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Advances in Radiation Oncology, 2022 Purpose: To assess the effect of a combination of compressed sensing and SENSitivity Encoding (SENSE) acceleration techniques on radiation therapy magnetic resonance imaging (MRI) simulation workflows.
Victoria Y. Yu, PhD +7 more
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TamaRISC-CS: An ultra-low-power application-specific processor for compressed sensing [PDF]
2012 IEEE/IFIP 20th International Conference on VLSI and System-on-Chip (VLSI-SoC), 2012 Compressed sensing (CS) is a universal technique for the compression of sparse signals. CS has been widely used in sensing platforms where portable, autonomous devices have to operate for long periods of time with limited energy resources. Therefore, an ultra-low-power (ULP) CS implementation is vital for these kind of energy-limited systems.
Jeremy Constantin +6 more
openaire +2 more sources
Compressed Measurements Based Spectrum Sensing for Wideband Cognitive Radio Systems
International Journal of Antennas and Propagation, 2015 Spectrum sensing is the most important component in the cognitive radio (CR) technology. Spectrum sensing has considerable technical challenges, especially in wideband systems where higher sampling rates are required which increases the complexity and ...
Taha A. Khalaf +2 more
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Applied Sciences, 2022 The majority of digital images are stored in compressed form. Generally, image classification using convolution neural network (CNN) is done in uncompressed form rather than compressed one.
Baba Fakruddin Ali B H +1 more
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Diagnostics, 2023 Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive undersampling impairs image quality when using conventional reconstruction techniques.
Philipp Fervers +8 more
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Deterministic Construction of Compressed Sensing Matrices via Vector Spaces Over Finite Fields
IEEE Access, 2020 Compressed Sensing (CS) is a new signal processing theory under the condition that the signal is sparse or compressible. One of the central problems in compressed sensing is the construction of sensing matrices.
Xuemei Liu, Lihua Jia
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Denoising-Based Turbo Compressed Sensing
IEEE Access, 2017 Turbo compressed sensing (Turbo-CS) is an efficient iterative algorithm for sparse signal recovery with partial orthogonal sensing matrices. In this paper, we extend the Turbo-CS algorithm to solve compressed sensing problems involving a more general ...
Zhipeng Xue, Junjie Ma, Xiaojun Yuan
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Compressed Sensing-Based MRI Reconstruction Using Complex Double-Density Dual-Tree DWT
International Journal of Biomedical Imaging, 2013 Undersampling k-space data is an efficient way to speed up the magnetic resonance imaging (MRI) process. As a newly developed mathematical framework of signal sampling and recovery, compressed sensing (CS) allows signal acquisition using fewer samples ...
Zangen Zhu +3 more
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Design of Robust Sensing Matrix for UAV Images Encryption and Compression
Applied Sciences, 2023 The sparse representation error (SRE) exists when the images are represented sparsely. The SRE is particularly large in unmanned aerial vehicles (UAV) images due to the disturbance of the harsh environment or the instability of its flight, which will ...
Qianru Jiang, Huang Bai, Xiongxiong He
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Deterministic Compressed Sensing Matrices From Sequences With Optimal Correlation
IEEE Access, 2019 Compressed sensing (CS) is a new method of data acquisition which aims at recovering higher dimensional sparse vectors from considerably smaller linear measurements. One of the key problems in CS is the construction of sensing matrices. In this paper, we
Zhi Gu +4 more
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