Results 21 to 30 of about 2,932,031 (355)
Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
IEEE Access, 2022 Compressive sensing allows the reconstruction of original signals from a much smaller number of samples as compared to the Nyquist sampling rate. The effectiveness of compressive sensing motivated the researchers for its deployment in a variety of ...
Irfan Ahmed +3 more
doaj +1 more source
Applied Sciences, 2022 Due to the small number of baselines (2–3), the traditional L1 norm compressive sensing method for layover solution in InSAR has poor separation ability and height estimation stability and a long operation time.
Bin Zhang +4 more
doaj +1 more source
From compression to compressed sensing [PDF]
2013 IEEE International Symposium on Information Theory, 2013 Can compression algorithms be employed for recovering signals from their underdetermined set of linear measurements? Addressing this question is the first step towards applying compression algorithms for compressed sensing (CS). In this paper, we consider a family of compression algorithms $\mathcal{C}_r$, parametrized by rate $r$, for a compact class ...
Arian Maleki, Shirin Jalali
openaire +4 more sources
Evaluation of the Effects of Compressive Spectrum Sensing Parameters on Primary User Behavior Estimation [PDF]
Port Said Engineering Research Journal, 2021 As the Internet of Things (IoT) technology is being deployed, the demand for radio spectrum is increasing. Cognitive radio (CR) is one of the most promising solutions to allow opportunistic spectrum access for IoT secondary users through utilizing ...
Ahmed Tawfik +2 more
doaj +1 more source
AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing [PDF]
IEEE Transactions on Image Processing, 2020 Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding ...
Zhonghao Zhang +4 more
semanticscholar +1 more source
Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning [PDF]
IEEE Access, 2019 Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals, for example by
A. Taha +2 more
semanticscholar +1 more source
Compression-Based Compressed Sensing [PDF]
IEEE Transactions on Information Theory, 2017 Modern compression algorithms exploit complex structures that are present in signals to describe them very efficiently. On the other hand, the field of compressed sensing is built upon the observation that "structured" signals can be recovered from their under-determined set of linear projections.
Farideh E. Rezagah +3 more
openaire +2 more sources
IEEE Open Journal of Circuits and Systems, 2022 Terahertz (THz) imaging system has great potentials for material identification, security screening, circuit inspection, bioinformatics and bio-imaging because it can penetrate various non-metallic materials and inhibits unique spectral fingerprints of a
Wei-Chieh Wang +4 more
doaj +1 more source
Compressed Sensing in Astronomy [PDF]
IEEE Journal of Selected Topics in Signal Processing, 2008 Recent advances in signal processing have focused on the use of sparse representations in various applications. A new field of interest based on sparsity has recently emerged: compressed sensing. This theory is a new sampling framework that provides an alternative to the well-known Shannon sampling theory.
Jérôme Bobin +2 more
openaire +4 more sources
Kinetic Compressive Sensing [PDF]
2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017 5 pages, 6 figures, Submitted to the Conference Record of "IEEE Nuclear Science Symposium and Medical Imaging Conference (IEEE NSS-MIC) 2017"
Scipioni Michele +6 more
openaire +4 more sources