Analog-to-Information Conversion with Random Interval Integration
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, th...
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doaj-09c32f6989914446864b103d61990ff92021-06-01T00:31:37ZengMDPI AGSensors1424-82202021-05-01213543354310.3390/s21103543Analog-to-Information Conversion with Random Interval IntegrationJán Šaliga0Ondrej Kováč1Imrich Andráš2Department of Electronics and Multimedia Communications, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 040 01 Kosice, SlovakiaDepartment of Technologies in Electronics, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 040 01 Kosice, SlovakiaDepartment of Electronics and Multimedia Communications, Faculty of Electrical Engineering and Informatics, Technical University of Kosice, 040 01 Kosice, SlovakiaA 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.https://www.mdpi.com/1424-8220/21/10/3543random interval integrationcompressed sensinganalog-to-information conversionsub-Nyquist sampling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ján Šaliga Ondrej Kováč Imrich Andráš |
spellingShingle |
Ján Šaliga Ondrej Kováč Imrich Andráš Analog-to-Information Conversion with Random Interval Integration Sensors random interval integration compressed sensing analog-to-information conversion sub-Nyquist sampling |
author_facet |
Ján Šaliga Ondrej Kováč Imrich Andráš |
author_sort |
Ján Šaliga |
title |
Analog-to-Information Conversion with Random Interval Integration |
title_short |
Analog-to-Information Conversion with Random Interval Integration |
title_full |
Analog-to-Information Conversion with Random Interval Integration |
title_fullStr |
Analog-to-Information Conversion with Random Interval Integration |
title_full_unstemmed |
Analog-to-Information Conversion with Random Interval Integration |
title_sort |
analog-to-information conversion with random interval integration |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-05-01 |
description |
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. |
topic |
random interval integration compressed sensing analog-to-information conversion sub-Nyquist sampling |
url |
https://www.mdpi.com/1424-8220/21/10/3543 |
work_keys_str_mv |
AT jansaliga analogtoinformationconversionwithrandomintervalintegration AT ondrejkovac analogtoinformationconversionwithrandomintervalintegration AT imrichandras analogtoinformationconversionwithrandomintervalintegration |
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