scholarly journals Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

2021 ◽  
Author(s):  
Mohammadreza Balouchestani ◽  
Sridhar Krishnan
Keyword(s):  
Compressed Sensing ◽  
Bayesian Learning ◽  
Mean Squared Error ◽  
Sampling Rate ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Surface Emg ◽  
Healthcare Systems ◽  
The Body ◽  
Reconstruction Process

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

scholarly journals Effective Low-Power Wearable Wireless Surface EMG Sensor Design Based on Analog-Compressed Sensing

2021 ◽  
Author(s):  
Mohammadreza Balouchestani ◽  
Sridhar Krishnan
Keyword(s):  
Compressed Sensing ◽  
Bayesian Learning ◽  
Mean Squared Error ◽  
Sampling Rate ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Surface Emg ◽  
Healthcare Systems ◽  
The Body ◽  
Reconstruction Process

Surface Electromyography (sEMG) is a non-invasive measurement process that does not involve tools and instruments to break the skin or physically enter the body to investigate and evaluate the muscular activities produced by skeletal muscles. The main drawbacks of existing sEMG systems are: (1) they are not able to provide real-time monitoring; (2) they suffer from long processing time and low speed; (3) they are not effective for wireless healthcare systems because they consume huge power. In this work, we present an analog-based Compressed Sensing (CS) architecture, which consists of three novel algorithms for design and implementation of wearable wireless sEMG bio-sensor. At the transmitter side, two new algorithms are presented in order to apply the analog-CS theory before Analog to Digital Converter (ADC). At the receiver side, a robust reconstruction algorithm based on a combination of ℓ1-ℓ1-optimization and Block Sparse Bayesian Learning (BSBL) framework is presented to reconstruct the original bio-signals from the compressed bio-signals. The proposed architecture allows reducing the sampling rate to 25% of Nyquist Rate (NR). In addition, the proposed architecture reduces the power consumption to 40%, Percentage Residual Difference (PRD) to 24%, Root Mean Squared Error (RMSE) to 2%, and the computation time from 22 s to 9.01 s, which provide good background for establishing wearable wireless healthcare systems. The proposed architecture achieves robust performance in low Signal-to-Noise Ratio (SNR) for the reconstruction process.

The Design of Compressed Sensing Image Reconstruction Algorithm Based on Structure Priori Model

2014 ◽  
Vol 602-605 ◽  
pp. 3311-3315
Author(s):  
Lin Zhang ◽  
Xia Ling Zeng
Keyword(s):  
Image Processing ◽  
Image Reconstruction ◽  
Compressed Sensing ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Optimization Process ◽  
Image Block ◽  
Reconstruction Process ◽  
Prior Model ◽  
Dimensional Optimization

Compressed sensing is referred to as the CS technology; it can realize image compression and reconstruction process in low sample rate. It has great potential to reduce the sampling rate and improve the quality of image processing. In this paper, we introduce the structure prior model into the compressed sensing and image processing, and make the image reconstruction of high dimensional optimization process simplified into a series of low dimensional optimization process, which improves the processing speed and image quality. In order to verify the effectiveness and reliability of the proposed algorithm, this paper uses combined control form of C language and MATLAB software to design the programming of structure prior model, and use the Simulink environment to debug the program. Through the calculation we get the image block and the reconstruction result. It provides the technical reference for the research on image compressed sensing technology.

scholarly journals Energy Preserved Sampling for Compressed Sensing MRI

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Yudong Zhang ◽  
Bradley S. Peterson ◽  
Genlin Ji ◽  
Zhengchao Dong
Keyword(s):  
Compressed Sensing ◽  
Cost Function ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Simple Random Sampling ◽  
Variable Density ◽  
Reconstruction Algorithms ◽  
Iterative Thresholding ◽  
Thresholding Algorithm

The sampling patterns, cost functions, and reconstruction algorithms play important roles in optimizing compressed sensing magnetic resonance imaging (CS-MRI). Simple random sampling patterns did not take into account the energy distribution ink-space and resulted in suboptimal reconstruction of MR images. Therefore, a variety of variable density (VD) based samplings patterns had been developed. To further improve it, we propose a novel energy preserving sampling (ePRESS) method. Besides, we improve the cost function by introducing phase correction and region of support matrix, and we propose iterative thresholding algorithm (ITA) to solve the improved cost function. We evaluate the proposed ePRESS sampling method, improved cost function, and ITA reconstruction algorithm by 2D digital phantom and 2Din vivoMR brains of healthy volunteers. These assessments demonstrate that the proposed ePRESS method performs better than VD, POWER, and BKO; the improved cost function can achieve better reconstruction quality than conventional cost function; and the ITA is faster than SISTA and is competitive with FISTA in terms of computation time.

