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Usc ee 301

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चैप्टर अनुसार सैंपल इंपोर्टेंट क्वेश्चन EE301(Power Ec)- ch1 - BTER New Exam Pattern - Explanation

Power-flow control from highly variable resources. Cost analysis and planning. Control and stability of large-scale systems such as the electric power grid.

Integration with information networks. Corequisites: EE EE Nonlinear Optics 3. Theory of nonlinear optical susceptibility and application to self-focusing, harmonic generation, and parametric interactions.

Raman and Brillouin scattering. Coherent spectroscopy. EE Wireless Communications 4. The mobile communication channel; techniques used to combat the channel; cellular communications; multiple-access techniques; example mobile communication systems.

Non-linear integrated circuits, data-converter architectures and implementations, comprehensive design project. Integrated-circuit technologies for mixed-signal communication and data systems.

Constituent device models and their limitations. Contemporary research topics. EE Introduction to Quantum Electronics 4.

Fundamentals of light amplification; laser amplifiers and oscillators; atomic pumping; maser and laser systems; definitions of coherence; measurements in quantum electronics.

EE a: Digital Control Systems 3. Design, analysis and implementation of digital control systems using microcomputers; Z-transform methods; frequency domain and state space approach; computational aspects; sampling and quantization.

Nanotechnology for advanced nanomaterials, nanoelectronics devices, and circuits, including the rigorous treatment of electronic band structures of carbon nanotubes, graphene, and two-dimensional materials.

Asynchronous channels and architectures; implementation design styles; controller synthesis; hazards, and races; Petri-nets; performance analysis, and optimization; globally asynchronous locally synchronous design.

Open only to graduate students. EE Broadband Network Architectures 3. Broadband network architectures and services, technologies for high-speed access and core networks, optical infrastructure for layered network architectures, high performance switch and router architectures.

Prerequisites: EE and EE EE Computer Systems Architecture 4. EE Mathematical Pattern Recognition 3. Distribution free classification, discriminant functions, training algorithms; statistical classification, parametric and nonparametric techniques; artificial neural networks.

Foundations of symbolic intelligent systems, search, logic, knowledge representation, planning, learning. EE Random Processes in Engineering 4.

Random vectors, sequences, and functions. Linear transformations, second moment theory, spectral densities, narrowband processes, Gaussian processes, correlation detection, linear minimum mean square error estimation.

EE Estimation Theory 3. Parameter estimation and state estimation technique including: least squares, BLUE, maximum-likelihood, maximum a posteriori, Kalman-prediction, Kalman-filtering and Kalman smoothing and extended Kalman filtering.

Digital modulations. Optimal reception. Performance analysis. Classical and modern codes. Viterbi, forward-backward, and iterative decoding.

Practical designs for channels with memory or nonlinearities. Example systems. Entropy and mutual information. Variable and fixed-length, lossless and lossy compression.

Universal compression. Text and multimedia compression. Channel capacity. Error-correcting codes. Erasure and Gaussian channels. Image sampling, 2-D image transform, image enhancement, geometric image modification, morphologic processing, edge detection, texture analysis, image filtering and restoration.

EE a: Advanced Electromagnetic Theory 4. Classical electromagnetic field theory and its usage in understanding and analytically modelling advanced applied electrical engineering devices.

Differential geometry of curves and surfaces, vectors, tensors, manifolds, curvature, Lie groups, Riemannian geometry, numerical PDEs, heat and Laplace equation, Applications to computer vision, machine learning, signal and imageprocessing.

Physical operation and implementation of modern solid-state memory structures. Emerging advanced memory technologies. Integrated circuit fabrication; circuit simulation; basic device physics; simple device layout: structured chip design; timing; project chip; MOS logic; system design; silicon compilers.

EE Nonlinear and Adaptive Control 3. Nonlinear systems, Lyapunov stability, parameter identification, direct and indirect adaptive control for linear and nonlinear systems.

Design analysis, stability, robustness and applications. EE Directed Research 1. Research leading to the master's degree. Maximum units which may be applied to the degree to be determined by the department.

Principles of magnetic resonance imaging. Spin physics, Fourier-based acquisition and reconstruction, generation of tissue contrast, fast imaging, artifact correction, advanced image reconstruction.

