Paper Topics
1: Sensor Array and Multichannel Signal Processing
1.1: Beamforming
1.2: Array calibration
1.3: Compressed sensing and sparse modeling
1.4: Direction of arrival estimation and source localization
1.5: Inverse methods and imaging with array data
1.6: Learning models and methods for multi-sensor systems
1.7: Multichannel processing, identification and modelling
1.8: Performance analysis and bounds
1.9: Source detection and separation
1.10: Target detection, classification, localization
1.11: Target tracking
1.12: Space-time adaptive methods
1.13: Tensor-based signal processing for multi-sensor systems
1.14: Multi-user and cooperative networks
1.15: Computational advances for multi-sensor systems
1.16: Waveform design for multi-sensor systems
1.17: Active and passive sensing
1.18: Adaptive sensing algorithms
2: Signal and Information Processing Over Networks
2.1: Adaptation and learning over graphs
2.2: Graph learning and network topology inference
2.3: Graph analysis for signal processing
2.4: Information-theoretic studies
2.5: Learning models and methods
2.6: Transforms, filtering, representation
2.7: Sampling, interpolation, denoising and reconstruction
2.8: Spectral graph theory and algebraic topology algorithms
2.9: Sparse SP methods for networks and graphs
2.10: Stochastic Processes over graphs
3: Machine Learning and Artificial Intelligence
3.1: Cognitive information processing
3.2: Deep learning techniques
3.3: Dictionary learning
3.4: Graphical and kernel methods
3.5: Matrix factorization/completion
3.6: Independent component analysis
3.7: Information-theoretic learning
3.8: Learning theory and algorithms
3.9: Learning from multimodal data
3.10: Applications in Music and Audio Processing
3.11: Pattern recognition and classification
3.12: Bounds on performance
3.13: Subspace and manifold learning
3.14: Sequential learning; sequential decision methods
3.15: Tensor-based signal processing
3.16: Other applications of machine learning
4: Computer-intensive Methods in Multi-sensor Signal Processing
4.1: Computational linear and multi-linear algebra
4.2: Convex optimization and relaxation for multi-sensor SP
4.3: Distributed optimization for multi-sensor SP
4.4: Programming techniques for multi-sensor SP
4.5: Non-convex methods for multi-sensor SP
4.6: Sparse optimization techniques for multi-sensor SP
5: Other Areas and Applications
5.1: Big Data
5.2: Cyber-physical systems
5.3: Computational Imaging
5.4: Waveform-agile multi-sensor systems
5.5: MIMO and massive MIMO communication systems
5.6: Microphone array processing
5.7: Synthetic aperture radar/sonar and imaging
5.8: Multi-sensor geophysical and seismic signal processing
5.9: Multi-sensor remote sensing of the environment
5.10: Biomedical signal processing for multi-sensor systems
5.11: Emerging techniques