A framework for low-complexity signal recovery and its application to structured sparsity

2016 ◽  
Author(s):  
Yann Traonmilin ◽  
Remi Gribonval
Keyword(s):  
Low Complexity ◽  
Signal Recovery ◽  
Structured Sparsity

scholarly journals Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

2020 ◽  
Author(s):  
Evan M. Dastin-van Rijn ◽  
Nicole R. Provenza ◽  
Jonathan S. Calvert ◽  
Ro’ee Gilron ◽  
Anusha B. Allawala ◽  
...  
Keyword(s):  
Real Time ◽  
Low Complexity ◽  
Signal Recovery ◽  
Complex Signals ◽  
Artifact Removal ◽  
Neural Recordings ◽  
Removal Method ◽  
The Central Nervous System ◽  
Novel Method

AbstractAdvances in device development have enabled concurrent stimulation and recording at adjacent locations in the central nervous system. However, stimulation artifacts obscure the sensed underlying neural activity. Here, we developed a novel method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker were used for validation. Performance was found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: it is 1) superior in signal recovery; 2) easily adaptable to several neurostimulation paradigms; and 3) low-complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.SummaryOnline, real-time artifact removal via PARRM will enable unbiased exploration of neural biomarkers previously obscured by stimulation artifact.

scholarly journals Cyber-Physical Systems: An Information-Theoretic Framework for Joint CS-ICA Recovery of Sparse Source Biosignals.

2020 ◽  
Author(s):  
Yahia Alghorani ◽  
salama Ikki
Keyword(s):  
Adaptive Design ◽  
Algorithm Design ◽  
Vital Signs ◽  
Low Complexity ◽  
Cyber Physical Systems ◽  
Signal Recovery ◽  
Digital To Analog Converter ◽  
Information Theoretic ◽  
Physical Systems ◽  
Recovery Algorithms

<div>The aim of this study is to propose an information-theoretic</div><div>framework that can be used for joint recovery of sparse</div><div>source biosignals. The proposed method supports medical cyber-physical systems (CPS) that enhance the detection, tracking, and monitoring of vital signs via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we develop an adaptive design methodology based on compressed sensing (CS) and</div><div>independent component analysis (ICA) to reduce and eliminate artifacts and interference in sparse biosignals. Our analysis and examples offer a low-complexity algorithm design for patient monitoring systems, where sparse source biosignals can be recovered at low hardware costs and power consumption. Also, we show that, under noisy measurement conditions, the joint CS-ICA recovery algorithms can outperform standard CS methods, where a sparse biosignal is retrieved in a few measurement. By implementing the joint sparse recovery algorithms, the error in reconstructing sparse biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution.</div>

scholarly journals A Low-Complexity Biosignal Recovery Algorithm using Compressed Sensing and Independent Component Analysis

2020 ◽  
Author(s):  
Yahia Alghorani ◽  
salama Ikki
Keyword(s):  
Independent Component Analysis ◽  
Compressed Sensing ◽  
Low Cost ◽  
Vital Signs ◽  
Low Complexity ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Recovery ◽  
Recovery Algorithm ◽  
Ica Algorithm

<div>The aim of this study is to propose a low-complexity algorithm that can be used for the joint sparse recovery of biosignals. The framework of the proposed algorithm supports real-time patient monitoring systems that enhance the detection, tracking, and monitoring of vital signs via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we develop an efficient computational framework using compressed sensing (CS) and independent component analysis (ICA) to reduce and eliminate artifacts and interference in sparse biosignals. Our analysis and examples indicate that the CS-ICA algorithm helps to develop low-cost, low-power wearable biosensors while improving data quality and accuracy for a given measurement. We also show that, under noisy measurement conditions, the CS-ICA algorithm can outperform the standard CS method, where a biosignal can be retrieved in only a few measurements. By implementing the sensing framework, the error in reconstructing biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution</div>

scholarly journals Sub-Sampling in OFDM with Constant Time Signal Recovery

2016 ◽  
Vol 21 (2) ◽  
pp. 19-32
Author(s):  
Nikos Petrellis
Keyword(s):  
Fourier Transform ◽  
Low Power ◽  
Fast Fourier Transform ◽  
Orthogonal Frequency Division Multiplexing ◽  
Forward Error Correction ◽  
Low Complexity ◽  
Time Signal ◽  
Signal Recovery ◽  
Analog To Digital ◽  
Forward Error

Abstract The sub-sampling method for Orthogonal Frequency Division Multiplexing proposed recently, has been extended in this paper allowing the Analog-to-Digital Converter on the receiver side to operate in low power mode, up to 3/4 of the time. The predictability of the parity patterns generated by the Forward Error Correction encoder of the transmitter, when sparse data are exchanged, is exploited in order to define appropriate Inverse Fast Fourier Transform input symbol arrangements. These symbol arrangements allow the substitution of a number of samples by others that have already been received. Moreover, several operations of the Fast Fourier Transform can be omitted because their result is zero when identical values appear at its input. The advantages of the proposed method are: low power, higher speed and fewer memory resources. Despite other iterative sub-sampling approaches like Compressive Sampling, the proposed method is not iterative and thus it can be implemented with very low complexity hardware. The simulation results show that full input signal recovery or at least a very low Bit Error Rate is achieved in most of the cases that have been tested.

scholarly journals Cyber-Physical Systems: An Information-Theoretic Framework for Joint CS-ICA Recovery of Sparse Source Biosignals.