Prerequisite: EE Recommended preparation: EE and EE Vector-space methods for solving inverse problems. Existence and uniqueness of solutions; conditioning and regularization; iterative algorithms; constrained optimization; applications in signal and image processing.

EE a: Master's Thesis 2. Credit on acceptance of Thesis. EE b: Master's Thesis 2. For the Master's Degree.

EE z: Master's Thesis 0. EE Software Design and Optimization 4. Software strategies for applications constrained by power consumption, memory space and verification time; assignments incorporate key elements of planning, modeling, design simulation and testing.

EE Wireless Networks 4. Introduction to wireless networking technologies; fundamental architectural and design principles used at all protocol layers; optimization, performance evaluation and implementation using mathematical analysis, simulations, and experiments.

Introduction to research in electrical engineering. Topics vary by semester. May be repeated for up to one unit of credit for MS students, two units of credit for PhD students.

EE Special Topics 1. The course content will be selected each semester to reflect current trends and developments in the field of electrical engineering.

EE Integrated Communication Systems 4. Analysis and design of integrated communication circuits at transistor and system levels.

Communication concepts, transceiver architectures, low-noise amplifiers, mixers, oscillators, phase-locked loops, power amplifiers.

EE Stochastic Network Optimization 3. Optimization of wireless and ad-hoc mobile networks; opportunistic scheduling, flow control; back-pressure routing; queue stability; energy-delay and utility-delay trade-offs.

EE Advanced Topics in Microarchitecture 3. Current research topics related to microprocessor architecture.

Power, performance, complexity, dependability issues. Impact of technology. Laboratory study of specific problems by candidates for the degree Engineer in Electrical Engineering.

EE Research 1. Research leading to the doctorate. EE a: Doctoral Dissertation 2. Credit on acceptance of Dissertation. EE b: Doctoral Dissertation 2.

EE c: Doctoral Dissertation 2. EE d: Doctoral Dissertation 2. EE z: Doctoral Dissertation 0. Information accurate as of October 6, pm.

Download as a spreadsheet. The Fall semester will begin with fully remote instruction, with limited exceptions for clinical education.

Faculty will contact students to provide information to login to classes. Read more. Mark Redekopp. Murali Annavaram.

Gandhi Puvvada. Photon-, electron-, and ion-based systems, advanced processes; resolution enhancement techniques; directed self assembly.

EE Linear Algebra for Engineering 4. Introduction to linear algebra and matrix theory and their underlying concepts; applications to engineering problems; mathematically rigorous and foundational to other classes in communication, control, and signal processing.

Project-oriented investigation of simulation methods used for the analysis and design of complex stochastic systems whose operation and performance are affected by random events.

Corequisite: EE Recommended preparation: MatLab programming experience. EE Stochastic Processes 3. Probability theory and stochastic processes, including renewal theory, Markov chains, Brownian motion, martingales, and stochastic calculus.

Applications in communication networks, queuing theory and financial systems. Design and operation of solar photovoltaic energy converters, thermovoltoic energy converters, thermoelectric energy converters, and solid state light emitters; their roles in renewal and conservation of energy.

Recommended Preparation EE Students will build a mathematical background for studying Financial Engineering. Emphasis is on analysis, proofs and examples.

Mathworks Financial toolbox will be introduced. Introduces the basics of quantum computation and quantum information theory: quantum bits and registers, unitary gates, algorithms, error correction, and quantum cryptography.

EE Power System Protection 4. Theory of system and equipment protection, characteristics of relays, relay coordination, and system considerations.

EE Power Electronics 4. Fundamentals of switched-mode power converters operating under steadystate and transient conditions.

Feedback control systems. Magnetic circuit design. EE Optics 4. Crosslist EE Materials Characterization 3. Integrated-circuit technologies for mixed-signal communication and data systems.

Constituent device models and their limitations. Contemporary research topics. EE Engineering Quantum Mechanics 4. Quantum mechanics for engineering majors who work with solid-state devices, quantum electronics, and photonics.

Schroedinger equation, perturbation theory, electronic and optical processes. EE Internet and Cloud Computing 3.

Recommended preparation: EE or EE Crosslist EE Robotics 4. Fundamental skills for modeling and controlling of dynamic systems for robotic applications and graphics animations; control theory; kinematics; dynamics; sensor processing; real-time operating systems; robot labs.

