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Signal Processing Theory and Methods

Technical Committee


Effective July 26, 2013

1 Sampling and Reconstruction
1.1 Sampling theory and methods
1.2 Quantization
1.3 Compressed Sampling
1.4 Nonuniform Sampling
1.5 Signal reconstruction, restoration, and enhancement
1.6 Multidimensional sampling and reconstruction

2 Signal and System Modeling and Estimation
2.1 System and signal modeling: Theory, performance analysis
2.2 System identification and approximation
2.3 Multidimensional systems
2.4 Non-stationary signals and time-varying systems
2.5 Time-frequency and time-scale analysis

3 Statistical Signal Processing
3.1 Detection and estimation theory and methods
3.2 Classification and pattern recognition
3.3 Cyclostationary signal analysis
3.4 Higher-order and fractional lower-order statistical methods
3.5 Performance analysis and bounds
3.6 Spectrum estimation theory and methods
3.7 Robust methods
3.8 Signal separation methods
3.9 Data driven methods (bootstrap, MCMC, sequential and particle filtering)
3.10 Non-parametric methods
3.11 Tracking algorithms
3.12 Hierarchical models & tree structured signal processing
3.13 Bayesian techniques

4 Adaptive Signal Processing
4.1 Adaptive filter analysis and design
4.2 Fast algorithms for adaptive filtering
4.3 Frequency-domain and transform-based adaptive filtering
4.4 Sequential decision theory and methods
4.5 Performance analysis and bounds
4.6 Distributed and collaborative learning algorithms

5 Nonlinear Systems and Signal Processing
5.1 Median, rank-order, and stack type filters
5.2 Non-Gaussian distribution filters
5.3 Polynomial and kernel methods for Signal Processing
5.4 Chaotic and fractal signals and systems
5.5 Applications of nonlinear signal processing

6 Digital and Multirate Signal Processing
6.1 Algorithm analysis
6.2 Filter bank design and theory
6.3 Multirate processing and multiresolution methods
6.4 Wavelets theory and applications
6.5 Transforms for signal Processing
6.6 Fast algorithms for digital signal processing
6.7 Filter design and structures
6.8 Applications of digital and multirate signal processing

7 Signal Processing Over Graphs
7.1 Statistical approaches (models, etc.)
7.2 Deterministic approaches (graph filtering, graph transforms, etc.)
7.3 Sparse graph representations
7.4 Graph analysis
7.5 Spectral graph theory and algebraic topology algorithms
7.6 Linear transforms (e.g., wavelets) over graphs

8 Sparsity-aware processing
8.1 Sparse/low-dimensional parameter estimation and signal recovery
8.2 Structured low-dimensional models (joint sparsity, manifolds, low-rank, ...)
8.3 Sparsity-promoting algorithms
8.4 Dictionary learning
8.5 Robust PCA
8.6 Subspace and manifold learning
8.7 Matrix Completion

9 Optimization Tools
9.1 Convex optimization and relaxation
9.2 Non-convex methods
9.3 Game theory solutions
9.4 Integer programming
9.5 Distributed optimization

10 Signal Processing on Networks
10.1 Distributed processing and optimization
10.2 Social networks, social learning models
10.3 Game theoretic analysis
10.4 Learning models and game theoretic analysis
10.5 Network utility maximization, resource allocation
10.6 Detection and inference
10.7 Estimation and filtering
10.8 Adaptation

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