Design of Bandwidth Efficient Compressed Sensing Based Prediction Measurement Encoder for Video Transmission in Wireless Sensor Networks

Angayarkanni, Veeraputhiran; Radha, S.

doi:10.1007/s11277-016-3176-1

Cited by 13 publications

(10 citation statements)

References 21 publications

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“…In paper [4], the authors introduced a novel compressive sensing based prediction measurement (CSPM) encoder. The sparse image undergoes CS by using Gaussian matrix and these measured values pass to CSPM.…”

Section: Abstract-compressivementioning

confidence: 99%

“…First the image will undergo 2-D wavelet transform. In paper [4], the whole wavelet coefficients are undergoing CS together and measured using a single measurement matrix. Low frequency components contain coarse information and high frequency components contain detail information.…”

Section: Basic Block Diagrammentioning

confidence: 99%

“…DARC uses weighting of three neighbouring pixels for prediction. This non-linear prediction gives better results than linear prediction which is used in paper [4]. DARC prediction and linear prediction are lossless prediction methods which help in reconstruction of medical images without visual degradation.…”

Section: Basic Block Diagrammentioning

confidence: 99%

“…These nonlinear techniques will result in good quality reconstructed image as in [3]. In short, CS helps to reduce sampling and computation costs for sensing signals that have a sparse or compressible representation.In paper [4], the authors introduced a novel compressive sensing based prediction measurement (CSPM) encoder. The sparse image undergoes CS by using Gaussian matrix and these measured values pass to CSPM.…”

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confidence: 99%

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Compressive Sensing Based Image Reconstruction using Wavelet Transform

Abraham¹,

Pathak²,

Jigna³

2017

IJET

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Abstract-CompressiveSensing is a novel technique where reconstruction of an image can be done with less number of samples than conventional Nyquist theorem suggests. The signal will pass through sensing matrix wavelet transformation to make the signal sparser enough which is a criterion for compressive sensing. The low frequency and high frequency components of an image have different kind of information. So, these have to be processed separately in both measurements and reconstruction techniques for better image compression. The performance further can be improved by using DARC prediction method. The reconstructed image should be better in both PSNR and visual quality. In medical field, especially in MRI scanning, compressive sensing can be utilized for less scanning time.Keyword-Compressive Sensing, Wavelet transform, Sparsity, DARC prediction I. INTRODUCTION Compressive sensing (CS) is a new compression technique where fewer samples of measurement are enough to reconstruct the image with good visual quality. The samples required are much lesser than Nyquist criterion suggests. But the non-linear reconstruction used in CS is more complex compared to linear reconstruction in conventional compression. CS will measure finite dimensional vectors. Now CS is actively researched in applications like MRI, RADAR, single pixel camera, etc. as in [1].We consider the application in medical field. MR images are sparse in Wavelet domain. The medical images will undergo CS so that the image reconstructed will have good visual quality but with less number of measurements. MRI is slow process due to the large number of data needed to be collected while scanning a patient as in [2]. With the help of CS we can reduce the number of samples, thus reducing scan time, which will benefit patient with less radiation exposure.In CS, there are three main principles -Sparsity, Measurements taking and Nonlinear reconstruction. The signal should be sparse -Information rate contained in the image should be much less than bandwidth -to undergo CS. If it's not sparse enough; we need to undergo the transformation of the image to make it sparse. We took wavelet transform as sparsity inducing matrix in this paper. The reconstruction of signals from lesser samples can only be possible if the chosen sparsity matrix and measurement matrix follows Restricted Isometry Property. The incoherence between these matrices is necessary for this. There are two approaches for reconstructing image at receiver side -basis pursuit and greedy algorithm. These nonlinear techniques will result in good quality reconstructed image as in [3]. In short, CS helps to reduce sampling and computation costs for sensing signals that have a sparse or compressible representation.In paper [4], the authors introduced a novel compressive sensing based prediction measurement (CSPM) encoder. The sparse image undergoes CS by using Gaussian matrix and these measured values pass to CSPM. In CSPM, the measured matrix undergoes linear prediction and entropy encoding. Since the sparsity ...

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Section: Abstract-compressivementioning

confidence: 99%

Section: Basic Block Diagrammentioning

confidence: 99%

Section: Basic Block Diagrammentioning

confidence: 99%

mentioning

confidence: 99%

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Compressive Sensing Based Image Reconstruction using Wavelet Transform

Abraham¹,

Pathak²,

Jigna³

2017

IJET

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“…CS reconstruction is also inherently immune to data loss [3] and the transferred data are naturally encrypted [4]. Since its introduction, CS was tested with WSNs performing temperature [5], EM emission [6], and ECG [7] monitoring, video streaming [8], etc. Other applications of the CS theory include WSN decentralized data storage [9], accelerated MRI [10], or digital modulation recognition [11].…”

Section: Introductionmentioning

confidence: 99%

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Andráš

Dolinský

Michaeli

et al. 2018

Measurement Science Review

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This paper presents a way of acquiring a sparse signal by taking only a limited number of samples; sampling and compression are performed in one step by the analog to information conversion. The signal is recovered with minimal information loss from the reduced data record via compressed sensing reconstruction. Several methods of analog to information conversion are described with focus on numerical complexity and implementation in existing embedded devices. Two novel analog to information conversion methods are proposed, distinctive by their computational simplicity - direct subsampling and subsampling with integration. Proposed sensing methods are intended for and evaluated with real water parameter signals measured by a wireless sensor network. Compressed sensing proves to reduce the data transfer rate by >80 % with very little signal processing performed at the sensing side and no appreciable distortion of the reconstructed signal.

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Scale‐free topology security mechanism of wireless sensor network against cascade failure

Gao

Liu

et al. 2021

Int J Communication

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As the basic architecture of the Internet of things, wireless sensor networks are the most important technical means to detect data on the Internet of things, and their security is essential to the development and implementation of the Internet of things. The wireless sensor network based on the scale-free theory has strong fault tolerance when random nodes fail and can better adapt to the unsupervised and complex environment of wireless sensor networks.However, in a wireless sensor network with scale-free function, the unevenness of its degree distribution causes some nodes to be very critical in the network. Once attacked, it will cause major damage to the network. Selective promotion attacks are relatively common. At the network level, the attacker destroys the normal collection of network data by selectively losing information or not forwarding sensitive information, which is extremely harmful to the network using this function. In order to overcome the influence of excessive dependence on a single path on the balance performance of the network, starting from the entire network, a secure multi-path routing algorithm for wireless sensor networks based on scale-free topology is proposed. Through the analysis of the network topology, the highest degree network node can be found. Optimize the load factor, use digital watermarking technology to locate the network path packet loss rate, and perform Gaussian modeling on the packet loss rate. Then the routing loading function and the routing packet loss rate are used as the confidence model factors to create the routing confidence model. We must first establish a model, select the transmission path according to the received function, and then obtain the global optimal security sensor routing algorithm. Through the algorithm in this thesis, the security of the wireless sensor network is improved by 17%.

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Design of Bandwidth Efficient Compressed Sensing Based Prediction Measurement Encoder for Video Transmission in Wireless Sensor Networks

Cited by 13 publications

References 21 publications

Compressive Sensing Based Image Reconstruction using Wavelet Transform

Compressive Sensing Based Image Reconstruction using Wavelet Transform

Sparse Signal Acquisition via Compressed Sensing and Principal Component Analysis

Scale‐free topology security mechanism of wireless sensor network against cascade failure

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