An introduction to the fundamentals of optimization with a focus on algorithms and applications in signal processing, control systems, machine learning, and robotics.
Studying biomedical image analysis techniques including image enhancement, analysis, classification, and interpretation for medical decision-making through practicals and projects. Cross-listed with BMED6780.
An introduction to the theory of statistical learning and practical machine learning algorithms with applications in signal processing and data analysis.
A study of the principles and design of medical imaging systems such
as X-ray, ultrasound, nuclear medicine, and nuclear magnetic
resonance. Cross-listed with BMED 6786.
Introduce application areas where signals are sampled over space and time.
Transfer knowledge of time-based techniques to spatial processing.
Develop algorithms unique to spatial processing.
Theory and algorithms of signal encoding and decoding for data compression. Applications in information systems, digital telephony, digital television, and multimedia Internet.
Signal modeling including radar cross section, multipath, and
clutter. Properties of the ambiguity function and coded waveforms.
Algorithms for doppler processing, detection, and radar imaging.
Introduction to discrete-time signal processing and linear systems. Sampling theorem. Filtering. Frequency response. Discrete Fourier Transform. Z Transform. Laboratory emphasizes computer-based signal processing.
Introduction to random signals and processes with emphasis on applications in ECE. Includes basic estimation theory, linear prediction, and statistical modeling.
Introduction to Digital Signal Processing. Sampling Theorem,Discrete-time
Fourier transform,power spectrum,discrete Fourier transform and the FFT
algorithm,z-Transform, digital filter design and implementation.
Applications of DSP in speech, image processing, radar, pattern
recognition, and adaptive filtering requiring working software
implementations applied to the analysis of real signals.
Introduction to Probability and Statistics for ECE
Introduction to probability, random variables, distributions, estimation, confidence intervals, linear regression and other tools for describing and managing uncertainty in electrical and computer engineering.
An introduction to the fundamentals and the theory of multidimensional signal processing and digital image processing, including key applications in multimedia products and services including machine learning
Algebraic and number theory approaches to cryptographic techniques, information security, secret key and public key encryption, signature schemes, hash functions, message authentication, and key distribution. Credit not allowed for both ECE 6280 and CS 6260.
Basic theory of adaptive filter design and implementation.
Steepest descent, LMS algorithm, nonlinear adaptive filters, and
neural networks. Analysis of performance and applications.