Nanotechnology for advanced nanomaterials, nanoelectronics devices, and circuits, including the rigorous treatment of electronic band structures of carbon nanotubes, graphene, and two-dimensional materials.

Applications of stochastic modeling and optimization techniques to communication network design and analysis. Data link control; performance models; multi-access channels; routing and flow control.

EE Principles of Radar 3. Signal propagation, reflections from targets; radar equation; detection of scintillating targets; resolution; ambiguity functions; clutter rejection; tracking radars.

Prerequisites: EE and EE EE Broadband Network Architectures 3. Broadband network architectures and services, technologies for high-speed access and core networks, optical infrastructure for layered network architectures, high performance switch and router architectures.

Prerequisites: EE and EE Stochastic system models, Dynamic programming, Linear quadratic control, Kalman filtering and estimation, System identification, approximate dynamic programming methods, adaptive control, reinforcement and online learning.

EE Computer Systems Architecture 4. State-of-the-art optical fiber communication system. Recommended preparation: EE ; basic knowledge of optics, semiconductor, and communications concepts.

Foundations of symbolic intelligent systems, search, logic, knowledge representation, planning, learning. EE Random Processes in Engineering 4.

Random vectors, sequences, and functions. Linear transformations, second moment theory, spectral densities, narrowband processes, Gaussian processes, correlation detection, linear minimum mean square error estimation.

EE Optical Information Processing 3. Coherent and incoherent optical transforming, imaging and two-dimensional information processing systems; optical image processing, spatial frequency response and filtering; optical and digital holography.

EE Communication Systems 3. Analysis of communication systems operating from very low to optical frequencies. Comparison of modulation and detection methods.

System components description. Optimum design of communication systems. Physical operation and implementation of modern solid-state memory structures.

Emerging advanced memory technologies. Integrated circuit fabrication; circuit simulation; basic device physics; simple device layout: structured chip design; timing; project chip; MOS logic; system design; silicon compilers.

Fundamental techniques underlying the methodologies for system design, from integrated circuits to cyber-physical systems.

Design flows, fundamental classes of models, and verification and synthesis techniques. EE Linear System Theory 3. Analysis of linear dynamical systems by state-space techniques; controllability, observability, stability, passivity.

Application of feedback control and network synthesis. Convex sets, functions, and optimization problems. Basic convex analysis and theory of convex programming.

Novel, efficient first-order algorithms. Applications in the information and data sciences. EE Directed Research 1. Research leading to the master's degree.

Maximum units which may be applied to the degree to be determined by the department. EE Robust Multivariable Control 3. Sensitivity and complementary sensitivity matrices; uncertainty representation; singular values; Bode plots.

Parameterization of internally stabilizing controllers. Algebraic Riccati Equations. Modern Wiener-Hopf and H-infinity designs.

EE a: Master's Thesis 2. Credit on acceptance of Thesis. EE b: Master's Thesis 2. For the Master's Degree.

EE z: Master's Thesis 0. EE Wireless Networks 4. Introduction to wireless networking technologies; fundamental architectural and design principles used at all protocol layers; optimization, performance evaluation and implementation using mathematical analysis, simulations, and experiments.

Introduction to research in electrical engineering. Topics vary by semester. May be repeated for up to one unit of credit for MS students, two units of credit for PhD students.

EE Special Topics 1. The course content will be selected each semester to reflect current trends and developments in the field of electrical engineering.

EE Microelectromechanical Systems 3. EE Science and Practice of Nanotechnology 3. In-depth discussions of important topics in nanotechnology, including both the implementation and the underlying theory.

EE Network Economics and Games 3. Economics of networks; Game theory, Mechanism design and auctions in networks; spectrum sharing mechanisms in communications; pricing of differentiated services; network security.

Fault models; test generation; fault simulation; self-checking and self-testing circuits; design for testability; fault tolerant design techniques; case studies.

Supervised, semisupervised, and unsupervised machine learning; classification and regression. Model complexity, assessment, and selection; performance error on unseen data.

EE Multimedia Data Compression 3. Recommended preparation: EE Fundamental issues in computer vision: theory, algorithms and applications.

Image formation, image segmentation, inference and measurement of 3-D, motion analysis, object and activity recognition. Laboratory study of specific problems by candidates for the degree Engineer in Electrical Engineering.

