SPP 1798:  Compressed Sensing in Information Processing (CoSIP)

• Subject Area: Computer Science, Systems and Electrical Engineering ; Biology ; Geosciences ; Mechanical and Industrial Engineering ; Mathematics ; Medicine ; Physics
• Term: from 2015 to 2023
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 255450733

Digital signal processing requires the conversion of analog signals in space and time to a discrete domain and vice versa. Conventional sampling relies on the Shannon Nyquist theorem which ensures complete reconstruction of a band limited signal by sampling at a rate twice the bandwidth. In contrast, compressed sensing follows the paradigm that a sparse signal may be sampled far below the Nyquist rate, but nevertheless may be completely recovered. Compressed sensing relies on two salient principles, sparsity and incoherence. Sparsity refers to the idea that the information rate of a signal is much smaller than expected from its bandwidth, so that the signal may be represented by a small number of elements in a proper basis or frame. Incoherence expresses the concept that signals with a sparse representation are spread out in the sampling domain.Sparsity is encountered in signals of numerous applications like wireless information and communication technology, radar surveillance, and visual and audio signal processing, to name a few. In this Priority Programme, applications of compressed sensing in information processing will be emphasised, however, it is expected that the mathematical theory behind will receive significant impact and new directions from applied issues. Paired cooperation projects between engineers and applied mathematicians are particularly encouraged.

• DFG Programme: Priority Programmes
• International Connection: Austria, Netherlands

Projects

• Bilinear Compressed Sensing - Efficiency, Structure, and Robustness (Applicants Gross, Ph.D., David ;  Jung, Peter ;  Krahmer, Ph.D., Felix )
• Complex-valued Reed-Solomon Codes for Deterministic Compressed Sensing (Applicant Bossert, Martin )
• Compressed Sensing Algorithms for Structured Massive MIMO -- Phase II: From Massive MIMO to Massive Wireless Networks (Applicants Caire, Ph.D., Giuseppe ;  Kutyniok, Gitta ;  Wunder, Gerhard )
• Compressed sensing for terahertz body scanners (Applicants Hübers, Heinz-Wilhelm ;  Zhu, Xiaoxiang )
• Compressed sensing radar imaging of polar mesospheric summer echoes using tracking and MIMO approaches (CS-PMSE-MIMO) (Applicants Chau, Ph.D., Jorge ;  Weber, Tobias )
• Compressive 2D/3D SAR (ComSAR) (Applicants Ender, Joachim ;  Loffeld, Otmar )
• Compressive Covariance Estimation for Massive MIMO (CoCoMiMo) (Applicants Caire, Ph.D., Giuseppe ;  Dirksen, Sjoerd ;  Rauhut, Holger )
• Coordination Funds (Applicants Kutyniok, Gitta ;  Rauhut, Holger )
• Coordination of the DFG-Priority Programm 1798 (Applicant Kutyniok, Gitta )
• CoS-MRXI - Compressed sensing for magnetorelaxometry imaging of magnetic nanoparticles (Applicants Baumgarten, Daniel ;  Wübbeling, Frank )
• Distributed Compressive Sensing: Theoretical Limits and Algorithmic Approaches (Applicants Müller, Ralf Reiner ;  Schulz-Baldes, Hermann )
• Dynamic measurement of sound fields using compressed sensing (Applicant Mertins, Alfred )
• Estimation of covariance matrices satisfying sparsity priors (Applicants Pfander, Ph.D., Götz Eduard ;  Pohl, Volker )
• Exploiting structure in compressed sensing using side constraints – from analysis to system design (EXPRESS II) (Applicants Haardt, Martin ;  Pesavento, Marius ;  Pfetsch, Marc Emanuel )
• Iterative Signal Recovery Algorithms --- A Unified View of Turbo and Message-Passing Approaches (Applicant Fischer, Robert )
• Joint design of compressed sensing and network coding for wireless meshed networks (Applicants Fitzek, Frank Hanns Paul ;  Stanczak, Slawomir )
• Learning and Recovery Algorithms for Multi-Sensor Data Fusion and Spectral Unmixing in Earth Observation (Applicants Fornasier, Massimo ;  Kramer, Gerhard ;  Zhu, Xiaoxiang )
• Quantized Compressive Spectrum Sensing (QuaCoSS) (Applicants Mathar, Rudolf ;  Rauhut, Holger )
• Security in the context of future communication challenges and compressive sensing (Applicants Eisert, Jens ;  Wunder, Gerhard )
• Structured Compressive Sensing via Neural Network Learning (SCoSNeL) (Applicants Caire, Ph.D., Giuseppe ;  Rauhut, Holger )
• Terahertz illumination concepts for reciprocal compressive imaging in silicon technologies (LumiCS) (Applicants Loffeld, Otmar ;  Pfeiffer, Ullrich )

• Spokesperson: Professorin Dr. Gitta Kutyniok