|
|
|
|
|
Jinjia Zhou and Jian Yang
Compressive Sensing (CS) has emerged as a transformative technique in image compression, offering innovative solutions to challenges in efficient signal representation and acquisition. This paper provides a comprehensive exploration of the key components...
ver más
|
|
|
|
|
|
|
|
|
|
|
Vasundhara Shukla and Preety D. Swami
This paper introduces a novel speech enhancement approach called dominant columns group orthogonalization of the sensing matrix (DCGOSM) in compressive sensing (CS). DCGOSM optimizes the sensing matrix using particle swarm optimization (PSO), ensuring se...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|