Signal Processing
The data utilized the IWR1642 FMCW radar and the DCA1000EVM data acquisition board from Texas Instruments. Three different environments—bedroom, shared office, and unoccupied conference room—were selected as potential scenarios for non-contact vital sign monitoring. Continuous monitoring was conducted at distances of 0.5, 1, 1.5, and 2m for 120s. The results were calculated using a 30-second data window, with 91 calculations performed during each monitoring session, starting from the 30th second and continuing until the 120th second.
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75 Views
This code accompanies the paper titled "An End-to-End Modular Framework for Radar Signal Processing: A Simulation-Based Tutorial," published in IEEE Aerospace and Electronics Systems Magazine (DOI:10.1109/MAES.2023.3334689). The simulation of a complete radar has been performed, and the results have been shown after each stage/module of a radar, enabling the reader to appreciate the impact of the specific process on the radar signal.
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163 Views
Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. We fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor.
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426 Views
Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. We recorded a dataset with our own sensor station at a German highway with eight interference classes and three non-interference classes.
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164 Views
Production quality control is an issue of great importance in the industry. Generating defective products leads to wasted time and money. For this reason, we have attempted to develop a production control system using computational artificial intelligence methods. The system, in its current version, has been developed and tested, using the example of controlling the operation of an injection moulding machine producing plastic elements.
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146 Views
Multimodal sensor fusion has been widely adopted in constructing scene understanding, perception, and planning for intelligent robotic systems. One of the critical tasks in this field is geospatial tracking, i.e., constantly detecting and locating objects moving across a scene. Successful development of multimodal sensor fusion tracking algorithms relies on large multimodal datasets where common modalities exist and are time-aligned, and such datasets are not readily available.
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676 Views
This dataset originates from a longitudinal study examining the factors contributing to the progression of cardiovascular disease. P This particular research employs the unprocessed sequential actigraph recordings collected from an actigraph device. We evaluate sleep quality based on the two indicators as proposed in our previous study [3] which are weekly sleep quality ‘SleepQualWeek’, and sleep consistency ‘SleepCons’. SleepQualWeek and SleepCons are calculated using the pre-processed attribute set derived from the MESA dataset.
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169 Views
The breath rate (BR), heart rate (HR), breathing-breathing interval (BBI) and heart rate variability (HRV) are the critical vital sign parameters. In this article, a novel method named adaptive separation variational mode extraction algorithm (ASVME) is proposed to accurately monitor multi-variable vital signs (MVVS) at the same time with a frequency-modulated continuous wave (FMCW) radar system in practical scenarios.
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121 Views
The breath rate (BR), heart rate (HR), breathing-breathing interval (BBI) and heart rate variability (HRV) are the critical vital sign parameters. In this article, a novel method named adaptive separation variational mode extraction algorithm (ASVME) is proposed to accurately monitor multi-variable vital signs (MVVS) at the same time with a frequency-modulated continuous wave (FMCW) radar system in practical scenarios.
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34 Views
The dataset was collected in the actual outdoor environment of Dalian, China, with specific areas carefully selected to encompass a diverse range of environmental characteristics. These environments are extensive, including not only the urban center with high-rise office buildings and high population density but also suburban areas with scattered low-rise buildings and relative tranquility; additionally, they encompass major urban arteries with constant traffic flow and urban side streets with lighter traffic.
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332 Views