2020 ◽  
Author(s):  
Yahia Alghorani ◽  
salama Ikki
Keyword(s):  
Adaptive Design ◽  
Algorithm Design ◽  
Vital Signs ◽  
Low Complexity ◽  
Cyber Physical Systems ◽  
Signal Recovery ◽  
Digital To Analog Converter ◽  
Information Theoretic ◽  
Physical Systems ◽  
Recovery Algorithms

<div>The aim of this study is to propose an information-theoretic</div><div>framework that can be used for joint recovery of sparse</div><div>source biosignals. The proposed method supports medical cyber-physical systems (CPS) that enhance the detection, tracking, and monitoring of vital signs via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we develop an adaptive design methodology based on compressed sensing (CS) and</div><div>independent component analysis (ICA) to reduce and eliminate artifacts and interference in sparse biosignals. Our analysis and examples offer a low-complexity algorithm design for patient monitoring systems, where sparse source biosignals can be recovered at low hardware costs and power consumption. Also, we show that, under noisy measurement conditions, the joint CS-ICA recovery algorithms can outperform standard CS methods, where a sparse biosignal is retrieved in a few measurement. By implementing the joint sparse recovery algorithms, the error in reconstructing sparse biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution.</div>

scholarly journals An efficient algorithm for compression-based compressed sensing

2018 ◽  
Vol 8 (2) ◽  
pp. 343-375 ◽  
Author(s):  
Sajjad Beygi ◽  
Shirin Jalali ◽  
Arian Maleki ◽  
Urbashi Mitra
Keyword(s):  
Compressed Sensing ◽  
Video Compression ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Robustness Analysis ◽  
Rate Distortion ◽  
Low Complexity ◽  
Signal Recovery ◽  
Recovery Algorithm

Abstract Modern image and video compression codes employ elaborate structures in an effort to encode them using a small number of bits. Compressed sensing (CS) recovery algorithms, on the other hand, use such structures to recover the signals from a few linear observations. Despite the steady progress in the field of CS, the structures that are often used for signal recovery are still much simpler than those employed by state-of-the-art compression codes. The main goal of this paper is to bridge this gap by answering the following question: can one employ a compression code to build an efficient (polynomial time) CS recovery algorithm? In response to this question, the compression-based gradient descent (C-GD) algorithm is proposed. C-GD, which is a low-complexity iterative algorithm, is able to employ a generic compression code for CS and therefore enlarges the set of structures used in CS to those used by compression codes. Three theoretical contributions are provided: a convergence analysis of C-GD, a characterization of the required number of samples as a function of the rate-distortion function of the compression code and a robustness analysis of C-GD to additive white Gaussian noise and other non-idealities in the measurement process. Finally, the presented simulation results show that, in image CS, using compression codes such as JPEG2000, C-GD outperforms state-of-the-art methods, on average, by about $2$–$3$ dB in peak signal-to-noise ratio.

Cyber-Physical Systems: An Information-Theoretic Framework for Joint Recovery of Sparse Source Biosignals.

2020 ◽  
Author(s):  
Yahia Alghorani ◽  
salama Ikki
Keyword(s):  
Adaptive Design ◽  
Algorithm Design ◽  
Vital Signs ◽  
Low Complexity ◽  
Cyber Physical Systems ◽  
Signal Recovery ◽  
Digital To Analog Converter ◽  
Information Theoretic ◽  
Physical Systems ◽  
Recovery Algorithms

<div>The aim of this study is to propose an information-theoretic</div><div>framework that can be used for joint recovery of sparse</div><div>source biosignals. The proposed method supports medical cyber-physical systems (CPS) that enhance the detection, tracking, and monitoring of vital signs via wearable biosensors. Specifically, we address the problem of sparse signal recovery and acquisition in wearable biosensor networks, where we develop an adaptive design methodology based on compressed sensing (CS) and</div><div>independent component analysis (ICA) to reduce and eliminate artifacts and interference in sparse biosignals. Our analysis and examples offer a low-complexity algorithm design for patient monitoring systems, where sparse source biosignals can be recovered at low hardware costs and power consumption. Also, we show that, under noisy measurement conditions, the joint CS-ICA recovery algorithms can outperform standard CS methods, where a sparse biosignal is retrieved in a few measurement. By implementing the joint sparse recovery algorithms, the error in reconstructing sparse biosignals is reduced, and a digital-to-analog converter operates at low-speed and low-resolution.</div>

scholarly journals Performance Bounds For Co-/Sparse Box Constrained Signal Recovery

2019 ◽  
Vol 27 (1) ◽  
pp. 79-106
Author(s):  
Jan Kuske ◽  
Stefania Petra
Keyword(s):  
Low Complexity ◽  
Signal Recovery ◽  
Performance Bounds ◽  
Linear Measurements ◽  
Complexity Measures ◽  
Rate Dependent ◽  
Intrinsic Complexity ◽  
Convex Regularization ◽  
Signal Image ◽  
Random Measurements

Abstract The recovery of structured signals from a few linear measurements is a central point in both compressed sensing (CS) and discrete tomography. In CS the signal structure is described by means of a low complexity model e.g. co-/sparsity. The CS theory shows that any signal/image can be undersampled at a rate dependent on its intrinsic complexity. Moreover, in such undersampling regimes, the signal can be recovered by sparsity promoting convex regularization like ℓ1- or total variation (TV-) minimization. Precise relations between many low complexity measures and the sufficient number of random measurements are known for many sparsity promoting norms. However, a precise estimate of the undersampling rate for the TV seminorm is still lacking. We address this issue by: a) providing dual certificates testing uniqueness of a given cosparse signal with bounded signal values, b) approximating the undersampling rates via the statistical dimension of the TV descent cone and c) showing empirically that the provided rates also hold for tomographic measurements.

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