Electrical Engineering Master of Science Degree

In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3, Recitation 1 (Fall, Spring).

This course covers communication networks in general and the internet in particular. Topics include layers service models, circuit and packet switching, queuing, pipelining, routing, packet loss and more. A five-layer model is assumed and the top four levels are covered in a top-down approach: starting with the application layer, going down through the transport layer to the network layer and finally the data link layer. Emphasis is placed on wireless networks and network security. Students would perform a basic research assignment consisting of a literature survey, performance analysis and dissemination of results in written and oral presentation. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).

Principles and practices of modern digital data communication systems. Topics include pulse code transmission and error probabilities, M-ary signaling and performance, AWGN channels, band-limited and distorting channels, filter design, equalizers, optimal detection for channels with memory, synchronization methods, non-linear modulation, and introduction to multipath fading channels, spread spectrum and OFDM. Students would perform a basic research assignment consisting of a literature survey, performance analysis and dissemination of results in written and oral presentation. (Prerequisites: EEEE-602 or equivalent course.) Lecture 3 (Spring).

This course offers a broad overview of sensor-array processing, with a focus on wireless communications. It aims at providing the students with essential and advanced theoretical and technical knowledge that finds direct application in modern wireless communication systems that employ multi-sensor arrays and/or apply user-multiplexing in the code domain (CDMA). Theory and practices covered in this course can be extended in fields such as radar, sonar, hyperspectral image processing, and biomedical signal processing. Topics covered: uniform linear antenna arrays (inter-element spacing and Nyquist sampling in space); linear beamforming, array beam patterns, array gain, and spatial diversity; interference suppression in the absence of noise (null-steering beamforming); optimal beamforming in AWGN (matched filter); optimal beamforming in the presence of colored interference; estimation of filters from finite measurements and adaptive beamforming (SMI and variants, RLS, LMS and variants, CMA, and AV); BPSK demodulation with antenna arrays (multiple users and AWGN); BPSK demodulation in CDMA (multiple users and AWGN); ML and subspace methods (MUSIC, root MUSIC, Minimum-norm, Linear Predictor, Pisarenko) for Direction-of-arrival estimation; BPSK demodulation with antenna arrays in CDMA systems (space-time processing). (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).

The course will cover topics in wireless communications, including: wireless propagation channels (propagation mechanisms, statistical description, channel characterization and modeling), modulation and demodulation, slow-flat fading channels, frequency selective channels, diversity methods, OFDM, spread spectrum, CDMA and channel coding. Applications of these systems, including wireless sensor networks would be discussed as well. (Prerequisites: EEEE-602 or equivalent course. Co-requisites: EEEE-593 or EEEE-693 or equivalent course.) Lecture 3 (Spring).

In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3, Recitation 1 (Fall, Spring).

This course deals with a complete description of physical systems its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input-output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for desired performance, state feedback, observers and their realizations. (Co-requisites: EEEE-707 or equivalent course.) Lecture 3 (Fall).

This first course in a graduate elective sequence will begin by presenting a general road map of real-time and embedded systems. The course will be conducted in a studio class/lab format with lecture material interspersed with laboratory work. This course will introduce a representative family of microcontrollers that will exemplify unique positive features as well as limitations of microcontrollers in embedded and real-time systems. These microcontrollers will then be used as external, independent performance monitors of more complex real-time systems. The majority of the course will present material on a commercial real-time operating system and using it for programming projects on development systems and embedded target systems. Some fundamental material on real-time operating systems and multiprocessor considerations for real-time systems will also be presented. Examples include scheduling algorithms, priority inversion, and hardware-software co-design. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).

The advanced topics on analysis, control and optimization of high-performance electromechanical systems are covered. Studies and learning are focused on electromechanical motion devices, amplifiers, controllers-drivers, multi-degree-of-freedom sensors, data acquisition, and, control systems. High-fidelity modeling, data-intensive simulations and experimental studies are pertain to industrial control systems as well as supervisory control and data acquisition systems. Novel sensing technologies, analog and digital control algorithms, and optimal design schemes are considered with applications to industrial platforms. Case studies include aerial, automotive, energy, robotic and servo systems. (Prerequisites: EEEE-353 or MECE-320 or equivalent courses.) Lecture 3 (Fall).

