Search

Compressive sensing (CS) is recognized for its adeptness at compressing signals, making it a pivotal technology in the context of sensor data acquisition. With the proliferation of image data in Internet of Things (IoT) systems, CS is expected to reduce the transmission cost of signals captured by various sensor devices. However, the quality of CS-reconstructed signals inevitably degrades as the sampling rate decreases, which poses a challenge in terms of the inference accuracy in downstream computer vision (CV) tasks. This limitation imposes an obstacle to the real-world application of existing CS techniques, especially for reducing transmission costs in sensor-rich environments. In response to this challenge, this paper contributes a CV-oriented adaptive CS framework based on saliency detection to the field of sensing technology that enables sensor systems to intelligently prioritize and transmit the most relevant data. Unlike existing CS techniques, the proposal prioritizes the accuracy of reconstructed images for CV purposes, not only for visual quality. The primary objective of this proposal is to enhance the preservation of information critical for CV tasks while optimizing the utilization of sensor data. This work conducts experiments on various realistic scenario datasets collected by real sensor devices. Experimental results demonstrate superior performance compared to existing CS sampling techniques across the STL10, Intel, and Imagenette datasets for classification and KITTI for object detection. Compared with the baseline uniform sampling technique, the average classification accuracy shows a maximum improvement of 26.23%, 11.69%, and 18.25%, respectively, at specific sampling rates. In addition, even at very low sampling rates, the proposal is demonstrated to be robust in terms of classification and detection as compared to state-of-the-art CS techniques. This ensures essential information for CV tasks is retained, improving the efficacy of sensor-based data acquisition systems. [ABSTRACT FROM AUTHOR]

This letter proposes an online energy management framework for smart buildings. Two low-complexity estimators for thermal comfort and electrical load are investigated and integrated into an optimization framework. The proposed framework optimizes operations of a battery system and multiple heating, ventilation, and air conditioning (HVAC) systems to minimize energy consumption and power peak while maximizing occupants’ thermal comfort. The effectiveness of the proposed framework is demonstrated using data measured in actual campus buildings in terms of system costs, thermal comfort, and computational complexity. The results show that the proposed framework can reduce electricity costs by 5.7% compared to the baseline. [ABSTRACT FROM AUTHOR]

This document discusses the development of intelligent sensor nodes for Internet of Things (IoT) applications. These nodes use embedded signal processing and machine learning algorithms to process data on board and transmit key information instead of raw sensing data, reducing data volume. The document focuses on image and video coding and processing techniques that can be implemented in low-cost sensor nodes to improve data compression and reconstruction quality. It also presents algorithms, hardware designs, and deep learning-based strategies for intelligent image sensors. The document concludes by expressing gratitude to the authors for their contributions and hopes that this collection will inspire further advancements in the field. [Extracted from the article]