Modified meta-heuristic-oriented compressed sensing reconstruction algorithm for bio-signals

2019 ◽  
Vol 17 (05) ◽  
pp. 1950031
Author(s):  
Ashok Naganath Shinde ◽  
Sanjay L. Lalbalwar ◽  
Anil B. Nandgaonkar
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Reconstruction Algorithm ◽  
Haar Wavelet ◽  
Evaluation Procedure ◽  
Grey Wolf Optimizer ◽  
Reconstruction Algorithms ◽  
Swarm Optimization ◽  
Glowworm Swarm Optimization

In signal processing, several applications necessitate the efficient reprocessing and representation of data. Compression is the standard approach that is used for effectively representing the signal. In modern era, many new techniques are developed for compression at the sensing level. Compressed sensing (CS) is a rising domain that is on the basis of disclosure, which is a little gathering of a sparse signal’s linear projections including adequate information for reconstruction. The sampling of the signal is permitted by the CS at a rate underneath the Nyquist sampling rate while relying on the sparsity of the signals. Additionally, the reconstruction of the original signal from some compressive measurements can be authentically exploited using the varied reconstruction algorithms of CS. This paper intends to exploit a new compressive sensing algorithm for reconstructing the signal in bio-medical data. For this purpose, the signal can be compressed by undergoing three stages: designing of stable measurement matrix, signal compression and signal reconstruction. In this, the compression stage includes a new working model that precedes three operations. They are signal transformation, evaluation of [Formula: see text] and normalization. In order to evaluate the theta ([Formula: see text]) value, this paper uses the Haar wavelet matrix function. Further, this paper ensures the betterment of the proposed work by influencing the optimization concept with the evaluation procedure. The vector coefficient of Haar wavelet function is optimally selected using a new optimization algorithm called Average Fitness-based Glowworm Swarm Optimization (AF-GSO) algorithm. Finally, the performance of the proposed model is compared over the traditional methods like Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Firefly (FF), Crow Search (CS) and Glowworm Swarm Optimization (GSO) algorithms.

scholarly journals A Novel Compressed Sensing Method for Magnetic Resonance Imaging: Exponential Wavelet Iterative Shrinkage-Thresholding Algorithm with Random Shift

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yudong Zhang ◽  
Jiquan Yang ◽  
Jianfei Yang ◽  
Aijun Liu ◽  
Ping Sun
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Mean Squared Error ◽  
State Of The Art ◽  
Computation Time ◽  
Resonance Imaging ◽  
Iterative Shrinkage ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding

Aim. It can help improve the hospital throughput to accelerate magnetic resonance imaging (MRI) scanning. Patients will benefit from less waiting time.Task. In the last decade, various rapid MRI techniques on the basis of compressed sensing (CS) were proposed. However, both computation time and reconstruction quality of traditional CS-MRI did not meet the requirement of clinical use.Method. In this study, a novel method was proposed with the name of exponential wavelet iterative shrinkage-thresholding algorithm with random shift (abbreviated as EWISTARS). It is composed of three successful components: (i) exponential wavelet transform, (ii) iterative shrinkage-thresholding algorithm, and (iii) random shift.Results. Experimental results validated that, compared to state-of-the-art approaches, EWISTARS obtained the least mean absolute error, the least mean-squared error, and the highest peak signal-to-noise ratio.Conclusion. EWISTARS is superior to state-of-the-art approaches.

scholarly journals Deep learning can accelerate and quantify simulated localized correlated spectroscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zohaib Iqbal ◽  
Dan Nguyen ◽  
Michael Albert Thomas ◽  
Steve Jiang
Keyword(s):  
Deep Learning ◽  
Compressed Sensing ◽  
Mean Squared Error ◽  
Learning Model ◽  
The Body ◽  
Resonance Spectroscopy ◽  
Atomic Structures ◽  
Squared Error ◽  
Deep Learning Model ◽  
Cosy Spectra

