Signal Processing and Machine Learning with Applications

Part A, Signal Processing and Spectral Analysis.- Signal Processing Background.- Fundamentals of Signal Transformation.- Digital Filters: Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) Filters.- Estimation and Detection.- Adaptive Signal Processing.- Spectral Analysis.- Part B, Machine Learning and Recognition.- General Learning.- Stochastic Processes.- Feature Extraction.- Fundamentals of Probability for Signal Processing.- Machine Learning and Its Role in Signal Processing.- Unsupervised Learning.- Markov Model and Hidden Stochastic Model (HSM).- Fuzzy Logic and Rough Sets.- Neural Networks.- Part C, Practical Aspects and Applications.- Noisy Signals.- Reasoning Methods and Noise Removal Techniques.- Audio Signals and Speech Recognition.- Noisy Speech.- Aspects of Human Hearing.- Speech Features.- Hidden Stochastic Model for Speech.- Different Speech Applications.- Biomedical Signals: ECG and EEG Signals.- Seismic Signals.- Radar Signal Processing.- Digital Processes and Multimedia.- Visual Analytics (VA) and Visual Story Telling (VST).- Visualizations for Emergency Operation Centre (EOC).- Intelligent Machine for Interactive Communications.- Comparisons.

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• Mineração de Dados
• Sistemas de imagem e tecnologia
• Inteligência artificial