- EE 613 Digital Signal Processing
- for Communications
- This course reviews fundamental digital signal processing concepts and emphasizes modern applications in the design of digital communication transmitters and receivers. Topics include digital filtering, processing of complex signals, synchronization and phase-locked loops, modulation/ demodulation techniques, sampling and baseband processing, channel equalization, pulse shaping, and speech compression.. (3 credits)
- EE 616 Signal Detection and Estimation
- for Communications
- Signal detection: Bayes principle, minimax rule, Neyman-Pearson rule; randomized decision; compound hypothesis testing; locally and universally most powerful tests, generalized likelihood-ratio test; Chernoff bound, asymptotic relative efficiency; sequential detection; nonparametric detection, sign test, rank test. Parameter estimation: Bayesian estimation, minimum variance unbiased estimation; sufficient statistics, minimum statistics; maximum a posteriori estimation, maximum likelihood estimation, invariance principle; statistical efficiency, Cramer-Rao lower bound, Fisher information matrix; least squares, weighted least squares, best linear unbiased estimation. Applications of detection and estimation techniques in modern communication systems. Prerequisites: EE602 and EE605 or equivalent. (3 credits).
|
- EE 663 Digital Signal Processing I
- Review of mathematics of signals and systems including sampling theorem, Fourier transform, z-transform, Hilbert transform; algorithms for fast computation: DFT, DCT computation, convolution; filter design techniques: FIR and IIR filter design, time and frequency domain methods, window method and other approximation theory based methods; structures for realization of discrete time systems: direct form, parallel form, lattice structure, and other state-space canonical forms (e.g., orthogonal filters and related structures); roundoff and quantization effects in digital filters: analysis of sensitivity to coefficient quantization, limit cycle in IIR filters, scaling to prevent overflow, role of special structures. Prerequisites: EE 602, EE 603 or equivalent. (3 credits).
-
- EE 616 Signal Detection and Estimation
- for Communications
- Signal detection: Bayes principle, minimax rule, Neyman-Pearson rule; randomized decision; compound hypothesis testing; locally and universally most powerful tests, generalized likelihood-ratio test; Chernoff bound, asymptotic relative efficiency; sequential detection; nonparametric detection, sign test, rank test. Parameter estimation: Bayesian estimation, minimum variance unbiased estimation; sufficient statistics, minimum statistics; maximum a posteriori estimation, maximum likelihood estimation, invariance principle; statistical efficiency, Cramer-Rao lower bound, Fisher information matrix; least squares, weighted least squares, best linear unbiased estimation. Applications of detection and estimation techniques in modern communication systems. Prerequisites: EE602 and EE605 or equivalent. (3 credits).
|
