Results 51 to 60 of about 63,884 (167)
Distributed Compressed Spectrum Sensing via Cooperative Support Fusion
International Journal of Distributed Sensor Networks, 2013 Spectrum sensing in wideband cognitive radio (CR) networks faces several significant practical challenges, such as extremely high sampling rates required for wideband processing, impact of frequency-selective wireless fading and shadowing, and limitation
Zha Song +3 more
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Shockwave Signal Downsampling Rate Acquisition Based on Sparse Fourier Transform
IEEE Access, 2022 The wireless distributed transient shockwave testing system based on Nyquist sampling needs to keep high sampling rate and front-end data processing rate, which leads to the shortening of the life cycle of wireless nodes.
Bo Xu +6 more
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Sample Distortion for Compressed Imaging
, 2013 We propose the notion of a sample distortion (SD) function for independent and identically distributed (i.i.d) compressive distributions to fundamentally quantify the achievable reconstruction performance of compressed sensing for certain encoder-decoder
Davies, Mike E., Guo, Chunli
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A Compressed Sampling and Dictionary Learning Framework for WDM-Based Distributed Fiber Sensing
, 2017 We propose a compressed sampling and dictionary learning framework for fiber-optic sensing using wavelength-tunable lasers. A redundant dictionary is generated from a model for the reflected sensor signal. Imperfect prior knowledge is considered in terms
Weiss, Christian, Zoubir, Abdelhak M.
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Multi Terminal Probabilistic Compressed Sensing [PDF]
, 2014 In this paper, the `Approximate Message Passing' (AMP) algorithm, initially developed for compressed sensing of signals under i.i.d. Gaussian measurement matrices, has been extended to a multi-terminal setting (MAMP algorithm).
Haghighatshoar, Saeid
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A Video Code Based on Distribution Compressive Sensing
Procedia Engineering, 2012 AbstractCompressed sensing (CS) is a new technique for simultaneous data sampling and compression. In this paper, we propose a new video coding algorithm based on Distributed Compressive Sampling(DCS) principles, where almost all computation burdens can be shifted to the decoder, resulting in a very lowcomplexity encoder.
WU Minghu, WU Minghu, Zhu Xiu-chang
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Digital Communications and Networks, 2023 Compressed Sensing (CS) is a Machine Learning (ML) method, which can be regarded as a single-layer unsupervised learning method. It mainly emphasizes the sparsity of the model.
Han Wang +5 more
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Distributed Compressed Sensing Based Ground Moving Target Indication for Dual-Channel SAR System
Sensors, 2018 The dual-channel synthetic aperture radar (SAR) system is widely applied in the field of ground moving-target indication (GMTI). With the increase of the imaging resolution, the resulting substantial raw data samples increase the transmission and storage
Jing Liu +3 more
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An Improved Distributed Multi-User Cooperative Spectrum Sensing Method Based on DCS
Dianxin kexue, 2013 Distributed compressed sensing theory extends the application of compressed sensing theory, which brings single signal compression sampling to signal group compression sampling.
Jianwu Zhang, Xiaoyan Chen, Xiaorong Xu
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Optimised projections for generalised distributed compressed sensing [PDF]
Electronics Letters, 2014 Different signals from the various sensors of the same scene form an ensemble. Distributed compressed sensing (DCS) rests on a new concept called the joint sparsity of the ensemble. JSM‐1 is a model that describes the joint sparsity by one dictionary.
Rong Rong +3 more
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