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Carmine Paolino, Alessio Antolini, Francesco Zavalloni, Andrea Lico, Eleonora Franchi Scarselli, Mauro Mangia, Alex Marchioni, Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, Mattia Luigi Torres, Marcella Carissimi and Marco Pasotti
Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fiel...
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Zuoqin Zhao, Yufei Nai, Zhiguo Yu, Xin Xu, Xiaoyang Cao and Xiaofeng Gu
Compressed Sensing (CS) has been applied to electrocardiogram monitoring in wireless sensor networks, but existing sampling and compression circuits consume too much hardware. This paper proposes a low-power and small-area sampling and compression circui...
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Subramanyam Shashi Kumar and Prakash Ramachandran
Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform f...
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Fangming Zhou, Lulu Zhao, Limin Li, Yifei Hu, Xinglong Jiang, Jinpei Yu and Guang Liang
The recently-emerging compressed sensing (CS) theory makes GNSS signal processing at a sub-Nyquist rate possible if it has a sparse representation in certain domain. The previously proposed code-domain compression acquisition algorithms have high computa...
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Lingji Xu, Lixing Chen, Yaan Li and Weihua Jiang
Due to the complex ocean propagation environments, the underwater acoustic (UWA) multipath channel often exhibits block sparse time-varying features, and while dynamic compressed sensing (DCS) can mitigate the time-varying effects of the UWA channel, DCS...
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Yisak Kim, Juyoung Park and Hyungsuk Kim
Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signa...
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Prateek Saurabh Srivastav, Lan Chen and Arfan Haider Wahla
Channel estimation is a formidable challenge in mmWave Multiple Input Multiple Output (MIMO) systems due to the large number of antennas. Therefore, compressed sensing (CS) techniques are used to exploit channel sparsity at mmWave frequencies to calculat...
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Xiang Li, Linlu Dong, Biao Li, Yifan Lei and Nuwen Xu
Microseismic signal denoising is of great significance for P wave, S wave first arrival picking, source localization, and focal mechanism inversion. Therefore, an Empirical Mode Decomposition (EMD), Compressed Sensing (CS), and Soft-thresholding (ST) com...
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Xingbo Lu, Weiwei Yang, Yueming Cai and Xinrong Guan
Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relax...
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Ziwei Lei, Peng Yang, Linhua Zheng, Hui Xiong and Hong Ding
Most of the earlier tracking and network sorting approaches with a high sampling rate for frequency hopping (FH) signals did not adapt to the wideband system during their implementation, whereas the sub-Nyquist based algorithms cannot satisfy the real-ti...
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