Sensors, signal, and artificial intelligent processing (SSAIP) is an overarching field of research focusing on the physic-mathematical foundations and practical applications of sensors, interpretation, signal processing, and artificial intelligence algorithms that learn, reason, and act. It bridges the boundary between theory and application, developing novel theoretically-inspired methodologies targeting both longstanding and emergent sensor-based applications.

The core of SSAIP lies in its use and physical meaning in sensors, nonlinear and non-Gaussian signal processing methodologies combined with convex and non-convex optimization, and sensor-based machine learning/deep learning neural networks. SSAIP encompasses new theoretical frameworks for interpreting sensing in 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 specialised to the processing of a variety of modalities including audio, chemical, bio-signals, electromagnetic thermal multi-physics signals, images, multispectral, and video, among others. In recent years, many sensor designs and signal processing algorithms have incorporated some form of intelligence as part of their framework in solving a problem. These algorithms have the capacity to generalize and discover knowledge for themselves and learning new information whenever unseen sensor data is captured.

The focus of the Special Issue will be on a broad range of sensors, physic interpretation, signal, and artificial intelligent processing involving the introduction and development of new advanced theoretical and practical algorithms. Original research and review articles are welcome.

Potential topics include but are not limited to the following:

• Different modality sensors and instrumentation
• Multimodality sensor fusion technique and its link to the physic interpretation
• Chemical sensing as well as baseline correction, self-calibration, drift counteraction
• Sensor pattern recognition and analysis
• Machine learning for sensor signal and image processing
• Multimodal information processing for healthcare and surveillance
• Computer vision and 3D reconstruction by multimodal sensor data fusion
• Wearable sensors and IoT for personalized health monitoring and social computing
• Data science and analytics for big sensor data
• Deep learning: theory, algorithms, and applications for sensing
• Sensor compressed sensing and sparsity aware processing
• Non-destructive testing and evaluation and structure health monitoring for material characterization, structural integrity, defect detection and identification, stress and lifecycle assessment
• Sensors and imaging with real-time data analytics