Fundamentals, mathematical aspects, concepts and algorithms of computational sensing including topics such as

• Compressed sensing and sparse modeling
• Low-rank models and low-rank matrix recovery
• Low-rank + sparse models
• Matrix and tensor decompositions
• Sampling theory and its role for sensing designs
• Theory of high-dynamic range sensing
• Machine-learning based methods in unified sensing pipelines
• Dictionary learning
• Nonlinearities in sensing designs (quantization, saturation, …)

Applications of computational sensing to:

• Radar systems (SAR and ISAR imaging, MIMO, waveform design, …)
• Imaging radar (SAR, ISAR),
• THz imaging and material analysis,
• Digital optics, time-resolved imaging (ToF depth imaging, ultrafast photography), hyperspectral imaging, microscopy
• Ultrafast imaging
• Systems for medical diagnostics (CT, MRT, X-ray, ultrasonography)
• X-ray crystallography,
• Acoustic systems, microphone arrays, SONAR systems,
• Radio astronomy
• Photoacoustic imaging

System design and hardware for computational sensing

• Analog to information converters
• Sampling strategies, sparse array design
• A/D-converter design

Application of matrix decomposition and tensor-based techniques for sensor systems

• Blind deconvolution, blind calibration
• Separation of foreground and background, change detection

Quality evaluation

• Estimation based on compressed sensing and the CRB
• Evaluation of super-resolution
• Off-grid evaluation
• Detection probabilities