A Survey Of First-Order Probabilistic Models: Fill & Download for Free

Physicists today remain largely unaware of the fact that quantum mechanics is perfectly choreographed by the mathematics of the de Broglie-Bohm theory, otherwise known as Bohmian mechanics. Despite the fact that Bohm’s formalism is entirely deterministic, and less vague than the standard interpretation of quantum mechanics, so far it has only been widely recognized and embraced among philosophers of physics.There are several historical events, or “unfortunate accidents,” that have led to the present ignorance of the superior mathematical clarity Bohm’s formalism offers. Understanding this historical posture goes a long way towards explaining why the orthodox or “standard” interpretation of quantum mechanics is still held by the majority of physicists today—something that I would argue is one of the greatest intellectual tragedies of our time.To dive right in, let us note that in addition to the Schrödinger equation, which is shared among all quantum mechanical interpretations, Bohmian mechanics [1] is completed by the specification of actual particle positions, which evolve (in configuration space) according to the guiding equation. This combination elegantly restores determinism into the dynamics of physical reality; accounting for all the phenomena governed by nonrelativistic quantum mechanics—from spectral lines and scattering theory to superconductivity, the quantum hall effect, quantum tunneling, nonlocality, and quantum computing.On top of this, Bohm’s theory magnificently elucidates state evolution without elevating the role of the observer to something mystical. [2] This reveals that the stochastic property of the orthodox approach of quantum mechanics, which manifests in state vector reduction, is merely a reflection of the incompleteness of that approach. [3]Therefore, by declaring that a particle’s wave function interacts with the particle and guides or pushes the particle around in a way that determines its subsequent motion, this approach explicitly captures nonlocality in a way that introduces a new level of clarity. For example, in the double-slit experiment Bohm’s approach explains that each individual particle goes through one slit or the other, while its wave function goes through both and suffers interference. Because the wave function guides the particle’s motion, the particle is likely to land where the wave function value is large and it is unlikely to land where it is small. [4]State vector reduction never occurs in this model (the wave function never collapses) because the state vector exists as a separate element of reality. Orthodox quantum mechanical interpretations, which are plagued with state vector reduction, describe a system as having many possible outcomes prior to observation and only one outcome after observation. This introduces a definite temporal asymmetry. Bohm’s model is not plagued with this problem. It portrays one single outcome as a possibility both before and after observation, because it sharply specifies an exact state of space. This restores time symmetry and allows a deterministic evolution.Bohm’s model has been praised as a cure to the conceptual difficulties that have plagued quantum mechanics because it elegantly does away with much of the subjectivity and vagueness found in the standard approach. Despite this, mainstream physicists haven’t embraced this interpretation, or examined it in depth. In fact, the large majority of them haven’t even heard of it. This is embarrassing, surprising and frustrating. [5] If Bohmian mechanics provides a cure to modern quantum mechanical philosophic complacency, then why have there been so few to study the richness of this elegant formalism?James Cushing notes that, Bohm’s formalism has been systematically ignored and misunderstood for “reasons having more to do with politics, positivism, and sloppy thought, than for reasons central to physics.” [6] Several historically perpetuated fallacies have discouraged people from giving Bohm’s formalism a real look. First off, the model suggests that there is something called configuration space, asserting additional variables and creating a dualism almost Platonic in scope. [7] This counts as a “strike against” Bohmian mechanics only in the sense that it conflicts with assumptions that have become popular among physicists. In addition to this, mainstream quantum physicists have been trying to map reality based on the assumption that wave functions somehow collapse upon measurement—contrary to the fact that Schrödinger’s equation demands that they do not. Bohm’s model denies wave function collapse. Therefore, although it is simple and in agreement with Schrödinger’s equation, it has been overlooked because it has not been in accord with popularized mainstream efforts.“New opinions are always suspected, and usually opposed, without any other reason but because they are not already common.” ~ John LockePhysicists also compulsorily reject Bohm’s construction because it explicitly builds nonlocality into its framework—even though violations of Bell’s inequality have conclusively shown that the stage of our universe is nonlocal. [8] This is perplexing. Nonlocality is unavoidable in any theory that recovers the predictions of quantum theory. [9] Therefore, any criticism of a theory that displays Nature’s nonlocal feature in an obvious way is both unfounded and counterproductive. Despite this, Bohm’s inherent explication of nonlocality continues to be obnoxiously mistaken as a strike against it instead of for it.“That the guiding wave, in the general case, propagates not in ordinary three-space but in a multidimensional-configuration space is the origin of the notorious ‘nonlocality’ of quantum mechanics. It is a merit of the de Broglie-Bohm version to bring this out so explicitly that it cannot be ignored.” ~ John BellFinally, and most significantly, Bohm’s theory has been neglected by physicists who thought that additional variable theories had been proven impossible. [10] Impossibility theorems, like the one produced by John Bell, [11] or the one independently and almost simultaneously introduced by Simon Kochen and Ernst Specker, [12] or John von Neumann’s original no-go theorem, [13] were widely interpreted to forbid additional variables in quantum mechanics. What these theorems actually show is that additional variable formulation of quantum mechanics must be nonlocal, and that “quantum theory itself is irreducibly nonlocal.” [14] To cite Bell’s inequality as something that forbids additional variables is to show a gross misunderstanding of the theorem. When it comes to ruling out additional variable theories, the theorem is empty and irrelevant.As Bell, [15] Bohm, [16] and Mermin [17] have pointed out, these impossibility proofs are logically unsatisfactory because they arbitrarily impose conditions that are relevant to the standard interpretation of quantum mechanics, but are not relevant to the theories they aim to dismiss—any theory with additional variables. [18] Nevertheless, it took a long time for the physics community to realize that the impossibility theorems were irrelevant. [19]John Bell himself, the original author of one of the impossibility theorems, recognized its irrelevance, but he was systematically misquoted, misunderstood, or ignored as he tried to call attention to it. Ironically, he was then portrayed as being against Bohmian mechanics, despite the fact that he was its prime supporter during his lifetime. [20] He said:“But in 1952 I saw the impossible done. It was in papers by David Bohm. Bohm showed explicitly how parameters could indeed be introduced, into nonrelativistic wave mechanics, with the help of which the indeterministic description could be transformed into a deterministic one. More importantly, in my opinion, the subjectivity of the orthodox version, the necessary reference to the ‘observer,’ could be eliminated…But why then had Bohm not told me of this ‘pilot wave’?... Why did von Neumann not consider it? More extraordinarily, why did people go on producing “impossibility” proofs, after 1952, and as recently as 1978?... Why is the pilot wave picture ignored in textbooks? Should it not be taught, not as the only way, but as an antidote to the prevailing complacency? To show us that vagueness, subjectivity, and indeterminism, are not forced on us by experimental facts, but by deliberate theoretical choice?” [21]The rest of the story as to why Bohmian mechanics is not currently favored as the mainstream interpretation of quantum mechanics can be traced back to orthodox philosophical intransigence. Those that fail to comprehend, or factor in, the ontological advantages that come from the determinism and mathematical clarity of Bohmian mechanics often attempt to downplay the formalism by pointing out that it “doesn’t make any predictions that differ from those of ordinary quantum mechanics.” Technically, that’s not much of an objection because we could equally argue that empirically “the standard theory doesn’t go beyond Bohm’s theory.” [22]In light of this empirical equivalence, physicist Hrvoje Nikolic of the Rudjer Boskovic Institute in Zagreb, Croatia has said, “If some historical circumstances had been only slightly different then it would have been very likely that Bohm’s deterministic interpretation would have been proposed and accepted first, and would be dominating today.” [23] The standard interpretation has simply become the standard as a happenstance of history. The tragedy is that, because of the overwhelming political momentum of the standard interpretation, valid alternative interpretations (of which there are many) have largely been ignored.The fact is that Bohmian mechanics completely accounts for nonrelativistic dynamics. It choreographs every dance in the quantum mechanical realm, and does so deterministically. For these reasons alone it is worthy of our attention. But we also might raise a brow in response to the way it frees us from the limiting assertion of the orthodox interpretation.The most controversial aspect of orthodox quantum mechanics is not the formalism itself, but rather “a further assertion to the effect that we cannot get beneath this formalism, to account for it in microscopic terms.” [24] The quantum formalism is touted as a “measurement” formalism. “Thus it is a phenomenological formalism describing certain macroscopic regularities.” [25] In this, and in many other ways, it is analogous to thermodynamics.The thermodynamic formalism details the dynamics and interrelated properties of the larger macroscopic system based on assumptions about the underlying behavior of a large number of microscopic constituents that it takes to be in equilibrium. For example, the ideal gas law relates the macroscopic properties of an ideal gas (pressure, volume, and temperature), and ultimately explains that relationship based on an underlying assumption that the system (the ideal gas) is made up of microscopic constituents (molecules) that interact elastically and are in a state of equilibrium. [26]Several averaged-over macroscopic mathematical relations automatically follow from such assumptions. Because these mathematical relations have reliably held up in our laboratory experiments our confidence in the substrate of elastically interactive constituents (molecules) is strengthened. We now believe that we can intuitively access what lies beneath the thermodynamic formalism by accounting for its microscopic substrate. Whether or not anyone ever directly sees a molecule, or an atom, having a picture of the underlying microscopic dynamics greatly improves the intuitive access we have of physical reality.Clearly, as we derive a quantum formalism, it is in our best interest to retain the ability to “get beneath it,” and explain it in microscopic terms. One way to do this is to start with the assumption that the system (the vacuum in this case) is composed of a large number of microscopic constituents that (at least to first approximation) interact elastically. Interestingly, when we assume that the vacuum can be represented this way—as a quantum field, or an infinite collection of coupled harmonic oscillators—a quantum formalism similar to Bohmian mechanics “emerges in such an inevitable manner that we are almost forced to conclude that philosophical prejudice must have played a crucial role in its nondiscovery.” [27]Today’s physicists have been brought up under the orthodox shadows of characters like Niels Bohr, Werner Heisenberg, and John von Neumann. [28] These figureheads loudly declared that a deterministic formalism of quantum mechanics is physically, philosophically, mathematically, and logically impossible. [29] They set in motion the assumptions that physicists would carry for decades after them. For some reason they were so intransigently stuck to the idea that quantum theory demands radical epistemological and metaphysical innovations that they appear to have never truly considered getting beneath the quantum formalism and accounting for it in microscopic terms. [30] These men possessed extraordinary intellects, and contributed powerfully to the development of quantum mechanics, but they missed out on Bohm’s obvious, elegant, and quite frankly trivial formalism.In my opinion, that stubbornness is the primary reason that Bohm’s interpretation of quantum mechanics is not the formal interpretation taught today. This intransigence has been quite lopsided. Craig Callender notes that, “For some reason or other, people often object to Bohm for reasons that they would never hold against other interpretations of quantum mechanics.” [31] I suspect that this has something to do with the fact that, without a map of the underlying molecular dynamics, people have a tremendously difficult time elevating their intuition to a higher dimensional realm where nonlocality is automatic.The orthodox interpretation of quantum mechanics maliciously severs the reach of our intuition. Its presumptions tautologically inhibit us from ever figuring out what is really going on by indefensibily asserting that Nature is not, and cannot be, described in a mathematically precise way. Many physicists and philosophers have felt the poignant sting of this truncation. Schrödinger himself never quite accepted the validity or completion of the wave function based on the intuitive damage it seemed to do. In reference to the wave function he said, “That it is an abstract, unintuitive mathematical construct is a scruple that almost always surfaces against new aids to thought and that carries no great message.” [32]A model’s value is to be measured by its ability to provide us with salient ontological and mathematical clarity of the domain it portrays. Unlike the orthodox interpretation of quantum mechanics, which restricts our intuitive reach by importing vaguely defined beables (additional variables called classical terms), Bohm’s formalism choreographs quantum mechanics in a way that is clear and mathematically precise. In short, instead of relegating Bohr’s classical terms (the additional variables from the Copenhagen interpretation) to the surrounding talk, [33] Bohm makes them mathematically precise.This leads to an interesting contrast. For example, despite the empirical equivalence between Bohmian mechanics and orthodox quantum theory, “there are a variety of experiments and experimental issues that don’t fit comfortably within the standard quantum formalism but are easily handled by Bohmian mechanics. Among these are swell and tunneling times, escape times and escape positions, scattering theory, and quantum chaos.” [34]The more striking contrast comes from the fact that Bohm’s model offers us a classical analogue by which to understand the quantum realm, while the orthodox interpretation attempts to forbid one. Let’s explore this point. In the orthodox interpretation we are asked to believe that, for example, photons form an interference pattern on the back wall because they all magically, in a way we cannot comprehend, manage to go through both slits. For systems with more than two slits every photon magically manages to go through every slit.In order to accept this interpretation we have to do more than abandon our notion of a particle—we have to accept that this magic is truly just that—magic. We have to accept that it really is impossible for us to ever have intuitive access to the process that causes photons, electrons, etc., to form interference patterns in the double-slit experiment—that it is impossible for us to comprehend, understand, or ever know what’s really going on during these experiments.The prevailing orthodox interpretation pushes this worldview upon us. Richard Feynman explains this by saying that the interference pattern made during the double-slit experiment is “a phenomenon which is impossible, absolutely impossible, to explain in any classical way, and which has in it the heart of quantum mechanics. In reality it contains the only mystery.” [35] Feynman later said, “Nobody can give you a deeper explanation of this phenomenon than I have given; that is, a description of it.” [36]If this were true it would be a pretty large pill to swallow. But it is not true. The truth is that Einstein understood the Copenhagen interpretation of quantum mechanics perfectly—he just wasn’t happy with its vagueness. [37] His intuition, that a deeper, more precise explanation is possible, has been fully justified. As we have seen, “Bohmian mechanics is just such a deeper explanation.” [38]From this precipice there is an apparent parallel between the advocates of the orthodox interpretation of quantum mechanics and the robed puppet masters of orthodox religions. Both preach that we are incapable of getting to know or discovering the truth for ourselves—that we should just give up and embrace unquestioning faith, or in this case, vagueness.That attitude is detrimental to our personal journeys and catastrophic to the overall scientific quest. Bohm’s interpretation frees us from the sins of orthodox unquestioning faith. It shows us that the path of the photon in our double-slit experiment reflects an interference pattern because the motion of that photon is governed by the wave function. Parts of the wave function pass through both slits while the particle passes through one slit. The parts of the wave function that pass through separate slits interfere with each other, developing an interference profile that guides the particle on its way.The interference pattern we see is, therefore, an unavoidable consequence of nonlocality—of the fact that the vacuum is a quantized fluid. It is not a magical, unexplainable effect. If the particles are emitted one by one, then this interference pattern still builds up over time—provided that the trajectories of the ensemble have a random distribution, or equilibrium distribution.If every particle were to follow completely identical trajectories, then they would all end up at one spot, creating a single bright spot on our photographic plate (or the wall). In Nature, this is not a real possibility for photons because Nature’s substrate ( the vacuum) is composed of interactive quanta constantly rearranging. For two particles to follow identical trajectories, identical trajectories must exist. They don’t because the vacuum is not static. The quanta that compose the vacuum are constantly mixing about in configuration space. In quantum mechanics, the best information about available four-dimensional trajectories is given by an equilibrium distribution because quantum mechanics explicitly codifies a vacuum that is in a state of equilibrium. The inherent mixing of the vacuum explains why extremely precise information about a trajectory in the familiar four dimensions can at best be described statistically, or probabilistically.For two photons to follow identical paths through space (identical trajectories) the positions and velocities of all the intermittent space quanta along that path (the additional variables) would have to be identically configured. On macroscopic scales this is extremely improbable. Therefore, according to Bohmian mechanics the interference pattern we see in the double-slit experiment is exactly what we should expect. That’s a rather significant improvement over the orthodox assertion that we should just accept the double-slit experiment as something that we will never make sense of.For more on this topic, and to discover how pilot-wave theory is elucidated by the assumption that the vacuum is a superfluid, check out my book 'Einstein's Intuition', available as a Kindle, in black and white softcover, full color softcover, full color hardcover, as an iBook, and in audiobook.I also heavily recommend this lecture by Mike Towler from Cambridge.Notes:[1] Bohmian mechanics is also called the de Broglie-Bohm theory, the pilot-wave model, and the causal interpretation of quantum mechanics. Louis de Broglie originally discovered this approach in 1927 and David Bohm rediscovered it 1952.