Abstract

Abstract
We spend about one-third of our life either sleeping or attempting to do so. Sleeping is a key aspect for most human body processes, affecting physical and mental health and the ability to fight diseases, develop immunity and control metabolism. Therefore, monitoring human sleep quality is extremely important for the detection of possible sleep disorders. Several technologies exist to achieve this goal, however, most of them are expensive proprietary systems, some require hospitalization and many use intrusive equipment that can, by itself, affect sleep quality. This paper presents an intelligent system, a complete low-cost hardware and software solution, for monitoring the sleep quality of an individual in a home environment. User privacy is guaranteed as all processing is done at the edge and no audio or video is stored. This system monitors several fundamental aspects of sleeping periods in real-time using a low cost single-board computer for processing, a camera for body motion detection (MD module) and for eye/sleep status detection (SSD module), and a microphone for audio recognition (AUDR module) of breath pattern analysis and snore detection. It can be strategically placed near the bed to avoid interfering with the natural sleep pattern. For each sleeping period, the system produces a final report that can be a valuable aid for improving the sleeping health of the monitored person. Functional unitary tests were carried successfully on the selected, low-cost, hardware platform (Raspberry Pi). The entire process was validated by an expert clinical psychologist, ensuring the reliability and effectiveness of the system. The visual and sound modules use sophisticated computer vision and machine learning techniques suitable for edge computing devices. Each of the system’s features have been independently tested, using properly organized audio and video datasets and the well established metrics of precision, recall and F1 score, to evaluate the binary classifiers in each of the three modules. The accuracy values obtained where 90.2% (MD), 79.1% (SSD) and 81.3% (AUDR), demonstrating the great application potential of our solution. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

Abstract
The exploitation of radio channels' inherent randomness for generating secret keys within a vehicular platoon offers a promising approach to securing communications in dynamic and unpredictable environments. The channel-based key generation leverages the fact that the physical characteristics of the radio channel, such as fading, shadowing, and multipath propagation, vary in a complex manner that makes it difficult for external adversaries to predict or replicate. A challenge lies in accurately assessing the channel's randomness to ensure the generated keys are both secure and consistent across the platooning vehicles, especially in vehicular environments with high mobility and the ever-changing urban landscape. This paper proposes a novel channel-based key generation (DRL-KeyAgree) technique to enhance communication security within vehicular platoons through combinatorial deep reinforcement learning (DRL). DRL-KeyAgree addresses key disagreement among platooning vehicles by training advantage Actor-Critic (A2C), which integrates policy-and value-based strategies to dynamically select optimal quantization intervals adapting to the random wireless channels. Further incorporation of Long Short-Term Memory (LSTM) allows DRL-KeyAgree to capture the characteristics of partially observable radio channels, significantly enhancing the key agreement rate among vehicles. DRL-KeyAgree is rigorously evaluated using the standard National Institute of Standards and Technology (NIST) test suite.

Abstract
This paper presents SpecRF-Posture, a novel low-cost approach for accurate Human Posture Recognition (HPR) using Radio Frequency (RF) signals. SpecRF-Posture leverages S21 parameters within the WiFi-6E frequency range for classification. We obtain a dataset of S21 parameters for different postures by performing beamscanning through mechanical rotation of a horn transmitter aimed at a reflective surface that illuminates the space of interest. We determine the S21 parameters of the signals that are then reflected back from the space onto an omni-directional receiver. Thus for each posture we attain the S21 parameters of each possible illumination direction of the space. Experimental results demonstrate that SpecRF-Posture achieves an accuracy of 99.17% in posture classification, highlighting its effectiveness. Additionally, an RF dataset was acquired using a software package for automatic data acquisition within the WiFi-6E frequency range, and both the dataset and the software package have been made publicly available.

Abstract
This paper introduces an advanced acoustic imaging system leveraging multibeam water column data at various frequencies to detect and classify marine litter. This study encompasses (i) the acquisition of test tank data for diverse types of marine litter at multiple acoustic frequencies; (ii) the creation of a comprehensive acoustic image dataset with meticulous labelling and formatting; (iii) the implementation of sophisticated classification algorithms, namely support vector machine (SVM) and convolutional neural network (CNN), alongside cutting-edge detection algorithms based on transfer learning, including single-shot multibox detector (SSD) and You Only Look once (YOLO), specifically YOLOv8. The findings reveal discrimination between different classes of marine litter across the implemented algorithms for both detection and classification. Furthermore, cross-frequency studies were conducted to assess model generalisation, evaluating the performance of models trained on one acoustic frequency when tested with acoustic images based on different frequencies. This approach underscores the potential of multibeam data in the detection and classification of marine litter in the water column, paving the way for developing novel research methods in real-life environments.

Abstract
[No abstract available]