EEE 203 – Signal and Systems (3 credits)<\/strong> EEE 407 – Digital Signal Processing (4 credits) <\/strong>More info<\/strong><\/a><\/p>\n Description\/Topics:<\/strong> EEE 506 – Digital Spectral Analysis<\/strong> (3 credits) EEE606 – Adaptive Signal Processing<\/a> (3 credits)<\/strong> EEE 607 – Speech Coding (3 credits) <\/p>\n INTERNET courses\u00a0 EEE509 MATLAB for DSP<\/a>\u00a0\u00a0 &\u00a0 EEE510 MULTIMEDIA SIGNAL PROCESSING<\/a><\/p>\n Hybrid\/ i-course EEE517 Sensors and Machine Learning<\/a><\/p>\n i-course EEE 307\u00a0Signal Processing for Digital Culture<\/a><\/p>\n i-course EEE598 Sensors and Machine Learning Applications (1 credit seminar course)<\/p>\n Books<\/a> used in DSP and Speech Coding\/Multimedia Courses<\/p>\n
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\n<\/strong>The purpose of this course is to introduce junior students to the principles of signals and linear systems. Topics: Introduction to continuous and discrete time signals and systems, Linearity, time-invariance, causality, Transient and Steady-State Response, Steady-State Response to Sinusoids, Convolution and Impulse Response, FIR and IIR Discrete-time Systems, FIR Filter Design, Feedback and BIBO stability, Transform Domain Analysis of Signals, Z-transform and region of convergence, Fourier transform and its properties, The sampling Theorem, State-variable representations. Prerequisites: EEE 202<\/p>\n
\nThe purpose of this course is to introduce senior students to the principles and applications of Digital Signal Processing. Topics: Difference equations, Digital Filters, BIBO Stability, Z-transforms and Frequency Response, FIR and IIR Digital Filter Design, impulse invariant methods, the bilinear transform, frequency-domain analysis, dft, fft, deterministic and random sequences, stationary and ergodic sequences, the mean and the autocorrelation. Uses Java-DSP <\/a> Lecture and Computer Lab. Prerequisites: EEE203.<\/p>\n
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\nThe goal of this course is to introduce graduate students to the principles and applications of spectral estimation. Topics: Review of matrix theory and random processes, Response of linear discrete systems to random inputs, Deterministic spectral analysis, The sample spectrum Autocorrelation and Crosscorrelation estimates, Periodograms and Correlograms AR, MA, and ARMA models, Pade approximations, Yule-Walker equations Linear prediction and lattice structures, Eigenanalysis Methods, The MUSIC Algorithm. Prerequisites: EEE407 and EEE350.<\/p>\n
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\nThe purpose of this course is to introduce to graduate students the principles and applications of adaptive filtering. Topics: The adaptive linear combiner, Mean square error, minimum mean square error, Wiener least-squares solution, Autocorrelation matrices, eigenvalues – eigenvectors and geometrical interpretation, Gradient search and performance surfaces, The LMS and the RLS algorithms, Block time and frequency domain LMS FIR and IIR adaptive filters, The Equation error model, Selected algorithms and applications from recent papers. Prerequisites: EEE407 and EEE506.<\/p>\n
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\nThe purpose of this course is to introduce to graduate students the principles and applications of speech coding. Topics: Signal Processing of Speech, Sampling, Speech Properties, Quantization, PCM, DPCM, ADPCM, Waveform Coding, Sub-band Coding, Transform Coding, Utility of STFT, Sinusoidal Models for Speech Coding, Linear Predictive Coding, Articulatory Systems, Analysis-by-Synthesis Linear Prediction, CELP, Speech and Audio Coding Standards. Prerequisites: EEE 407 and EEE506.<\/p>\n