[2] S. Goldstein. Bohmian Mechanics. Stanford Encyclopedia of Philosophy. For more information on the identical success of Bohmian mechanics with the traditional quantum formalism see: Detlef Dürr, Sheldon Goldstein and Nino Zanghí. Quantum Physics Without Quantum Philosophy. Physical Review Letters, vol. 93, p 090402; Ward Struyve & Hans Westman, Proceedings of the Royal Society A, vol. 463, p. 3115; D. Bohm. (1953). Proof that probability density approaches psi squared in causal interpretation of quantum theory. Physical Review 89, 458–466; D. Bohm. (1952). A suggested interpretation of the quantum theory in terms of “hidden” variables’, Physical Rev. 85, p 166–193; M. Daumer, D. Dürr, S. Goldstein, & N. Zanghí. A survey of Bohmian mechanics, Il Nuovo Cimento.[3] Much of this chapter follows Sheldon Goldstein’s publication, Bohmian Mechanics, found in the online Stanford Encyclopedia Of Philosophy.[4] Brian Greene, (2004). The Fabric of the Cosmos, p. 206.[5] I have had enlightening discussions with Sheldon Goldstein and Daniel Victor Tausk about this very matter. Both of them have devoted considerable energy toward correcting this historical problem. But they have run into a lot of resistance. They have noted to me that many people are just too intransigent to consider a solution to a problem they have been working on their whole life, even if it is placed right in front of them. After spending a career on the problems of quantum mechanics to no avail many of them would prefer that the problem remain unsolved.[6] Cushing. (1994).[7] Vacuum quantization leads to a model that rides between Aristotelian naturalism and Platonic idealism. Aristotelian naturalism holds that reality consists only of the natural world. It is completely monistic and therefore denies the existence of a separate non-material order of reality. It also holds strongly to the belief that Nature follows orderly, discoverable laws. Platonic idealism, on the other hand, asserts that there is a non-material second transcendental realm. It is therefore dualistic. This non-spatiotemporal realm is believed to be accessible to the mind, but only to the mind. Vacuum quantization adjoins these two perspectives and ends up with a hierarchical monism. It proclaims that there is nothing outside natural order; there is no supernatural. It carries the explicit requirement for non-spatiotemporal realms that are directly accessible only to the mind (via vacuum quantization), but these realms are still part of the natural world—they follow orderly, discoverable laws.[8] Technically these violations show that the vacuum does not conform to local realism. To assume that realism is out is to assume that the entire scientific endeavor makes no connection to the real world (or that there isn’t a real world to begin with). If we don't go that route, then we must assume that the vacuum is nonlocal.[9] For a presentation of the argument and the experimental results that secure this point, see: Quantum Non-Locality and Relativity, Second Edition, by Tim Maudlin.[10] Tim Maudlin. (2002). Quantum Non-Locality and Relativity, second Edition, Blackwell Publishing, MA, p.124. For more on this see Joy Christian, Disproof of Bell’s Theorem by Clifford Algebra Valued Local Variables: http://www.arxiv.org/abs/quawnt-ph/0703179[11] J. S. Bell. (1966). On the problem of hidden variables in quantum mechanics. Rev. Mod. Phys. 28, 447–452; reprinted in Quantum Theory of Measurement, J. A. Wheeler & W. H. Zurek editors, Princeton University Press (1983), 396–402; and in Chapter 1 of J. S. Bell, Speakable and Unspeakable in Quantum Mechanics, Cambridge University Press (1987); second augmented edition (2004), which contains the complete set of J. Bell’s articles on quantum mechanics.[12] S. Kochen & E. P. Specker. (1967). The problem of hidden variables in quantum mechanics. J. Math. Mech. 17, 59–87.[13] John von Neumann. (1932). Mathematische Grundlagen der Quantenmechanik. Springer, Berlin.[14] Quoted from personal discussions with Sheldon Goldstein.[15] J. S. Bell. (1966). On the problem of hidden variables in quantum mechanics. Rev. Mod. Phys. 28, 447–452; reprinted in Quantum Theory of Measurement, J. A. Wheeler and W. H. Zurek editors, Princeton University Press (1983), 396–402; J. S. Bell, Speakable and Unspeakable in Quantum Mechanics, Cambridge University Press (1987); second augmented edition (2004), which contains the complete set of J. Bell’s articles on quantum mechanics.[16] D. Bohm & J. Bub. (1966). A proposed solution of the measurement problem in quantum mechanics by a hidden variable theory. Rev. Mod. Phys. 38, 453–469; D. Bohm & J. Bub. (1966). A refutation of the proof by Jauch and Piron that hidden variables can be excluded in quantum mechanics. Rev. Mod. Phys. 38, 470–475.[17] N. D. Mermin. (1993). Hidden variables and the two theorems of John Bell. Rev. Mod. Phys. 65, 803–815; in particular see § III.[18] “[T]he assumption of Kochen and Specker… appear, in fact, to be quite reasonable indeed. However, they are not. The impression that they are arises from a pervasive error, a naïve realism about operators…” Sheldon Goldstein, Bohmian Mechanics, published 10-26-2001; substantive revision 5-19-2006, Stanford Encyclopedia Of Philosophy.In other words, supporters of the standard interpretation of quantum mechanics often fail to recognize that Bohr’s classical variables are additional variables in their theory. Bohm takes these variables and makes them mathematically precise. Technically, attacking additional variable theories also attacks the standard model.[19] Franck Laloë. Do We Really Understand Quantum Mechanics?, p. 37. There are still members in the physics community that are held back by this misunderstanding. I have interacted with many physicists that are deeply convinced that these no-go theorems forbid additional variable theories.[20] See Wigner. (1976).[21] J. S. Bell. (1987), p. 160.[22] Mark Buchanan. (2008, March 22). No dice. New Scientist, pp. 28–31.[23] Ibid.[24] Detlef Dürr, Sheldon Goldstein, & Nino Zanghí. Quantum Physics Without Quantum Philosophy, p. 4. The mathematics to follow, as well as much of the remaining discussion, follows this work.[25] Ibid., p. 4.[26] Boyle’s law is another example of this. Technically, pressure and temperature are macroscopic properties that also rely on this underlying assumption. These properties result from the group behavior of a large number of elastically interactive molecules in motion.[27] Detlef Dürr, Sheldon Goldstein, & Nino Zanghí. Quantum Physics Without Quantum Philosophy, p. 4.[28] J. von Neumann. (1932); R. T. Beyer. (1955), pp. 324–325; J. S. Bell. (1982). 989–999; J.S. Bell. (1987), pp. 159–168.[29] J. von Neumann. (1932); J. S. Bell. (1982), (1987).[30] D. Dürr et al., pp. 4–6.[31] Craig Callender. (1998). Review, Brit. J. Phil. Sci. 49, 332–337.[32] Schrödinger, E. (1935). 23: 807–812, 923–828, 844–849.[33] John S. Bell. (1976). The theory of local beables. Epistemological Lett. 9, 11-24; Reprinted in John S. Bell. (2004). Speakable and Unspeakable in Quantum Mechanics, 2nd ed. Cambridge U.P., Cambridge, pp. 52–62.[34] Sheldon Goldstein. Bohmian Mechanics. For more on how the formalism of Bohmian mechanics naturally points to a formalism richer than the standard orthodox theory see: Anthony Valentini of the Perimeter Institute in Waterloo, Ontario, Journal of Physics A: Mathematical and theoretical, vol.of the popular one used today e bove the i.ld ever fall into it."me so great an absurdity that I believe no man who has in philo 40, p. 3285; For discussion on escape times and escape positions see Daumer et al., 1997a, for scattering theory see Dürr et al., 2000, and for quantum chaos see Cuching, 1994; Dürr et al., 1992a.[35] R. P. Feynman, R. B. Leighton, & M. Sands. (1963). The Feynman Lectures on Physics, I, New York: Addison-Wesley; Sheldon Goldstein. Bohmian Mechanics.[36] Richard Feynman. (1967). The Character of Physical Law. Cambridge MA: MIT Press; Sheldon Goldstein. Bohmian Mechanics.[37] It could be argued that Einstein initiated the pilot-wave approach with the concept of the Führungsfeld or guiding field. Wigner. (1976), 262; Goldstein. Bohmian Mechanics. Stanford Encyclopedia of Philosophy.Einstein failed to complete the formalism of such an approach, but he independently encouraged both de Broglie and Bohm to press on with their efforts.[38] Sheldon Goldstein. Bohmian Mechanics.For more on this topic, and to see how the pilot-wave theory can be further elucidated by the assumption that the vacuum is a superfluid, check out my book, 'Einstein's Intuition', available as Kindle, black and white softcover, full color softcover, full color hardcover, an iBook, and as an audiobook.I also heavily recommend this lecture by Mike Towler from Cambridge.

Curriculum for Bachelor of Technology in Information Technology at MNNITData Structures (III Semester CSE & IT 4L)SyllabusData Structure (CS1301)Prerequisite: C Programming, Basic Mathematics.Objective: Implementation of databases, designing efficient algorithms, memory management etc.Data structures provide the necessary data abstraction for the development of large software systems and their central role in software engineering. Data structure covers include sets, linked lists, stacks, queues, hash tables, trees, heaps, and graphs. Students are introduced to algorithms for searching, sorting, and data structure manipulation and learn the techniques to analyze program efficiency. Programming using recursion and dynamic data structures are covered.Course DescriptionThis course introduces the students fundamentals of data structures and takes them forward to software design along with the course on Algorithms. It details how the choice of data structures impacts the performance of programs for given software application. This is a precursor to DBMS and Operating Systems. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 40 lectures)1. Introduction, Elementary Data Organization, Data Structure Operations, Algorithms Complexity, Time-Space Trade off (6)2. Arrays, Linked List, stacks and Queues (10)3. Tree, Binary tree, Search tree, Heap, B+ tree (12)4. Sorting methods, External Sorting/Searching, Hashing (8)5. Graphs (6)Text Books1. The Art of Computer Programming (Volume 1 and Volume 3) - D E Knuth,2. Data Structures Using C & C++, Langsam, Augenstein & Tenenbaum,3. Data Structures – A Programming Approach with C, Kushwaha & Mishra,4. R.L. Kruse, B.P. Leary, C.L. Tondo, “Data structure and program design in C”5. Fundamentals of Data Structures in C, by Ellis Horowitz, Sartaj Sahni, and Susan Anderson-Freed7Principles of IT Industry Management (III Sem CSE & IT 3L)SyllabusManagement of IT Industries (MS-1301)Prerequisite: NoneObjective: Competently employ broad-based analytical tools and computers for decision-making and system design, analysis and performance. Assume managerial and leadership roles in their chosen professional careers while working in multidisciplinary teams. Engage in continuous learning by seeking out opportunities for higher education or ongoing training related to their employment.Effectively adapt to the changing demands in workplace and are able to perform increasingly complex tasks, and tasks outside their field of expertise.Course DescriptionThis course introduces students the working and management of IT industries. The emphasis of the course will be on the skills and knowledge needed to understand and successfully manage an IT based organization. A central concept of the course is that there is a general framework for understanding management that applies to managers in all organizations-large or small, public or private, product-oriented or service-oriented.Course Outline (To be covered in 30 lectures)1. Introduction, Nature & Concept of Management; Managerial skills; Evolution of management thought; Concept of functional management; Management styles, Productivity measurement, productivity index, types of production system. (3)2. Human Resource Management: Definition and theories of Managing People for IT Industry, Human Resource Planning, responsibility assignment matrix, resource management, developing and managing the project team, Case Studies (6)3. IT Industry Supply Chain Management: Types, Business processes, Strategic, tactical, and operational decisions in supply chains, performance measures, inventory management, bullwhip effect, e-marketplaces, e-procurement, e-logistics, e-fulfillment, customer relationship management, web services, ERP and supply chains, Case Studies (6)4. IT Project Quality Management: Tools and techniques for quality control (Pareto Analysis, Statistical sampling, testing), process control, SQC control charts, single, double and sequential sampling, TQM. Case Studies (6)5. Environmental Issues, Pollution Control Acts, Green IT Practices, Establishing a Green IT Action Plan, techniques and technologies available to enable Green IT Case Studies6. Comprehensive Case studies: Any three from TCS, Cisco, Infosys, Wipro, Facebook, Accenture, Google, IBM, Microsoft etc (3)Text Books1. Managemenet :Global Perspectives, by Koontz and Weihrich2. Principles of Management by Prasad, L.M.,3. Environmental and Pollution Awareness by Sharma B.R.8Analog and Digital Electronics (III Semester CSE & IT 3L)SyllabusAnalog & Digital Electronics(CS-1303)Prerequisite: Basic circuits, Semiconductor devices, digital logic.Objective:Analog and Digital Circuits is an introductory course on circuit design that aims to develop a combination of design, analysis and experimental skills among the students. In addition, the course will help students understand mechanisms of sensing and actuation that are commonly used. The laboratory component will expose the student to topics in measurement and instrumentation.Course DescriptionThis course introduces the students fundamentals of basic electronics and takes them forward to digital circuits. The course provides introduction to (semiconductor) electronic devices. Conduction of electric currents in semiconductors, the semiconductor p-n junction, the transistor. Analysis and synthesis of linear and nonlinear electronic circuits containing diodes and transistors. Biasing, small signal models, frequency response, and feedback. Operational amplifiers. Further, this course covers combinational and sequential logic circuits. Topics include Boolean algebra, logic families, MSI and LSI circuits. This is an precursor to Computer Organization course. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 40 lectures)1. Introduction to semiconductor physics. Diode, Zener Diode, Diode as a switch, Rectifier, Clipping and Clamping Circuits (6)2. Bipolar Junction Transistor, Biasing of Transistor, Transistor configurations, Transistor as an Amplifier, Transistor as a Switch. (8)3. Introduction to FET, MOSFET, Operational Amplifier, SCR, UJT and other devices (6)4. Introduction to Boolean Algebra and fundamental theorems, Basic Logic Gates, Realization of combinational circuits using universal gates, Gate level minimization (8)5. Important Digital Circuits Decoder, Multiplexer, PLA, ROM, RAM (4)6. Flip Flops, Design of Sequential Circuits, Registers, Counters (8)Text Books1. Digital Design by M Morris Mano, M D Ciletti2. Integrated Electronics by Millman & Halkias3. Electronic Principles by Malvino4. Foundations of Analog and Digital Electronic Circuits by Anant Agarwal and Jeffrey Lang9Foundations of Logical Thought ( III Semester CSE & IT 4L)SyllabusFoundations of Logical Thought (CS-1304)Prerequisite: NoneObjective:This course is aimed at Computer Science majors who have never taken any type of mathematical theory courses before, though it is also a useful course for developing general reasoning and problem solving skills. For those that continue studying Computer Science, this course serves as excellent preparation for the required course Discrete Math For Computer Scientists. However, all students taking this course should benefit by improving their reasoning and abstract thinking skills, learning how to construct sound, logical arguments, and by learning to detect flaws in unsound arguments.Course DescriptionThis course offers a presentation of fundamental tools required in advanced computer science. The main topics covered in this subject include propositional and first-order logic, recursion, proofs, other kinds of logic. This forms the basis for the subjects like Automata theory and formal methods.Course Outline (to be covered in 40 lectures)1. Introduction, Set theory, Notion of proofs , Linear congruence (8)2. Formal logic: Propositional Logic, Relational logic, First order logic, and related issues (8)3. Lattices and related issues (8)4. Group Theory and related issues (6)5. Finite Fields and related issues (6)6. Generating Functions and related issues (4)Text Books1. The Essence of Logic, by John Kelly, Ed.2. Logic for Applications, Anil Nerode and Richard A. Shore, Ed.3. Logic, Sets, and Recursion, by Robert L. Causey, Ed.4. Concrete mathematics: a foundation for computer science, by R. Graham, D. Knuth, O. Patashnik,5. A Mathematical Introduction to Logic, Enderton, H6. Discrete Mathematical Structure with Application to Computer Science”, J.P Trembley,. & R. Manohar10Technical Writing (III Semester CSE & IT 3L)SyllabusTechnical Writing (CS-1305)Prerequisite: English, Foundations.Objective:English is a dedicated writing course offered to students in an online classroom environment. Students enrolled in the course will be expected to work in three ways: independently; in consultation with their instructor; and also collaboratively in writing teams to be established by the instructor following the first module (unit) of the course.Course DescriptionThis course is an introduction to Technical Writing. To help students analyze the communication situation fully and accurately: which includes needs, audiences, and users. To gather, interpret, and document information logically, efficiently, and ethically. To develop professional work and teamwork habits. To be able to design usable, clear, persuasive, accessible documents. To educate the students to select the appropriate format for presenting information and organize information using reader-based principles. To motivate them to use graphics effectively. And finally develop an effective, clear writing style.Course Outline (To be covered in 30 lectures)1. Introduction, Introduction To Latex, Introduction to Xfig and other drawing software. (8)2. English usage, when English is a foreign language. (6)3. Reading a draft, Writing a draft, revising a draft, Introduction to IEEE, ACM style files (6)4. Writing a technical talk, presenting the technical talk (4)5. Writing a project/thesis. Introduction to various styles. (4)6. Copyright issues and plagiarism (2)Text Books1. Handbook of Writing for the Mathematical Sciences By Nicholas J. Higham2. The Elements of Style, William Strunk, ISBN 0-205-30902-X3. LaTeX: A document preparation system, User's guide and reference manual Leslie Lamport, ISBN 0-201-52983-14. Cambridge English for Engineering, Mark Ibbotson11Analysis of Algorithms (IV Semester CSE & IT 3L)SyllabusAnalysis of Algorithms (CS-1401)Prerequisites: Discrete Mathematics (counting arguments, induction, recurrence relations and discrete probability)Objectives:This is an introductory course in the analysis and design of combinatorial algorithms. Emphasis is given on (i) familiarizing the students with fundamental algorithmic paradigms and (ii) rigorous analysis of combinatorial algorithms. This is a modern introduction to combinatorial algorithms and it maintains some consistency with previous courses.Course DescriptionThis course teaches techniques for the analysis of efficient algorithms, emphasizing methods useful in practice. Algorithms are recipes for solving computational problems. In this course we will study fundamental algorithms for solving a variety of problems, including sorting, searching, divide-and-conquer, dynamic programming, greediness, and probabilistic approaches. Algorithms are judged not only by how well they solve a problem, but also by how effectively they use resources like time and space. Techniques for analyzing time and space complexity of algorithms and to evaluate tradeoffs between different algorithms. Analysis of algorithms is studied - worst case, average case, and amortized - with an emphasis on the close connection between the time complexity of an algorithm and the underlying data structures. NP-Completeness theory is examined along with methods of coping with intractability, such as approximation and probabilistic algorithms. A basic understanding of mathematical functions and data structures is a prerequisite for the subject. