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Signal Processing Theory and Methods

Technical Committee


Effective: June 2017

11.1 Sampling and Reconstruction
11.1.1 Sampling theory and methods
11.1.2 Quantization
11.1.3 Compressed and non-uniform sampling
11.1.4 Signal reconstruction, restoration and enhancement

11.2 Signal and System Modeling and Estimation
11.2.1 System and signal modeling: Theory, performance analysis
11.2.2 System identification and approximation
11.2.3 Non-stationary signals and time-varying systems
11.2.4 Time-frequency and time-scale analysis

11.3 Statistical Signal Processing
11.3.1 Detection theory and methods
11.3.2 Estimation theory and methods
11.3.3 Classification and pattern recognition
11.3.4 Performance analysis and bounds
11.3.5 Robust methods
11.3.6 Signal separation methods
11.3.7 Bayesian signal processing

11.4 Adaptive Signal Processing
11.4.1 Adaptive filter analysis and design
11.4.2 Tracking algorithms

11.5 Nonlinear Systems and Signal Processing

11.6 Digital and Multirate Signal Processing

11.7 Signal Processing over Graphs
11.7.1 Statistical approaches (models, etc.)
11.7.2 Deterministic approaches (graph filtering, graph transforms, etc.)
11.7.3 Graph representations and analysis

11.8 Sparsity-Aware Processing
11.8.1 Sparse/low-dimensional signal recovery, parameter estimation and regression
11.8.2 Structured matrix factorization, low-rank models, matrix completion
11.8.3 Dictionary learning; subspace and manifold learning

11.9 Optimization Tools
11.9.1 Convex optimization and relaxation
11.9.2 Non-convex methods

11.10 Signal Processing on Networks
11.10.1 Distributed processing and optimization
11.10.2 Social networks, social learning models

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