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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Perez, Dailys Arronde Schoeffmann, Christian Zangl, Hubert Univ Klagenfurt Inst Smart Syst Technol Sensors & Actuators Klagenfurt Austria Silicon Austria Labs AAU SAL USE Lab Graz Austria

ISBN: (纸本)9781665483605

The reconstruction of frequency-sparse signals from a low number of samples using a compressed sensing concept is a widely studied problem and solutions based on greedy and relaxation methods are available. However, their performance is degraded in presence of spectral leakage, which affects the sparsity of the signal representation and consequently, its estimation accuracy. In this paper a two-stage optimization approach, called Opti2, is proposed for the reconstruction of periodic signals that can be expressed in terms of fundamental frequency and harmonics. The estimation provided by basis pursuit denoising (BPDN) sparse optimization approach is computed in the first stage and used as initial guess for the second stage, where a constrained non-linear optimization problem is solved in an iterative fashion, aiming to improve the estimation of the signal parameters. The evaluation of the proposed method with simulated and experimental data demonstrates that it outperforms existing approaches in term of accuracy, showing its robustness to noise, compression rate and dictionary refinement factor.

关键词： periodic signals compressive sampling recovery algorithm frequency sparse spectral leakage

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On the Reconstruction of Multiple Sinusoidal Signals from Compressed Measurements 30

On the Reconstruction of Multiple Sinusoidal Signals from Co...

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30th European Signal Processing Conference (EUSIPCO)

作者： Perez, Dailys Arronde Zangl, Hubert Univ Klagenfurt Inst Smart Syst Technol Sensors & Actuators Klagenfurt Austria AAU SAL USE Lab Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9789082797091

The introduction of compressive sensing in wireless smart transducers can substantially reduce the high impact of sampling rate on their overall power consumption. Such systems are often dealing with signals that can be expressed as a sum of multiple sinusoids, having a frequency-sparse representation. Although the reconstruction of frequency-sparse signals has been widely studied and solutions based on greedy and relaxation methods exist, their performance is degraded in presence of spectral leakage, which affects the sparse representation of the signal and consequently, its estimation accuracy. In this paper, a two-stage optimization approach, named Opti2, is presented for the reconstruction of frequency-sparse signals that can be expressed as a sum of multiple real-valued sinusoidal waveforms. The estimation provided by basis pursuit denoising (BPDN) sparse optimization is computed in the first stage and used as initial guess for the second stage, where a non-linear least squares (NLLS) problem is formulated to improve the estimation of the signal parameters from undersampled data. Simulation results demonstrate that the proposed approach outperforms existing methods in terms of accuracy, showing its robustness to noise and compression rate.

关键词： compressive sampling frequency-sparse signals multiple sinusoids recovery algorithm optimization spectral leakage

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Construction of Wideband Radio Environment Map Based on Compressed Sensing 14

Construction of Wideband Radio Environment Map Based on Comp...

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14th IEEE International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Yang, Ensong Yu, Hui Mo, Xiaohao Gao, Ao Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781665450850

The ever-growing demand for wireless communication has led to an increasingly complex radio environment, which has caused the construction of wideband radio environment map (WREM) as an effective means to manage and utilize spectrum resources. In this paper, we investigate the low-complexity method for building a high-accuracy WREM. By jointly exploiting the sparsity in spatial and frequency domains, we transform the WREM construction problem into a compressed sensing (CS) problem to reduce the construction complexity and resource consumption. Then we propose a spatial sampling points (SSP) optimization algorithm based on reducing columns correlation to improve the recovery accuracy. Inspired by the quasi-block sparse structure of the vector to be recovered, we propose a quasi-block sparse block matching pursuit (QBSBMP) algorithm to recover the locations and power spectral density (PSD) of all transmitting sources. Simulation results show that the proposed scheme significantly reduces the amount of sampled data and achieves the high-precision of constructing WREM.