The course covers different optimization techniques, as applied to feedback control systems. The main emphasis will be on the design of optimal controllers for digital control systems. The major topics are: Different performance indices, formulation of optimization problem with equality constraints, Lagrange multipliers, Hamiltonian and solution of discrete optimization problem. Discrete Linear Quadratic Regulators (LQR), optimal and suboptimal feedback gains, Riccati equation and its solution, linear quadratic tracking problem. Dynamic Programming - Bellman's principle of optimality - Optimal controllers for discrete and continuous systems - Systems with magnitude constraints on inputs and states. (Prerequisites: EEEE-661 or equivalent course.) Lecture 3 (Spring).

The purpose of this course is to expose students to complete, custom design of a CMOS digital system. It emphasizes equally analytical and CAD based design methodologies, starting at the highest level of abstraction (RTL, front-end)), and down to the physical implementation level (back-end). In the lab students learn how to capture a design using both schematic and hardware description languages, how to synthesize a design, and how to custom layout a design. Testing, debugging, and verification strategies are formally introduced in the lecture, and practically applied in the lab projects. Students are further required to choose a research topic in the area of digital systems, perform bibliographic research, and write a research paper following a prescribed format. (Prerequisites: EEEE-420 and EEEE-480 or equivalent courses or graduate standing in EEEE-MS.) Lab 3, Lecture 3 (Fall, Spring).

The purpose of this course is to expose students to the design of single and multicore computer systems. The lectures cover the design principles of instructions set architectures, non-pipelined data paths, control unit, pipelined data paths, hierarchical memory (cache), and multicore processors. The design constraints and the interdependencies of computer systems building blocks are being presented. The operation of single core, multicore, vector, VLIW, and EPIC processors is explained. In the first half of the semester, the lab projects enforce the material presented in the lectures through the design and physical emulation of a pipelined, single core processor. This is then being used in the second half of the semester to create a multicore computer system. The importance of hardware/software co-design is emphasized throughout the course. Students are further required to choose a research topic in the area of computer systems, perform bibliographic research, and write a research paper following a prescribed format. (Prerequisites: EEEE-420 or equivalent course or graduate standing in EEEE-MS.) Lab 2, Lecture 3 (Fall).

In this course the student is introduced to a multitude of advanced topics in digital systems design. It is expected that the student is already familiar with the design of synchronous digital systems. The lecture introduces the operation and design principles of asynchronous digital systems, synchronous and asynchronous, pipelined and wave pipelined digital systems. Alternative digital processing paradigms are then presented: data flow, systolic arrays, networks-on-chip, cellular automata, neural networks, and fuzzy logic. Finally, digital computer arithmetic algorithms and their hardware implementation are covered. The projects reinforce the lectures material by offering a hands-on development and system level simulation experience. (Prerequisites: EEEE-520 or EEEE-620 or equivalent courses.) Lecture 3 (Spring).

In this course the student is introduced to advanced topics in computer systems design. It is expected that the student is already familiar with the design of a non-pipelined, single core processor. The lectures cover instruction level parallelism, limits of the former, thread level parallelism, multicore processors, optimized hierarchical memory design, storage systems, and large-scale multiprocessors for scientific applications. The projects reinforce the lectures material, by offering a hands-on development and system level simulation experience. (Prerequisites: EEEE-521 or EEEE-621 or equivalent courses.) Lecture 3 (Spring).

Due to continually rising system complexity, verification has become the critical inflection point for complex digital system success or failure. In this course students will study various concepts and technologies related to complex digital system verification with an emphasis on functional verification, top down design flows and advanced methodologies. The class projects reinforce the lectures material by offering hands-on development of a verification environment for a complex digital system. (Prerequisite: This course is restricted to students with graduate standing in EEEE-MS.) Lecture 3 (Fall).

In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3, Recitation 1 (Fall, Spring).

The primary objective is to study the fundamentals of microwave engineering with emphasis on microwave network analysis and circuit design. Topics include microwave transmission lines such as wave-guides, coax, microstrip and stripline, microwave circuit theory such as S- matrix, ABCD matrices, and even odd mode analysis, analysis and design of passive circuits and components, matching networks, microwave resonators and filters. Microwave circuit design projects will be performed using Ansoft's Designer software. (Prerequisites: EEEE-374 or equivalent course or graduate standing in EEEE-MS.) Lecture 3 (Spring).

