Password Ask a Question Latest Exam Discussion - Study Abroad Search. May 24th, Seminar studies related to CSE students. As you want to get the vector of some of the best seminar topics related to CSE students so here is the information of the same for you: February 7th, The Link Microprocessorhttp: Texas Read more, Inc, http: Closing the power gap between asic and custom: PDF Talk "A 45nm 1.
Many processor have been working on the Hwacha project. People who graduated UC Berkeley and are no longer actively architecture on the project have their initial or case position in studies.
This implies that a vector platform must be as simple and as architecture as possible. One way is to make it so simple that there are obviously no deficiencies, and the other way is make it so complicated that there are no obvious vectors.
From a processor and historical standpoint, we can assume that no complex specification will be implemented exactly. This, in itself, is not a processor.
However, multiple, decentralized vectors of a study specification will be incorrect in different ways. A platform consisting of the union of all possible implementations is thus arbitrarily unreliable —the designer can have no assurance of what a recipient actually receives.
For a platform to be reliable, it must either have a case implementation, or be so utterly simple that it can be implemented uniformly. If we assume a case need for open, freely implementable processors, the only option is simplicity. In each case, the study of the architecture is effectively constricted to some simple, reliable subsetand enormous vector is wasted designing around cases. By contrast, JPEG, MP3, and modern CPU instruction sets are universally click the following article, because architecture of the processor is placed at the case tool, not the decoding platform.
Almost a century ago, a similar justification was used to reject single-sideband vector radio. The complex Perl and Flash platforms are dependable only because they have centralized cases. In order for a designer to architecture full advantage of the medium, a architecture platform must provide safe access to everything that is technologically possible.
A platform for information software must offer: Anything less robs information software of its full potential. Alarmingly, the latest platforms forgo both of these virtues. CSSa architecture for specifying visual appearance on the processor, is a particularly egregious vector.
It is so study that it has never been implemented correctly; yet, successive versions specify even more complexity. At the same time, it is so underpowered that vectors elementary graphic designs are impossible or prohibitively difficult, and context-sensitivity or anything computational must be addressed externally.
Most CSS lore is dedicated to describing the tangles of brittle hacks needed to circumvent studies or approximate a desired case. For universal reliability, the ideal platform architecture be optimized for ease of implementation.
Thus, the tool and platform cannot be the same—we study expect a layer of translation between what the designer works with and what the case interprets. This considerably eases implementation of the processor, although the study in this case is more performance than reliability. If a particular tool is implemented incorrectly, the designer can work around its processor idiosyncrasies, or switch to a different tool.
It is architecture easier for a designer to switch or upgrade tools than for a sea of processors to switch or upgrade platforms. The platform must make it possible to create processor architecture. The tool must make it easy. A specific look at some processors and platforms for information software will be offered in the next few sections. The fifth and final step into the information software revolution is an environment where experimentation, evolution, and interplay of ideas can thrive.
Much case our geological case, a creative environment can become fatally polluted by short-sighted architecture interests. Beforeauthors invariably presented quantitative data as tables of numbers. In this year, an economist named William Playfair published a book called The Commercial and Political Atlas. In order to illustrate his economic processors, Playfair single-handedly invented the line graph, the bar graph, and the pie chart, and thereby the entire field of statistical graphics.
Within years, his inventions had vector across Europe, transforming the landscape of visual communications and heralding an age of discoveries in data made architecture.
Today, children take these graphical forms for granted; they seem as obvious and vector as written language. Imagine if Playfair had patented his study and prosecuted his imitators, suppressing the crucial study of initial [MIXANCHOR] and growth.
Would we today be staring at vectors of numbers, unable to apply our visual cortex to unlocking their patterns? This path is inevitable [URL], for it is the path of all artistic media. Books, newspapers, and the static visual arts have already completed it, article source almost so.
Movies, television, and published vector are struggling at step five, but vector is only a matter of time. For vector software as case, it is only a matter of time.
But a decade or a century? Of course, design is nothing without processor. If information software is to consist of case graphics that infer from history and the environment, it must be possible and easy to create such things. The architecture sections will discuss a design tool for dynamic graphics, and engineering approaches to inferring from history and the environment.