关键词： WREM compressed sensing sampling optimization recovery algorithm

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Compressive spectrum sensing for 5G cognitive radio networks - LASSO approach

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HELIYON 2022年第6期8卷 e09621页

作者： Koteeshwari, R. S. Malarkodi, B. Natl Inst Technol Dept Elect & Commun Engn Trichy 620015 India EGS Pillay Engn Coll Dept Elect & Commun Engn Nagapattinam 611002 India

In recent years, the importance of Artificial Intelligence is inevitable for effective performance in communication area. The progressing in standards from beyond 5G networks are compatible gadgets for incorporate wireless communication. Cognitive radio (CR) is a sensible and advanced scientific communication that can effectively handle the radio spectrum applications. Spectrum sensing (SR) is the primary role in CR. In SR, various Wide Band techniques suited for 5G were investigated in this paper. (Least Absolute Shrinkage and Selection Operator) LASSO is the suitable choice for communication in compressive sensing and recovery in wideband 5G networks. The obtained results were correlated with recent report. Further, the relative merit and demerits are discussed significantly.

关键词： 5G networks Compressed sensing recovery algorithm LASSO

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Analysis of three dimensional recovery algorithms' influence on the ranging accuracy of Gm-APD Ladar 10

Analysis of three dimensional recovery algorithms' influence...

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10th International Conference on Information Optics and Photonics

作者： Gao, Shang Sun, Jianfeng Jiang, Peng Liu, Di Zhou, Xin Fu, Hongming Wang, Penghui Harbin Inst Technol Natl Key Lab Sci & Technol Tunable Laser Inst Opto Elect Harbin 150001 Heilongjiang Peoples R China Beijing Electro Mech Engn Inst Beijing 100074 Peoples R China China Airborne Missile Acad Luoyang 471009 Peoples R China

ISBN: (纸本)9781510625808

Geiger-mode Avalanche Photo Diode(Gm-APD) Ladar is a probabilistic device outputting three dimensional(3D) images based on the multi-frame imaging statistics, which makes the 3D recovery algorithm one of the key techniques of imaging system. Besides, performance of algorithm plays a crucial role in improving recovery quality of 3D images. This paper researches three 3D algorithms based on histograms, containing peak-selecting algorithm, range-selecting algorithm and Gaussian fitting algorithm. Firstly, the triggered model of Gm-APD is analyzed based on the work timing sequence and imaging theory of Gm-APD Ladar. Meanwhile, the recovery principles of three algorithms are analyzed and clarified. Secondly, two evaluation criterions, average range error and accuracy of range recovery, are raised to evaluate range accuracy. Finally, range images are obtained with above three algorithms in statistics of different frames, based on original data obtained from 64 x 64 Gm-APD Ladar imaging experiment. With the three construction algorithms, the result shows that the range accuracy of recovery range images improves and converges to 0.2 similar to 0.3m with the increment of number of frames participating in the statistics, and the accuracy of range recovery can be up to 90%. In low frame numbers, the range accuracy of recovery range profile is worst with peak-selecting algorithm, and the average range error with range-selecting algorithm performs best while accuracy of range recovery with Gaussian fitting algorithm is highest among all algorithms. The result has important guiding significance for the choice of recovery algorithm under different requests.

关键词： Geiger-mode Avalanche Photo Diode Ladar recovery algorithm average range error accuracy of range recovery

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DSESP: Dual sparsity estimation subspace pursuit for the compressive sensing based close-loop ecg monitoring structure

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2019年第5期12卷 1311-1322页

作者： Yu, Wenbin Chen, Cailian Liu, Zhe Yang, Bo Guan, Xinping Shanghai Jiao Tong Univ Dept Automat Minist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China

Compressive Sensing based ECG collecting system is an effective way for long time heart disease monitoring because of its precision as well as energy efficiency. Traditional Compressive Sensing methods are open-loop and the coordinator cannot obtain the recovery quality, which will impact the diagnosis results based on the recovered data. In this paper, a novel close-loop structure is designed for wireless ECG monitoring, which can maintain the recovery quality and the energy efficiency at a high level. Moreover, an improved Subspace Pursuit recovery method is designed to optimize the recovery quality. Simulations are made to prove that the recovery method is more efficient than most state-of-art recovery algorithm. Finally, an experimental testbed is built for evaluate the whole monitoring system. The results show that the recovery error can be controlled within the medical request while the energy efficiency can be improved by 12%.

关键词： ECG monitoring Compressive sensing recovery algorithm recovery quality

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A novel recovery algorithm of time encoded signals

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BMC Neuroscience 2013年第1期14卷 1-2页

作者： Dorian Florescu Daniel Coca Department of Automatic Control and Systems Engineering University of Sheffield Sheffield South Yorkshire S1 3JD UK

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Comparison on Binary MDS Array Codes for Single Disk Failure recovery 18

Comparison on Binary MDS Array Codes for Single Disk Failure...