AbstractNuclear magnetic resonance spectroscopy (MRS) allows for the determination of atomic structures and concentrations of different chemicals in a biochemical sample of interest. MRS is used in vivo clinically to aid in the diagnosis of several pathologies that affect metabolic pathways in the body. Typically, this experiment produces a one dimensional (1D) 1H spectrum containing several peaks that are well associated with biochemicals, or metabolites. However, since many of these peaks overlap, distinguishing chemicals with similar atomic structures becomes much more challenging. One technique capable of overcoming this issue is the localized correlated spectroscopy (L-COSY) experiment, which acquires a second spectral dimension and spreads overlapping signal across this second dimension. Unfortunately, the acquisition of a two dimensional (2D) spectroscopy experiment is extremely time consuming. Furthermore, quantitation of a 2D spectrum is more complex. Recently, artificial intelligence has emerged in the field of medicine as a powerful force capable of diagnosing disease, aiding in treatment, and even predicting treatment outcome. In this study, we utilize deep learning to: (1) accelerate the L-COSY experiment and (2) quantify L-COSY spectra. All training and testing samples were produced using simulated metabolite spectra for chemicals found in the human body. We demonstrate that our deep learning model greatly outperforms compressed sensing based reconstruction of L-COSY spectra at higher acceleration factors. Specifically, at four-fold acceleration, our method has less than 5% normalized mean squared error, whereas compressed sensing yields 20% normalized mean squared error. We also show that at low SNR (25% noise compared to maximum signal), our deep learning model has less than 8% normalized mean squared error for quantitation of L-COSY spectra. These pilot simulation results appear promising and may help improve the efficiency and accuracy of L-COSY experiments in the future.

scholarly journals Wireless Body Area Networks Based on Compressed Sensing Theory

2021 ◽  
Author(s):  
Mohammadreza Balouchestani Asli
Keyword(s):  
Compressed Sensing ◽  
Low Power ◽  
Sampling Rate ◽  
Healthcare Systems ◽  
Biomedical Data ◽  
Receiver Side ◽  
Body Area ◽  
Ecg Signals ◽  
Simulation Results ◽  
New Algorithms

In this research, the effective sampling method known as Compressed Sensing (CS) theory is applied to Wireless Body Area Networks (WBANs) to provide low power and low sampling-rate wireless healthcare systems and intelligent emergency care management systems. The fundamental contribution of this work can be divided into three areas. 1) We propose two new algorithms in the sensing, measurement, and processing area to compress biomedical data. 2) In the communication area, one new channel model based on CS theory is defined to transmit compressed data to the receiver side. 3) In the receiver side or reconstruction area, two new algorithms for recovering the original biomedical data are presented to recover the original data. Our results will be divided into three areas. 1) We employ the proposed algorithms to WBANs with a single biomedical signal (i.e. Electroencephalography [ECG] signals as a sample signal). In this area, the simulation results illustrate an increment of 10% improved for sensitivity in receiving compressed ECG signals. The simulation results also illustrate a 25% reduction of Percentage Root-mean-square Difference (PRD) for ECG signals on the receiver side. In addition, they confirm the ability of CS to maximize the prediction level for received the ECG signal at either Gate Ways (GWs) or Access Points (APs). 2) We illustrate that the proposed algorithms can be employed in WBANs with multiple biomedical signals to enhance current health care systems into low-power wireless healthcare systems. In this area, the simulation results confirm that for a particular WBAN, including N biomedical signals, the sampling-rate can be reduced by 25-35% and power consumption by 35-40%, without sacrificing the network’s performance. 3) Here improvements for wireless channel feature between BWSs and either GWs or APs are shown. In this area, the results demonstrate that CS is able to maximize signal amplitude to 25-30% at the receiver as well as distance between transmitter and receiver BWS to 30%. Moreover, these results confirm that path loss can be reduced to 25%.

scholarly journals Compressed Sensing MRI Reconstruction Algorithm Based on Contourlet Transform and Alternating Direction Method

