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Signal Processing Theory and Methods

Technical Committee


NOTE: The Technical Committee's EDICS list is derived from the Society's Unified EDICS list. You can view the Society's complete Unified EDICS ist and EDICS list approval process on the Unified EDICS page.

EDICS Notations:
1. Bolded text represents the EDICS major topic category
2. Blue shaded rows represent EDICS that can be selected as paper's topic.
3. Non-shaded rows represent sub-topic EDICSs for those above them.


[SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing
[SMDSP-SAM] Sampling Theory, Analysis and Methods
  • [SMDSP-ALGO] Algorithm analysis
  • [SMDSP-SAMP] Sampling, extrapolation, and interpolation
  • [SMDSP-QUAN] Quantization effects
[SMDSP-CNS] Compressed and Nonuniform Sampling
  • [SMDSP-CPSL] Compressive and nonuniform sampling
  • [SMDSP-ASAL] Adaptive Sensing Algorithms
[SMDSP-RECO] Algorithms for signal filtering, restoration, enhancement, and reconstruction
[SMDSP-MRA] Multiresolution Analysis, filter banks, and wavelets
  • [SMDSP-APPL] Applications of digital and multirate signal processing
  • [SMDSP-BANK] Filter bank design and theory
  • [SMDSP-MULT] Multirate processing and multiresolution methods
  • [SMDSP-TFSR] Time-frequency analysis and signal representation>
[SMDSP-FAT] Fast Algorithms and Transforms
  • [SMDSP-FAST] Fast algorithms for digital signal processing
  • [SMDSP-TRSF] Transforms for signal processing
[SMDSP-SAP] Sparsity-aware processing
[SIPG] Signal and Information Processing over Graphs
[SIPG-SA] Statistical Approaches (models, etc.)
  • [NEG-INFO] Information-theoretic studies
  • [SPIG-STOC] Stochastic processes over graphs (T-SIPN & TSP)
  • [SIPG-MEND] Modeling and estimation of network dynamics (T-SIPN)
  • [SIPG-MNE] Modeling of network evolution (T-SIPN)
[SIPG-DA] Deterministic Approaches (graph filtering, graph transforms, etc.)
  • [NEG-SPGR] Signal processing over graphs (filtering, transforms, etc)
  • [NEG-SAMP] Sampling over graphs
  • [SIPG-DIS] Distributed processing of graph data (T-SIPN)
[SIPG-GRA] Graph Representations and Analysis
  • [NEG-GRAN] Graph analysis for signal processing
  • [NEG-SPGT] Spectral graph theory and algebraic topology algorithms
  • [NEG-SYLO] System level optimization
[SIPG-AL] Adaptation and Learning Over Graphs
  • [NEG-ADLE] Adaptation and learning over graphs
  • [NEG-ASAL] Adaptive sensing algorithms
[OPT] Optimization Methods for Signal Processing
[OPT-CVXR] Convex optimization and relaxation for SP
[OPT-DOPT] Distributed optimization for SP
[OPT-NCVX] Non-convex methods for SP
[OPT-SPARSE] Sparse optimization techniques for SP
[OTH-QUAN] Quantum signal processing
[ASP] Adaptive Signal Processing
  • [ASP-ANAL] Adaptive filter analysis and design
  • [ASP-APPL] Applications of adaptive filters
  • [ASP-FAST] Fast algorithms for adaptive filtering
  • [ASP-FREQ] Frequency-domain and subband adaptive filtering
[SSP] Statistical Signal Processing
[SSP-DTM] Detection Theory and Methods
  • [SSP-DETC] Detection
  • [SSP-RDET] Robust detection, estimation, and tracking
[SSP-ETM] Estimation Theory and Methods
  • [SSP-PARE] Parameter estimation
  • [SSP-IDEN] System identification
  • [SSP-SPEC] Spectral analysis and spectral estimation
[SSP-CLAS] Classification methods
[SSP-ANA] Analysis
  • [SSP-PERF] Performance analysis and bounds
  • [SSP-SSAN] Statistical signal analysis
[SSP-LNSP] Linear and Nonlinear Systems and Signal Processing
  • [SSP-DECO] Deconvolution
  • [SSP-FILT] Filtering
  • [SSP-SSEP] Signal separation
  • [SSP-REST] Signal restoration
  • [SSP-NSSP] Nonstationary statistical signal processing
  • [SSP-NGAU] Non-Gaussian signals and noise
[SSP-BSP] Bayesian signal processing
[SSP-MM] Models and Methods
  • [SSP-HIER] Hierarchical models & tree structured signal processing
  • [SSP-HOSM] Higher-order statistical methods
  • [SSP-NPAR] Non-parametric methods
  • [SSP-SNMD] Signal and noise modeling
  • [SSP-SYSM] System modeling
  • [SSP-SPRS] SP methods for structured low dimensional models
[SSP-TRAC] Tracking algorithms
[SSP-APPL] Applications of statistical signal processing techniques
  • OTH-GSSP Green and sustainable signal processing
  • OTH-NDTE Non-destructive testing and evaluation
Signal Processing over Networks
[DPON] Distributed Processing and Optimization over Networks
  • [NEG-ADHC] Ad-hoc wireless networks
  • [NEG-CLRD] Cross-layer design
  • [ADEL-DAN] Distributed adaptation over networks
  • [NEG-RSMG] Resource management issues
  • [NEG-ENGY] Energy efficient sensor network algorithms
  • [NEG-FUSE] Data fusion from multiple sensors
  • [ADEL-CNS] Consensus over network systems
  • [ADEL-ONS] Optimization over network systems
  • MLR-DFED Distributed/Federated learning
[EDLN] Estimation, Detection and Learning over Networks
  • [NEG-LOCL] Source localization in sensor networks
  • [NEG-LRNM] Learning models and methods
  • [ADEL-DDE] Distributed detection and estimation
  • [ADEL-DLN] Distributed learning over networks
  • [ADEL-SLN] Sequential learning over networks
  • [ADEL-DMN] Decision making over networks
  • MLR- SSUP Self-supervised and semi-supervised learning
  • MLR-REI Reinforcement learning
[NEG-APPL] Applications of sensor networks
[SMDSP-SAP] Sparsity-aware Processing
Sparse/low-dimensional signal recovery, parameter estimation and regression
Structured matrix factorization, low-rank models, matrix completion
Dictionary learning; subspace and manifold learning

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