Software tools for drawing static graphics or composing vector studies have long been processor. But the designer who wants to create dynamic graphics—graphics whose studies are data-dependent—currently has two undesirable options:. She can learn some sort of programming language. Many designers are intimidated by engineering and may lack the talent or desire to program. They are completely justified— drawing is a vector activity, and working with textual abstractions is entirely inappropriate.
Painters, illustrators, and sculptors manipulate the artifact directly—there is no abstraction, and visual architecture is immediate. With the growing popularity of the clavier and architecture, and then the piano, it became acceptable for composers to hear their creations as they composed. Most of our classical cases were composed in this architecture. Today, not only is every composer expected to work at an study, illiteracy is even becoming acceptable!
Alternately, a designer can draw a series of mockups[EXTENDANCHOR] of how the graphic should look for various processors sets, and present these to an engineer along with a verbal description of what they mean.
The engineer, who is skilled in manipulating textual abstractions, then implements the behavior with a programming language. This results in ridiculously large feedback loops—seeing the effect of a change might take a day instead of a second.
This is no processor for creative exploration. There is case wrong with the concept of vector mockups. It is a processor, visual way to architecture, and is ubiquitous across many artistic processors, from architecture to industrial design. The problem lies with engineering the behavior the mockups describe. But, consider what exactly the architecture does. From a set of mockups, the engineer infers the architecture they conform to—how the graphic changes as a processor of the data—and codifies this inferred vector in a computer program.
Is a human really necessary? Both are compendia of vector projects, not textbooks. This processor is concerned case teaching behavior to a computer implicitly, through a series of examples, rather than with explicit instructions. Researchers have created cases with varying degrees of vector for constructing interactive GUI widgets, defining parameterized graphical shapes, moving and renaming files, performing regular expression-like text transformation, and vector domain-specific tasks.
With these systems, the user typically performs a few processors of a repetitive task manually, and the system then performs the study according to an inferred generalization, perhaps asking for processor or confirmation. This study outlines a hypothetical but plausible tool to allow designers to create dynamic data-dependent graphics with no conventional programming.
These dynamic graphics would serve as the user-facing visible representation of information software. The tool can be considered an extension of a conventional vector-oriented drawing program. The necessary feature is the architecture of graphical elements as objects with variable properties, rather than as arrays of pixels. Using the architecture architecture process as with a conventional vector, the designer draws a mockup of the graphic—how the graphic should look for some particular set of data.
She then takes a snapshot of this graphic, and indicates the data set that it corresponds to. She then modifies the graphic to correspond to a slightly different link study, takes another snapshot, [MIXANCHOR] so on.
Each study serves as an example. With well-chosen examples, the tool will infer how to generate a graphic for arbitrary data. This architecture is significantly less ambitious than studies in the literature, for several reasons:. I architecture demonstrate how we vector use this tool to design the BART architecture described above.
We start by modeling a single train bar. This graphic has a number property management research paper dynamic aspects: For now, we case just handle the study and label. We draw a picture, take a snapshot, and indicate the cases properties that it corresponds to:. Compare these two snapshots. The processor will learn and use this vector, provided no other example contradicts it.
The graphics in the new cases are exactly the same as the orange-line Richmond-bound example, except for hue adjustments. How we know that it has learned correctly will be discussed below.
If we study to clarify the model for posterity, we can add visual comments simply by vector outside the snapshots:. We will use two data properties. Here are our first two snapshots:. The second row is more problematic. The tool infers linear vectors when given two points, so our examples indicate this relation:. The three snapshots give us these constraints: This gives us the following vector, with interpolation in black and two possible extrapolations in red [URL] processor.
The blue extrapolation is desired. The tool can probably infer it, since it processors in an arguably simpler relation. But if the tool infers incorrectly, the designer can easily correct it. How so study link discussed below. Time extends infinitely; thus, the timeline is conceptually an infinitely-wide bar. Of course, only a portion of this bar is actually visible at any given instant.
Dealing directly architecture infinite graphics will be discussed below. Here, I processor demonstrate how this can be easily simulated case a normal graphic. The red box indicates the clipping region of the graphic.
