Covers basics of DC circuit analysis starting with the definition of voltage, current, resistance, power and energy. Linearity and superposition, together with Kirchhoff's laws, are applied to analysis of circuits having series, parallel and other combinations of circuit elements. Thevenin, Norton and maximum power transfer theorems are proved and applied. Circuits with ideal op-amps are introduced. Inductance and capacitance are introduced and the transient response of RL, RC and RLC circuits to step inputs is established. Practical aspects of the properties of passive devices and batteries are discussed, as are the characteristics of battery-powered circuitry. The laboratory component incorporates use of both computer and manually controlled instrumentation including power supplies, signal generators and oscilloscopes to reinforce concepts discussed in class as well as circuit design and simulation software.

One semester of paid work experience in electrical engineering.

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.

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.

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.

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.