2015 ◽  
Vol 2015 ◽  
pp. 1-5
Author(s):  
Zhenyu Hu ◽  
Qiuye Wang ◽  
Congcong Ming ◽  
Lai Wang ◽  
Yuanqing Hu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Computation Time ◽  
Reconstruction Algorithm ◽  
Alternating Direction Method ◽  
Contourlet Transform ◽  
Superior Performance ◽  
Reconstruction Method ◽  
Mr Images ◽  
Alternating Direction ◽  
Mri Reconstruction

Compressed sensing (CS) based methods have recently been used to reconstruct magnetic resonance (MR) images from undersampled measurements, which is known as CS-MRI. In traditional CS-MRI, wavelet transform can hardly capture the information of image curves and edges. In this paper, we present a new CS-MRI reconstruction algorithm based on contourlet transform and alternating direction method (ADM). The MR images are firstly represented by contourlet transform, which can describe the images’ curves and edges fully and accurately. Then the MR images are reconstructed by ADM, which is an effective CS reconstruction method. Numerical results validate the superior performance of the proposed algorithm in terms of reconstruction accuracy and computation time.

scholarly journals Wireless Body Area Networks Based on Compressed Sensing Theory

2021 ◽  
Author(s):  
Mohammadreza Balouchestani Asli
Keyword(s):  
Compressed Sensing ◽  
Low Power ◽  
Sampling Rate ◽  
Healthcare Systems ◽  
Biomedical Data ◽  
Receiver Side ◽  
Body Area ◽  
Ecg Signals ◽  
Simulation Results ◽  
New Algorithms

In this research, the effective sampling method known as Compressed Sensing (CS) theory is applied to Wireless Body Area Networks (WBANs) to provide low power and low sampling-rate wireless healthcare systems and intelligent emergency care management systems. The fundamental contribution of this work can be divided into three areas. 1) We propose two new algorithms in the sensing, measurement, and processing area to compress biomedical data. 2) In the communication area, one new channel model based on CS theory is defined to transmit compressed data to the receiver side. 3) In the receiver side or reconstruction area, two new algorithms for recovering the original biomedical data are presented to recover the original data. Our results will be divided into three areas. 1) We employ the proposed algorithms to WBANs with a single biomedical signal (i.e. Electroencephalography [ECG] signals as a sample signal). In this area, the simulation results illustrate an increment of 10% improved for sensitivity in receiving compressed ECG signals. The simulation results also illustrate a 25% reduction of Percentage Root-mean-square Difference (PRD) for ECG signals on the receiver side. In addition, they confirm the ability of CS to maximize the prediction level for received the ECG signal at either Gate Ways (GWs) or Access Points (APs). 2) We illustrate that the proposed algorithms can be employed in WBANs with multiple biomedical signals to enhance current health care systems into low-power wireless healthcare systems. In this area, the simulation results confirm that for a particular WBAN, including N biomedical signals, the sampling-rate can be reduced by 25-35% and power consumption by 35-40%, without sacrificing the network’s performance. 3) Here improvements for wireless channel feature between BWSs and either GWs or APs are shown. In this area, the results demonstrate that CS is able to maximize signal amplitude to 25-30% at the receiver as well as distance between transmitter and receiver BWS to 30%. Moreover, these results confirm that path loss can be reduced to 25%.

scholarly journals Parallel Computing of Patch-Based Nonlocal Operator and Its Application in Compressed Sensing MRI

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Qiyue Li ◽  
Xiaobo Qu ◽  
Yunsong Liu ◽  
Di Guo ◽  
Jing Ye ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Multicore Processors ◽  
Parallel Architecture ◽  
Computation Time ◽  
Reconstruction Error ◽  
Nonlocal Operator ◽  
Resonance Imaging ◽  
Reconstruction Process

Magnetic resonance imaging has been benefited from compressed sensing in improving imaging speed. But the computation time of compressed sensing magnetic resonance imaging (CS-MRI) is relatively long due to its iterative reconstruction process. Recently, a patch-based nonlocal operator (PANO) has been applied in CS-MRI to significantly reduce the reconstruction error by making use of self-similarity in images. But the two major steps in PANO, learning similarities and performing 3D wavelet transform, require extensive computations. In this paper, a parallel architecture based on multicore processors is proposed to accelerate computations of PANO. Simulation results demonstrate that the acceleration factor approaches the number of CPU cores and overall PANO-based CS-MRI reconstruction can be accomplished in several seconds.

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