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 30 lectures)1. Introduction, Review of basic concepts, advanced data structures like Binomial Heaps, Fibonacci Heaps (5)2. Divide and Conquer with examples such as Sorting, Matrix Multiplication, Convex hull etc(6)3. Dynamic programming with examples such as Kanpsack, All pair shortest paths etc (4)4. Backtracking, Branch and Bound with examples such as Travelling Salesman Problem etc (6)5. Algorithms involving Computational Geometry (4)6. Selected topics such as NP-completeness, Approximation algorithms, Randomized algorithms, String Matching (5)Text Books1. Introduction to Algorithms by Thomas H. Coreman, Charles E. Leiserson and Ronald L. Rivest2. Fundamentals of Computer Algorithms by E. Horowitz & S Sahni3. The Design and Analysis of Computer Algorithms by Aho, Hopcraft, Ullman,12Graph Theory and Combinatorics (IV Semester CSE & IT 3L)SyllabusGraph Theory & Combinatorics (CS-1402)Prerequisites: Discrete Mathematics, Computer Algorithms and ProgrammingObjectives: The course aims to introduce the students about topics and techniques of Graph Theory and Combinatorial analysis. The course provides a large variety of applications and, through some of them, the algorithmic approach to the solution of problems in computer science and related areas. This helps in developing mathematical maturity skills of students. To present a survey of essential topics for computer science students who will encounter some of them again in more advanced courses.Course DescriptionThe course provides an introduction to graph theory and combinatorics, the two cornerstones of discrete mathematics. The student will gain an insight into the basic definitions of relevant vocabulary from graph theory and combinatorics, and know the statements and proofs of many of the important theorems in the subject. It helps to simulate real world problems, with applications in communication and networks, operating systems, robotics, wireless and sensor networks, VLSI and many more. Topics that will be discussed include Euler formula, Hamilton paths, planar graphs and coloring problem; the use of trees in sorting and prefix codes; useful algorithms on networks such as shortest path algorithm, minimal spanning tree algorithm and min-flow max-cut algorithm. The Prerequisite is basic knowledge of set and matrix theoryCourse Outline (To be covered in 30 lectures)1. Combinatorics Basic counting techniques, pigeon-hole principle, recurrence relations, Polya's counting theorem. Introduction to probabilistic method in combinatorics (6)2. Fundamental concepts of graphs and digraphs, (4)3. Spanning tree, connectivity, optimal graph traversals (5)4. Planarity of Graphs, Drawing graphs and maps, graph coloring (5)5. Special digraph models, network flow and applications (6)6. Algebraic specifications of Graphs, Non planar layouts (4)Text Books1. Introduction to Enumerate Combinatorics, M. Bona,2. Introduction to Graph Theory, D.B.West3. Graph Theory and Applications J.A. Bondy and U.S.R.Murty: ( Freely downloadable from Bondy's website; Google-Bondy)4. Graph Theory: Modeling, Applications, and Algorithms, by Geir Agnarsson and Raymond Greenlaw5. Introductory Combinatorics by R A Brualdi,13Computer Organization (IV Semester CSE & IT 3L)SyllabusComputer Organization (CS-1403)Prerequisites: Discrete Structures and Digital LogicObjectives: The objective of this course is to master the basic hardware and software issues of computer organization. The students are expected to know the inner workings of a computer and have the ability to analyze the hardware and software issues related to computers and the interface between the two. This allows the students to work out the trades off involved in designing a modern computer.Course DescriptionThis is a first course dealing with layout and design principles of a computing system and its peripherals. It requires understanding of digital electronics. It prepares foundations for the operating system, microprocessor and embedded systems courses.Course Outline (To be covered in 30 lectures)1. Introduction, Register Transfer Language, Bus and Memory Transfers, Bus Architecture, Arithmetic Logic Unit (6)2. Fundamental concepts of controller design. (6)3. Processor design and related issues (8)4. Input/Output Organization and related concepts(4)5. Optical, magnetic and semiconductor memory devices, Memory organization (6)Text Books1. Computer Organization and Design: The Hardware-Software Interface, by David Patterson and John Hennessy.2. Computer Organization, by Vravice, Zaky & Hamatcher3. Structured Computer Organization, by Tannenbaum4. Computer System Architecture, by M. Mano14Automata Theory(IV Semester CSE & IT 4L)SyllabusAutomata Theory (CS-1404)Prerequisites: Knowledge corresponding to the Formal languages and automata, Computability and complexity.Objectives: At the end of the course students should be able to understand and explain selected advanced parts of automata theory, including parsing techniques for deterministic context-free languages, relationship between finite-state automata and MSO logic, automata on infinite words, and process specifications. Further, students should be able to make reasoned decisions about computational models appropriate for the respective area and to understand methods and techniques of their applications.Course DescriptionAutomata theory is the study of abstract computational devices. They have applications in modelling hardware, lexical analysis, machine design, syntax analysis, parser generation, program verification, text editing and so on. The class of formal languages, context free grammar, DFA, NFA and PDA are being covered up in the course. The knowledge of these concepts form the foundations of computer science and continues towards the development of the student's skills in understanding mathematical models. The prerequisite is basic knowledge of mathematics. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 40 lectures)1. Introduction, inductive Proofs Relations and Functions (4)2. Regular Languages DFA, NFA Machines and their equivalence, Regular Expressions, Equivalence of Regular Expressions and Finite State Machines, Closure Properties of Regular Languages Proving Non-Regularity (8)3. Context-free Languages Context-free Grammars, Derivations, Leftmost, Rightmost, Inherent Ambiguity, Parse Trees, Normal Forms, Proof of Containment of the Regular Languages Pushdown Automata, Equivalence of PDAs and Context-free Grammars Closure Properties of Context-free Languages (12)4. Pumping Lemma for both Regular & Context-free Languages, Proving Some Languages are not Context-free. (6)5. Recursive and Recursively Enumerable Languages, Turing Machines Definition of Recursive and Recursively Enumerable, Church's Hypothesis, Computable Functions, Methods for Turing Machine Construction (10)Text Books1. Introduction to the Theory of Computation, by Michael Sipser2. Introduction to Automata Theory, Languages, and Computation, by Hopcroft, Motwani, and Ullman (ISBN 0-321-45536-3)3. Theory of Computer Sciences Korral,4. Automata, Computability and Complexity: Theory and Applications. by E Rich15Communication Foundations (IV Semester CSE & IT 3L)SyllabusCommunication Foundations (EC-1405)Prerequisites: Developing Windows Communication Foundation (WCF)–based applications including experience with .NET Framework.Objectives: This course introduces the .NET Windows Communication Foundation (WCF) technology and its architecture. It shows how to create a basic WCF service and how to host the service in a managed application, a Windows Service, Internet Information Service (IIS), or Windows Process Application Activation Services (WAS). It also covers how to generate a client proxy class and configuration file to access a WCF service. This course is one of a series in the Skillsoft learning path that covers the objectives for the Microsoft Technology Specialist: Microsoft Windows Communication Foundation Development with Microsoft .NET Framework. This course is ideal for Application Developers, .Net Programmers, Web Developers.Course DescriptionIn this course students will study fundamentals of analog and digital communication. The course includes the basics of Electromagnetic waves, antennas, modulation, information theory, sampling and quantization, coding, signal detection and system performance in the presence of noise. This is a prerequisite for the course on Computer Networks. A lab course is also associated with it.Course Outline (To be covered in 30 lectures)1. Introduction, Elements of communication systems, review of signal2. epresentations in time and frequency domain, bandwidth, filters, Electromagnetic spectrum (6)3. Sky waves, ground waves and space waves, Antenna fundamentals and types of antennas (4)4. Amplitude Modulation, Frequency modulation, Radio receivers (4)5. Sampling theorem, quantization and pulse code modulation, digital modulation techniques (6)6. Fundamentals of guided waves, wave guides, coaxial cables, fiber optic cables, cable types and specifications. (6)7. Case studies: FM Broadcast, satellite communication, telephone systems, mobile telephonyText Books1. Communication Systems Engineering by Proakis, John, and Masoud Salehi2. Electronic Communication Systems by Kennedy D3. Computer Networks by Tanenbaum, Andrew4. Communication Systems by Haykin, Simon.16Contemporary Issues in Information Technology (IV Sem CSE & IT 2L)SyllabusContemporary Issues in Information Technology (CS-1405)Prerequisites: Application areas of Information TechnologyObjectives: This course addresses the current issues that surround the use of information technology (IT) and the development of IT-based solutions. Using an overview of the IT components utilized in the areas of computer hardware and software, information processing and telecommunications as a foundation, this course explores the current issues and trends which challenge IT professionals. The primary purpose of this course is to teach students how to approach, investigate, consider, analyze, use and apply information technology in order to address specific information based needs. The course is intended to serve as a foundation formore advanced work in the information Technology concentration.Course DescriptionA survey of the computer engineering profession's contemporary role in society, including concerns for business principles, safety, and the environment; the role of computer engineers in achieving economic stability, growth, and improving the human condition. Course prepares general awareness amongst students pertaining CS/IT by motivating them to go through the debates, and discussions taking place in national and international societies like CSI, ACM and IEEE and articles published in their respective magazines.Course Outline (To be covered in 10 lectures of two hour duration per week)1. Introduction, Information technology in the past, present, and in the future (4)2. Contemporary theoretical and research issues which include the digital divide, optical and quantum computing, human computer interfaces and computing limitations. (8)3. Applying information technology across disciplines (4)4. Case study of famous IT professionals (4)Text Books (Not Applicable)1. CSI Communications ( latest 12 issues)2. Communications of ACM ( latest 12 issues)3. IEEE Software ( latest 12 issues)4. IEEE Computer ( latest 12 issues)5. IEEE Spectrum ( latest 12 issues)17Computer Graphics (V Semester IT 3L)SyllabusComputer Graphics (CS-1507)Prerequisites: Data Structure and Linear Algebra.Objective: understand the internal workings of commercial systems for the rendering of digital images from 3D models write their own software for 3D modeling and rendering use 3D graphics API's undertake creative work and research in 3D graphicsCourse DescriptionIn this course students will study the fundamental concepts in creating graphical images on the computer. Computer graphics uses ideas from Art, Mathematics, and Information Technology to create images. The students are expected to be comfortable writing programs in an upper level language, and have sound background in mathematics, as a great deal of computer graphics is best described mathematically. This course leads to courses on multimedia and image processing. This course has an associated lab course with it.Course Outline (To be covered in 30 lectures)1. Introduction, Input-Output devices, Line Scan algorithms, Mid –point Circle and Ellipse Generating algorithms, Polygon Filling, Clipping (7)2. Geometrical Transformations (2D & 3D), Projections, Visible-Surface Determination (9)3. Representation of Curves and Surfaces, Solid Modeling (6)4. Color models and applications(4)5. CAD/CAM and Applications of computer Graphics (4)Text Books1. Computer Graphics, by Hearn and Bakerand2. Procedural Elements of Computer Graphics by Rogers3. Principle of Interactive Computer Graphics by Newman and Sproul4. Computer Graphics, A programming Approach by Steven Harrington18Operating System (V Semester CSE & IT 4L)SyllabusOperating Systems (CS-1502)Prerequisite: C, Java, and data structures.Objective: - gain extensive knowledge on principles and modules of operating systems- understand key mechanisms in design of operating systems modules- understand process management, concurrent processes and threads, memory management, virtual memory concepts, deadlocks- compare performance of processor scheduling algorithms- produce algorithmic solutions to process synchronization problems- use modern operating system calls such as Linux process and synchronization libraries- practice with operating system concepts such as process management, synchronization, networked processes and file systemsCourse DescriptionIn this course students will study the basic facilities provided in modern operating systems. The emphasis will be on understanding general concepts that are applicable to a wide range of operating systems, rather than a discussion of the features of any one specific system. Topics that will be covered in the course include: protected kernels, processes and threads, concurrency and synchronization, memory management, virtual memory, file systems, secondary storage, protection, and security. This course requires as prerequisite the course on computer programming, data structures and computer organization. This course has an associated lab with it.Course Outline (To be covered in 40 lectures)1. Introduction and Overview (2)2. Process fundamentals, scheduling, synchronization (12)3. Inter-process communication, Deadlock (8)4. Memory management and virtual memory (7)5. File system and secondary storage (5)6. Protection and security issues, Case studies e.g. Linux, Solaris and Android (6)Text Books1. Operating Systems, by William Stallings2. Operating Systems Concepts by Silberschatz, Galvin, and Gagne3. The Design of the UNIX Operating System, by Maurice J. Bach4. Advanced Programming in the UNIX Environment, by W. R. Stevens & S. A. Rago5. The Design and implementation of the 4.4 BSD UNIX operating system by Marshall Kirk McKusick, Keith Bostic, Michael J. Karels, John S. Quarterman19Computer Networks (V Semester CSE & IT 4L)SyllabusComputer Networks (CS-1503)Prerequisite : C or Java programming, Course in algorithms, Course in probability.Objective:1. Build an understanding of the fundamental concepts of computer networking.2. Familiarize the student with the basic taxonomy and terminology of the computernetworking area.3. Introduce the student to advanced networking concepts, preparing the student forentry Advanced courses in computer networking.4. Allow the student to gain expertise in some specific areas of networking such as the design and maintenance of individual networks.Course DescriptionIn this course students will study computer networks within the context of the Internet. It will build on prior knowledge in Communication foundations, computer organization, basic algorithms, data structures and C programming. Students will study the fundamental principles, elements, and protocols of computer networks. Course will investigate how the different protocols work, why they work that way, and their performance trade-offs. This course prepares foundations for wireless networks and distributed systems. This has a lab course associated with it.Course Outline (To be covered in 40 lectures)1. Introduction, Fundamental requirements of network, OSI & TCP/IP model (3)2. Physical and Link layer issues (4)3. Medium Access protocols (IEEE 802.3 ...) and related issues (8)4. Network layer: IP and other protocols, Routing protocols, and LAN design. (11)5. Transport layer Protocols and related Issues (8)6. Basic client server architecture, introduction to different application layer protocols like ftp, telnet, mail(SMTP), HTTP, DNS, DHCP and peer to peer (6)Text Books1. Computer Network – Top down approach by James. F. Kurose & Keith W. Rose,2. Compuer Network – A system approach by Larry.L.Peterson & Bruce.S.Davie3. Data Communication & Networking by Behrouz Forouzan4. Unix Network Programming –volume-I by W.Richard Stevens20Object Oriented Modeling (V Semester CSE & IT 3L)SyllabusObject Oriented Modeling(CS-1504)Prerequisite : Basic Concepts of Object Oriented Programming, Software Engineering.Objective: Analyze and Design a real – world problem into Object- Oriented form. Create a requirements model using UML class notations and use-cases based onstatements of user requirements, and to analyze requirements models given to them forcorrectness and quality. Create the OO design of a system from the requirements model in terms of a high-levelarchitecture description, and low-level models of structural organization and dynamicbehavior using UML class, object, and sequence diagrams. Comprehend enough Java to see how to create software that implements the OO designsmodeled using UML. Comprehend the nature of design patterns by understanding a small number of examplesfrom different pattern categories, and to be able to apply these patterns in creating an OOdesign. Given OO design heuristics, patterns or published guidance, evaluate a design forapplicability, reasonableness, and relation to other design criteria.Course DescriptionIn this course students will study the fundamental principles of object-oriented approaches to modeling software requirements and design. Topics include strategies for identifying objects and classes of objects, specification of software requirements and design, the design of class hierarchies, software reuse considerations, graphical notations, system implementation using object-oriented and object-based programming languages, and comparison of object-oriented approaches to more traditional approaches based on functional decomposition.Course Outline (To be covered in 30 lectures)1. Introduction, Need for formal and semi-formal modeling, UML-2 Meta-model (4)2. UML-2 Concepts and Examples: Object, Class, Relationship, Interface, Types, roles, Use Case, Interaction and Activity Diagrams, State Machine and State-chart Diagram, Events, signals, Process and threads (8)3. Software System Design, Design Patterns, Pattern Classification, Creational, Structural and Behavioral patterns, Idoms (12)214. Agents and Agent Modeling, Multi-Agent Systems Modeling, Case Study (6)Text Books1. Object-Oriented Modeling and Design with UML - Michael Blaha, James Rumbaugh2. Pattern-Oriented Software Architecture A System of Patterns, Volume 1 - Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal3. Object-Oriented Analysis and Design with Applications - Grady Booch et al4. Object-Oriented Design with UML and JAVA - K. Barclay, J. Savage5. Practical Object-Oriented Design with UML - Mark Priestley22Operations Research (V Semester CSE & IT 3L)SyllabusOperation Research (CS-1505)Prerequisite: Basic Engineering Mathematics.Objective: This module aims to introduce students to use quantitative methods and techniques for effective decisions–making; model formulation and applications that are used in solving business decision problems.Course DescriptionIn this course students will study some common operations research models and algorithms. Operations Research (OR) refers to the science of informed decision making. The goal is to provide rational basis for decision-making by analyzing and modeling complex situations, and to utilize this understanding to predict system behaviour and improve system performance. The application of OR involves problem formalization, model construction and validation; other activities include a computational part, analysis of solutions, arriving at conclusions, and implementation of the decision. It extensively uses the concepts of mathematical modeling, statistical analysis and optimization techniques. The emphasis is on applications rather than the details of methodology. This would act as a tool to the courses namely data mining, business intelligence and decision support systems.Course Outline (To be covered in 30 lectures)1. Introduction, Linear programming (LP) models, (4)2. Simplex & revised simplex algorithms, Duality and sensitivity analysis in LP (6)3. Basics of Game theory, Transportation and assignment problems, Project scheduling (critical path method & PERT) (10)4. Integer programming models, Stochastic processes: Markov chains and birth/death processes, Queuing theory (6)5. Network Analysis and Inventory Control(4)Text Books1. Operations Research Models and Methods, by Paul A. Jensen and Jonathan F. Bardto2. Operation Research by Hamdy.A Taha3. Introduction to Operations Research, by Frederick Hillier & Gerald Lieberman4. Linear Programming by Hadely G.23Cryptography (V Semester CSE & IT 3L)SyllabusCryptography (CS-1506)Prerequisite: Coding Theory, Information Security.Objective:Appreciate the core techniques of cryptography and how they can be applied to meet various security objectives. Understand both the importance of cryptographic key management, and the different key management requirements and practices associated with the use of different security techniques. Appreciate how the techniques described are employed in practice in a variety of security applications, from SSL enabled websites through to disk encryption.Course DescriptionIn this course students will study the essential mathematical foundations for Information Security. This course features a rigorous introduction to modern cryptography, with an emphasis on the fundamental cryptographic primitives of public-key encryption, digital signatures, pseudo-random number generation, and basic protocols and their computational complexity requirements. After crediting this course students can look forward to wireless network security and E-commerce courses.Course Outline (To be covered in 30 lectures)1. Introduction, Prime Number Generation, Shannon's Theory of Perfect Secrecy (5)2. Asymmetric Key Cryptosystem and related issues (5)3. Public Key Cryptography and related concepts/methodologies (10)4. Cryptographic Hash Functions design and implementation issues. (5)5. Digital Signatures and related issues (5)Text Books1. Modern Cryptography : Theory and Practice by W Mao2. Applied cryptography by Bruce Schiener3. “Cryptography: Theory & Practice” D R Stinson,4. Introduction to cryptography by Johannes A Buchmann5. Network Security and Cryptography by Bernard Menezes24Multimedia Technology (VI Semester IT 3L)SyllabusMultimedia Technology (CS-1607)Prerequisites: Knowledge of basic application software.Objective: Knowledge of utilization of multimedia applications.Course DescriptionIn this course students will study multimedia technologies, both standard and newly developed. Course coverage will include both theoretical understanding of multimedia technologies, and hands-on experience with applications and hardware. Topics may include perception, cognition, and communication issues, multimedia interface standards, multimedia evaluation, digitizing and manipulating images, voice, and video materials. Courses namely Computer graphics, Operating System and Computer Networks are prerequisites. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 30 lectures)1. Introduction, Multimedia Information, Multimedia Objects, Convergence of Computer, Communication and Entertainment products, Digital representation (6)2. Multimedia hardware, Memory & storage devices, Communication devices, Multimedia software's, presentation tools, tools for object generations, video, sound, image capturing, authoring tools, card and page based authoring tools (6)3. Introduction to Text, hypertext & hypermedia, Sound, MIDI, Digital Audio concepts, audio file formats Sampling Variables, Loss less compression of sound, Audio Capture. (6)4. Introduction to video& images :Multiple monitors, bitmaps, Vector drawing, Image format conversion, image compression, JPEG Compression, image & video file formats, animation, animation file formats. Video representation, Video Compression, color models, MPEG standards, Video Streaming on net, Video on demand. (6)5. Introduction to multimedia communications. multimedia over I.P, multimedia Over ATM Networks, multimedia Data Base, content based retrieval in Digital libraries, multimedia over wireless networks. Serial port programming and interrupts (6)Text Books1. Fundamental of Multimedia by Li and Drew2. Principle of Multimedia by Rajan Parekh3. Multimedia, Making it Work by Tay Vaughan25Scientific Computing(VI Semester CSE & IT 3L)SyllabusScientific Computing (CS-1602)Objective: The goal of this course is to introduce students to the fundamental concepts of Scientific Programming using Matlab/Octave and similar programming languages (e.g. sage math) and we will introduce the necessary mathematical concepts as we go (including linear algebra, differential equations, probability and statistics). The course will cover the syntax and semantics of Matlab/Octave including data types, control structures, comments, variables, functions, and other abstraction mechanisms.Prerequisites: Calculus, Algebra, The ability to write and run programs under a UNIX operating system, in one of the languages C, C++, or Fortran.The ability to create executables involving multiple files and libraries either by a script or a makefile .Write programs that read and write formatted data from and to files.Course DescriptionScientific computing has become an indispensable tool in many branches of research, and is vitally important for studying a wide range of physical and social phenomena. This course will examine the mathematical foundations of well-established numerical algorithms and explore their use through practical examples drawn from a range of scientific and engineering disciplines. It gives the computational algorithms for analyzing and solving mathematical problems such as model fitting, calculus operations, finding roots for equations and other statistical computation. The prerequisites for this course are linear algebra, calculus, and elementary probability theory along with computer programming.Course Outline (To be covered in 30 lectures)1. Introduction, Algebraic and Transcendental Equations and related issues (8)2. Discussion on different Interpolation concepts and methods (8)3. Curve Fitting, Cubic Spline & Approximation(7)4. Numerical Integration and Differentiation. (6)5. Numerical Linear Algebra (5)6. Statistical Computations (6)Text Books1. Numerical Recipes in C The Art of Scientific Computing by W H Press, S A Teukolesky, W T Vellerling and B P Flannery2. Numerical Methods for Scientific and Engineering by M.K.Jain, S.R.K.Iyenger and R.K.Jain3. Numerical Methods and Analysis by James I. Buchman and Peter R.Turner4. Applied Numerical Analysis by C.F.Gerald and P.O.Wheatley26Business Intelligence (VI Semester IT 3L)SyllabusBusiness Intelligence (CS-1608)Prerequisites: Clear understanding of what kind of business Questions must be answered.Objective: Gather all needed information from the business applications, to link them into the business context and to reduce the amount of information to answer the Business questions represented by the key indicators.Course DescriptionIn this course students will study the features, uses, and design strategies for IT-enabled managerial decision support and business intelligence. The course includes an overview of business intelligence framework, business process management and application-based business analytics and reporting.Course Outline (To be covered in 30 lectures)1. Introduction, Overview of Business Intelligence, deduction, induction, machine learning and neural networks, (5)2. Introduction to analysis, quantitative methods for data analysis and knowledge extraction: classification and regression, Bayesian approaches, belief networks. (8)3. Introduction to DSS development, Traditional system development life cycle, Alternate development methodologies, Prototyping: DSS Methodology, Tools for DSS development, DSS Technology levels and tools (8)4. Enterprise system : Concept and definition, Enterprise Decision Support System, Evolution of executive and enterprise information system (EIS), Characteristics and capabilities of EDSS , Comparing and integrating EIS and DSS (6)5. BI applications: Knowledge management, Decision analysis, Investment Strategies, Marketing Campaigns (3)Text Books1. Decision Support Systems and Intelligent Systems by Efrain Turbon.2. Adaptive Business Intelligence by Michalewicz Z., Schmidt M., Michalewicz M. and Chiriac C.3. Business Intelligence: A Managerial Approach by Turban E., Sharda R., Aronson J.E. and King, D.4. Advanced Management Information Systems by W.S. Jawadeka27Wireless Network Security (VI Semester CSE & IT 3L)SyllabusWireless Network Security (CS-1604)Objective: In this course, description and classification of security goals and attacks in wireless networks, security architectures of the following wireless systems and networks: 802.11, GSM/UMTS, RFID, ad hoc/sensor networks, reason about security protocols for wireless network will be covered.Prerequisites: This course assumes prior knowledge of Computer Networks, A basic understanding of TCP/IP networking, Mobile Computing & understanding of Computer Security concepts. Knowledge of data structures, databases, and mathematical logic are useful.Course DescriptionIn this course students will study wireless networks and their security. In this course, many recent, current and emerging developments will be discussed including advances in cellular, personal communications systems (PCS), wireless LANs, satellites, and fixed wireless networks. Significant details of wireless devices and middleware will be included. Many emerging challenges and solutions including ad hoc wireless networks, broadband wireless and quality of service, and location management besides security would be taken up. Communication Foundations, Computer Network and cryptography are prerequisite courses. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 30 lectures)1. Introduction, Wireless Communications (2)2. Wireless devices and Middleware, Design of Wireless Networks (2)3. Ad-hoc wireless networks, wireless sensor networks(2)4. Security threats in wireless networks. Security requirements of wireless networks (4)5. Security case studies for Wireless LAN and Ad-hoc wireless networks (6)6. Speech Cryptology (5)7. Protocols and Applications of Cellular, Personal Communications Systems, and Bluetooth. Security issues and services. (9)Text Books1. Wireless Security Models, Threats, and Solutions By: Randall K. Nichols, Panos C. Lekkas2. Wireless Communications: Principles & Practice, by Ted Rappaport,3. Wireless Network Design: Optimization Models and Solution Procedures, by J. Kennington et. al.4. Security and Cooperation in Wireless Networks, by Levente Buttyán and Jean-Pierre Hubaux [Available Online]5. The IEEE 802.11 Handbook: A designers companion by Bob O Hara, Al Petrick28Database Management System(VI Semester CSE & IT 4L)SyllabusDatabase Management System (CS-1605)Objective: This course will give principles and practical solutions for storage and retrieval of information using a computer system, particularly for large quantities of data, and with an emphasis both on the use of relational database management systems.Prerequisites: Elementary knowledge about computers including some experience using Unix or Windows. Knowledge of programming in some common programming language. Understanding of data structures and algorithms are required.Course DescriptionIn this course students will study the basic functions and capabilities of database management systems (DBMS). Emphasis is placed on the use of DBMS in solving information processing problems which will include database design case studies as well as SQL programming assignments along with transactions. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 40 lectures)1. Database system concept and architecture, Entity Relationship and Enhanced E-R (5)2. Relational Data Model and Relational Algebra, SQL, Indexing, Query Optimization (10)3. Relational Database Design, Normalization principles and normal forms (8)4. Transaction concept and concurrency control (8)5. Web Interface to DBMS, Semi-structured databases, Object oriented databases (6)6. DBMS Case studies (3)Text Books1. Database system concepts, by Korth, Silberschatz, and Sudarshan2. Fundamentals of Database Systems by Elmasari and Nawathe3. Databases by O Neil,4. Database Systems The Complete Book by Garcia-Molina, Ullman, & Widom5. Database Management System by Ramakrishnan and Gehrke29Software Engineering (VI Semester CSE & IT 3L)SyllabusSoftware Engineering (CS-1606)Objective: The course assists to understand the basic theory of software engineering, and to apply these basic theoretical principles to a group software development project.Prerequisites: Data Structures & Algorithms, Programming Language abstract and Concrete Syntax, Logic Propositional and Predicate Logic, Proofs - Inference Rules, Proof Methods.Course DescriptionIn this course students will study the fundamentals of software engineering, including understanding system requirements, finding appropriate engineering compromises, effective methods of design, coding, and testing, team software development, and the application of engineering tools. The course will combine a strong technical focus with a mini project (offered alongside), providing the opportunity to practice engineering knowledge, skills, and practices in a realistic development setting.Course Outline (To be covered in 30 lectures)1. Introduction, Software life-cycle models (4)2. Software requirements, Requirements Specification (6)3. Software design and Software user interface design(7)4. Coding Issues, Software integration and testing. (6)5. Software support processes and Quality Assurance, IEEE Software Engineering Standards (4)6. Software maintenance, Software reuse, (3)Text Books1. Software Engineering – A Practitioner’s Approach, by Pressman R. S. and Ince D2. Software Engineering by Sommerville3. Software Engineering, Volume 1 and Volume 2, by Thayer, and Christiansen,4. Fundamentals of Software Engineering by Rajib Mall30Image Processing (VII Semester IT 4L)SyllabusImage Processing (CS-1703)Prerequisites: This course assumes that students have strong programming skill in MATLAB, and a working knowledge of Intermediate Calculus, Linear Algebra, basic estimation techniques, and some statistical topics on the level of introductory courses in statistics.Objective: This course will provide students a detailed overview of Digital Image Processing and its applications. Image processing has found applications in many areas from medical imaging to computer graphics. This course covers the fundamental concepts of visual perception and image acquisition, basic techniques of image manipulation, segmentation and coding, and a preliminary understanding of Computer Vision. With successful completion of the course, students will be able to perform image manipulations and analysis in many different fields.Course DescriptionIn this course students will study the theoretical foundations and modern applications in digital image processing. Insight into the basic operations like image acquisition, enhancement, restoration, transformations, compression, segmentation, object recognition and visual interpretation would be taken up along with the numerical interpretation. Wide variety of research applications ranging from pattern recognition, security