Prototype design of multicoset sampling based on compressed sensing

Liu, Peizhuo; Fu, Ning; Peng, Xiyuan

doi:10.1109/icemi.2015.7494524

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…However, despite practical implementation difficulties, efficient prototypes of compressive multi-coset samplers have been recently developed. 31,37 For illustration, Figure 2 shows a discrete-time model of an ideal multi-coset sampler, 30 . .…”

Section: Psbs-based Approachmentioning

confidence: 99%

“…the covariance matrix or, equivalently, the PSD matrix) through a power spectrum blind sampling (PSBS) step assuming a deterministic periodic non-uniform-in-time sampling scheme known as multi-coset sampling. [29][30][31][32] From a practical viewpoint, it is emphasized that contrary to the signal sparsity assumption made by the CS-based approach, the stationarity signal assumption of the PSBS-based approach does not limit the applicability of the method for OMA as the same assumption is made by the FDD algorithm. 1,2 However, it is recognized that the PSBS-based approach cannot retrieve, by default, the time-histories of response acceleration signals.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the method treats signals as realizations of an underlying wide-sense stationary random process and estimates second-order statistics (i.e., the covariance matrix or, 6 equivalently, the PSD matrix) through a power spectrum blind sampling (PSBS) step assuming a deterministic periodic non-uniform-in-time sampling scheme known as multi-coset sampling [29][30][31][32] .…”

Section: Introductionmentioning

confidence: 99%

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Assessment of sub-Nyquist deterministic and random data sampling techniques for operational modal analysis

Gkoktsi

Giaralis

2017

Structural Health Monitoring

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This article assesses numerically the potential of two different spectral estimation approaches supporting non-uniformin-time data sampling at sub-Nyquist average rates (i.e. below the Nyquist frequency) to reduce data transmission payloads in wireless sensor networks for operational modal analysis of civil engineering structures. This consideration relaxes transmission bandwidth constraints in wireless sensor networks and prolongs sensor battery life since wireless transmission is the most energy-hungry on-sensor operation. Both the approaches assume acquisition of sub-Nyquist structural response acceleration measurements and transmission to a base station without on-sensor processing. The response acceleration power spectral density matrix is estimated directly from the sub-Nyquist measurements, and structural mode shapes are extracted using the frequency-domain decomposition algorithm. The first approach relies on the compressive sensing theory to treat sub-Nyquist randomly sampled data assuming that the acceleration signals are sparse/compressible in the frequency domain (i.e. have a small number of Fourier coefficients with significant magnitude). The second approach is based on a power spectrum blind sampling technique considering periodic deterministic sub-Nyquist ''multi-coset'' sampling and treating the acceleration signals as wide-sense stationary stochastic processes without posing any sparsity conditions. The modal assurance criterion is adopted to quantify the quality of mode shapes derived by the two approaches at different sub-Nyquist compression rates using computer-generated signals of different sparsity and field-recorded stationary data pertaining to an overpass in Zurich, Switzerland. It is shown that for a given compression rate, the performance of the compressive sensing-based approach is detrimentally affected by signal sparsity, while the power spectrum blind sampling-based approach achieves modal assurance criterion .0.96 independently of signal sparsity for compression ratios as low as 22% the Nyquist rate. It is concluded that the power spectrum blind sampling-based approach reduces effectively data transmission requirements in wireless sensor networks for operational modal analysis, without being limited by signal sparsity and without requiring a priori assumptions or knowledge of signal sparsity.

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Section: Psbs-based Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of sub-Nyquist deterministic and random data sampling techniques for operational modal analysis

Gkoktsi

Giaralis

2017

Structural Health Monitoring

View full text Add to dashboard Cite

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“…Regarding MC-based sampling systems, the main findings show how it has gained popularity. A MC sampler prototype based on discrete integrated circuits is presented in [17], where an FPGA can sample signals with a bandwidth of 100 kHz and 10 sampling channels. In [18], the authors present an implementation of a MC-based sub-Nyquist sampling scheme for a surveillance radar with 1.1 GHz bandwidth; however, no details of the hardware implementation parameters are offered.…”

Section: Introductionmentioning

confidence: 99%

Testbed for Sub-Nyquist Wideband Spectrum Monitoring

Lee

López-Parrado

Tasamá

et al. 2019

rev.ing.univ.Medellin

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Radioelectric spectrum management is a concern for today’s world, mainly due to the misuse that has been given to this resource through the years, especially on the UHF band. To address this problem, a testbed for sub-Nyquist Wideband Spectrum Monitoring was built, that includes a web interface to remotely measure occupancy of the UHF band. To achieve the above, an RF interface that allows tuning UHF frequencies with an instantaneous bandwidth of 95 MHz was built. Afterwards, a Random Demodulator was connected, and then an embedded system performed sub--Nyquist sampling and spectrum recovery. The embedded system connected to an information system that serves a web page, through which remote users can perform UHF band monitoring. Experimental results showed that spectrum sensing can be achieved by using different algorithms on certain sparse spectra. In addition, the aforementioned web interface allowed simultaneous user connections, in order to perform independent measurements by sharing a hardware subsystem.

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A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications

Gkoktsi

Giaralis

2019

Mechanical Systems and Signal Processing

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A novel multi-sensor power spectrum blind sampling (PSBS) approach is proposed supporting low-power wireless sensor networks (WSN) for Operational Modal Analysis (OMA) applications. The developed approach relies on arrays of wireless sensors, employing deterministic non-uniform in time multi-coset sampling to acquire structural response acceleration signals at sub-Nyquist sampling rates, treated as realizations of stationary random processes without making any assumption about the average signal frequency content and spectral support. The acquired compressed measurements are transmitted to a central server and collectively processed via a PSBS technique, herein extended to the multi-sensor case, to estimate the power spectral density matrix of an underlying spatially correlated stationary response acceleration random process directly from the compressed measurements. Structural modal properties are then extracted through standard frequency domain decomposition (FDD). The efficacy of the proposed approach to resolve closelyspaced modes is numerically tested for various data compression levels using noisy response acceleration signals of a white-noise excited finite element model of a space truss as well as fieldrecorded acceleration time-histories of an instrumented bridge under operational loading. It is shown that accurate mode shapes based on the modal assurance criterion can be obtained from as low as 89% less measurements compared to conventional non-compressive FDD at Nyquist sampling rate. Further, significant gains in energy consumption and battery lifetime prolongation of the order of years are estimated, assuming wireless sensors operating on multi-coset sampling at different data compression levels. It is, therefore, concluded that the proposed PSBS approach could provide long-term structural health monitoring systems with low-maintenance cost once wireless sensors with multi-coset sampling capabilities become commercially available.

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Prototype design of multicoset sampling based on compressed sensing

Cited by 4 publications

References 16 publications

Assessment of sub-Nyquist deterministic and random data sampling techniques for operational modal analysis

Assessment of sub-Nyquist deterministic and random data sampling techniques for operational modal analysis

Testbed for Sub-Nyquist Wideband Spectrum Monitoring

A multi-sensor sub-Nyquist power spectrum blind sampling approach for low-power wireless sensors in operational modal analysis applications

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