COPYRIGHT NOTICE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.     Special Topics on Digital Signal Processing Master level No. of credits: 7  Textbooks:  Sayed, A.H., Fundamentals of Adaptive Filtering, Wiley, 2003 Haykin, S., Adaptive Filter Theory, 4/E, Prentice-Hall, 2001 J.C. Principe et al., Neural and Adaptive Systems, Wiley, 2000 Lab info  Tutorials: Linear adaptive filtering  (Chapter 3 from my DSP book, in Romanian) Introduction to Artificial Neural Networks  (Chapter 1 from my ANN book, in Romanian)   General description The main themes of the course are related to linear and nonlinear adaptive filtering, multirate analysis using wavelet transforms, and compression algorithms. Applications include pattern recognition, data transmission channel equalization and analog decoding, biomedical signals analysis and compression, biometrics. Special focus is placed on implementation aspects, including quantization effects, floating-point to fixed-point conversion, and algorithmic transformations (pipeline, retiming). Software support is provided by MATLAB/Simulink and NeuroSolutions neural networks simulator.  Course outline  Lecture 1:  More info: 1  Lab 1 General introduction to adaptive linear filtering      Optimal filtering problem and Wiener solution. Definition and characterization of random processes      Classification criteria of adaptive algorithms. Cost functions.      Applications of linear adaptive filters.  Lecture 2:  Lab 2 First order adaptive algorithms: gradient descent, Newton algorithm. Case studies: linear prediction, system identification.  Lecture 3:  More info: 1, 2, 3  Lab 3 LMS algorithm and its variants. Adaptive filtering in the frequency domain. Case studies: channel equalization, linear prediction, noise cancellation.  Homework 1 Adaptive channel equalization using the LMS algorithm. theoretical background | homework | M_files  Lecture 4:  More info: 1 Least-Squares (LS) algorithm. Recurrent Least-Squares (RLS) algorithm. General introduction to Kalman filters.  Lecture 5:   General introduction to Artificial Neural Networks (ANN's).      Motivations for studying ANN's. Definition and classification criteria for ANN's.      Applications of ANN's.  Lecture 6: Multilayer perceptron (MLP).      Standard backpropagation training algorithm.      Variants of backpropagation algorithm.  Lecture 7:  More info: 1, 2 Multirate signal processing: Interpolation and decimation. Continuous and discrete wavelet transform. Case study: ECG signal analysis - QRS complex detection.  Lectures 8-9:  More info: 1 Data compression algorithms. General introduction to compressed sensing. Case study: ECG signal compression.  Homework 2 : Face recognition using PCA algorithm. theoretical background | homework | M_files  Lectures 10-11: Implementation of DSP algorithms: finite precision effects, floating to fixed-point conversion. Algorithmic transformations: pipeline, retiming.  Lectures 12-13: Applications of artificial neural networks: pattern recognition, channel equalization, digital filter design. General introduction to biometrics and face recognition.  Lecture 14: Course review. Q&A.