The primary objective is to study the fundamental principles of antenna theory applied to the analysis and design of antenna elements and arrays including synthesis techniques and matching techniques. Topics include antenna parameters, linear antennas, array theory, wire antennas, microstrip antennas, antenna synthesis, aperture antennas and reflector antennas. A significant portion of the course involves design projects using some commercial EM software such as Ansoft Designer, Ansoft HFSS and SONNET and developing Matlab codes from theory for antenna synthesis and antenna array design. The measurement of antenna input and radiation characteristics will be demonstrated with the use of network analyzers, and spectrum analyzers in an anechoic chamber. (Prerequisites: EEEE-374 or equivalent course or graduate standing in EEEE-MS.) Lecture 3 (Fall).

The primary objective is to provide the mathematical and physical fundamentals necessary for a systematic analysis of electromagnetic field problems. Topics included: electromagnetic theorems and principles, scattering and radiation integrals, TE and TM in rectangular and circular waveguides, hybrid LSE and LSM modes in partially filled guides, dielectric waveguides, the Green's function. The course will also include projects using advanced EM modeling software tools. (Prerequisites: EEEE-617 and EEEE-629 or equivalent course.) Lecture 3 (Spring).

There are two primary course objectives. Design of experiments to characterize or measure specific quantities, working with the constraints of measurable quantities using the vector network analyzer, and in conjunction with the development of closed form analytical expressions. Design, construction and characterization of microstrip circuitry and antennas for specified design criteria obtaining analytical models, using software tools and developing measurements techniques. Microwave measurement will involve the use of network analyzers, and spectrum analyzers in conjunction with the probe station. Simulated results will be obtained using some popular commercial EM software for the design of microwave circuits and antennas. (Prerequisites: EEEE-617 and EEEE-629 or equivalent courses. Co-requisite: EEEE-790 or EEEE-792 or equivalent course.) Lecture 3 (Fall).

This is a foundation course in analog integrated circuit design and is a prerequisite for the graduate courses in RF & mixed-signal IC design (EEEE-726 and EEEE-730). The course covers the following topics: (1) Review of CMOS technology, MOSFET models and Frequency Response (2) Single-stage amplifiers (3) Current mirrors and biasing (4) Current and voltage references (5) Differential amplifiers (6) Cascoding (7) Feedback and Stability (8) OTAs (9) Matching and layout techniques (10) Multi-stage op-amps (11) Noise Analysis (12) Linearity in analog circuits (13) Switched-cap circuits. (Prerequisites: EEEE-480 or equivalent course or graduate standing in EEEE-MS.) Lab 2, Lecture 3 (Fall).

A graduate course in the fundamental principles and operating characteristics of carrier-injection-based semiconductor devices. Advanced treatments of pn junction diodes, metal-semiconductor contacts, and bipolar junction transistors form the basis for subsequent examination of more complex carrier-injection devices, including tunnel devices, transferred-electron devices, thyristors and power devices, light-emitting diodes (LEDs), and photodetectors. Topics include heterojunction physics and heterojunction bipolar transistors (HBT). (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).

An advanced-level course on MOSFETs and submicron MOS devices. Topics include MOS capacitors, gated diodes, long-channel MOSFETs, subthreshold conduction and off-state leakage, short-channel effects, hot-carrier effects, MOS scaling and advanced MOS technologies. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).

This is the first course in the graduate course sequence in analog integrated circuit design EEEE-726 and EEEE-730. This course covers the following topics: (1)Fundamentals of data conversion (2) Nyquist rate digital-to-analog converters (3) Quantization noise and analysis (4) Nyquist rate analog-to-digital converters (5) Sample and hold circuits (6) Voltage references (7) Static and dynamic testing of digital-to-analog converters (8) Cell based design strategies for integrated circuits (9)Advanced topics in data conversion. (Prerequisites: EEEE-510 or EEEE-610 or equivalent course.) Lecture 3 (Spring).

This course introduces the beginning graduate student to the fabrication of solid-state devices and integrated circuits. The course presents an introduction to basic electronic components and devices, lay outs, unit processes common to all IC technologies such as substrate preparation, oxidation, diffusion and ion implantation. The course will focus on basic silicon processing. The students will be introduced to process modeling using a simulation tool such as SUPREM. The lab consists of conducting a basic metal gate PMOS process in the RIT clean room facility to fabricate and test a PMOS integrated circuit test ship. Laboratory work also provides an introduction to basic IC fabrication processes and safety. (Prerequisites: Graduate standing in the MCEE-MS or MCEMANU-ME program or permission of instructor.) Lab 3, Lecture 3 (Fall).