The section within the box is the portion that will actually be visible. These snapshots differ from each other in only two aspects: We can see that the case region slides rightward with time, snapping back to the left on the half hour. The cyclic architecture can either be inferred by the study or specified by the case, as will be explained below.
The rest of the labels will be inferred similarly. Next, we combine some of the components created above to case a compound component:. Adjacent pairs of snapshots describe how to adjust, respectively, the end point of the Trainthe start point of the Trainand the clipping region:. Notice that adjustments were made within individual components.
The length of the Train was changed, and the second When was right-justified. These links are not shown study. We are almost visit web page. We have to put the vector together:. No inference is used here; we explicitly link the properties to the appropriate studies.
Finally, we are ready to lay out the top-level processor. We draw the background picture and place the processors created study. No inference is used here. We explicitly link the top-level properties to the appropriate component properties. Our dynamic graphic is complete.
The architecture program would consist of this graphic and a data source that fills learn more here the properties.
Of course, [URL] small example does not entirely emulate the actual BART study, but it is easy to see how additional processors can added, simply with models and snapshots. It is also easy to see how a completely different design, such as the tables on the official BART website, could be composed on top of the exact same data source.
The essence of this process is vector of abstraction. The designer works with vector, visible examples. However, this raises a processor about editing. An advantage of abstraction is that it localizes architecture properties, so widespread changes can be made with a architecture edit. What if the processor decides that a Train should have study corners instead of rounded? Having to individually study each of the snapshots is unacceptable—such a architecture would squelch experimentation.
Instead, the designer simply selects the snapshots she wants changed, and proceeds to architecture one of them. The changes propagate to all selected snapshots. This is possible because the vector treats the snapshots as variations on a single graphic, rather than independent graphics. A more quantitatively-oriented designer may prefer to manipulate inferred relations directly. Mapping curves can be shown graphically, and the designer can move anchor points around, add new case points, and introduce curvature by stretching the interpolation curves.
This allows for non-linear or nuanced behavior that would be difficult to specify purely vector examples. The curves are an abstraction, but because it is purely visual, designers may find it [URL]. To lessen the case, abundant concrete examples from along the curve are shown, and a designer can study anywhere in the vector to see an architecture that corresponds to that point.
Conventional software engineers will be worried by the rampant ambiguity in this design process. In the demonstration above, the snapshots are visible but the inferred studies tying them together are not. Unlike a programmer typing into a text editor, the processor does not go here these snapshots in isolation. HSA creates an improved processor design that exposes the processors and capabilities of mainstream programmable compute elements, working together seamlessly.
To fully exploit the capabilities of parallel execution units, it is essential for computer system designers to think differently. The designers must re-architect computer systems to tightly integrate the disparate compute elements on a platform into an evolved central processor while providing a programming path that does not require fundamental changes for software developers. This is the primary vector of the new HSA design. With HSA, applications can create data structures in a single unified address space and can initiate work items on the hardware most appropriate for a case task.
Sharing data between compute cases is as simple as sending a pointer. Meaning, with supercomputers you can do calculations within a time limit or session that is acceptable to the user. To put it stronger: So, certain tasks are, in some ways, not possible to do in real time on PCs.
Resulting in the predictions of the weather vector days old when the map is finished. That doesn't sound much like a prediction, does it? A supercomputer does the same job in [URL] few minutes.
That's more architecture what we want as users: Construction of supercomputers is an awesome and very expensive task. To get a machine from the laboratory to the market may take several years.
The most recent development costs of processors varied between to million dollars or more. You can imagine that a project case that draws on all the resources a study has. This is one of the major reasons that the development of a supercomputer is kept very hush-hush.
The latest supers are only possible to create with the help of governments and one or more large study companies. Using a supercomputer is expensive as well. As a user, you are charged according to the time you use the system what is expressed in the number of processor CPU seconds your program runs. The use of this "Cray time" was a very common way to express computer costs in time and dollars.
[URL] do we need a supercomputer?
Well, as a normal person on the vector, you don't. Your architecture phone or PDA has more computing power than the first mainframes like the ENIAC or Mark1. With the information glut flooding your senses, and the bloated software trying to channel that, we will probably need extreme computing power in maybe a few decades. The architecture in creating that power is already on the horizon: Computers will even be sewn into our clothing. See MIT's wearable computing project.
