Titlebook: Optimization of Spiking Neural Networks for Radar Applications; Muhammad Arsalan Book 2024 The Editor(s) (if applicable) and The Author(s)

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Book 2024g energy consumption through strategies such as tiny networks, pruning approaches, and weight quantization. Additionally, the book sheds light on the challenges posed by the physical size of AI accelerators for edge devices. The central focus of the book is an in-depth examination of SNNs‘ capabilit

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on reducing energy consumption through strategies such as tiny networks, pruning approaches, and weight quantization. Additionally, the book sheds light on the challenges posed by the physical size of AI accelerators for edge devices. The central focus of the book is an in-depth examination of SNNs‘ capabilit978-3-658-45317-6978-3-658-45318-3

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978-3-658-45317-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Fachmedien Wies

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Conclusion and Future Directions,dge computing. The study primarily considers consumer radar as an application area, with a specific use case of gesture sensing and air-writing. To generalize the scope of this dissertation, the Fitbit tracker application is also utilized, covering a use case of time series forecasting for health data.

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Time Series Forecasting of Healthcare Data,The use of health tracking devices has grown significantly over the last 30 years [135]. These gadgets are utilized by clinicians to monitor the health of their patients and by individuals for self-tracking purposes.

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Radar-based Air-writing for Embedded Devices,ition, air-writing involves varying directions, speeds, and styles, making it more challenging to recognize. AW has been implemented using different types of sensors, and the systems can be categorized based on the sensor used.