Usc Ee 301 - Dateiverwendung

Aus dem Zusammenspiel von Patentrecht, Zivilprozessrecht und dem Bürgerlichen Recht ergeben sich vielschichtige wirtschaftliche und rechtliche Probleme, die bei internationalen Verträgen noch stärker zu Tage treten. Das Vertragsgleichgewicht ist durch die Nichtigkeitserklärung des Patents erheblich gestört. So war es dem Lizenznehmer lange Zeit aufgrund des sog. Usc ee 301

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Introduction to concepts of randomness and uncertainty: probability, random variables, statistics. Applications to digital communications, signal processing, automatic control, computer engineering and computer science.

Optimization design methods for system applications. Introduction to micro-electro-opto-mechanical systems; scaling effects on material properties, fluid flows, dynamical behavior; fabrication methods; design considerations for MEMS sensors and actuators.

Experiments and design project in digital signal processing e. Recommended preparation: EE Capstone design experience.

EE Introduction to Power Systems 4. Components of power systems; analysis techniques in electrical power generation transmission and utilization; and environmental and economic considerations in system operations and planning.

Designing, building and programming mobile robots; sensors, effectors, basic control theory, control architectures, some advanced topics, illustrations of state-of-the-art.

Teamwork; final project tested in a robot contest. Junior standing or higher. Prerequisites: CSCI Application of solid-state electronic devices to the design of linear and mixed-signal systems.

Laboratory experiments and projects involving the design of electronic hardware. EE Introduction to Computer Networks 4. Network architectures; layered protocols, network service interface; local, wide area, wireless networks; Internet protocols; link protocols; addressing; routing; flow control; software defined network; multimedia networks.

EE Parallel and Distributed Computation 4. Introduction to parallel programming techniques, models and optimization strategies; Application mapping to multi-core, accelerator, GPU and cloud platforms; High Performance Computing and Data Science applications.

Intensive introduction to programming principles, discrete mathematics for computing, software design and software engineering concepts.

Not available for credit to computer sciencemajors, graduate or undergraduate. EE Computer Systems Organization 4. Register Transfer level machine organization; performance; arithmetic; pipelined processors; exceptions, out-of-order and speculative execution, cache, virtual memory, multi-core multi-threaded processors, cache coherence.

EE Introduction to Communication Systems 3. Analog and digital communication systems. Review of wireless, networking, and optical systems.

Laboratory assignments including design, layout, extraction, simulation and automatic synthesis. Vibration measurement and analysis; simulation, design, and experimental verification of mechanical control systems; system identification; implementation of controllers; performance evaluation via experimentation and stimulation.

Fundamentals of digital signal processing covering: discrete time linear systems, quantization, sampling, Z-transforms, Fourier transforms, FFTs and filter design.

EE x: Directed Research 1. Individual research and readings. Not available for graduate credit. EE a: Undergraduate Thesis 2.

For the undergraduate degree. Credit on acceptance of thesis. EE b: Undergraduate Thesis 2. Crosslist EE Solid State 4. Atomic electronic states, molecular bonding, crystal structures, diffraction, reciprocal lattice, Brillouin zones, crystal binding, lattice vibrations, specific heat, anharmonic effects, energy bands, metals, conductivity.

Rigorous coverage of probability, discrete and continuous random variables, functions of multiple random variables, covariance, correlation, random sequences, Markov chains, estimation, and introduction to statistics.

Duplicates credit in former EE and EE Laboratory oriented with lectures keyed to practical procedures and processes. Solid-state fabrication and analysis fundamentals; basic device construction techniques.

Prerequisite: BSEE. Physical basis of technologies for the fabrication of micro- and nano-scale devices. Thin-film deposition, etching, and material modification processes; patterntransfer methods.

EE Linear Algebra for Engineering 4. Introduction to linear algebra and matrix theory and their underlying concepts; applications to engineering problems; mathematically rigorous and foundational to other classes in communication, control, and signal processing.

Project-oriented investigation of simulation methods used for the analysis and design of complex stochastic systems whose operation and performance are affected by random events.

Corequisite: EE Recommended preparation: MatLab programming experience. EE Stochastic Processes 3. Probability theory and stochastic processes, including renewal theory, Markov chains, Brownian motion, martingales, and stochastic calculus.

Applications in communication networks, queuing theory and financial systems. EE Quantum Error Correction 4. A comprehensive introduction to quantum error correction and decoherence control, from the basics to the cutting edge, enabling students to delve into current research topics.

