Lightweight Time-Series Signal Compression Period Extraction and Multiresolution Using Difference Sequences
| Autor: | Gajraj Kuldeep |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Lossless compression
Computer Networks and Communications Computer science Period estimation Computation Resource-constrained Signal compression Ramanujan sums Circular shift Internet of Things (IoT) Computer Science Applications Discrete derivatives Orthogonality Hardware and Architecture Signal Processing Linear independence Multiresolution Algorithm Energy (signal processing) Information Systems Data compression |
| Zdroj: | Kuldeep, G 2022, ' Lightweight Time-Series Signal Compression Period Extraction and Multiresolution using Difference Sequences ', IEEE Internet of Things Journal, vol. 9, no. 9, pp. 7043-7050 . https://doi.org/10.1109/JIOT.2021.3113951 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3113951 |
| Popis: | In the Internet of Things (IoT), connected devices generate a massive amount of data that need to be processed and transmitted to the data aggregator or edge device. The connected devices are resource-constrained in terms of memory, computation power, and energy. In this paper, we propose a novel transform using difference sequences. The proposed transform is multiplierless, which makes it very promising for resource-constrained IoT devices. Various properties of the difference sequences, such as orthogonality, linear independence, and circular shift, are studied in detail. These sequences are sparse and take values from the set {0,1,-1}, which make these sequences very efficient in computation. Applications of the proposed transform are shown for lossless compression, period extraction, and multiresolution using electrocardiogram, accelerometer, images, and photoplethysmography datasets. Furthermore, the proposed transform is compared with the state-of-the-art data compression transforms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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