This course deals with a complete description of physical systems its analysis and design of controllers to achieve desired performance. The emphasis in the course will be on continuous linear systems. Major topics are: state space representation of physical systems, similarities/differences between input-output representation (transfer function) and state spate representations, conversion of one form to the other, minimal realization, solution of state equations, controllability, observability, design of control systems for desired performance, state feedback, observers and their realizations. (Co-requisites: EEEE-707 or equivalent course.) Lecture 3 (Fall).

Microelectromechanical systems (MEMS) are widely used in aerospace, automotive, biotechnology, instrumentation, robotics, manufacturing, and other applications. There is a critical need to synthesize and design high performance MEMS which satisfy the requirements and specifications imposed. Integrated approaches must be applied to design and optimized MEMS, which integrate microelectromechanical motion devices, ICs, and microsensors. This course covers synthesis, design, modeling, simulation, analysis, control and fabrication of MEMS. Synthesis, design and analysis of MEMS will be covered including CAD. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall).

This course focuses on evaluation of MEMS, microsystems and microelectromechanical motion devices utilizing MEMS testing and characterization. Evaluations are performed using performance evaluation matrices, comprehensive performance analysis and functionality. Applications of advanced software and hardware in MEMS evaluation will be covered. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Spring).

This course introduces the beginning graduate student to the fabrication of solid-state devices and integrated circuits. The course presents an introduction to basic electronic components and devices, lay outs, unit processes common to all IC technologies such as substrate preparation, oxidation, diffusion and ion implantation. The course will focus on basic silicon processing. The students will be introduced to process modeling using a simulation tool such as SUPREM. The lab consists of conducting a basic metal gate PMOS process in the RIT clean room facility to fabricate and test a PMOS integrated circuit test ship. Laboratory work also provides an introduction to basic IC fabrication processes and safety. (Prerequisites: Graduate standing in the MCEE-MS or MCEMANU-ME program or permission of instructor.) Lab 3, Lecture 3 (Fall).

In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3, Recitation 1 (Fall, Spring).

Cybernetics refers to the science of communication and control theory that is concerned especially with the comparative study of automatic control systems (as in the nervous system and brain and mechanical-electrical communications systems). This course will present material related to the study of cybernetics as well as the aspects of robotics and controls associated with applications of a biological nature. Topics will also include the study of various paradigms and computational methods that can be utilized to achieve the successful integration of robotic mechanisms in a biological setting. Successful participation in the course will entail completion of at least one project involving incorporation of these techniques in a biomedical application. Students are required to write an IEEE conference paper on their projects. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lab 2, Lecture 3 (Spring).

The course will start with the history of artificial intelligence (AI) and its development over the years. There have been many attempts to define and generate artificial intelligence. As a result of these attempts, many AI techniques have been developed and applied to solve real life problems. This course will explore a variety of AI techniques and their applications and limitations. Some of the AI topics to be covered in this course are intelligent agents, problem-solving, knowledge and reasoning, uncertainty, decision making, machine learning, reinforcement learning, and real-world applications of AI. Students are expected to have solid programming skills, understanding of probability and linear algebra, and statistics. Students will write a conference-style paper based on a research project. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3 (Fall).

An introduction to a wide range of robotics-related topics, including but not limited to sensors, interface design, robot devices applications, mobile robots, intelligent navigation, task planning, coordinate systems and positioning image processing, digital signal processing applications on robots, and controller circuitry design. Pre- requisite for the class is a basic understanding of signals and systems, matrix theory, and computer programming. Software assignments will be given to the students in robotic applications. Students will prepare a project, in which they will complete software or hardware design of an industrial or mobile robot. There will be a two-hour lab additional to the lectures. Students are required to write an IEEE conference paper on their projects. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lab 3, Lecture 3 (Fall).

This course explores advance topics in mobile robots and manipulators. Mobile robot navigation, path planning, room mapping, autonomous navigation are the main mobile robot topics. In addition, dynamic analysis of manipulators, forces and trajectory planning of manipulators, and novel methods for inverse kinematics and control of manipulators will also be explored. The pre-requisite for this course is Principles of Robotics. However, students would have better understanding of the topics if they had Control Systems and Mechatronics courses as well. The course will be a project based course requiring exploration of a novel area in Robotics and writing an IEEE conference level paper. (Prerequisites: EEEE-585 or EEEE-685 or equivalent course.) Lab 2, Lecture 3 (Spring).

