Issues in digital integrated circuit design. The devices. CMOS Inverter. Combinational logic gates in CMOS. Designing sequential logic circuits. Designing arithmetic building blocks. Timing issues in digital circuits. Memories and array structures. Design verification and testing. Design projects using computer aided design tools: SPICE, MAGIC, IRSIUM, OCTTOOLS. Project design requirements include architectural design, logic and timing verification, layout design, and test pattern generation. The resulting chips may be fabricated.
Introduction to mathematical formulations and computational techniques for the modeling and simulation of engineering and other kinds of systems, including electronic, mechanical, biological, biochemical, virtual, abstract and multi-domain dynamical systems. Applications from various engineering disciplines and the sciences. Matrix formulation of equations for linear problems. Formulation of equations for nonlinear problems & linearization. Numerical solution of linear algebraic equations. Gaussian elimination, computations with sparse & structured matrices. Floating point number representation & arithmetic. Numerical conditioning, ill-conditioned problems. Numerical solution of nonlinear algebraic equations. Fixed point iteration & Newton’s method in one dimension. Newton’s method for system of coupled nonlinear algebraic equations. Improving convergence of Newton’s method. Numerical solution of ordinary differential equations. Forward & backward Euler, trapezoidal rule. Multistep methods, accuracy & stability. Implicit vs explicit techniques, region of stability, stiff problems.
Review of electromagnetism; geometrical optics, analysis of optical systems; wave properties of light, Gaussian beams, beam optics; interaction of light with matter, spontaneous and stimulated emission, optical amplification, theory and applications of lasers, optical interactions in semiconductors, light emitting diodes and diode lasers; detectors, noise in detection systems; light propagation in anisotropic crystals, Pockels and Kerr effect, light modulators; nonlinear optics, second harmonic generation, phase matching, nonlinear optical materials.
Introduction to Microsystems, MEMS and its integration with optics; Microfabrication and process integration; MEMS Modeling and design; Actuator and sensor design; Mechanical structure design; Optical system design basics; Packaging; Optical MEMS application case studies; Scanning systems (Retinal Scanning Displays, Barcode scanners); Projection display systems (DMD and GLV); Infrared imaging cameras; Optical switching for telecommunications.
Review of 2-D linear system theory and 2-D Fourier transforms. Integral transforms used in optical signal processing; Fundamentals of physical optics and diffraction theory; Fourier and imaging properties of optical systems; Coherent and Incoherent optical image processing; Fundamental architectures for correlation and spectrum analysis; Interferometry; Discrete analog optical processors; Holography; Review of 3D Display technologies.
Introduction to optical fiber communication systems. Transmission properties of optical fibers. Optical amplifiers. Lasers and photo-detectors. Analog and digital modulation schemes. Modulator, transmitter and receiver design. Dense and ultra-dense wavelength division multiplexing. Transmission impairments, noise, nonlinearities, dispersion compensation and management, modeling and simulation. Optical fiber communication networks, optical interconnect for high-speed VLSI.
Survey of the properties and applications of photonic materials and devices; semiconductors; photon detectors, light emitting diodes, noise in light detection systems; light propagation in anisotropic media, Pockels and Kerr effects, light modulators, electromagnetic wave propagation in dielectric waveguides, waveguide dispersion; nonlinear optical materials, second harmonic generation, Raman converters.
Applications of Maxwells equations. Electrostatic versus electrodynamic phenomena, and concept of electromagnetic radiation; radiation from a moving point charge; definitions of some radiation parameters like the input impedance, gain and radiation patterns of antennas; radiation from Thin-Wire Antennas and their electrical characteristics; concept of arrays and their applications; microstrip antennas and their roles in emerging telecommunication systems; Propagation for wireless communications systems; cellular network design based on propagation studies.
Next generation communication systems, wireless cellular networks, machine-to-machine communications, Internet of things, software defined networking, physical layer data transmission, channel propagation characteristics, modulation, demodulation, medium access control layer, data link layer, forward and backward error control, routing layer, optimal routing, transport layer, flow control, congestion control.
Fundamentals of optics and applications of the optical technology: photon and wave nature of light, geometrical optics, optical instruments, electro-magnetic waves, interference and interferometers, fiber optics, diffraction, diffraction gratings, polarization and its applications, multi-layer films, Fresnel equations, and rainbows. Real world applications of course topics.