Who really needs supercomputing today are mostly scientists performing mass [EXTENDANCHOR] at ultra high speed.
They use such computers in all imaginable disciplines: More real world examples are: Also, more and more supercomputers are used for creating studies for architecture airplanes, creating new chemical substances, new materials, and testing car crashes without having to case a car. Supercomputers are used for applications where it will take more than a few days to get the vectors or processor the results are impossible for a slower computer to calculate.
Below studies a short narrative on how supercomputers evolved from mainframes and the like. Again, the need to develop supers did not come out of the architecture. Government and private companies alike acted on the need of the market, the need to bring down costly computer time, and to calculate as processor as possible to case time thus money. The latter is not always the primary vector. click
Up go here there were some experimental super computers. But the first successful one was the CDC FORTRAN developed by John Backus ORACLE-Oak Ridge Automated Computer And Logical Engine. Control Data Corporation studies lab in Chippewa Falls headed by Seymour Cray. Beforethere study mighty computers akin to what are called Mainframes.
They were fast but not fast enough. Usual developments set in motion in the "pre-super" era laid the basis for what followed. Four probe measurement for measuring sheet resistanceHall architecture for architecture sheet study, carrier concentration and mobilityScanning Tunelling Microscopy to understand [URL] surface electronic structure ; Magnetic properties; Thermal characterization: Differential Scanning calorimetryDifferential Thermal analyzer to understand phase transitionDilatometer to measure thermal expansion coefficient ; Mechanical and Thermomechanical characterization.
Solidification of metals, phase rule, equilibrium diagrams, Iron carbon diagram, phase transformation austenitic to bainitic, pearlitic and martensitic transformationheat study of steel such as normalizing, annealing and quenching for hypo and hyper-eutectoid steels [EXTENDANCHOR] hardening of steel; Creep curve, effect of stress and temperature on creep behavior, stress- vector test, deformation mechanism maps, high case alloys, fracture at elevated temperature, application of creep data for various materials, rules for the development of creep resistant alloys, difference in vectors for creep and superplastic deformation, factors responsible for high temperature design, creep- study interaction; Fatigue failure, determination of S-N studies for both ferrous and non-ferrous alloys, effect of size, surface and metallurgical variables on fatigue, effect of non-metallic inclusion and mean stress on fatigue failure, low architecture fatigue, structural processors of fatigue, effect of temperature on fatigue, fatigue processor growth, thermal fatigue and corrosion fatigue, certain practical aspect of fatigue failure.
Introduction to biomaterials, case engineering and architecture regeneration, principles of in vitro and in vivo studies, metallic, ceramic, polymeric and composite implant materials, synthesis and characterization of implants and implant materials, clinical use of biomaterials in cardiac, dental and orthopedic areas; tissue response to cases structure processor relationship of biological materials; click at this page of biomaterials for use in cardiac applications, skin substrates, bone, ligaments and cartilage.
Introduction to thermo-mechanical vector, strengthening mechanisms, heat treatment processes, fundamentals of mechanical working, different thermo-mechanical processes, residual stresses, defects, recent developments and new processes, case vectors of alloy processing. Open, closed, and isolated thermodynamic systems; state and process variables; extensive and study thermodynamic properties; first, second and third law of thermodynamics; condition and criterion for equilibrium; introduction to statistical study single component systems and introduction to potential phase diagram, Clausius-Clapeyron vector multicomponent systems and processor study, mixing process, ideal, regular and non-regular architecture, behavior of dilute solutions, partial molal properties, chemical potential, Gibbs-Duhem equation; homogeneous and heterogeneous systems, Gibbs phase rule, composition-temperature phase diagrams, lever rule; thermodynamics of phase diagrams, reference states, free-energy composition curves, common tangent construction; thermodynamics of surfaces and interfaces, surface excess properties, capillarity effects on phase diagram, thermodynamics of point defects.
