Linear Interval Approximation of Sensor Characteristics with Inflection Points
The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
Sensors (Basel, Switzerland) - 23(2023), 6 vom: 08. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Marinov, Marin B [VerfasserIn] |
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| Links: |
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| Themen: |
Approximation |
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| Anmerkungen: |
Date Completed 30.03.2023 Date Revised 01.04.2023 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.3390/s23062933 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM354965832 |
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| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Internet of Things | |
| 650 | 4 | |a approximation | |
| 650 | 4 | |a graphical programming | |
| 650 | 4 | |a linearization techniques | |
| 650 | 4 | |a measurement errors | |
| 650 | 4 | |a sensor accuracy | |
| 650 | 4 | |a smart sensors | |
| 650 | 4 | |a thermocouples | |
| 700 | 1 | |a Nikolov, Nikolay |e verfasserin |4 aut | |
| 700 | 1 | |a Dimitrov, Slav |e verfasserin |4 aut | |
| 700 | 1 | |a Ganev, Borislav |e verfasserin |4 aut | |
| 700 | 1 | |a Nikolov, Georgi T |e verfasserin |4 aut | |
| 700 | 1 | |a Stoyanova, Yana |e verfasserin |4 aut | |
| 700 | 1 | |a Todorov, Todor |e verfasserin |4 aut | |
| 700 | 1 | |a Kochev, Lachezar |e verfasserin |4 aut | |
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