Sensor Signal and Information Processing IV

Dear Colleagues,

Sensor signal and information processing (SSIP) is an overarching field of research focusing on the mathematical foundations and practical applications of signal processing algorithms that learn, reason, and act. It bridges the boundary between theory and application, developing novel theoretically-inspired methodologies targeting both longstanding and emergent signal processing applications. The core of SSIP lies in its use of nonlinear and non-Gaussian signal processing methodologies combined with convex and non-convex optimization. SSIP encompasses new theoretical frameworks for statistical signal processing (e.g., hidden Markov model, latent component analysis, tensor factorization, Bayesian methods) coupled with information-theoretic learning, and novel developments in these areas specifically related to the processing of a variety of signal modalities, including audio, bio-signals, multi-physics signals, images, multispectral, and video, among others. In recent years, many signal processing algorithms have incorporated some form of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and can learn to learn new information whenever unseen data is captured.

The focus of the Special Issue will be on a broad range of sensors, signals, and information processing involving the introduction and development of new advanced theoretical and practical algorithms. Potential topics include, but are not limited to the following:

• Biomedical signal processing and instrumentation
• Pattern recognition and analysis
• Machine learning for signal and image processing
• Multimodality sensor fusion techniques
• Compressed sensing and sparsity aware processing
• Data science and analytics for big data
• Deep learning: theory, algorithms, and applications
• Multi-objective signal processing optimization
• Multimodal information processing for healthcare, monitoring, and surveillance
• Computer vision and 3D reconstruction with multimodal data fusion
• Wearable sensors and IoT for personalized health monitoring and social computing
• Non-destructive testing and evaluation for material characterization, structural integrity, defect detection and identification, and stress and lifecycle assessment
• Signal processing for smart grid, load forecasting, and energy management
• Precision farming combining sensors and imaging with real-time data analytics
• Other emerging applications of signal and information processing

Prof. Dr. Wai Lok Woo
Prof. Dr. Bin Gao
Guest Editors

Manuscript Submission Information

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