measures such as digital signatures, watermarking; traffic video surveillance, medical imaging, remote sensing applications would be illustrated. Pre-requisite is the basic knowledge of mathematics and programming. A lab course is associated with it to strengthen the concepts.Course Outline (To be covered in 40 lectures)1. Introduction, digital image fundamentals Elements of digital image processing systems, Elements of visual perception, brightness, contrast, hue, saturation, Color image fundamentals - RGB, HSI models, Image sampling, Quantization, dither, Two-dimensional mathematical preliminaries, 2D transforms - DFT, DCT, KLT, SVD. (6)2. Image enhancement Histogram equalization and specification techniques, Noise distributions, Spatial averaging, Directional Smoothing, Median, Geometric mean, Harmonic mean, Contraharmonic mean filters, Homomorphic filtering, Color image enhancement. (8)3. Image Restoration - degradation model, Unconstrained restoration - Lagrange multiplier and Constrained restoration, Inverse filtering-removal of blur caused by uniform linear motion, Wiener filtering, Geometric transformations-spatial transformations. (8)4. Image segmentation, Edge detection, Edge linking via Hough transform –31Thresholding - Region based segmentation – Region growing – Region splitting and Merging – Segmentation by morphological watersheds – basic concepts – Dam construction – Watershed segmentation algorithm. (8)5. Need for image compression, Huffman, Run Length Encoding, Shift codes, Arithmetic coding, Vector Quantization, Transform coding, JPEG standard, MPEG. (8)Text Books1. Digital Image Processing by Rafael C. Gonzalez, Richard E. Woods,2. Fundamentals of Digital Image Processing by Anil K. Jain,3. Digital Image Processing by William K. Pratt4. Professional Ethics(VII Semester CSE & IT 2L)32Professional Ethics(VII Semester CSE & IT 2L)SyllabusProfessional Ethics (CS-1702)Objective : The course will develop a framework on which professional and ethical issues can be analyzed, and build up an awareness of various views of ethical issues as well as professionals and engineering ethical rights and responsibilities.Instructional Goal:1. To familiarize students with different professional codes of ethics.2. To familiarize students with the goals and possible effects of professional codes of ethics.Performance Objective:1. To develop awareness of different codes of ethics.2. To be able to distinguish between the goals of different societies and organizations.3. To be able to distinguish between the effects of different codes of ethics.Prerequisites: A course in public speaking and a course in writing a research papers.Course DescriptionIn this course students will study application of moral reasoning to established profession of computer engineering. Moral reasoning entails the search for values and principles that promote a good life and human flourishing. As a professional, one has to employ ones expertise in the ways that greatly affect the lives of others. After crediting the course the students are expected to identify ethical conflicts, identify their responsibilities and options, and think through the implications of possible solutions to ethical conflicts.Course Outline (To be covered in 20 lectures)1. Introduction, Ethical theories (4)2. Ethics in IT societies, Intellectual rights and privacy (6)3. Professional Relationships, Professional Responsibilities, Professional Ethics in Computing (6)4. Online crime, hacking, Legal aspects of Professional Ethics (4)Text Books1. IEEE/ACM Software Engineering Code of Ethics and Professional Practice (online)2. Computer Ethics by Deborah Johnson3. Ethics in Engineering by Martin M.W., Schinzinger R.4. Ethics in Information Technology by George Reynolds5. Readings in Cyber Ethics, Edited by Richard Spinello and Herman Tavani.33Privacy Preserving Publishing (VIII Semester IT 4L )SyllabusPrivacy Preserving Publishing (CS-1803)Prerequisites: Discrete Mathematics and Computer Systems Technologies.Objective: Primary goal is to develop critical understanding and thinking with respect to current research challenges in cyber security.Course DescriptionIn this course students will understand mechanisms and principles involved in the methods and tools of privacy-preserving publishing enabling the publication of useful information while protecting data privacy. In this course students will explore not only the privacy and information utility issues but also efficiency and scalability challenges.Course Outline (To be covered in 40 lectures)1. Introduction, Attack Models and Privacy Models (7)2. Anonymization Operations and Algorithms, Anonymization for Cluster Analysis. (9)3. Anonymizing Incrementally Updated Data Records, Collaborative Anonymization for Vertically Partitioned Data and Horizontally Partitioned Data. (8)4. Anonymizing Complex Data e.g. Anonymizing Transaction Data, Anonymizing Trajectory Data. (8)5. Anonymization for data mining, Anonymizing Social Networks . (8)Text Books1. Introduction to Privacy-Preserving Data Publishing Concepts and Techniques By Benjamin C.M. Fung, Ke Wang, Ada Wai-Chee Fu, Philip S. Yu2. Privacy-Preserving Data Publishing: An Overview by Raymond Chi-Wing Wong & Ada Wai-Chee Fu3. Research papers34Research Trends in IT (VIII Semester IT 3L)SyllabusResearch Trends in IT(CS-1804)Prerequisites: Discrete Mathematics and Computer Systems Technologies.Objective: Primary goal is to develop critical understanding and thinking with respect to currentResearch challenges in Information Technology.Course DescriptionIn this course students will study newer areas of research in Information Technolgy. In the last fitty years have seen computer science and communication technology evolve as major academic disciplines. Today the field is undergoing a fundamental change. Some of the drivers of this change are the Internet, the World Wide Web, large quantities of information in digital form, wide spread use of computers for accessing information and semantic web. In this course the students would be encouraged to identify their area of interest and to prepare and present a term paper pertaining recent advances taking place in that area.Course Outline (To be covered in 30 lectures)1. Introduction, History of information technology, (3)2. Presentation ScheduleText Books (Not applicable)1. DBLP to identify areas and TOC of Journals and Conference Proceedings2. INDEST, ACM digital Library, IEEE Digital Library etc to browse papers3. Handbook of Writing for the Mathematical Sciences By Nicholas J. Higham35Professional Elective I & II(Pool – 1)36Artificial Intelligence (Professional Elective for CSE & IT 3L)SyllabusArtificial Intelligence: (OE)Prerequisites: Basic Program, Logical Program, Probability, Discrete Mathematics.Objective: Identify problems that are amenable to solution by AI methods, and which AI methods may be suited to solving a given problem. Formalize a given problem in the language/framework of different AI methods (e.g., as a search problem, as a constraint satisfaction problem, as a planning problem, as a Markov decision process, etc). Implement basic AI algorithms (e.g., standard search algorithms or dynamic programming). Design and carry out an empirical evaluation of different algorithms on a problem formalisation, and state the conclusions that the evaluation supports. Develop an expert system. Learn Logical Programming Skills.Course DescriptionThis course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence (AI). It covers basic elements of AI, such as knowledge representation, inference, machine learning.Course Outline (To be covered in 30 lectures)1. Introduction, Intelligent agents, reactive, deliberative, goal-driven, utility-driven, and learning agents, Artificial Intelligence programming (5)2. Defining problems at state space search, Production system, Problem and production system characteristics, Forward and backward, state-space, blind, heuristic, problem-reduction, A, A*, AO*, minimax, constraint propagation, neural, stochastic, and evolutionary search algorithms, sample applications. Issues in design of search programs (7)3. foundations of knowledge representation and reasoning, issues in knowledge representation, representing and reasoning about objects, relations, events, actions, time, and space; predicate logic, situation calculus, description logics, reasoning with defaults, sample applications. (6)4. Planning as search, partial order planning, construction and use of planning graphs, planning and acting in the real world (3)5. Basics of utility theory, decision theory, sequential decision problems, elementary game theory, sample applications. (4)6. Learning from memorization, examples, explanation, and exploration. Supervised and un-supervised learning, learning nearest neighbor, naive Bayes, and decision37tree classifiers, Q-learning for learning action policies, applications. Sample Applications of AI (5)Text Books1. Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig,2. Artificial Intelligence by Eliane Rich, Kevin Knight and Shivashankar B Nair,3. Introduction to Artificial Intelligence by Charniak, McDermott38Data Compression (Professional Elective for CSE & IT 3L)SyllabusData Compression (OE)Prerequisites:Basic data structures and algorithms, Fundamental concepts of computer architecture.Objective:Develop theoretical foundations of data compression, concepts and algorithms for lossy and lossless data compression, signal modeling and its extension to compression with applications to speech, image and video processing.Course DescriptionThe course discusses the theory and methods of data compression of signals, images, and video. Data Compression is the computational problem of how to encode a data file (text, image, audio, video) so that the new file has fewer bits the original file. Techniques covered include: Quantization, Vector Quantization, Differential Schemes, Filterbanks and Subband Coding, Wavelet Transform, JPEG 2000, and MPEG. Coverage of selected topics of recent research issues in data compression is also taken up.Course Outline (To be covered in 30 lectures)1. Information theoretic foundations, Arithmetic coding (6)2. Dictionary techniques, Context modeling (6)3. Lossless image compression, Lossy coding preliminaries (6)4. Scalar and vector quantization (6)5. Differential encoding, Transform coding (6)Text Books1. Introduction to Data Compression by Sayood, Khalid,2. Data Compression: The Complete Reference by M. Nelson,39Data Warehousing and Mining (Professional Elective for CSE & IT 3L)SyllabusData Warehousing and Mining (OE)Prerequisites: An upper-level undergraduate course(s) in algorithms and data structures, a basic course on probability and statistics programming in Java, C++, C.Objective:Understand data mining principles and techniques: Introduce DM as a cutting edge business intelligence method and acquaint the students with the DM techniques for building competitive advantage through proactive analysis, predictive modeling, and identifying new trends and behaviors. Learning objectives include:a. Building basic terminology.b. Learning how to gather and analyze large sets of data to gain useful business understanding.c. Learning how to produce a quantitative analysis report/memo with the necessary information to make decisions.d. Describing and demonstrating basic data mining algorithms, methods, and tools .e. Identifying business applications of data miningf. Overview of the developing areas - web mining, text mining, and ethical aspects of data mining.Develop and apply critical thinking, problem-solving, and decision-making skills.Course DescriptionThe course is an introduction to data mining techniques for the data stored in a data warehouse. Data mining, or knowledge discovery in databases, has during the last few years emerged as one of the most exciting fields in Computer Science. Data mining aims at finding useful regularities in large data sets. Interest in the field is motivated by the growth of computerized data collections which are routinely kept by many organizations and commercial enterprises, and by the high potential value of patterns discovered in those collections. This course will cover data warehousing and data cleaning, clustering, classification, and association rules mining.Course Outline (To be covered in 30 lectures)1. Introduction and overview of data mining processes (3)2. Data Warehousing: Overview, Definition, Delivery Process, Multi Dimensional Data Model, Data Cubes, Stars, Snow Flakes, Fact Constellations, Concept hierarchy, Process Architecture, 3 Tier Architecture, Data Marting. (5)3. Data clustering and classification techniques (9)4. Association rule mining (5)5. Tuning Data Warehouse, Testing Data Warehouse Data Mining interface,40Historical information, Query Facility, OLAP function and Tools. OLAP Servers, ROLAP, MOLAP, HOLAP, Security, Backup and Recovery (5)6. Applications and case studies (3)Text Books1. Data Mining: Concepts and Techniques by J. Han and M. Kamber,2. Introduction to Data Mining by Pang-Ning Tan, Michael Steinbach and Vipin Kumar3. Data Warehousing in the Real World : A Practical Guide for Building Decision Support Systems by Sam Anahory, Dennis Murray41Design Patterns (Professional Elective for CSE & IT 3L)SyllabusDesign Pattern (OE)Prerequisites: Prior knowledge of object-oriented programming is essential for this course. The students are expected to be proficient in Java, Principle of Programming Languages.Objective:• Understand and be able to apply incremental/iterative development• Understand common design patterns• Be able to identify appropriate patterns for design problems• Be able to evaluate the quality software source code• Be able to refactor badly designed program properly using patternsCourse DescriptionThis course is an introduction to software design patterns. Each pattern represents a best practice solution to a software problem in context of some application. The course will cover both the rationale and benefits of object-oriented software design patterns. Several example problems need to be studied to investigate the development of good design patterns. Specific patterns, such as Observer, State, Adapter, Strategy, Decorator and Abstract Factory would be covered.Course Outline1. Introduction To Design Patterns, Introduction To Java, Some OO Design Principles , The Observer Pattern, The Template Method Pattern (6)2. Factory Patterns: Factory Method and Abstract Factory, The Singleton Pattern, The Iterator Pattern, The Composite Pattern, The Facade Pattern (6)3. The State and Strategy Patterns, Functors and the Command Pattern, The Proxy Pattern (5)4. RMI, The Adapter Pattern, The Decorator Pattern (4)5. Dynamic Proxies In Java, The Chain of Responsibility Pattern, Concurrency Patterns, The Visitor Pattern, Anti Patterns (5)6. Layer, Pipe and Filters, Black Board Broker, Case Studies (4)Text Books1. Design Patterns - Elements Of Reusable Object-Oriented Software, Erich Gamma, Richard Helm, Ralph Johnson, and John Vlissides,2. Head First Design Patterns, Eric Freeman and Elisabeth Freeman3. Applied Java Patterns, Stephen Stelting and Olav Maassen,4. Java Design Patterns - A Tutorial, James W. Cooper,5. Refactoring To Patterns, Joshua Kerievsky,42Functional Programming (Professional Elective for CSE & IT 3L)SyllabusFunctional Programming: (OE)Prerequisites: Basic Mathematics.Objective: master foundational techniques from the paradigm of functional programming. be trained in using abstraction to structure programs. be able to explain and use recursion in general, as well as know how to distinguish between recursive and iterative processes. be able to write and use higher-order functions. master techniques for delayed evaluation for working with infinite data structures such as streams. have insight in different models for understanding how code is evaluated.Course DescriptionThis course aims to make functional techniques and thought patterns part of programming skills of the students. This course presents the functional programming paradigm, based on the idea of functions as "first-class" values that can be computed and operated. Functional languages provide great power of expression while maintaining simplicity, making it easier to write correct and maintainable software. Upon successful completion of the course, students would be able to analyze problems and apply functional programming concepts and techniques to solve the problems.Course Outline (To be covered in 30 lectures)1. Introduction, Problem Solving with Functional Language, Programming with functions, List constructors and selectors, Recursive functions, Accumulating parameters, Local definitions, Higher Order functions, Dot notation, and example simple functional programs (12)2. Un-typed and Typed Lambda Calculus and Combinators, Term structure and substitution, alpha and Beta reductions and Beta Equality, Normal Form, Combinators, Church Numerals, Reduction Rules, Y-Combinator, Bracket Abstraction, Standard Combinator Expressions, Typed Lambda Calculus and Reduction Rules (10)3. Lambda Calculus Semantics: Reduction Machines SECD Machine , Graph Reduction Machine, Lazy/delayed Evaluation, (8)Text Books1. Functional Programming : Application and Implementation by Peter Henderson2. Lambda Calculus, Combinators and Functional Programming by G. Revesz3. Lambda Calculus and Combinators : An Introduction by J. Roger Hindley and Jonathan P. Seldin43Genetic Algorithm (Professional Elective for CSE & IT 3L)SyllabusGenetic Algorithm: (OE)Prerequisites: Fundamentals of Artificial Intelligence, Basic Mathematics, Knowledge of a programming language.Objective: The aim of the course is to introduce genetic algorithms and to give students practical experience in implementing and experimenting with them. The course will equip them to be able to assess the suitability of genetic algorithms for specific problems.Course DescriptionIn this course students will study Genetic Algorithm and its application to optimization problems. The course covers Basics of Optimization, Optimization Problems, Point to Point Algorithms, Simulated Annealing, Population Based Algorithms, Brief Overview of Evolutionary Computation, Genetic Algorithms (Theory and Advanced Operators), Genetic Representation, search operators, selection schemes and selection pressure, Operators on Real-valued Representations, Niche and fitness sharing, Particle Swarm Optimization, Memetic Algorithms and Real Life application of Evolutionary Algorithms.Course Outline (To be covered in 30 lectures)1. Basics of Optimization, Optimization Problems, Point to Point Algorithms, Simulated Annealing (3)2. Population Based Algorithms, Brief Overview of Evolutionary Computation, Genetic Algorithms (Theory and Advanced Operators), Genetic Representation, search operators, selection schemes and selection pressure. (7)3. Operators on Real-valued Representations, Niche and fitness sharing, Particle Swarm Optimization, Memetic Algorithms (7)4. Evolution Strategies, Genetic Programming, Evolutionary Programming, Differential Evolution (6)5. Constraint Handling in optimization problems , Real Life application of optimization Algorithms, Introduction of Multi-objective Evolutionary Algorithms (7)Text Books1. Genetic Algorithms in Search, Optimization & Machine Learning by D E Goldberg2. Multi-Objective Optimization Using Evolutionary Algorithms by K.Deb3. Handbook on Evolutionary Computation by T. Baeck, D. B. Fogel, and Z. Michalewicz (eds.)44Network Administration (Professional Elective