Low-complexity soft ML detection for generalized spatial modulation

Simarro, M. Ángeles; Garcia-Mollá, Víctor M.; Martínez-Zaldívar, Francisco-Jose; González, Alberto

doi:10.1016/j.sigpro.2022.108509

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Data Pre-processing Module1

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“…The module is paramount for temporal sensor data (especially energy data) since the data are collected from heterogeneous data sources (sensors, databases, etc.) [38,39].…”

Section: Data Pre-processing Modulementioning

confidence: 99%

TSxtend: A Tool for Batch Analysis of Temporal Sensor Data

2023

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Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

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“…The module is paramount for temporal sensor data (especially energy data) since the data are collected from heterogeneous data sources (sensors, databases, etc.) [38,39].…”

Section: Data Pre-processing Modulementioning

confidence: 99%