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2nd International Conference on Computer Science and Artificial Intelligence (CSAI) / 10th International Conference on Information and Multimedia Technology (ICIMT)

作者： Ze, Junrong Hou, Hanxu Han, Yunghsiang S. Shenzhen Univ Sch Informat Engn Shenzhen Peoples R China Dongguan Univ Technol Sch Elect Engn & Intelligentizat Dongguan Peoples R China

ISBN: (纸本)9781450366069

Binary Maximum Distance Separable (MDS) array codes are widely used in storage systems such as RAID systems that can provide fault tolerance with minimum storage and low computational complexity. As data loss is normal in storage system, especially in large-scale distributed storage systems, recent studies have been focused on repair performance. This paper implements a class of binary MDS array codes of which the amount of network bandwidth required in repairing a failure is much less than that of the existing repair methods with three parity columns. We conduct experiments and present the performance comparison with other existing works. We show that the implemented binary MDS array code can save 40% repair bandwidth compared to Cauchy Reed-Solomon codes. Compared to the existing repair method X-code, the implemented binary MDS array code can save 20% repair time and 20% the amount of network bandwidth required in a repair process, respectively.

关键词： Binary array code single disk failure contrast experiment repair bandwidth data availability recovery algorithm

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A High-Resolution Joint Angle-Doppler Estimation Sub-Nyquist Radar Approach Based on Matrix Completion

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INFORMATION 2019年第4期10卷 124页

作者： Wang, Quanhui Sun, Ying Lingnan Normal Univ Sch Informat Engn Zhanjiang 524000 Peoples R China Huawei Technol Co Ltd Shenzhen 518000 Peoples R China

In order to reduce power consumption and save storage capacity, we propose a high-resolution sub-Nyquist radar approach based on matrix completion (MC), termed as single-channel sub-Nyquist-MC radars. While providing the high-resolution joint angle-Doppler estimation, this proposed radar approach minimizes the number of samples in all three dimensions, that is, the range dimension, the pulse dimension (also named temporal dimension), and the spatial dimension. In range dimension, we use a single-channel analog-to-information converter (AIC) to reduce the number of range samples to one;in both spatial and temporal dimensions, we employ a bank of random switch units to regulate the AICs, which greatly reduce the number of spatial-temporal samples. According to the proposed sampling scheme, the samples in digital processing center forwarded by M receive nodes and N pulses are only a subset of the full matrix of size M times N. Under certain conditions and with the knowledge of the sampling scheme, the full matrix can be perfectly recovered by using MC techniques. Based on the recovered full matrix, this paper addresses the problem of the high-resolution joint angle-Doppler estimation by employing compressed sensing (CS) techniques. The properties and performance of the proposed approach are demonstrated via simulations.

关键词： compressed sensing radar sub-Nyquist sampling matrix completion array signal processing recovery algorithm

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Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation algorithm 14

Cyclic Spectrum Estimation Under Compressive Sensing by the ...

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14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Gao, Yulong Wang, Song Chen, Yanping Wei, Yuming Harbin Inst Technol Commun Res Ctr Harbin 150080 Heilongjiang Peoples R China Harbin Univ Commerce Sch Comp & Informat Engn Harbin 150028 Heilongjiang Peoples R China

ISBN: (纸本)9781538620700

Cyclic spectrum is a basic tool to study the cyclostationary of signal. It has the advantages of high resolution, anti-interference ability and low channel environment sensitivity. These properties are beneficial to process digital modulated signals. There are two time-smoothed algorithms: the FFT Accumulation Method (FAM) and the Strip Spectral Correlation algorithm (SSCA). SSCA is suitable for fast estimation and engineering realizable, while FAM has shortcoming of complex computation. But both methods need high sampling rate that requires high performance analog-to-digital converter. To improve the efficiency of cyclic spectrum estimation, we propose CS-SSCA algorithm in the framework of Compressive Sensing (CS). The cyclic spectrum can be recovered via compressed samples by SSCA. The theoretical analysis and simulation results prove that the improved algorithm displays better performance than CS-FAM under the same conditions.

关键词： compressive Sensing cyclic spectrum density recovery algorithm

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