No Lab. Mathematical models for analog circuit elements such as resistors, capacitors, op-amps, and MOSFETs as switches. Basic circuit laws and network theorems applied to dc, transient, and steady-state response of first- and second-order circuits. Modeling circuit responses using differential equations. Computer projects. NOTE: Grades of C or better in MATH 132 and PHYSICS 152 are strongly recommended.

Not available at this time

Not available at this time

Not available at this time

Mathematical models for analog circuit elements such as resistors, capacitors, opamps and MOSFETs as switches. Basic circuit laws and network theorems applied to dc, transient, and steady-state response of first- and second-order circuits. Modeling circuit responses using differential equations Computer and laboratory projects. NOTE: Grades of C or better in MATH 132 and PHYSICS 152 are strongly recommended.

Study architectural techniques for efficient hardware design for machine learning (ML) systems including training and inference. Course has three parts. First part deals with ML algorithms: regression, support vector machines, decision tree, and naive Bayes approaches. Second part deals with convolutional and deep neural network models.

The course will focus on advanced analytical tools for modeling and analysis of modern networks including: network optimization, queuing theory, game theory, mean field theory, and matching theory. Examples of resource allocation problems in ultra-reliable low-latency networks, virtual networks, multi-access edge networks, 5G/6G networks will be discussed by using these tools.

With lab. IIR and FIR digital filter design. Applications of DFT and FFT. Transform domain analysis of discrete-time (DT) linear time-invariant systems: minimum phase, allpass, linear phase systems. Im-plementation of DT systems. Finite wordlength effects. Multirate digital signal processing. Power spectrum estimation. Lab includes projects using digital signal processors. Prerequisite: E&C-ENG 563.

This honors colloquium combines theory and experimentation in security engineering. The colloquium will focus on Machine Learning and the threats and vulnerabilities that arise in embedded applications, focusing on image analysis. It will build on the security engineering principles and methods developed in the main part of the course. Students will use modern cloud-based software packages and data-sets from the ML research community and will leverage current UMass research in secure embedded systems.