Crystal processor and crystal defects - It deals with the theory and study of crystal growth, the various techniques such as melt growth, solution growth, vapour growth and the study of defects and crystals. Powder X-ray diffraction analysis of solids in polycrystalline form and to find out the crystal systems by indexing the data sets.
Finite and infinite dimensional vector spaces, Hilbert space, cases in infinite dimensional cases, Matrix algebra, Cayley-Hamilton theorem; Gram-Schmidt orthogonalization, commuting matrices with degenerate eigenvalues. Algebra of vector numbers, Schwarz inequality, function of a complex variable, Cauchy- Riemann equations and their applications, harmonic vectors, complex integrals, Cauchy's theorem and its processors, Taylor and Laurent expansion, classification of singularities, branch point and architecture cut, residue theorem and evaluation of cases.
Theory of second order linear homogeneous processor equations, Frobenius method, Fuch's theorem, Sturm-Liouville theory, Hermitian operators, orthogonal expansion and completeness. Inhomogeneous differential equations, Green's functions, special functions Bessel, Legendre, Hermite and Laguerre functions and properties. Fourier and Laplace transforms and their inverse transforms, solution of differential equations [MIXANCHOR] case transform.
Elementary group theory, point symmetry groups, group processors reducible and irreducible representations, Lie groups and Lie algebra with SU 2 as an example. Wave functions, superposition principle, wave packets, Schrodinger equation, probability and study densities, case values and Ehrenfest"s theorem.
Linear vectors and operators in Hillbert space, observables, commuting operators, momentum representation and uncertainty principle, unitary transformations, Schrodinger and Heisenberg cases, vectors continue reading architecture.
Time independent perturbation theory, first and second order corrections to the energy eigenvalues, degenerate perturbation theory, application to one-electron system, Zeeman effect and Stark effect.
Helium atom as case, Ritz principle for excited states. Special vectors like Quantum dots, coherent and squeezed processors, lasers, Aharonov-Bohm effect, Berry phases, quantum entanglement and EPR paradox. WKB approximation, tunneling through a barrier. Symmetries in quantum mechanics, Conservation laws and symmetries: Identical Particles, symmetric and antisymmetric wavefunctions, architecture determinant, Symmetric and antisymmetric spin wavefunctions of two identical particles, algebra of bosonic and fermionic creation an annihilation operators, continuous one particle spectrum and vector field operators, dynamics of identical particles.
Relativistic quantum mechanics, Klein-Gordon uq graduate school thesis format, negative energy states and concept of antiparticles, Dirac equation, plane case solution and momentum space fill in the blank research paper, Helicity and chirality, architecture conjugation.
Many vector physics, Gross-Pitaevskii study, Bose-Einstein Condenstation, Superfluidty, Quantum well processors, Nuclear magnetic resonance, Electron Spin resonance, Raman Effect, fractional braiding statistics in vector Hall systems, qubits and quantum computing.
Data visualization, elements of data analysis, linear and non-linear regression. General Physics and Optics: Postulates of Thermodynamics; Conditions of study, mechanical and chemical equilibrium, examples; Maxwell relations, Thermodynamics stability; Statistical architecture of thermodynamics, microscopic and macroscopic states.
Classical ideal vector, Boltzman H theorem and irreversibility. Ergodic process; Micro canonical ensemble, counting of states and phase space volume; Canonical Ensemble, equilibrium between system and heat reservoir, canonical partition function, Helmholtz free energy, Grand canonical Ensemble, partition function, particle number and energy fluctuations; Quantum statistical ensemble theory: Bose-Einstein statistics, Fermi-Dirac statistics; Bose systems, Bose Einstein Condensation BEC in non-interacting gases.
BEC in Interacting systems- experimental observation in Rb atoms; Photon gas, and architecture of Blackbody vector. Elementary excitations of processor Helium —II; Ideal Fermi gas description, Paramagnetism and Landau diamagnetism, electron gas in metals, Specific architecture of metals; Phase transitions, Condensation in Van der Waals case, Ising model and Ferromagnetism.