for CSE & IT 3L)SyllabusNetwork Administration: (OE)Prerequisites: Basic knowledge of Computer Networks.Objective: To learn about the network and how the data route.Course DescriptionThe course is designed to provide students with essential knowledge and skills that an effective network administrator must possess. It provides an overview of the essential TCP/IP protocols, and discusses how to properly configure and manage the network services based on these protocols (including DNS, DHCP, AD/LDAP directory services, print and file servers, NFS/NIS, and routing services). The course also takes up various issues like Configuration management, accounting management, Fault and disaster management, security management and performance management.Course Outline (To be covered in 30 lectures)1. Introduction, Basic System Administration (3)2. Windows Installation, Linux Installation and Package Management, Backup and Security, Monitoring and Managing Processes/Daemons, Scripting basics and start-up scripts (8)3. Unix Networking, Network Protocols - TCP, IP, UDP, NetBIOS, TCP/IP Concepts and Configuration - the basics, Sub netting Implementation, Basic Network Trouble-Shooting and Monitoring Tools (8)4. Server configuration and management, DHCP, NIS, NFS, LDAP and Samba (6)5. Apache Web Server with PHP, DNS, BIND and Sendmail, Tools like Webmin, Webalizer, and Phpmyadmin; Security and firewall (5)Text Books1. TCP/IP Network Administration?, by Craig Hunt,2. Neural Networks and Learning Machines by S. Haykin3. Artificial Neural Networks by Robert J. Schalkoff4. Multi-Objective Optimization Using Evolutionary Algorithms by Deb Kalyanmoy5. Genetic Algorithms + Data Structures = Evolution Programs by Z Michalewicz45Neural Networks (Professional Elective for CSE & IT 3L)SyllabusNeural Network(OE) Prerequisites: Multivariate calculus and linear algebra.Objective: gain familiarity with a wide variety of neural network models and their applications Develop capabilities for creating and using neural network models. develop knowledge of the state-of-the-art in neural networks, and Gain some mathematical understanding of neural network models. Gain experience in using computational tools such as neural networks to perform computational experiments leading to new theoretical insights.Course DescriptionThe course is an introduction to neural networks. Neural networks provide a model of computation drastically different from traditional computers. Typically, neural networks are not explicitly programmed to perform a given task; rather, they learn to do the task from examples of desired input/output behavior. The course introduces biological information processing followed by an overview of the most important artificial neural network architectures and algorithms such as perceptrons, backpropagation, Hopfield and Boltzmann networks, self-organizing maps, adaptive resonance theory, reinforcement learning, and neuroevolution.Course Outline (To be covered in 30 lectures)1. Introduction, Brain Physiology, Neuron Model and Network Architectures (4)2. Nonlinear dynamical system theory (6)3. The Hopfield Model, Spin Glasses, Stochastic Neural Networks, Boltzmann Machine (8)4. Multilayer Feedforward Networks For Supervised Learning(6)5. Unsupervised and Competitive Learning Algorithms, Bifurcating Neural Networks (6)Text Books1. Neural Networks: A Comprehensive Foundation by S. Haykin,2. Neural Networks and Learning Machines by S. Haykin3. Artificial Neural Networks by Robert J. Schalkoff4. Multi-Objective Optimization Using Evolutionary Algorithms by Deb Kalyanmoy5. Genetic Algorithms + Data Structures = Evolution Programs by Z Michalewicz46Service Oriented Software Engineering (Professional Elective for CSE & IT 3L)SyllabusService Oriented Software Engineering (OE)Objective:1. To introduce the idea of service-oriented architectures2. To explain the notion of a reusable service, based on web service standards, that provides a mechanism for inter-organisational computing;3. To describe the service engineering process that is intended to produce reusable web services.4. To introduce service composition as a means of application development;5. To show how business process models may be used as a basis for the design of service-oriented systemsPrerequisites: Software Engineering, Service-oriented analysis and design, Service oriented Modeling.Course DescriptionService oriented software development paradigm is becoming the delivery model by all major IT companies. This course is intended to introduce the students with this paradigm. In this course students shall study the fundamentals of Service Oriented Software Engineering. Prerequisite for this course is course on Software Engineering.Course Outline (To be covered in 30 lectures)1. Concepts of Service orientation (8)2. Service oriented Software architecture concepts (5)3. Requirements Analysis & Design Process (7)4. Service Testing and Estimation models (6)5. Cloud based services models (4)Text Books1. Service Oriented Architecture – Concept Technology & Design by Thomas Earl2. Enterprise SOA – Designing IT for Business Innovation by Woods & Mattem3. Web Service Essentials, Eiban Cerami, O’Reilly47XML and Applications (Professional Elective for CSE & IT 3L)SyllabusXML Based Applications (OE)Objective:1. To familiarize students with various XML based applications with the help of case studies.2. To be able to develop new applications using XML schema.Prerequisites: Fundamental concepts of XML including document and language creation and implementation, XML SchemaCourse DescriptionThis course introduces students to the basic concepts of the eXtensible markup language (XML). XML has made a major impact in almost every aspect of software development. Designed as an open, extensible, self-describing language, it has become the world-wide standard for data and document delivery on the Web. Students will be instructed as to the purpose of an XML document and what it consists of, in how a Document Type Definition (DTD) or schema is used to validate an XML document and the extensible style language (XSL) to transform XML documents into HTML/XHTML. XML-related technologies continue to develop, to enable validation, navigation, transformation, linking, querying, description, and messaging of data. Students would be exposed to such wide range of application domains.Course Outline (To be covered in 30 lectures)1. Emerging Technologies; XML Documents: Syntax, Well formed and Valid; CCS and XHTML; Document Type Definition(DTD); XML Schema : XSD, XDR, Examples; JavaScript (12)2. SAX and DOM Parser and APIs, Example of API usage; XPATH, XLink, Xpointer; XSL: XSLT (10)3. Applications: RDF and RDFS, JENA API, Case Study (8)Text Books1. XML The Microsoft Way By Peter G. Aitken2. Learning XML By Erik T. Ray and Christopher R. Maden3. XML How to Program By Harvey M. Deitel, Paul J. Deitel, Tem R. Nieto, Ted Lin and Praveen Sadhu48Professional Elective III & IV(Pool – 2)49Distributed and Parallel Algorithms (Professional Elective for CSE & IT 3L-0T)SyllabusDistributed & Parallel Algorithms (OE)Prerequisites: Basic Algorithms and Data Structures.Objective: Parallel and distributed architectures appear in a wide range of areas including networking, computer architecture, databases, image processing, artificial intelligence, numerical computing, symbolic computing, and other areas. Distributed and parallel systems are characterized by concurrency, large scale, peculiar demands for resources, etc. Such systems require skills and knowledge that dicer substantially from sequential programming experience. This course serves to introduce the students to the computational and algorithmic aspects of parallel and distributed computing. Thus, this course is appropriate for students wishing to do research and thesis work in a variety of areas of computer science.Course DescriptionThis course is an introduction to distributed and parallel algorithms design. Aim is to acquaint students with the basic concepts of parallel and distributed computing. The course aims to look into the general principles of parallel and distributed algorithms and their time complexity.Course Outline (To be covered in 30 lectures)1. Introduction, architectures and languages for parallel and distributed processing. (3)2. Abstract models of parallel computing, PRAM (Parallel Random Access Machine). Distributed and parallel algorithms and their complexity. Interaction between processes, communication, synchronization. (9)3. Topologies, synchronous and asynchronous algorithms. Algorithms for parallel sorting. Algorithms for parallel searching. (6)4. Parallel matrix operations. All prefix sums and their applications. Graph and list algorithms. Synchronization algorithms and tasks. (6)5. Mechanisms and language constructs for synchronization. Recently published algorithms.(6)Text Books1. Parallel Computation, Model and Methods by Akl,2. An Introduction to Parallel Algorithms, by J’aJ’a, J3. Introduction to Parallel Algorithms and Architectures: Arrays, Trees, Hypercubes by Leighton,4. Synthesis of Parallel Algorithms by J. H. Rief,5. Introduction to Distributed Algorithms by Gerard Tel,50E-Commerce (Professional Elective CSE & IT 3L)SyllabusE-Commerce (OE)Prerequisites: Computer Information Systems , Business Data Management , System Analysis and Design .Objective: have an understanding of essential e-Commerce concepts and technologies and skills related to the management and application of e-Commerce and e-Business approaches . have an understanding of the technological, capital and social infrastructure for commercial activities such as buying and selling, marketing and advertising, supply-chain management etc. have hands on, real-life experience with electronic commerce applications . be able to define and explain the main issues facing businesses engaged in the planning and implementation of e-Business strategies . identify and define the main e-Business models currently being adopted by organizations have an understanding and ability to assess the strategic relevance of e-Commerce in shaping both inter-organisational relationships and intra-organisational structures and processes critically evaluate the design of e-Business sites and discuss human, organisational and social implications of electronic commerceCourse DescriptionThe growth of the Internet continues to have a tremendous influence on business. Companies and organizations of all types and sizes are rethinking their strategies and how they run their operations. This new course in the Temple E-Marketing program challenges students to explore the realities and implications of e-commerce from a marketer's perspective. Business-to-consumer (B2C) and business-to-business (B2B) e-commerce markets are examined. The course introduces students to a wide range of electronic commerce issues for marketers, as a foundation for continual learning in the dynamic e-commerce environment.Course Outline (To be covered in 30 lectures)1. Introduction to e-Commerce and Network Infrastructure for e-commerce. [4]2. E-commerce Models, e-Advertising & Marketing [6]3. Electronic Payment Systems and Electronic Data Exchange [6]4. E-commerce Security [4]515. e-CRM [6]6. Mobile Commerce [4]Text Books1. Introduction to E-commerce by Jeffrey F.Rayport & Bernard J.Jaworski2. Frontiers of E-commerce by Kalakota & Winston3. E-Commerce- Strategy technologies and Applications by David Whiteley4. E-Commerce-Concepts, Models & Strategies by C.S.V. Murthy5. E-Commerce by Perry52Gaming and Animation (Professional Elective for CSE & IT 3L)SyllabusGaming and Animation (OE)Prerequisites: This course requires general familiarity with computer concepts, an interest in and experience with games, and a vivid imagination.Objective: This course gives students a solid understanding of designing, modeling and implementing a game.Course DescriptionThe purpose of this course is to give students a thorough understanding of computer animation and gaming. The course introduces camera and vehicle animation, parent/child hierarchies, character rigging, character animation, facial animation, lip syncing, physical simulations, motion capture for gaming.Course Outline (To be covered in 30 lectures)1. Introduction, Fundamental Principles of Animation and gaming (6)2. Rigging & Posing Techniques, Fundamentals of Character Animation, Facial Animation and Lip Sync Techniques (8)3. Fundamentals of Motion Capture, Principles of Motion Simulation (6)4. Game design principles and processes (8)Text Books1. Fundamentals of Game Design. By E. Adams.2. The Art of Game Design by J. Schell3. Computer Animation: Algorithms and Techniques by Rick Parent53Information Retrieval (Professional Elective for CSE & IT 3L)SyllabusInformation Retrival (OE)Prerequisites:Basic knowledge of web design, Basic Programming, data structures, Algorithms, Basic linear algebra, Basic statistics.Objective:To give students a solid understanding of: the genesis and variety of information retrieval situations. the variety of information retrieval models and techniques. design principles for information retrieval systems. methods for implementing information retrieval systems. characteristics of operational and experimental information retrieval systems. methods and principles for the evaluation of information retrieval systems.Course DescriptionThis course will cover traditional material, as well as recent advances in Information Retrieval (IR). The course includes the study of indexing, processing, and querying textual data basic retrieval models, algorithms, and IR system implementations. The course will also address advanced topics in IR, including Natural Language Processing techniques, and Web agents.Course Outline (To be covered in 30 lectures)1. Introduction to IR models and methods, Text analysis / Web spidering Text properties (5)2. Vector-based model, Boolean model, Probabilistic model, other IR models; IR evaluation and IR test collections; Relevance feedback, query expansion (8)3. Web search: link based and content based; Query-based and content sensitive link analysis; Search engine technologies (8)4. Text classification and clustering; Question answering on offline and online collections (5)5. Personalized IR, Cross-language IR, Web 2.0, (4)Text Books1. Introduction to Information Retrieval by Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze (available online)2. Information Retrieval: Algorithms and Heuristics. By D.A. Grossman, O. Frieder3. Readings in Information Retrieval by K.Sparck Jones and P. Willett54Pattern Recognition (Professional Elective for CSE & IT 3L)SyllabusPattern Recognition (OE)Prerequisites: Analysis of algorithms, Calculus, Introductory Statistics, Linear Algebra.Objective: Learn the fundamental concepts and applications of pattern recognition. Learn the concepts of Bayes decision theory. Understand the concepts of linear and nonlinear classifiers. Understand the concepts of feature selection and generation techniques. Understand the concepts of supervised learning and system evaluation. Develop some applications of pattern recognition.Course DescriptionThe emphasis of the course is on algorithms used for pattern recognition. Pattern Recognition is assigning a meaningful or classifying label to the elements of the input data. It uses the concepts of classification and clustering to separate the interclass elements. This information can then be used to classify or recognize new data using supervised or unsupervised learning methods and classifiers such as Support Vector Machine, Hidden Markov Model and Linear Discriminant Analysis. Pattern recognition has several important applications in the fields of data mining, artificial intelligence, networking and image processing. The prerequisites of the course are basic knowledge of statistics and linear algebra along with the concepts of probability theory.Course Outline (To be covered in 30 lectures)1. Introduction to Pattern Recognition, Feature Detection, Classification, Decision Theory, ROC Curves, Likelihood Ratio Test, Linear and Quadratic Discriminants, Fisher Discriminant, Sufficient Statistics, Coping with Missing or Noisy Features, Template-based Recognition, Feature Extraction, Eigenvector and Multilinear Analysis (10)2. Training Methods, Maximum Likelihood and Bayesian Parameter Estimation, Linear Discriminant/Perceptron Learning, Optimization by Gradient Descent, Support Vector Machines, K-Nearest-Neighbor Classification (6)3. Non-parametric Classification, Density Estimation, Parzen Estimation,Unsupervised Learning, Clustering, Vector Quantization, K-means, Mixture Modeling, Expectation-Maximization (6)4. Hidden Markov Models, Viterbi Algorithm, Baum-Welch Algorithm, Linear Dynamical Systems, Kalman Filtering, Decision Trees, Multi-layer Perceptrons, Reinforcement Learning with Human Interaction (8)Text Books1. Pattern Classification by Richard O. Duda, Peter E. Hart and David G. Stork2. Pattern Recognition and Machine Learning by C. M. Bishop3. Pattern Recognition by S. Theodoridis and K. Koutroumbas55Semantic Web (Professional Elective for CSE & IT 3L)SyllabusSemantic Web (OE)Prerequisites: Basic Web technology like html.Objective: The aim of the course is to make the students familiar with the Semantic Web, with technologies used on the Semantic Web, and with applications using Semantic Web technologies. The course will focus on the theoretical background of various languages on the Semantic Web such as RDF, SPARQL, OWL, and F-Logic (Programming), and the practical use of these languages on the Semantic Web. In addition, the course will focus on important application areas for Semantic Web technology, namely Web Services and Life Sciences.Course DescriptionThis course introduces techniques that are useful stand-alone and can be integrated for building a semantic web. It will review XML with Document Type Definitions and Schemas; transformation/inference rules in XSLT, metadata with RDF (Resource Description Framework); metadata taxonomies with RDF Schema; description logic and the W3C ontology language OWL 2; as well as integrating these techniques for ontology/rule-based multi-agent systems. Students may note that besides enabling quick and accurate web search, semantic web may also allow the development of intelligent internet agents and facilitate communication between a multitude of heterogeneous web-accessible devices.Course Outline (To be covered in 30 lectures)1. Review of XML; Meta-model and Meta-data, RDF & RDFS; OWL; Ontology Engineering and tools (12)2. Description Logic(DL); Programming with DL; Example Application (12)3. Knowledge Acquisition and Management System, (6)Text Books1. A Semantic Web Primer by Antoniou, Grigoris and Frank van Harmelen2. The Description Logic Handbook: Theory, Implementation and Applications by Franz Baader, Deborah L. Guinness, Daniele Nardi, and Peter F. Patel-Schneider (Eds.)3. An Introduction to Description Logic by Daniele Nardi and Ronald J. Brachman56Software Metrics & Quality Assurance (Professional Elective for CSE and IT 3L)SyllabusSoftware Metrics & Quality Assurance (OE)Prerequisites: Software engineering process, analysis, design etc.Objective: This course introduces concepts, metrics, and models in software quality assurance. The course covers components of software quality assurance systems before, during, and after software development. It presents a framework for software quality assurance and discuss individual components in the framework such as planning, reviews, testing, configuration