Compressed sensing with continuous parametric reconstruction

Andráš, Imrich; Michaeli, L.; Šaliga, Ján

doi:10.11591/ijece.v11i1.pp851-862

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…An update of the firmware is used in this model with a 12 bit resolution of the sensors. Thus, the authors claim that they could reduce the power consumption of the sensors by 60% [24,25].…”

Section: Related Workmentioning

confidence: 99%

Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems

Amaya

Inga

2022

Sensors

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The present work proposes to locate harmonic frequencies that distort the fundamental voltage and current waves in electrical systems using the compressed sensing (CS) technique. With the compressed sensing algorithm, data compression is revolutionized, a few samples are taken randomly, a measurement matrix is formed, and according to a linear transformation, the signal is taken from the time domain to the frequency domain in a compressed form. Then, the inverse linear transformation is used to reconstruct the signal with a few sensed samples of an electrical signal. Therefore, to demonstrate the benefits of CS in the detection of harmonics in the electrical network of this work, power quality analyzer equipment (commercial) is used. It measures the current of a nonlinear load and issues its results of harmonic current distortion (THD-I) on its screen and the number of harmonics detected in the network; this equipment acquires the data based on the Shannon–Nyquist theorem taken as a standard of measurement. At the same time, an electronic prototype senses the current signal of the nonlinear load. The prototype takes data from the current signal of the nonlinear load randomly and incoherently, so it takes fewer samples than the power quality analyzer equipment used as a measurement standard. The data taken by the prototype are entered into the Matlab software via USB, and the CS algorithm run and delivers, as a result, the harmonic distortions of the current signal THD-I and the number of harmonics. The results obtained with the compressed sensing algorithm versus the standard measurement equipment are analyzed, the error is calculated, and the number of samples taken by the standard equipment and the prototype, the machine time, and the maximum sampling frequency are analyzed.

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Section: Related Workmentioning

confidence: 99%

Compressed Sensing Technique for the Localization of Harmonic Distortions in Electrical Power Systems

Amaya

Inga

2022

Sensors

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“…Options in such cases are virtually limitless, as witnessed by a large number of publications [4][5][6][7][8][9][10][11][12] concerning various applications. On the other hand, when the acquisition of an analog signal has to be performed by physical circuitry, the conditions are more challenging [13]. The AIC circuitry should be as simple as possible, and it has to be describable with sufficient precision by a known matrix.…”

Section: Introductionmentioning

confidence: 99%

Analog-to-Information Conversion with Random Interval Integration

Šaliga

Kováč

Andráš

2021

Sensors

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A novel method of analog-to-information conversion—the random interval integration—is proposed and studied in this paper. This method is intended primarily for compressed sensing of aperiodic or quasiperiodic signals acquired by commonly used sensors such as ECG, environmental, and other sensors, the output of which can be modeled by multi-harmonic signals. The main idea of the method is based on input signal integration by a randomly resettable integrator before the AD conversion. The integrator’s reset is controlled by a random sequence generator. The signal reconstruction employs a commonly used algorithm based on the minimalization of a distance norm between the original measurement vector and vector calculated from the reconstructed signal. The signal reconstruction is performed by solving an overdetermined problem, which is considered a state-of-the-art approach. The notable advantage of random interval integration is simple hardware implementation with commonly used components. The performance of the proposed method was evaluated using ECG signals from the MIT-BIH database, multi-sine, and own database of environmental test signals. The proposed method performance is compared to commonly used analog-to-information conversion methods: random sampling, random demodulation, and random modulation pre-integration. A comparison of the mentioned methods is performed by simulation in LabVIEW software. The achieved results suggest that the random interval integration outperforms other single-channel architectures. In certain situations, it can reach the performance of a much-more complex, but commonly used random modulation pre-integrator.

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