All Results
EECredits
Machine Learning (ML) is the study of algorithms that learn from data, and it has become pervasive in technology and science. This course is an introductory course on the application of Artificial intelligence (AI) & ML in the field of Electrical and Computer Engineering. The course has three units. The first unit introduces several ML algorithms and Python programming languages. The second unit deals with autonomous driving. The last part deals with AI & ML-based wireless network design.
Individual studies of problems of special interest. Open only to advanced students.
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Application of EE computer modeling and simulation tools. Design of experiments, Taguchi methods, automated data acquisition, and analysis methods.
This course covers the analysis of continuous and discrete multivariate systems, linear models of stochastic and non-stochastic systems, and analog and digital sampled data systems. Issues examined include controllability, stability, observability, tensor properties, signal spectra, state equations, optimization, and computer simulation. A variety of case studies of advanced systems also examined.
This course covers the analysis of non-linear continuous and discrete systems and devices. Topics covered include non-linear circuit analysis, non-linear stochastic and non-stochastic system models, limit cycles, oscillators, stability, non-linear wave functions. Computer simulation will be utilized in conjunction with selected case studies in advanced non-linear systems.
Study of major paradigms used in the evaluation and execution of algorithms. Algorithm analysis will include complexity measure, hardware requirements, organization and storage system requirement.
A treatment of computer architecture covering new technological developments, including details of multiprocessor systems. Special emphasis will be devoted to new concepts. Architectures of FPGAs and CPLDs will be explored and Hardware Description Languages such as VHDL and VERILOG will be used in project assignments.
Computer architecture for parallel processors designed for high computation rates. Primary emphasis is on image processing, pattern recognition, etc. Performance of various systems with regard to interconnect network, fault tolerance, and programming.
This course covers the programming model of a contemporary microprocessor/microcontroller. The course encompasses the interfacing and application of parallel and serial I/O devices using the parallel and serial ports such as SPI, I2C, and CAN. Industrial standard interface such as USB and Ethernet would be discussed. Development tools would be reviewed and used in projects. Multi-tasking and real-time kernal would be presented and projects would be assigned. Memory technologies and expansion issues would be reviewed and taught.
Programmable logic design, simulation, synthesis, verification, and implementation using a Hardware Description Language (HDL), industry standard tools, and prototyping hardware. Mixed-level modeling including gate-level, dataflow and behavioral levels. HDL language constructs and design techniques. Logic timing and circuit delay modeling. Programming Language Interface (PLI). Advanced verification techniques.
Study the ZigBee and IEEE 802.15.4 wireless specifications and develop embedded products with wireless communication capabilities for sensor intensive and control applications. An 8-bit or a 16-bit microcontroller will be used to implement the target hardware and software.
Wave equations, solutions, wave propagation and polarization, reflection and transmissions, rectangular wave guides and cavities, strip line and microstrip lines, and geometric theory of diffraction.
Active and passive microwave devices, microwave amplifiers and oscillators, mircowave filters, cavity resonators, microwave antennas, microwave receivers, microwave transmitters.
Coherent and incoherent radiation, optical resonators, laser oscillators and amplifiers, propagation in optical fibers, integrated optical dielectric wave guides, semiconductor lasers, wave propagation in anisotropic, and non linear media, detection and noise.
Selected topics in the theory of probability and statistics. Spectral analysis. Rayleigh, Rician, Gaussian, and Poisson processes. Noise figure. Signal-to-noise ratio requirements for analog and digital communications, remote sensing, radar and sonar. Random signals in linear and nonlinear systems. Signal-to-noise enhancement techniques. Source encoding. Shannon's theorems.
Digital communication system modulation techniques. A/D conversion. Additional noise sources from sampling and encoding. Error detection and correction. Speech encoding. Data compression. Data networks. Companding. Multiplexing. Packet switching. Performance of digital baseband. Digital Signal Processing. Digital system design trade-offs.
Principles of silicon integrated circuit fabrication processes and design limitations. Process modeling, crystal growth, oxidation, implantation, diffusion, deposition. Processing of bipolar and MOS devices and circuits. Photolithography and design rules. Introduction to GaAs technology. Use of SUPREME.
Design and layout of passive and active electronic devices in silicon integrated circuits, both digital and analog. CMOS and bipolar circuit design principles will be developed. Assembly techniques and process control measurements and testing for yield control will be introduced.
This course will introduce students to nanotechnology, and focus on the atomic conduction in material leading to the fundamentals of nanoscale transistors. Models for nanoscale devices, processes, and circuit considerations in the development of integrated circuits.
Mathematical modeling of living systems. Entropy and information. Thermodynamic constraints. Feedback and feedforward mechanisms in metabolic processes. Metabolic heat generation and loss. Energy flow in living systems. Atomic and molecular bonds in biological systems. Engineering analysis of the cardiovascular, renal, immune, endocrine and nervous systems; analysis of specific disease states.
Physiological transport phenomena (intercellular, intracellular and membrane transport), strength and properties of tissue, bioelectric phenomena, muscle contraction, cardiovascular and pulmonary mechanics, design of artificial organs, diagnostic tools, therapeutic techniques in the treatment of cancer, material compatibility problems in prosthetics, and ethical dilemmas in biomedicine.
Fundamentals of RF, microwave, millimeter wave, and optical communication systems. Link power budgets. Bandwidth constraints. Phase-locked loop receivers. Matched filters. Spread spectrum communication systems. Modulation formats. Comparison of active and passive sensing systems. Signal processing.
Students will be introduced to Statistical Signal Processing. Weiner filters and Adaptive filters will be studied. Methods of steepest descent algorithm and the least squares algorithm. Applications of these filters using special purpose software for digital signal processing.
Develops analysis and design techniques for multivariable feedback systems. Definitions of poles and zeros of multivariable systems are established. Study of design methods such as LQG, Youla parametrization and H optimal control.