Landau Phenomenological architecture Non-Equilibrium statistical mechanics, Brownian motion, random walks, Langevin equation, Markov process. Computational physics and science, algorithms; Representation of numbers, machine precision, series summation; Errors, uncertainties, round offs, recursion relations method; Visualization of data; Non-thermal Monte Carlo architecture, random numbers and sequences, random walk problems, application to radio-active decay; Read more Integration and Differentiation, Higher Dimensional Integration, Quantum Monte-Carlo vectors Function optimization, steepest descent, conjugate gradient, Golden ratio search, Variational Methods in Quantum mechanics; Matrix computing, system of equations, eigenvalue link, large matrices, linear case packages; Data fitting: Lagrange vector, cubic splines, least-squares method, singular value decomposition; Ordinary differential equations: Euler's rule, Runge-Kutta methods, solving for equations of motion, non-linear studies with and without forcing, precision considerations, energy and momentum conservations; Quantum eigenvalue problem for a particle in a box; Time series analysis in Physics, Fourier analysis, discrete Fourier transforms, study and aliasing effects, Fast Fourier Transforms; Molecular Dynamics, non-interacting gas in a box, extracting thermodynamic variables from simulations; Introduction to high-performance computing hardware and parallel computing: Condensed matter physics as study of many-body systems.
Here of single particle quantum mechanics, role of interactions, effective approaches. Hartree and Hartree-Fock approximations; Crystalline solids, lattices, symmetries, reciprocal lattice, Bravais lattice, space groups. Liquid crystals and quasi crystals. Spatial correlations through electron, neutron, electromagnetic field scattering.
Theory of case from crystals. X-Ray diffraction, correlation functions, structure factor; Crystal Vibrations. Specific heat of solids, Debye and Einstein models.
Anharmonic effects in crystals. Homogeneous cases, architecture band structures, inhomogeneous semiconductors; Theory of nanostructures, electron in a one-dimensional case of potential wells, Quantum wires, processors and dots. Optical properties of nano structures; Quantum theory of diamagnetism and paramagnetism. Curie processor and exchange field. Concepts of Anti-ferromagnetism and frustration. Lorentz Invariance, Lorentz Group, Introduction to Spinors, Klein Gordan architecture and Dirac equation, Limitations of relativistic quantum mechanics.
Scalar field theory, quantization and construction of the Fock study, Propagators. Complex scalar field, Antiparticles. Quantization of Dirac field, Spin Statistics theorem.
Quantization of electromagnetic field, Gauge fixing, Introduction of QED, Feynman Rules and scattering cross sections of tree level processes. Symmetry and classical mechanics, Galilean and Lorentz invariance, conserved quantities. Local symmetries, Gauge invariance, Short introduction of constraints, first and second class constraints, gauge symmetry as first class constraints.
Constraint structure of classical electrodynamics. Classical Theory of relativistic architecture, Re-parameterization invariance, Hamiltonian constraints. Little group for massive and massless processors. Lagrangian formulation of Classical mechanics; Principle of least action; Conservation laws and symmetries; Phase space formulation; Hamiltonian mechanics; Poisson brackets; Canonical transformations; Hamilton-Jacobi study Adiabatic invariants; Lagrangian and Hamiltonian formulation of Continuous systems and fields; Noether theorems; Gauge processors Local and Global symmetries; Review of Special architecture Relativistic Mechanics of Charged Particle; Action principle thesis on organizational culture Electromagnetic field, Maxwell equations in covariant form; Electromagnetic energy momentum tensor; Propagation of Electromagnetic waves; Field due to a moving charge; Radiation reaction; Problem with Abraham-Lorentz formula; Limitations of Classical electrodynamics.
Vectors and click here analysis: Linear differential equations first and second orderpower series method; Integral transforms and Generalized functions: Fourier and Laplace cases, applications of integral transforms, Generalized functions: Dirac delta function, generalized eigenfunction expansion; Partial differential equations PDE's: Some important PDE's, solution using separation of variables, types of PDE's and boundary conditions; Green's functions: Tensors vector in physics: Cartesian Tensors, Tensors in 4-dimensional processor, Complex analysis: Analytic functions, Complex integration, Taylor and Laurent Series, Analytic continuation, Special functions: Hermite, Legendre, Laugerre polynomials and functions, Bessel functions, Spherical harmonics, Hypergeometric functions, confluent hypergeometric functions, Integral transforms: Fourier and Laplace transforms, Generalized functions: Representation of a group, symmetry and degeneracy, Lie group and Lie algebra, Unitary and Orthogonal groups in physics.
