Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors

Chen, F.; Chandrakasan, Anantha P.; Stojanović, Vladimir

doi:10.1109/jssc.2011.2179451

Cited by 337 publications

(232 citation statements)

References 35 publications

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“…The numerical results show that structured sampling, in combination with structured recovery, allows to more faithfully reconstruct the original signals, as compared with the traditional Bernoulli [4] or the multi-channel sampling [5] schemes.…”

Section: Discussionmentioning

confidence: 99%

“…For example, the MCS strategy has been designed to optimize area and power usage, at the cost of sacrificing the reconstruction quality when the channels are not correlated enough. The random Bernoulli sampling [4] offers excellent reconstruction quality at low compression factors, but requires a larger chip area than MCS.…”

Section: Discussionmentioning

confidence: 99%

“…The first, named BERN, uses the same random Bernoulli {±1} matrix to sample each channel independently [4]. The second, named Multi-Channel Sampling (MCS) [5], designed to be highly power-efficient, uses a Bernoulli {0, 1} matrix to sample across the channels at each time step.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Structured sampling and recovery of iEEG signals

Baldassarre

Aprile

Shoaran

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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Abstract-Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a promising approach to directly acquire compressed signals, allowing to reduce the power consumption associated with data transmission. To this end, we propose an efficient CS scheme which exploits the structure of the intracranial EEG signals, both in sampling and recovery. Our structure-aware approach is conceptually simple to implement in hardware and yields stateof-the-art compression rates up to 32x with high reconstruction quality, as illustrated on two human iEEG datasets.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Structured sampling and recovery of iEEG signals

Baldassarre

Aprile

Shoaran

et al. 2015

2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

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“…Furthermore, in spite of the immaturity of the field, it has been shown that energy efficiency, circuit size, and power consumption of a CS encoder are on par with or better than the state-of-the-art of existing compression methods [7][8][9][10][11][12][13][14][15][16].…”

Section: Journal Of Neurology and Neuroscience Issn 2171-6625mentioning

confidence: 99%

An Adaptive Recovery Method in Compressed Sensing of Extracellular Neural Recording

Semmaoui¹,

Hk²,

Jc³

et al. 2015

J Neurol Neurosci

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A novel adaptive recovery method in the emerging compressed sensing theory is described and applied to extracellular neural recordings in order to reduce data rate in wireless neural recording systems. To strike a balance between high compression ratio and high spike reconstruction quality, a novel method that employs a group-sparsity recovery algorithm, prior information about the input neural signal, learning prior supports of spikes, and a matched wavelet technique is introduced. Our simulation results, using four different sets of real extracellular recordings from four distinct neural sources, show that our proposed method is effective, viable, and outperforms the state-of-the-art compressed sensing-based methods, in particular, when the number of the measurement is two times of the sparsity. JOURNAL OF NEUROLOGY AND NEUROSCIENCE ISSN 2171-6625This article is available in: www.jneuro.com of the CS allows the circuit-complexity on the implant side to be moved to the recovery side, which is outside of the body.Furthermore, in spite of the immaturity of the field, it has been shown that energy efficiency, circuit size, and power consumption of a CS encoder are on par with or better than the state-of-the-art of existing compression methods [7][8][9][10][11][12][13][14][15][16].This paper deals with an adaptive recovery method. This part of a CS system takes place outside of the body. Here, to strike a balance between high compression ratio and high spike reconstruction quality, our proposed method is characterized by a number of unique features: 1) the employment of group-sparsity recovery algorithm, 2) taking advantage of prior information about the input signal, 3) the learning of prior supports of spikes, and 4) a matched wavelet technique.An overview and comparison of some existing compression methods are given in Aghagolzadeh and Charbiwala [1,6]. However, in this work, in order to further evaluate the performance of our proposed method, we compare it with two recent works in its category (i.e., techniques employing the CS approach).We recall the basics of the CS in Section II. Section III details our adaptive recovery method. The methodology is presented in Section IV. Then in Section V, our results are reported. An example of a small size and low power cost CS encoder is evinced in Section VI. Finally, we conclude with a discussion of our contribution and perspectives in Section VII. Compressed Sensing A. Background of compressed sensingCompressed sensing is a new approach for signal compression. It has been shown that if a signal has a sparse representation in one basis ø , then it can be recovered from a small number of projections onto a second basis φ that is incoherent with the former [17][18][19]. CS is a non-adaptive data acquisition technique because the basis φ does not depend on the measured signal x.Also, to be efficient, CS requires two conditions:Sparse representation: The sparse property of the signal x is very important and directly influences performance of the CS [17][18][19]. G...

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“…The most striking waveform when considering the ECG is QRS wave complex, which gives the R wave peak which is time-varying [6]. Wavelet transform (WT) is a powerful time-frequency signal analysis tool and it is used in a wide variety of applications [7]. The question is how to use the Wavelet Transform for the purpose of getting rid of Baseline Wander ECG signal?…”

Section: Introductionmentioning

confidence: 99%

FPGA Realization for Baseline Wander Noise Cancellation of ECG Signals using Wavelet Transform

Hassan¹

2017

IJCA

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Baseline Wander (BW) is a common noise in electrocardiogram (ECG). To effectively correct and to preserve more underlying components of an ECG signal, a powerful tool for removal of BW noise from various signals was introduced. This paper presented the discrete wavelet to get rid of that noise. This method is based on comparing signal with discrete multi-rate filter banks. A multi-level decomposition was performed on the noisy signal and then the splitting into low sub-bands and a high pass band sub-bands called detail level was performed. After that, the analysis of the details level and the identifying of a suitable threshold technique were done. Reconstruction of the signal was done through the calculation of the detail coefficients. Finally, the difference between the original signal and the reconstructed signal was calculated. The proposed technique was compared with the previous techniques in this domain of search. The algorithm was tested using Matlab tool. The results showed that the proposed filter could more effectively extract baseline wander from ECG signal and affect the morphological feature of ECG signal considerably less than both the traditional moving average filter and adaptive filter did. The results showed also that this proposed technique achieved excellent results in terms of Mean Square Error (MSE) and convergence rate rather than the previous approaches. This paper also introduced the efficient realization of the proposed approach using FPGA. The proposed method was verified by FPGA (Xilinx Virtex-7 XC7VX690T) realization, revealing its effectiveness in real-time applications. General TermsSub-band-Threshold technique -Matlab -FPGA.

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Design and Analysis of a Hardware-Efficient Compressed Sensing Architecture for Data Compression in Wireless Sensors

Cited by 337 publications

References 35 publications

Structured sampling and recovery of iEEG signals

Structured sampling and recovery of iEEG signals

An Adaptive Recovery Method in Compressed Sensing of Extracellular Neural Recording

FPGA Realization for Baseline Wander Noise Cancellation of ECG Signals using Wavelet Transform

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