Statistics and data analysis emphasizing computation and problem solving: confidence intervals, hypothesis tests, bootstrap and Monte Carlo estimation, regression, Bayesian and statistical learning techniques.

Students will build a mathematical background for studying Financial Engineering. Emphasis is on analysis, proofs and examples.

Mathworks Financial toolbox will be introduced. Speech production, acoustics, perception, synthesis, compression, recognition, transmission. Coding for speech, music, and CD-quality.

Feature extraction. Echo cancellation. Audio, visual synchronization. Multimedia, Internet use. Power system planning, studies, and design; time-domain modeling and analysis of power-system networks; power flow, stability, fault, and economic dispatch analysis; symmetrical components.

Fundamentals of digital audio signal processing, room acoustics, and psychoacoustics. Algorithms for real-time implementation of immersive audio systems for integrated media applications.

Advanced scientific and engineering principles of biomedical imaging including magnetic resonance, X-ray computed tomography, ultrasound, and single photon and positron emission tomography.

Open only to master and doctoral students. Renewable energy sources and their integration in electrical networks. Power-flow control from highly variable resources.

Cost analysis and planning. Control and stability of large-scale systems such as the electric power grid. Integration with information networks.

Corequisites: EE EE Nonlinear Optics 3. Theory of nonlinear optical susceptibility and application to self-focusing, harmonic generation, and parametric interactions.

Raman and Brillouin scattering. Coherent spectroscopy. EE Wireless Communications 4. The mobile communication channel; techniques used to combat the channel; cellular communications; multiple-access techniques; example mobile communication systems.

Non-linear integrated circuits, data-converter architectures and implementations, comprehensive design project. Integrated-circuit technologies for mixed-signal communication and data systems.

Constituent device models and their limitations. Contemporary research topics. EE Introduction to Quantum Electronics 4. Fundamentals of light amplification; laser amplifiers and oscillators; atomic pumping; maser and laser systems; definitions of coherence; measurements in quantum electronics.

EE a: Digital Control Systems 3. Design, analysis and implementation of digital control systems using microcomputers; Z-transform methods; frequency domain and state space approach; computational aspects; sampling and quantization.

Nanotechnology for advanced nanomaterials, nanoelectronics devices, and circuits, including the rigorous treatment of electronic band structures of carbon nanotubes, graphene, and two-dimensional materials.

Asynchronous channels and architectures; implementation design styles; controller synthesis; hazards, and races; Petri-nets; performance analysis, and optimization; globally asynchronous locally synchronous design.

Open only to graduate students. EE Broadband Network Architectures 3. Broadband network architectures and services, technologies for high-speed access and core networks, optical infrastructure for layered network architectures, high performance switch and router architectures.

Prerequisites: EE and EE EE Computer Systems Architecture 4. EE Mathematical Pattern Recognition 3. Distribution free classification, discriminant functions, training algorithms; statistical classification, parametric and nonparametric techniques; artificial neural networks.

Foundations of symbolic intelligent systems, search, logic, knowledge representation, planning, learning.

EE Random Processes in Engineering 4. Random vectors, sequences, and functions. Linear transformations, second moment theory, spectral densities, narrowband processes, Gaussian processes, correlation detection, linear minimum mean square error estimation.

EE Estimation Theory 3. Parameter estimation and state estimation technique including: least squares, BLUE, maximum-likelihood, maximum a posteriori, Kalman-prediction, Kalman-filtering and Kalman smoothing and extended Kalman filtering.

Digital modulations. Optimal reception. EE Internet and Cloud Computing 3. Recommended preparation: EE or EE Crosslist EE Robotics 4.

Fundamental skills for modeling and controlling of dynamic systems for robotic applications and graphics animations; control theory; kinematics; dynamics; sensor processing; real-time operating systems; robot labs.

Nanotechnology for advanced nanomaterials, nanoelectronics devices, and circuits, including the rigorous treatment of electronic band structures of carbon nanotubes, graphene, and two-dimensional materials.

Applications of stochastic modeling and optimization techniques to communication network design and analysis. Data link control; performance models; multi-access channels; routing and flow control.

EE Principles of Radar 3. Signal propagation, reflections from targets; radar equation; detection of scintillating targets; resolution; ambiguity functions; clutter rejection; tracking radars.