management, and so on. It also discusses metrics and models for software quality as a product, in process, and in maintenance. The course will include case studies and hands on experiences. Students will develop an understanding of software quality and approaches to assure software quality.Course DescriptionIn this course students will study the foundational concepts of measurement of various aspects of software during the entire course of its development. The course takes up various existing metrics and tools that measure various activities of the software development. Topics such as Property-oriented measurement, Meaningfulness in measurement, Measurement quality, Measurement process, Scale, Measurement validation, Object-oriented measurement are covered. Students may note that the course is credited only after having undergone Software Engineering.Course Outline (To be covered in 30 lectures)1. The state of IT project management & basics of measurement (6)2. Measuring internal product attributes: size and structure (6)3. Measuring cost and effort (6)4. Measuring external product attributes: Quality & Reliability (6)5. Software test metrics (6)Text Books1. Software Metrics: A Rigorous and Practical Approach by N.E. Fenton and S.L. Pfleeger2. Metrics and Models in Software Quality Engineering by Stephen H. Kan3. Software Project Management in practice by Pankaj Jalote4. Software Project Management by Bob Hughes and Mike Cotterell57Software Testing (Professional Elective for CSE & IT 3L)SyllabusSoftware Testing (OE)Prerequisites: Software engineering and Software project management.Objectives: To understand the fundamental of software testing, different approaches to testing, managing test cases and different testing strategies.Course DescriptionIn this course students shall study the fundamentals of testing, various approaches to testing, managing test cases and various testing strategies. Students may note that the course is credited only after having undergone Software Engineering and/or Software Project Management.Course Outline (To be covered in 30 lectures)1. Fundamentals of Testing and its current state of art (8)2. Various approaches to Testing (6)3. Test planning and Management (6)4. Test Strategies - Preventive, Reactive Approach, Analytical, Heuristic, Configuration Management (6)5. Mutation Testing & Testing Object Oriented Software (4)Text Books1. Software Testing Techniques by Borris Beizer2. Software Testing – A Craftman’s Approach by Paul C. Jorgensen3. Software Testing by Hambling, Samaroo & Williams.4. Software Testing Practice: Test Management by Spillner, Rossner, Winter & Linz58Theory of Virtualization (Professional Elective for CSE & IT 3L)SyllabusTheory of Virtualization (OE)Prerequisites: Operating system and Computer network.Objectives: Understanding the skills and knowledge related to the concepts and principles of virtualization, the mechanisms and techniques of building virtualized system and virtualization-enabled processing scenario.Course DescriptionThis course provides description of the concepts of virtualization and the properties of virtualization that make it a powerful technology. It contrast different forms of virtualization and focus on system level virtualization which has become very popular lately in the computer industry. It describes various architectures for implementing system-level virtualization. Upon completion of this course, students will possess the skills and knowledge related to the concepts and principles of virtualization, the mechanisms and techniques of building virtualized systems, as well as the various virtualization-enabled computing paradigms. Further, they will also gain knowledge about some State-of-the-art virtualization software and systems through their course projects. The basic courses on Operating System and Computer Networks are prerequisites.Course Outline (To be covered in 30 lectures)1. Introduction, Overview of virtualization (8)2. Hardware/Server virtualization (8)3. Network virtualization (8)4. Virtual machines (6)Text Books1. Virtual Machines: Versatile Platforms for Systems and Processes by James E. Smith, Ravi Nair,2. Virtualization: From the Desktop to the Enterprise by Chris Wolf, Erick M. Halter3. Network virtualization by Kumar Reddy, Victor Moreno,4. Advanced Server Virtualization: VMware and Microsoft Platform in the Virtual Data Center by David Marshall, Wade A. Reynolds,59Web Mining (Professional Elective for CSE & IT 3L)SyllabusWeb Mining (OE)Prerequisites: Data mining, Data Base.Objectives: Web usage mining is the process of extracting useful information from server. Web Usage Mining is the application of data mining techniques to discover interesting usage patterns from Web data in order to understand and better serve the needs of Web-based applications.Course DescriptionThe course is an introduction to web mining technologies. Though the Web is rich with information, gathering and making sense of this data is difficult because the documents of the Web are largely unorganized. The course will cover machine learning techniques to mine the Web and other information networks, social networks, and social media. Applications to search, retrieval, classification, and recommendation would be studied. Various models to explain the dynamics of Web processes will also be emphasized.Course Outline (To be covered in 30 lectures)1. Introduction, Practical web mining applications overview (3)2. Natural Language Processing methods used for web information retrieval (6)3. Web Content Mining (5)4. Web Structure Mining (5)5. Web Usage Mining (6)6. Specific applications and case studies (5)Text Books1. Web data mining: exploring hyperlinks, contents, and usage data by LIU, B.2. Mining the Web - Discovering knowledge from hypertext data, by Soumen Chakrabarti,3. Ontology learning and population from text : algorithms, evaluation and applications by CIMIANO, P.60Programming Tools I ( III Semester CSE and IT 3P)Lab DescriptionThis is first independent lab course in programming tools which intends to introduce shell programming skills. UNIX is popular alternative to the Windows environment, especially in high-performance PC Linux servers and other UNIX-based web servers. Topics include: Unix utilities and file structure, Links and symbolic links, Data processing and process control in the Unix shell, Shell programming, Regular expressions, Exposure to different shells like bash, csh, ksh. Introduction to the Python/Perl programming in the Unix environment.Programming Tools II ( IV Semester CSE and IT 3P)Lab DescriptionThis is second independent lab course in programming tools which intends to introduce programming involving system calls. System calls are commands that are executed by the operating system. System calls are the only way to access kernel facilities. In this lab course students would learn to use these system calls as file system, multitasking mechanisms and the interprocess communication primitives.Programming Tools III ( V Semester CSE and IT 3P)Lab DescriptionThis is third independent lab course in programming tools which intends to introduce web programming skills. The web is an integral part of society and our lives. The web browser has also grown to be a critical piece of software on many platforms: PC, Laptop, mobile devices, and video game consoles. This course will follow the course tradition of "looking under the hood," exploring ways to create web content and applications.Note: Other labs are associated with respective theory courses and hence do not require explicit description.61Computer Programming ( I/II Semester All Branches)SyllabusCourse DescriptionThis is a first course in programming which intends to introduce students to the foundations of computing, programming and problem-solving. Aim is to develop basic programming skills necessary for engineering education. Students would learn C/C++ programming in a Linux environment. This course has an associated lab with it.Course Outline1. Introduction, LINUX Commands, editors, Files & Directories, Design of algorithms (4)2. Writing a Simple Program: Learning the form of a C program, Declaring variables, designing program flow and control, using standard terminal I/O functions. (4)3. Fundamental Data Types and Storage Classes, Operators and Expressions Conditional Program Execution Loops and Iteration, Introduction to Abstraction, functions, (6)4. Arrays, Pointers, Structures (6)5. Introduction to Object Oriented Programming concepts, Classes and Objects, Important C++ constructs (6)6. The Standard C/C++ Preprocessor, The Standard C/C++ Library (4)Text Books1. How to solve it by Computer by R. J. Dromey2. The C Programming Language by Brian W. Kernighan, Dennis M. Ritchie3. On to C++ by P H Winston ( also available online)4. Structure and Interpretation of Computer Programs by Harold Abelson and Gerald Sussman with Julie Sussman, (Also available online)5. Herbert Schield, Complete reference in C,

This is a great question. The de Broglie-Bohm theory (otherwise known as pilot wave theory, or Bohm's interpretation of quantum mechanics) has been ignored mostly because it wasn't liked by significant figures who held strong convictions about how the world is supposed to work that were contested by Bohm's interpretation. The shadows that those figures (e.g. Niels Bohr) have cast over the politics of physics have been so dominating that today's physicists are still rarely encouraged to explore anything outside of the standard interpretation of quantum mechanics. As a consequence, most of today's physicists are unaware that quantum mechanics is perfectly choreographed by the mathematics of the de Broglie-Bohm theory, or that it is less vague than the standard interpretation of quantum mechanics and entirely deterministic.Several historical events, or “unfortunate accidents,” have given momentum to a modern ignorance of the superior mathematical clarity Bohm’s formalism offers. Understanding this historical posture goes a long way towards explaining why the orthodox or “standard” interpretation of quantum mechanics is still held by the majority of physicists today—something that I would argue is one of the greatest intellectual tragedies of our time.To dive right in, let us note that in addition to the Schrödinger equation, which is shared among all quantum mechanical interpretations, Bohmian mechanics [1] is completed by the specification of actual particle positions, which evolve (in configuration space) according to the guiding equation. This combination elegantly restores determinism into the dynamics of physical reality; accounting for all the phenomena governed by nonrelativistic quantum mechanics—from spectral lines and scattering theory to superconductivity, the quantum hall effect, quantum tunneling, nonlocality, and quantum computing.On top of this, Bohm’s theory magnificently elucidates state evolution without elevating the role of the observer to something mystical. [2] This reveals that the stochastic property of the orthodox approach of quantum mechanics, which manifests in state vector reduction, is merely a reflection of the incompleteness of that approach. [3]Therefore, by declaring that a particle’s wave function interacts with the particle and guides or pushes the particle around in a way that determines its subsequent motion, this approach explicitly captures nonlocality in a way that introduces a new level of clarity. For example, in the double-slit experiment Bohm’s approach explains that each individual particle goes through one slit or the other, while its wave function goes through both and suffers interference. Because the wave function guides the particle’s motion, the particle is likely to land where the wave function value is large and it is unlikely to land where it is small. [4]State vector reduction never occurs in this model (the wave function never collapses) because the state vector exists as a separate element of reality. Orthodox quantum mechanical interpretations, which are plagued with state vector reduction, describe a system as having many possible outcomes prior to observation and only one outcome after observation. This introduces a definite temporal asymmetry. Bohm’s model is not plagued with this problem. It portrays one single outcome as a possibility both before and after observation, because it sharply specifies an exact state of space. This restores time symmetry and allows a deterministic evolution.Bohm’s model has been praised as a cure to the conceptual difficulties that have plagued quantum mechanics because it elegantly does away with much of the subjectivity and vagueness found in the standard approach. Despite this, mainstream physicists haven’t embraced this interpretation, or examined it in depth. In fact, the large majority of them haven’t even heard of it. This is embarrassing, surprising and frustrating. [5] If Bohmian mechanics provides a cure to modern quantum mechanical philosophic complacency, then why have there been so few to study the richness of this elegant formalism?James Cushing notes that, Bohm’s formalism has been systematically ignored and misunderstood for “reasons having more to do with politics, positivism, and sloppy thought, than for reasons central to physics.” [6] Several historically perpetuated fallacies have discouraged people from giving Bohm’s formalism a real look. First off, the model suggests that there is something called configuration space, asserting additional variables and creating a dualism almost Platonic in scope. [7] This counts as a “strike against” Bohmian mechanics only in the sense that it conflicts with assumptions that have become popular among physicists. In addition to this, mainstream quantum physicists have been trying to map reality based on the assumption that wave functions somehow collapse upon measurement—contrary to the fact that Schrödinger’s equation demands that they do not. Bohm’s model denies wave function collapse. Therefore, although it is simple and in agreement with Schrödinger’s equation, it has been overlooked because it has not been in accord with popularized mainstream efforts.“New opinions are always suspected, and usually opposed, without any other reason but because they are not already common.” ~ John LockePhysicists also compulsorily reject Bohm’s construction because it explicitly builds nonlocality into its framework—even though violations of Bell’s inequality have conclusively shown that the stage of our universe is nonlocal. [8] This is perplexing. Nonlocality is unavoidable in any theory that recovers the predictions of quantum theory. [9] Therefore, any criticism of a theory that displays Nature’s nonlocal feature in an obvious way is both unfounded and counterproductive. Despite this, Bohm’s inherent explication of nonlocality continues to be obnoxiously mistaken as a strike against it instead of for it.“That the guiding wave, in the general case, propagates not in ordinary three-space but in a multidimensional-configuration space is the origin of the notorious ‘nonlocality’ of quantum mechanics. It is a merit of the de Broglie-Bohm version to bring this out so explicitly that it cannot be ignored.” ~ John BellFinally, and most significantly, Bohm’s theory has been neglected by physicists who thought that additional variable theories had been proven impossible. [10] Impossibility theorems, like the one produced by John Bell, [11] or the one independently and almost simultaneously introduced by Simon Kochen and Ernst Specker, [12] or John von Neumann’s original no-go theorem, [13] were widely interpreted to forbid additional variables in quantum mechanics. What these theorems actually show is that additional variable formulation of quantum mechanics must be nonlocal, and that “quantum theory itself is irreducibly nonlocal.” [14] To cite Bell’s inequality as something that forbids additional variables is to show a gross misunderstanding of the theorem. When it comes to ruling out additional variable theories, the theorem is empty and irrelevant.As Bell, [15] Bohm, [16] and Mermin [17] have pointed out, these impossibility proofs are logically unsatisfactory because they arbitrarily impose conditions that are relevant to the standard interpretation of quantum mechanics, but are not relevant to the theories they aim to dismiss—any theory with additional variables. [18] Nevertheless, it took a long time for the physics community to realize that the impossibility theorems were irrelevant. [19]John Bell himself, the original author of one of the impossibility theorems, recognized its irrelevance, but he was systematically misquoted, misunderstood, or ignored as he tried to call attention to it. Ironically, he was then portrayed as being against Bohmian mechanics, despite the fact that he was its prime supporter during his lifetime. [20] He said:“But in 1952 I saw the impossible done. It was in papers by David Bohm. Bohm showed explicitly how parameters could indeed be introduced, into nonrelativistic wave mechanics, with the help of which the indeterministic description could be transformed into a deterministic one. More importantly, in my opinion, the subjectivity of the orthodox version, the necessary reference to the ‘observer,’ could be eliminated…But why then had Bohm not told me of this ‘pilot wave’?... Why did von Neumann not consider it? More extraordinarily, why did people go on producing “impossibility” proofs, after 1952, and as recently as 1978?... Why is the pilot wave picture ignored in textbooks? Should it not be taught, not as the only way, but as an antidote to the prevailing complacency? To show us that vagueness, subjectivity, and indeterminism, are not forced on us by experimental facts, but by deliberate theoretical choice?” [21]The rest of the story as to why Bohmian mechanics is not currently favored as the mainstream interpretation of quantum mechanics can be traced back to orthodox philosophical intransigence. Those that fail to comprehend, or factor in, the ontological advantages that come from the determinism and mathematical clarity of Bohmian mechanics often attempt to downplay the formalism by pointing out that it “doesn’t make any predictions that differ from those of ordinary quantum mechanics.” Technically, that’s not much of an objection because we could equally argue that empirically “the standard theory doesn’t go beyond Bohm’s theory.” [22]In light of this empirical equivalence, physicist Hrvoje Nikolic of the Rudjer Boskovic Institute in Zagreb, Croatia has said, “If some historical circumstances had been only slightly different then it would have been very likely that Bohm’s deterministic interpretation would have been proposed and accepted first, and would be dominating today.” [23] The standard interpretation has simply become the standard as a happenstance of history. The tragedy is that, because of the overwhelming political momentum of the standard interpretation, valid alternative interpretations (of which there are many) have largely been ignored.The fact is that Bohmian mechanics completely accounts for nonrelativistic dynamics. It choreographs every dance in the quantum mechanical realm, and does so deterministically. For these reasons alone it is worthy of our attention. But we also might raise a brow in response to the way it frees us from the limiting assertion of the orthodox interpretation.The most controversial aspect of orthodox quantum mechanics is not the formalism itself, but rather “a further assertion to the effect that we cannot get beneath this formalism, to