Advanced Techniques in Signal Filtering and Analysis
( 30 Modules )
Module #1 Introduction to Advanced Signal Filtering and Analysis Overview of signal filtering and analysis, importance of advanced techniques, and course objectives
Module #2 Review of Fundamentals:Signal Filtering and Analysis Review of basic signal filtering and analysis concepts, including Fourier transform, convolution, and filtering
Module #3 Advanced Filtering Techniques:Adaptive Filtering Introduction to adaptive filtering, least mean squares (LMS) algorithm, and normalized LMS algorithm
Module #4 Advanced Filtering Techniques:Kalman Filtering Introduction to Kalman filtering, state-space model, and Kalman gain calculation
Module #5 Advanced Filtering Techniques:Wavelet Filtering Introduction to wavelet filtering, wavelet transform, and applications
Module #6 Signal Analysis Techniques:Time-Frequency Analysis Introduction to time-frequency analysis, short-time Fourier transform, and continuous wavelet transform
Module #7 Signal Analysis Techniques:Empirical Mode Decomposition Introduction to empirical mode decomposition, intrinsic mode functions, and Hilbert-Huang transform
Module #8 Signal Analysis Techniques:Independent Component Analysis Introduction to independent component analysis, blind source separation, and ICA algorithms
Module #9 Advanced Spectral Analysis Techniques:Periodogram and Welchs Method Introduction to periodogram, Welchs method, and spectral estimation
Module #10 Advanced Spectral Analysis Techniques:MUSIC and ESPRIT Introduction to MUSIC and ESPRIT algorithms, and frequency estimation
Module #11 Signal Denoising Techniques:Wavelet Denoising Introduction to wavelet denoising, thresholding, and shrinkage
Module #12 Signal Denoising Techniques:Filtering and Thresholding Introduction to filtering and thresholding methods for signal denoising
Module #13 Advanced Signal Compression Techniques:Wavelet Compression Introduction to wavelet compression, lossless and lossy compression
Module #14 Advanced Signal Compression Techniques:Fractal Compression Introduction to fractal compression, self-similarity, and iterated function systems
Module #15 Signal Feature Extraction Techniques:Time-Domain Features Introduction to time-domain features, including mean, variance, and skewness
Module #16 Signal Feature Extraction Techniques:Frequency-Domain Features Introduction to frequency-domain features, including spectral power and coherence
Module #17 Signal Classification Techniques:Machine Learning Approaches Introduction to machine learning approaches for signal classification, including supervised and unsupervised learning
Module #18 Signal Classification Techniques:Deep Learning Approaches Introduction to deep learning approaches for signal classification, including convolutional neural networks
Module #19 Case Studies in Advanced Signal Filtering and Analysis Real-world examples and case studies of advanced signal filtering and analysis techniques
Module #20 Advanced Topics in Signal Filtering and Analysis Advanced topics, including compressive sensing, sparse signal representation, and graph signal processing
Module #21 Lab 1:Adaptive Filtering and Kalman Filtering Hands-on lab exercise on adaptive filtering and Kalman filtering
Module #22 Lab 2:Time-Frequency Analysis and Empirical Mode Decomposition Hands-on lab exercise on time-frequency analysis and empirical mode decomposition
Module #23 Lab 3:Wavelet Filtering and Denoising Hands-on lab exercise on wavelet filtering and denoising
Module #24 Lab 4:Spectral Analysis and Feature Extraction Hands-on lab exercise on spectral analysis and feature extraction
Module #25 Lab 5:Signal Classification using Machine Learning Hands-on lab exercise on signal classification using machine learning approaches
Module #26 Project Development and Implementation Guided project development and implementation of advanced signal filtering and analysis techniques
Module #27 Project Presentations and Feedback Project presentations and feedback from instructors and peers
Module #28 Advanced Signal Filtering and Analysis Tools and Software Overview of advanced signal filtering and analysis tools and software, including MATLAB, Python, and R
Module #29 Signal Filtering and Analysis in Real-World Applications Real-world applications of signal filtering and analysis in fields such as biomedical engineering, finance, and climate science
Module #30 Course Wrap-Up & Conclusion Planning next steps in Advanced Techniques in Signal Filtering and Analysis career
