全部文献期刊会议图书|学者科研项目
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作者:Nabil Sabor
来源:[J].Applied Soft Computing Journal(IF 2.14), 2020, Vol.88
摘要:Abstract(#br)The reconstruction aspect is the main core of the compressive sensing theory, in which the sparse signal is reconstructed from an incomplete set of random measurements. The constraint of spare signal reconstruction is the minimization of l 0 -norm, especially under n...
作者:Devaraj Ponnaian , Kavitha Chandranbabu
来源:[J].Optik - International Journal for Light and Electron Optics(IF 0.524), 2017, Vol.147, pp.263-276
摘要:Abstract(#br)Compressive sensing acquires image in the form of linear measurements. Utilizing compressive sensing in image encryption makes most of the algorithm vulnerable, due to the linearity property of compressive sensing retained in the encryption process. In this paper...
作者:Ping Zhang , Jianxin Wang , Kehua Guo
来源:[J].Computer Communications(IF 1.079), 2018, Vol.129
摘要:... Compressive sensing can break through the classical Shannon-Nyquist boundary and reduce the total energy consumption. Random walk technology has a significant advantage in the energy balance. Random walk based compressive sensing data collection mechanism is a combination of ...
作者:Jin Liu , Jian-cheng Fang , Gang Liu
来源:[J].Optik - International Journal for Light and Electron Optics(IF 0.524), 2017
摘要:Abstract(#br)In the traditional compressive sensing methods, to realize highly-accurate pulsar time-of-arrival (TOA) estimation, a large-size measurement matrix has to be adopted due to the low radiation flux of X-ray pulsars. However, a large-size measurement matrix results in a...
作者:Xue-xia Han , Ting-xuan Du , Chao Pan ...
来源:[J].Optik(IF 0.524), 2019, Vol.197
摘要:... To solve this problem, a similar Hadamard-based compressive sensing is proposed and applied to the pulsar Time-of-Arrival (TOA) estimation. In this method, a similar Hadamard is developed. Similar to Hadamard matrix, the similar Hadamard matrix has zero-mean and non-relevance...
作者:Ramon Fuentes , Carmelo Mineo , Stephen G. Pierce ...
来源:[J].Mechanical Systems and Signal Processing(IF 1.913), 2019, Vol.117, pp.383-402
摘要:Abstract(#br)The field of Compressive Sensing (CS) has provided algorithms to reconstruct signals from a much lower number of measurements than specified by the Nyquist-Shannon theorem. There are two fundamental concepts underpinning the field of CS. The first is the use of rando...
作者:Sreejith Kallummil , Sheetal Kalyani
来源:[J].Signal Processing(IF 1.851), 2020, Vol.168
摘要:Abstract(#br)Recovering the support of sparse vectors in underdetermined linear regression models, aka , compressive sensing is important in many signal processing applications. High SNR consistency (HSC), i.e., the ability of a support recovery technique to correctly identify th...
作者:Samrat Mukhopadhyay , Siddhartha Satpathi , Mrityunjoy Chakraborty
来源:[J].Signal Processing(IF 1.851), 2020, Vol.168
摘要:Abstract(#br)Orthogonal least square (OLS) is an important sparse signal recovery algorithm in compressive sensing, which enjoys superior probability of success over other well known recovery algorithms under conditions of correlated measurement matrices. Multiple OLS (mOLS) is a...
作者:Amit Satish Unde , P.P. Deepthi
来源:[J].Journal of Visual Communication and Image Representation(IF 1.195), 2017, Vol.44, pp.187-197
摘要:Abstract(#br)Compressive sensing provides simultaneous sensing and compression of data. Block compressive sensing (BCS) of images has gained a prominence in recent years due to low encoding complexity. In this paper, we propose the reconstruction algorithm for BCS framework based...
作者:Jianjun Yuan , Jianjun Wang
来源:[J].Magnetic Resonance Imaging(IF 2.06), 2018, Vol.51, pp.79-86
摘要:Abstract(#br)Compressive sensing can be used to reduce noise. However, some details also are sparsified. This paper presents a new denoising model based on compressive sensing with L 1 and Hessian regularizations for magnetic resonance images denoising. Firstly, the proposed mode...

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