account for it in microscopic terms.” [24] The quantum formalism is touted as a “measurement” formalism. “Thus it is a phenomenological formalism describing certain macroscopic regularities.” [25] In this, and in many other ways, it is analogous to thermodynamics.The thermodynamic formalism details the dynamics and interrelated properties of the larger macroscopic system based on assumptions about the underlying behavior of a large number of microscopic constituents that it takes to be in equilibrium. For example, the ideal gas lawrelates the macroscopic properties of an ideal gas (pressure, volume, and temperature), and ultimately explains that relationship based on an underlying assumption that the system (the ideal gas) is made up of microscopic constituents (molecules) that interact elastically and are in a state of equilibrium. [26]Several averaged-over macroscopic mathematical relations automatically follow from such assumptions. Because these mathematical relations have reliably held up in our laboratory experiments our confidence in the substrate of elastically interactive constituents (molecules) is strengthened. We now believe that we can intuitively access what lies beneath the thermodynamic formalism by accounting for its microscopic substrate. Whether or not anyone ever directly sees a molecule, or an atom, having a picture of the underlying microscopic dynamics greatly improves the intuitive access we have of physical reality.Clearly, as we derive a quantum formalism, it is in our best interest to retain the ability to “get beneath it,” and explain it in microscopic terms. One way to do this is to start with the assumption that the system (the vacuum in this case) is composed of a large number of microscopic constituents that (at least to first approximation) interact elastically. Interestingly, when we assume that the vacuum can be represented this way—as a quantum field, or an infinite collection of coupled harmonic oscillators—a quantum formalism similar to Bohmian mechanics “emerges in such an inevitable manner that we are almost forced to conclude that philosophical prejudice must have played a crucial role in its non-discovery.” [27]Today’s physicists have been brought up under the orthodox shadows of characters like Niels Bohr, Werner Heisenberg, and John von Neumann. [28] These figureheads loudly declared that a deterministic formalism of quantum mechanics is physically, philosophically, mathematically, and logically impossible. [29] They set in motion the assumptions that physicists would carry for decades after them. For some reason they were so intransigently stuck to the idea that quantum theory demands radical epistemological and metaphysical innovations that they appear to have never truly considered getting beneath the quantum formalism and accounting for it in microscopic terms. [30] These men possessed extraordinary intellects, and contributed powerfully to the development of quantum mechanics, but they missed out on Bohm’s obvious, elegant, and quite frankly trivial formalism.In my opinion, that stubbornness is the primary reason that Bohm’s interpretation of quantum mechanics is not the formal interpretation taught today. This intransigence has been quite lopsided. Craig Callender notes that, “For some reason or other, people often object to Bohm for reasons that they would never hold against other interpretations of quantum mechanics.” [31] I suspect that this has something to do with the fact that, without a map of the underlying molecular dynamics, people have a tremendously difficult time elevating their intuition to a higher dimensional realm where nonlocality is automatic.The orthodox interpretation of quantum mechanics maliciously severs the reach of our intuition. Its presumptions tautologically inhibit us from ever figuring out what is really going on by indefensibily asserting that Nature is not, and cannot be, described in a mathematically precise way. Many physicists and philosophers have felt the poignant sting of this truncation. Schrödinger himself never quite accepted the validity or completion of the wave function based on the intuitive damage it seemed to do. In reference to the wave function he said, “That it is an abstract, unintuitive mathematical construct is a scruple that almost always surfaces against new aids to thought and that carries no great message.” [32]A model’s value is to be measured by its ability to provide us with salient ontological and mathematical clarity of the domain it portrays. Unlike the orthodox interpretation of quantum mechanics, which restricts our intuitive reach by importing vaguely defined beables (additional variables called classical terms), Bohm’s formalism choreographs quantum mechanics in a way that is clear and mathematically precise. In short, instead of relegating Bohr’s classical terms (the additional variables from the Copenhagen interpretation) to the surrounding talk, [33] Bohm makes them mathematically precise.This leads to an interesting contrast. For example, despite the empirical equivalence between Bohmian mechanics and orthodox quantum theory, “there are a variety of experiments and experimental issues that don’t fit comfortably within the standard quantum formalism but are easily handled by Bohmian mechanics. Among these are swell and tunneling times, escape times and escape positions, scattering theory, and quantum chaos.” [34]The more striking contrast comes from the fact that Bohm’s model offers us a classical analogue by which to understand the quantum realm, while the orthodox interpretation attempts to forbid one. Let’s explore this point. In the orthodox interpretation we are asked to believe that, for example, photons form an interference pattern on the back wall because they all magically, in a way we cannot comprehend, manage to go through both slits. For systems with more than two slits every photon magically manages to go through every slit.In order to accept this interpretation we have to do more than abandon our notion of a particle—we have to accept that this magic is truly just that—magic. We have to accept that it really is impossible for us to ever have intuitive access to the process that causes photons, electrons, etc., to form interference patterns in the double-slit experiment—that it is impossible for us to comprehend, understand, or ever know what’s really going on during these experiments.The prevailing orthodox interpretation pushes this worldview upon us. Richard Feynman explains this by saying that the interference pattern made during the double-slit experiment is “a phenomenon which is impossible, absolutely impossible, to explain in any classical way, and which has in it the heart of quantum mechanics. In reality it contains the only mystery.” [35] Feynman later said, “Nobody can give you a deeper explanation of this phenomenon than I have given; that is, a description of it.” [36]If this were true it would be a pretty large pill to swallow. But it is not true. The truth is that Einstein understood the Copenhagen interpretation of quantum mechanics perfectly—he just wasn’t happy with its vagueness. [37] His intuition, that a deeper, more precise explanation is possible, has been fully justified. As we have seen, “Bohmian mechanics is just such a deeper explanation.” [38]From this precipice there is an apparent parallel between the advocates of the orthodox interpretation of quantum mechanics and the robed puppet masters of orthodox religions. Both preach that we are incapable of getting to know or discovering the truth for ourselves—that we should just give up and embrace unquestioning faith, or in this case, vagueness.That attitude is detrimental to our personal journeys and catastrophic to the overall scientific quest. Bohm’s interpretation frees us from the sins of orthodox unquestioning faith. It shows us that the path of the photon in our double-slit experiment reflects an interference pattern because the motion of that photon is governed by the wave function. Parts of the wave function pass through both slits while the particle passes through one slit. The parts of the wave function that pass through separate slits interfere with each other, developing an interference profile that guides the particle on its way.The interference pattern we see is, therefore, an unavoidable consequence of nonlocality—of the fact that the vacuum is quantized. It is not a magical, unexplainable effect. If the particles are emitted one by one, then this interference pattern still builds up over time—provided that the trajectories of the ensemble have a random distribution, or equilibrium distribution.If every particle were to follow completely identical trajectories, then they would all end up at one spot, creating a single bright spot on our photographic plate (or the wall). In Nature, this is not a real possibility for photons because the substrate of the vacuum is composed of interactive quanta. For two particles to follow identical trajectories, identical trajectories must exist. They don’t because the vacuum is not static. The quanta that compose the vacuum are constantly mixing about in configuration space. In quantum mechanics, the best information about available four-dimensional trajectories is given by an equilibrium distribution because quantum mechanics explicitly codifies a vacuum that is in a state of equilibrium. The inherent mixing of the vacuum explains why extremely precise information about a trajectory in the familiar four dimensions can at best be described statistically, or probabilistically.For two photons to follow identical paths through space (identical trajectories) the positions and velocities of all the intermittent space quanta along that path (the additional variables) would have to be identically configured. On macroscopic scales this is extremely improbable. So from the ontological vantage of Bohmian mechanics the interference pattern we see in the double-slit experiment is exactly what we should expect. That’s a rather significant improvement over the orthodox assertion that we should just accept the double-slit experiment as something that we will never make sense of.For more on this topic, and to see how the pilot-wave theory can be further elucidated by the assumption that the vacuum is a superfluid, check out Einstein's Intuition, available in black and white softcover, full color softcover, full color hardcover, an iBook, and as an audiobook.I also heavily recommend this lecture by Mike Towler from Cambridge.Notes:[1] Bohmian mechanics is also called the de Broglie-Bohm theory, the pilot-wave model, and the causal interpretation of quantum mechanics. Louis de Broglie originally discovered this approach in 1927 and David Bohm rediscovered it 1952.[2] S. Goldstein. Bohmian Mechanics. Stanford Encyclopedia of Philosophy. For more information on the identical success of Bohmian mechanics with the traditional quantum formalism see: Detlef Dürr, Sheldon Goldstein and Nino Zanghí. Quantum Physics Without Quantum Philosophy. Physical Review Letters, vol. 93, p 090402; Ward Struyve & Hans Westman, Proceedings of the Royal Society A, vol. 463, p. 3115; D. Bohm. (1953). Proof that probability density approaches psi squared in causal interpretation of quantum theory. Physical Review 89, 458–466; D. Bohm. (1952). A suggested interpretation of the quantum theory in terms of “hidden” variables’, Physical Rev. 85, p 166–193; M. Daumer, D. Dürr, S. Goldstein, & N. Zanghí. A survey of Bohmian mechanics, Il Nuovo Cimento.[3] Much of this chapter follows Sheldon Goldstein’s publication, Bohmian Mechanics, found in the online Stanford Encyclopedia Of Philosophy.[4] Brian Greene, (2004). The Fabric of the Cosmos, p. 206.[5] I have had enlightening discussions with Sheldon Goldstein and Daniel Victor Tausk about this very matter. Both of them have devoted considerable energy toward correcting this historical problem. But they have run into a lot of resistance. They have noted to me that many people are just too intransigent to consider a solution to a problem they have been working on their whole life, even if it is placed right in front of them. After spending a career on the problems of quantum mechanics to no avail many of them would prefer that the problem remain unsolved.[6] Cushing. (1994).[7] Vacuum quantization leads to a model that rides between Aristotelian naturalism and Platonic idealism. Aristotelian naturalism holds that reality consists only of the natural world. It is completely monistic and therefore denies the existence of a separate non-material order of reality. It also holds strongly to the belief that Nature follows orderly, discoverable laws. Platonic idealism, on the other hand, asserts that there is a non-material second transcendental realm. It is therefore dualistic. This non-spatiotemporal realm is believed to be accessible to the mind, but only to the mind. Vacuum quantization adjoins these two perspectives and ends up with a hierarchical monism. It proclaims that there is nothing outside natural order; there is no supernatural. It carries the explicit requirement for non-spatiotemporal realms that are directly accessible only to the mind (via vacuum quantization), but these realms are still part of the natural world—they follow orderly, discoverable laws.[8] Technically these violations show that the vacuum does not conform to local realism. To assume that realism is out is to assume that the entire scientific endeavor makes no connection to the real world (or that there isn’t a real world to begin with). If we don't go that route, then we must assume that the vacuum is nonlocal.[9] For a presentation of the argument and the experimental results that secure this point, see: Quantum Non-Locality and Relativity, Second Edition, by Tim Maudlin.[10] Tim Maudlin. (2002). Quantum Non-Locality and Relativity, second Edition, Blackwell Publishing, MA, p.124. For more on this see Joy Christian, Disproof of Bell’s Theorem by Clifford Algebra Valued Local Variables: http://www.arxiv.org/abs/quawnt-ph/0703179[11] J. S. Bell. (1966). On the problem of hidden variables in quantum mechanics. Rev. Mod. Phys. 28, 447–452; reprinted in Quantum Theory of Measurement, J. A. Wheeler & W. H. Zurek editors, Princeton University Press (1983), 396–402; and in Chapter 1 of J. S. Bell, Speakable and Unspeakable in Quantum Mechanics, Cambridge University Press (1987); second augmented edition (2004), which contains the complete set of J. Bell’s articles on quantum mechanics.[12] S. Kochen & E. P. Specker. (1967). The problem of hidden variables in quantum mechanics. J. Math. Mech. 17, 59–87.[13] John von Neumann. (1932). Mathematische Grundlagen der Quantenmechanik. Springer, Berlin.[14] Quoted from personal discussions with Sheldon Goldstein.[15] J. S. Bell. (1966). On the problem of hidden variables in quantum mechanics. Rev. Mod. Phys. 28, 447–452; reprinted in Quantum Theory of Measurement, J. A. Wheeler and W. H. Zurek editors, Princeton University Press (1983), 396–402; J. S. Bell, Speakable and Unspeakable in Quantum Mechanics, Cambridge University Press (1987); second augmented edition (2004), which contains the complete set of J. Bell’s articles on quantum mechanics.[16] D. Bohm & J. Bub. (1966). A proposed solution of the measurement problem in quantum mechanics by a hidden variable theory. Rev. Mod. Phys. 38, 453–469; D. Bohm & J. Bub. (1966). A refutation of the proof by Jauch and Piron that hidden variables can be excluded in quantum mechanics. Rev. Mod. Phys. 38, 470–475.[17] N. D. Mermin. (1993). Hidden variables and the two theorems of John Bell. Rev. Mod. Phys. 65, 803–815; in particular see § III.[18] “[T]he assumption of Kochen and Specker… appear, in fact, to be quite reasonable indeed. However, they are not. The impression that they are arises from a pervasive error, a naïve realism about operators…” Sheldon Goldstein, Bohmian Mechanics, published 10-26-2001; substantive revision 5-19-2006, Stanford Encyclopedia Of Philosophy.In other words, supporters of the standard interpretation of quantum mechanics often fail to recognize that Bohr’s classical variables are additional variables in their theory. Bohm takes these variables and makes them mathematically precise. Technically, attacking additional variable theories also attacks the standard model.[19] Franck Laloë. Do We Really Understand Quantum Mechanics?, p. 37. There are still members in the physics community that are held back by this misunderstanding. I have interacted with many physicists that are deeply convinced that these no-go theorems forbid additional variable theories.[20] See Wigner. (1976).[21] J. S. Bell. (1987), p. 160.[22] Mark Buchanan. (2008, March 22). No dice. New Scientist, pp. 28–31.[23] Ibid.[24] Detlef Dürr, Sheldon Goldstein, & Nino Zanghí. Quantum Physics Without Quantum Philosophy, p. 4. The mathematics to follow, as well as much of the remaining discussion, follows this work.[25] Ibid., p. 4.[26] Boyle’s law is another example of this. Technically, pressure and temperature are macroscopic properties that also rely on this underlying assumption. These properties result from the group behavior of a large number of elastically interactive molecules in motion.[27] Detlef Dürr, Sheldon Goldstein, & Nino Zanghí. Quantum Physics Without Quantum Philosophy, p. 4.[28] J. von Neumann. (1932); R. T. Beyer. (1955), pp. 324–325; J. S. Bell. (1982). 989–999; J.S. Bell. (1987), pp. 159–168.[29] J. von Neumann. (1932); J. S. Bell. (1982), (1987).[30] D. Dürr et al., pp. 4–6.[31] Craig Callender. (1998). Review, Brit. J. Phil. Sci. 49, 332–337.[32] Schrödinger, E. (1935). 23: 807–812, 923–828, 844–849.[33] John S. Bell. (1976). The theory of local beables. Epistemological Lett. 9, 11-24; Reprinted in John S. Bell. (2004). Speakable and Unspeakable in Quantum Mechanics, 2nd ed. Cambridge U.P., Cambridge, pp. 52–62.[34] Sheldon Goldstein. Bohmian Mechanics. For more on how the formalism of Bohmian mechanics naturally points to a formalism richer than the standard orthodox theory see: Anthony Valentini of the Perimeter Institute in Waterloo, Ontario, Journal of Physics A: Mathematical and theoretical, vol.of the popular one used today e bove the i.ld ever fall into it."me so great an absurdity that I believe no man who has in philo 40, p. 3285; For discussion on escape times and escape positions see Daumer et al., 1997a, for scattering theory see Dürr et al., 2000, and for quantum chaos see Cuching, 1994; Dürr et al., 1992a.[35] R. P. Feynman, R. B. Leighton, & M. Sands. (1963). The Feynman Lectures on Physics, I, New York: Addison-Wesley; Sheldon Goldstein. Bohmian Mechanics.[36] Richard Feynman. (1967). The Character of Physical Law. Cambridge MA: MIT Press; Sheldon Goldstein. Bohmian Mechanics.[37] It could be argued that Einstein initiated the pilot-wave approach with the concept of the Führungsfeld or guiding field. Wigner. (1976), 262; Goldstein. Bohmian Mechanics. Stanford Encyclopedia of Philosophy.Einstein failed to complete the formalism of such an approach, but he independently encouraged both de Broglie and Bohm to press on with their efforts.[38] Sheldon Goldstein. Bohmian Mechanics.For more on this topic, and to see how the pilot-wave theory can be further elucidated by the assumption that the vacuum is a superfluid, check out my book, 'Einstein's Intuition', available in black and white softcover, full color softcover, full color hardcover, an iBook, and as an audiobook.I also heavily recommend this lecture by Mike Towler from Cambridge.