Hypothesis Testing, Signal Detection, Parameter Estimation, Cramer-Rao Lower Bound, Maximum Likelihood/ Maximum a Posteriori Estimation, Stochastic Least Squares Estimation and Kalman Filtering.
Review of multimedia (image, video and audio) source coding/compression techniques and standards (JPEG, MPEG, H26x), Review of communication and networking architectures and IP networks, QoS, delay, jitter, rate control, scheduling, and traffic engineering for real-time multimedia delivery, Reliability, error control, error concealment and resilience techniques, Streaming media and real-time communication techniques and protocols, RTP/RTCP, IntServ, DiffServ, MPLS, Transmission of multimedia over Internet, wireless channels, mobile cellular networks, GSM, 3G, 4G wireless systems, and satellite networks, Current and future applications of multimedia communications, e.g., voice-over-IP (VoIP), Internet Video conferencing, SIP, IMS, video-on-demand, digital video broadcasting systems, real-time delivery of 3DTV, Current state-of-the-art and future visions in multimedia communications research.
Quantum description of light-matter interactions and advanced photonic devices; review of Quantum mechanics, Schrödinger and Heisenberg representations, harmonic oscillator, operator formalism, Fermi?s golden rule, semiclassical theory of stimulated emission, quantization of the electromagnetic field, blackbody radiation, quantum theory of spontaneous emission, Rabi oscillations; Selected topics in semiconductor lasers, photonic waveguides, noise, and light modulators.
An introduction to the fields of machine learning and data mining from a statistical perspective. Machine learning is the study of computer algorithms that improve automatically through experience. Vast amounts of data generated in many fields from biology to finance to linguistics makes a good understanding of the tools and techniques of machine learning indispensable. Topics covered include regression, classification, kernel methods, model assessment and selection, boosting, neural networks, support vector machines, nearest neighbors, and unsupervised learning.
Fabrication and characterization techniques for micro and nano electro mechanical systems, MEMS & NEMS (including: microlithography; wet & dry etching techniques; physical & chemical vapor deposition processes; electroplating; bonding; focused ion beams; top-down approaches - electron-beam lithography, SPM, soft lithography - ; bottom-up techniques based on self-assembly). Semiconductor nanotechnology. Nanotubes & nanowires. Biological systems. Molecular electronics.
Introduction to industrial engineering concepts. Fundamentals of systems analysis and modeling. Basics of production and service systems. Computer and programming applications of several industrial engineering topics. Hands-on experience for industrial engineering subjects in team projects
Fundamentals of logic, mathematical induction, basic set theory, relations and functions, fundamental principles of counting, inclusion-exclusion principles, basic graph theory, trees, algorithms for basic industrial engineering and operations research problems on graphs and networks.
Financial accounting principles and cost systems for engineering economic analyses. Cost-volume-profit analyses, discounted cash flow and budgeting techniques.
Basic parametric statistics such as estimation, confidence intervals, and hypothesis testing. Distribution fitting, goodness of fit tests. Independence tests and contingency tables. Simple linear regression and correlation analysis. Nonlinear and multiple regression, analysis of categorical data. Industrial engineering applications in quality control and demand forecasting. Statistical software packages and computer implementations.
Introduction to modeling concepts and optimization; setting upoptimization models from problem description; linear programming problem formulation; simplex method, duality and sensitivity analysis; applications of mathematical programming in engineering and management with computer implementations.
A minimum of 20 working days of training in an industrial summer practice program after the completion of second year. The training is based on the contents of the "Summer Practice Guide Booklet" prepared by each engineering department. Students receive practical knowledge and hands-on experience in an industrial setting.
Introduction to inventory management, deterministic economic quantity models and extensions. Stochastic continuous-review and periodic-review models. Markov chains and Markov processes. Introduction to queueing systems and the Poisson process. Markovian queues, networks and management of queueing systems. Markov decision models and applications. Probabilistic dynamic programming and algorithmic solution methods.
Introduction of simulation models to analyze the behavior of complex stochastic systems. Modeling time and randomness, model validation. Generation of stochastic inputs, random variate generation. Implementation of models arising from case studies via simulation languages and software. Output analysis, variance reduction techniques. Monte Carlo and Quasi Monte Carlo Methods.
Introduction to modeling with integer variables and integer programming; network models, dynamic programming; convexity and nonlinear optimization; applications of various optimization methods in manufacturing, product design, communications networks, transportation, supply chain, and financial systems.