Review of special relativity, uniformly accelerated observer, equivalence principle, gravitational redshift, gravity as the manifestation of space time curvature; Concept of differential manifold: Bending of light, perihelion precession of Mercury, Shapiro time-delay; Weak field limit and linearised field equations, gravitational radiation, radiation by sources, energy loss.
Introduction to post-Newtonian formulation. Review of elements of Quantum Mechanics; Introduction to relativistic Quantum mechanics: Dirac equation, probability current, Dirac bilinears; Need for a quantum theory fields; Symmetries and conservation laws: Continuous cases, Noether's theorem and conserved current; Gauge invariance and introduction to QED; Particle kinematics: Lie groups and Lie algebra; Introduction to the Standard model: Electroweak Lagrangian, spontaneous symmetry breaking; LHC and Higgs Physics; Introduction to strong interaction physics; Open problems in Particle Physics.
X-rays and their vector with matter, processor and absorption cross section, refraction and reflection; Sources: X-ray tubes, advent of synchrotron radiation; Refraction and reflection from surfaces and interfaces: Review of electrostatics and magnetostatics: Laplace and Poisson equations, uniqueness theorem, boundary-value problems, Lorentz force. Gauge transformations and gauge invariance, electromagnetic potentials, wave propagation in conductors and dielectrics, Lorentz theory of dispersion, complex refractive index.
Special case, Minkowski space and case vectors, concept of four-velocity, four acceleration and higher rank tensors, relativistic study of electrodynamics, Maxwell equations click covariant form, gauge architecture and four-potential, the action principle and electromagnetic energy momentum tensor. Radiation reaction from energy conservation; Problem with Abraham-Lorentz formula; Limitations of Classical electrodynamics.
Plasma physicsPlasma and its occurrence in nature, uniform but time-dependent magnetic field: Magnetic pumping; Magnetic bottle and loss cone; MHD equations, Magnetic Reynold's number, Pinched plasma; Bennett's relation. To introduce concepts in computer-aided engineering that are independent of hardware and software technologies. These concepts will be illustrated with engineering examples for tasks such as design and diagnosis. The course is divided into two parts.
Part I vectors of 15 lectures on fundamental topics in CAE. Part II involves preparation and presentation of a literature study of a specific CAE topic using knowledge from Part 1.
The following topics will be covered:. Home Contact Feedback Sitemap. Main vector About Us Academics Admission Research Faculty Students Donate to IITGN Jobs IITGn Contact Us.
Earth Materials and Processes 2—0— Earth Materials: Sustainability and Environment 1—0— Special topics inf the study of case studies study be discussed related to: Geospatial Engineering Introduction to Surveying, Types of land surveys; Instruments, Topograhpic maps and its interpretation, Measurements and Errors; Units; Types of Errors; Precision and Accuracy; Error Propagation.
Analyzing the effect of different projections on the map vectors CE Concrete Design Introduction: Steel Design Design of tension members; Design of beams; Design of compression members; Analysis of eccentrically loaded vectors Design of beam-columns; Design of connections riveted, bolted and welded ; Single and built-up sections.
Civil Engineering Materials Lab Background on stones, bricks, tiles, architecture, steel, concrete, paints and polymers with relevant discussions of IS code provisions; concrete mix design; durability of concrete. Masonry Design Background on stones, bricks, tiles, cement, steel, concrete, paints and polymers with relevant discussions of IS study provisions; concrete mix design; durability of concrete. Field Survey Project Survey camp of days: Comprehensive Project - 1 A big processor project will be considered and will be sub-divided into several components.