In this course the student is introduced to random variables and stochastic processes. Topics covered are probability theory, conditional probability and Bayes theorem, discrete and continuous random variables, distribution and density functions, moments and characteristic functions, functions of one and several random variables, Gaussian random variables and the central limit theorem, estimation theory , random processes, stationarity and ergodicity, auto correlation, cross-correlation and power spectrum density, response of linear prediction, Wiener filtering, elements of detection, matched filters. (Prerequisites: This course is restricted to graduate students in the EEEE-MS, EEEE-BS/MS program.) Lecture 3, Recitation 1 (Fall, Spring).

This course provides a rigorous introduction to the principles and applications of pattern recognition. The topics covered include maximum likelihood, maximum a posteriori probability, Bayesian decision theory, nearest-neighbor techniques, linear discriminant functions, and clustering. Parameter estimation and supervised learning as well as principles of feature selection, generation and extraction techniques, and utilization of neural nets are included. Applications to face recognition, classification, segmentation, etc. are discussed throughout the course. (Prerequisites: EEEE-602 and EEEE-707 and EEEE-709 or equivalent courses.) Lecture 3 (Spring).

In this course, the student is introduced to the concept of multi rate signal processing, Poly phase Decomposition, Transform Analysis, Filter Design with emphasis on Linear Phase Response, and Discrete Fourier Transforms. Topics covered are: Z- Transforms, Sampling, Transform Analysis of Linear Time Invariant Systems, Filter Design Techniques, Discrete Fourier Transforms (DFT), Fast Algorithms for implementing the DFT including Radix 2, Radix 4 and Mixed Radix Algorithms, Quantization Effects in Discrete Systems and Fourier Analysis of Signals. (Prerequisites: EEEE-707 or equivalent course.) Lecture 3 (Fall, Summer).

The first half of the course contains a detailed study of the mathematical tools required for understanding and implementing specific digital image processing algorithms such as an overview of the human visual system, Cartesian-separable vs. isotropic filters, fast approximation of Gaussian filters, a comprehensive review of 2-D digital spatial filters (LP, HP, sharpening, edge detection), the integral image, 2-D sampling strategies (e.g., Cartesian, Hexagonal, or general grid), fundamentals of image resizing (bilinear, bicubic, Lanczos, etc.), geometric transforms and image warping, and detailed coverage of 2-D discrete Fourier transform. The second half of the course focuses on specific digital image processing algorithms including contrast enhancement, noise reduction, sharpening, deblurring and segmentation. Some specific techniques for contrast enhancement are linear and nonlinear look-up tables, histogram equalization and modification, and contrast-limited adaptive HE (CLAHE). Algorithms for linear and nonlinear noise reduction include selective averaging, the sigma filter, the K-NN filter, bi-lateral filtering, median filtering, and deep networks. Sharpening techniques include nonadaptive and adaptive unsharp masking and relaxation of the boosting parameter. Deblurring techniques include the inverse filter and the Wiener filter. Finally, segmentation algorithms include various edge detection masks, the Otsu algorithm and adaptive thresholding. This course relies heavily on the knowledge of an undergraduate EE course in linear systems such as shift-invariant linear systems, impulse response, continuous and discrete Fourier transforms, the sampling theorem and the convolution operation. Additionally, EEEE678 serves as a good background or can be taken simultaneously. (Prerequisites: EEEE-353 or equivalent course or graduate student standing.) Lecture 3 (Fall).

This course studies the fundamental technologies used in image and video compression techniques and international standards such as JPEG and MPEG. At the highest level, all visual data compression techniques can be reduced to three fundamental building blocks: transformation or decomposition (examples are discrete cosine transform or DCT, wavelets, differential pulse code modulation or DPCM and motion compensation), quantization (strategies include scalar vs. vector quantization, uniform vs. nonuniform, Lloyd-Max and entropy-constrained quantization) and symbol modeling and encoding (the concept of Markov source and its entropy, context modeling, variable length coding techniques such as Huffman and arithmetic coding and Golomb-Rice coding). This course studies all of these fundamental concepts in great detail in addition to their practical applications in leading image and video coding standards. The study cases include a comprehensive review of the JPEG lossless compression standard (based on pixel prediction and Huffman coding), the JPEG lossy compression standard (based on DCT and Huffman coding), a detailed study of wavelet decomposition and a brief overview of the MPEG family of standards (employing motion compensation in addition to aforementioned techniques). Course concepts rely heavily on knowledge of probability theory. It is strongly recommended that the students have the equivalent knowledge of a senior undergraduate or graduate level probability course such as EEEE602. The course assignments require proficient programming skills (either Matlab or Python). (Prerequisites: EEEE-353 or equivalent course or graduate student standing.) Lecture 3 (Summer).