Universal distributed sensing via random projections

Duarte, Marco F.; Wakin, Michael B.; Baron, Dror; Baraniuk, Richard G.

doi:10.1145/1127777.1127807

Cited by 71 publications

(84 citation statements)

References 33 publications

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“…Actually, the aliased images from all channels share the common sparse support in the sparsifying transform domain. Distributed CS (DCS) is an extension of CS for simultaneous reconstruction of multiple signals with intra-and intersignal correlations (33). With distributed CS algorithms (34)(35)(36), all aliased images with common sparse support may be simultaneously reconstructed using fewer samples.…”

Section: Extensionsmentioning

confidence: 99%

Accelerating SENSE using compressed sensing

Liang

Liu

Wang

et al. 2009

Magnetic Resonance in Med

403

390

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Both parallel MRI and compressed sensing (CS) are emerging techniques to accelerate conventional MRI by reducing the number of acquired data. The combination of parallel MRI and CS for further acceleration is of great interest. In this paper, we propose a novel method to combine sensitivity encoding (SENSE), one of the standard methods for parallel MRI, and compressed sensing for rapid MR imaging (SparseMRI), a recently proposed method for applying CS in MR imaging with Cartesian trajectories. The proposed method, named CS-SENSE, sequentially reconstructs a set of aliased reducedfield-of-view images in each channel using SparseMRI and then reconstructs the final image from the aliased images using Cartesian SENSE. The results from simulations and phantom and in vivo experiments demonstrate that CS-SENSE can achieve a reduction factor higher than those achieved by SparseMRI and SENSE individually and outperform the existing method that combines parallel MRI and CS. Magn Reson Med 62:1574 -1584, 2009.

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Section: Extensionsmentioning

confidence: 99%

Accelerating SENSE using compressed sensing

Liang

Liu

Wang

et al. 2009

Magnetic Resonance in Med

403

390

View full text Add to dashboard Cite

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“…We get progressively better results as we compute more measurements M [7]. Because of the selective nature of the sparse random matrix, computational complexity is reduced to O(dN ), where d = O(log(N/K)) [4,8].…”

Section: Low-complexity Cs With Downsamplingmentioning

confidence: 99%

“…The downsampling process takes every Lth sample and the upsampling process inserts L−1 zeros between samples, where L is a downsampling factor. Note that the sparse random matrix generation can be synchronized between encoder and decoder using pseudorandom number generator, which is a common practice in CS literatures [7]. Another important thing is that our downsampling at the encoder does not involve prior low-pass filtering, which inevitably incurs aliasing of the signal.…”

Section: Low-complexity Cs With Downsamplingmentioning

confidence: 99%

Low-complexity compressive sensing with downsampling

Lee

Choi

Shin

2014

IEICE Electron. Express

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Abstract:Compressive sensing (CS) with sparse random matrix for the random sensing basis reduces source coding complexity of sensing devices. We propose a downsampling scheme to this framework in order to further reduce the complexity and improve coding efficiency simultaneously. As a result, our scheme can deliver significant gains to a wide variety of resource-constrained sensors. Experimental results show that the computational complexity decreases by 99.95% compared to other CS framework with dense random measurements. Furthermore, bitrate can be saved up to 46.29%, by which less bandwidth is consumed.

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“…The goal is to reduce communication of Nyquist rate sampled data, as opposed to facilitating subNyquist rate signal sampling for low-power devices. [15] proposes a distributed compressed sensing approach, wherein each sensor independently collects random Gaussian measurements. This work relies on a random projection of the signal source, which can be generated by specialized hardware.…”

Section: Related Workmentioning

confidence: 99%

RHA: A robust hybrid architecture for information processing in wireless sensor networks

Dang

Bulusu

Feng

et al. 2010

2010 Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing

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Abstract-The paradox of wireless sensor networks (WSNs) is that the low-power, miniaturized sensors that can be deeply embedded in the physical world, are too resource-constrained to capture high frequency phenomena. In this paper, we propose RHA, a robust hybrid architecture for information processing to extend sensing capacity while conserving energy, storage and bandwidth. We evaluate RHA with two real world applications, bio-acoustic monitoring and spatial monitoring. RHA provides accurate signal reconstruction with significant down sampling compared to traditional multi-rate sampling, is robust to noise, enables fast triggering and load balancing across sensors.

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Universal distributed sensing via random projections

Cited by 71 publications

References 33 publications

Accelerating SENSE using compressed sensing

Accelerating SENSE using compressed sensing

Low-complexity compressive sensing with downsampling

RHA: A robust hybrid architecture for information processing in wireless sensor networks

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