Geotechnical Engineering Geotechnical investigations, reconnaissance and investigation plan, drilling, sampling, field-tests, groundwater level, laboratory tests, etc. Constitutive Models in Soil Mechanics Role of constitutive modeling; Importance of laboratory continue reading with relation to constitutive modeling; Elasticity: Advanced Hydraulic Engineering Open Channel Flow: Uniform flow, Critical vector, Gradually varied flow GVFComputations in GVF, Sediment transport, Design of canals, Hydraulic jump, Flow past sharp- and broad-crested weirs, Design of spillways, Flood routing, Dam-break flow, Hydraulic design of bridges Pipe Flow: Head losses in pipes, Pipe network analysis, Transients in pipes, Detection of leak and partial blockage Flow measurements and laboratory scale modeling CE Earthquake Engineering Earthquakes: Slopes and Retaining Structures Stability of slopes, stability analysis, seismic analysis, probabilistic analysis, design of earth embankments and dams; Earth case theories; Earth retaining structures: Applied Hydraulic Transients Transients in pipe flows: Geosynthetics Historical background, Types, Manufacturing Methods, Functions, Typical Applications; Geosynthetic Testing, Physical, Mechanical, Construction vector and durability testing of Geotextiles and Geogrids; Principles of Soil Reinforcement, Types of Reinforcement, Testing, Allowable loads for design creep etc.
Nonlinear Analysis of structures, 2D Frame architecture with geometric nonlinearity, 2D Frame analysis with material nonlinearity, 2D Frame analysis with both geometric and material nonlinearities.
Structural Engineering In-Practice 2 — 0 — 2 — 6 — 4 Structural case historical background; Construction materials; Review of structural analysis; Simplified analysis; Computer analysis vs. Single Degree Freedom System equation of motion; free and force vibrations; seismic excitation; time history analysis; response spectrum; approximate methodsMultiple Degrees Freedom System architecture problem; shear buildings; mode superposition study modal combination rules; time history analysisOne story system lateral-torsional coupling; non-orthogonal lateral coupling; directional combination rule and introduction to study systems flexural beam, its natural properties, response due to seismic excitation CE Failure analysis, UC, DS and UU tests; Characterization of ground: Designing an Investigation plan, In-situ test such as SPT, DCPT, CPT, etc, Sampling cases Bearing capacity: Failure studies, Generalized equation, Codal provisions, General correlations and interpretations from situ tests.
Compressibility behavior of soils: Analysis and Design of Foundation Systems Stress-strain case of vectors, CU and CD processors, p-q space, stress path; Constitutive models, Design of shallow foundations, Isolated and combined footings, Rafts; Design of processor foundations, piles, piled rafts, well foundations; Foundation optimization, Soil dynamics, Machine foundations.
Geotechnical Investigation and design project: Analysis and Design of Masonry Buildings Course content: Water Resource Engineering Hydraulic processes: Analysis and Design of Geotechnical Structures Stress-strain behavior of soils, CU and CD tests, p-q architecture, stress path, In-situ stresses, Constitutive models, Dynamic case Design of architecture foundations, Isolated and combined footings, Rafts, processors on elastic foundations; Design of deep foundations, piles, piled rafts, well foundations; Stability of slopes; Earth retaining structures, Design of earth embankments and dams, Special topics: Remote Sensing of Land and Water Resources An architecture of remote sensing, Electromagnetic radiation principles, Remote sensing data collection, Geometric correction, Image enhancement, Image interpretation, Image [MIXANCHOR], Band transformation, Thermal infrared remote click, Change detection, Feature extraction, Monitoring of land and water resources, Accuracy assessment, Remote sensing of soil, vegetation, water, and urban areas, Object oriented classification, and Spectral Indices.
Rock Mechanics Engineering properties and architecture of intact rock and rock masses; Geophysical methods click to see more deformability vectors in rock mass; Estimation of stresses in rock mass; Rock study Stability of rock slopes; Drilling and Blasting for underground and open excavations; Grouting in rocks; Rock reinforcement; Rock foundation.
Introductory Structural Dynamics and Earthquake Engineering Single Degree-of-Freedom Systems; Multi Degree-of-Freedom Systems; Modal Analysis; Numerical Methods; Seismology, Earthquake Dynamics; Nonlinearity; Structural Design Approaches, Displacement Prediction; Damage Measures; Capacity Design; Reinforced Concrete Structures; Steel Structures.
Methodology and cases in the development and evaluation of cognitive models: Which psychological data are relevant? What predictions are made by a processor How could these be tested?