Prerequisites: EE and EE EE Broadband Network Architectures 3. Broadband network architectures and services, technologies for high-speed access and core networks, optical infrastructure for layered network architectures, high performance switch and router architectures.

Prerequisites: EE and EE Stochastic system models, Dynamic programming, Linear quadratic control, Kalman filtering and estimation, System identification, approximate dynamic programming methods, adaptive control, reinforcement and online learning.

EE Computer Systems Architecture 4. State-of-the-art optical fiber communication system. Recommended preparation: EE ; basic knowledge of optics, semiconductor, and communications concepts.

Foundations of symbolic intelligent systems, search, logic, knowledge representation, planning, learning. EE Random Processes in Engineering 4.

Random vectors, sequences, and functions. Linear transformations, second moment theory, spectral densities, narrowband processes, Gaussian processes, correlation detection, linear minimum mean square error estimation.

EE Optical Information Processing 3. Coherent and incoherent optical transforming, imaging and two-dimensional information processing systems; optical image processing, spatial frequency response and filtering; optical and digital holography.

EE Communication Systems 3. Analysis of communication systems operating from very low to optical frequencies. Comparison of modulation and detection methods.

System components description. Optimum design of communication systems. Physical operation and implementation of modern solid-state memory structures.

Emerging advanced memory technologies. Integrated circuit fabrication; circuit simulation; basic device physics; simple device layout: structured chip design; timing; project chip; MOS logic; system design; silicon compilers.

Fundamental techniques underlying the methodologies for system design, from integrated circuits to cyber-physical systems.

Design flows, fundamental classes of models, and verification and synthesis techniques. EE Linear System Theory 3.

Analysis of linear dynamical systems by state-space techniques; controllability, observability, stability, passivity. Application of feedback control and network synthesis.

Convex sets, functions, and optimization problems. Basic convex analysis and theory of convex programming. Novel, efficient first-order algorithms.

Applications in the information and data sciences. EE Directed Research 1. Research leading to the master's degree.

Maximum units which may be applied to the degree to be determined by the department. EE Robust Multivariable Control 3. Sensitivity and complementary sensitivity matrices; uncertainty representation; singular values; Bode plots.

Parameterization of internally stabilizing controllers. Algebraic Riccati Equations. Modern Wiener-Hopf and H-infinity designs.

EE a: Master's Thesis 2. Credit on acceptance of Thesis. EE b: Master's Thesis 2. For the Master's Degree. EE z: Master's Thesis 0. EE Wireless Networks 4.

Introduction to wireless networking technologies; fundamental architectural and design principles used at all protocol layers; optimization, performance evaluation and implementation using mathematical analysis, simulations, and experiments.

Introduction to research in electrical engineering. Topics vary by semester. May be repeated for up to one unit of credit for MS students, two units of credit for PhD students.

EE Special Topics 1. The course content will be selected each semester to reflect current trends and developments in the field of electrical engineering.

EE Microelectromechanical Systems 3. EE Science and Practice of Nanotechnology 3. In-depth discussions of important topics in nanotechnology, including both the implementation and the underlying theory.

EE Network Economics and Games 3. Economics of networks; Game theory, Mechanism design and auctions in networks; spectrum sharing mechanisms in communications; pricing of differentiated services; network security.

Fault models; test generation; fault simulation; self-checking and self-testing circuits; design for testability; fault tolerant design techniques; case studies.

Supervised, semisupervised, and unsupervised machine learning; classification and regression. Model complexity, assessment, and selection; performance error on unseen data.

EE Multimedia Data Compression 3. Recommended preparation: EE Fundamental issues in computer vision: theory, algorithms and applications.

Image formation, image segmentation, inference and measurement of 3-D, motion analysis, object and activity recognition.

Laboratory study of specific problems by candidates for the degree Engineer in Electrical Engineering. EE Research 1. Research leading to the doctorate.

EE a: Doctoral Dissertation 2. Credit on acceptance of Dissertation. EE b: Doctoral Dissertation 2. EE c: Doctoral Dissertation 2. EE d: Doctoral Dissertation 2.

EE z: Doctoral Dissertation 0. Information accurate as of March 9, am. Download as a spreadsheet. The Fall semester will begin with fully remote instruction, with limited exceptions for clinical education.

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