A Structurally Regularized CNN Architecture via Adaptive Subband Decomposition.

We propose a generalized convolutional neural network (CNN) architecture that first decomposes the input signal into subbands by an adaptive filter bank structure, and then uses convolutional layers to extract features from each subband independently. Fully connected (FC) layers finally combine the extracted features to perform classification. The proposed architecture restrains each of the subband CNNs from learning using the entire input signal spectrum, resulting in structural regularization.

Systematic Review of IoT-Based Solutions for User Tracking: Towards Smarter Lifestyle, Wellness and Health Management.

The Internet of Things (IoT) base has grown to over 20 billion devices currently operational worldwide. As they greatly extend the applicability and use of biosensors, IoT developments are transformative. Recent studies show that IoT, coupled with advanced communication frameworks, such as machine-to-machine (M2M) interactions, can lead to (1) improved efficiency in data exchange, (2) accurate and timely health monitoring, and (3) enhanced user engagement and compliance through advancements in human-computer interaction.

Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange.

In an era dominated by rapid digitalization of sensed data, the secure exchange of sensitive information poses a critical challenge across various sectors. Established techniques, particularly in emerging technologies like the Internet of Things (IoT), grapple with inherent risks in ensuring data confidentiality, integrity, and vulnerabilities to evolving cyber threats. Blockchain technology, known for its decentralized and tamper-resistant characteristics, stands as a reliable solution for secure data exchange.

Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring.

This paper explores the use of low earth orbit (LEO) satellite links in long-term monitoring of water levels across remote areas. Emerging sparse LEO satellite constellations maintain sporadic connection to the ground station, and transmissions need to be scheduled for satellite overfly periods. For remote sensing, the energy consumption optimization is critical, and we develop a learning approach for scheduling the transmission times from the sensors.

An Ultrasound-Based Biomedical System for Continuous Cardiopulmonary Monitoring: A Single Sensor for Multiple Information.

Biomedical wearable sensors enable long-term monitoring applications and provide instantaneous diagnostic capabilities. Physiological monitoring can help in both the diagnosis and the ongoing treatment of a vast number of cardiovascular and pulmonary diseases such as hypertension, dysrhythmia, and asthma. In this paper, we present a system capable of monitoring several vital signals and physiological variables that determine the cardiopulmonary activity status.

A Software Defined Radio Evaluation Platform for WBAN Systems.

In recent years, the Wireless Body Area Network (WBAN) concept has attracted significant academic and industrial attention. WBAN specifies a network dedicated to collecting personal biomedical data from advanced sensors that are then used for health and lifestyle purposes. In 2012, the 802.

Ultrasound Sensors for Diaphragm Motion Tracking: An Application in Non-Invasive Respiratory Monitoring.

This paper introduces a novel respiratory detection system based on diaphragm wall motion tracking using an embedded ultrasound sensory system. We assess the utility and accuracy of this method in evaluating the function of the diaphragm and its contribution to respiratory workload. The developed system is able to monitor the diaphragm wall activity when the sensor is placed in the zone of apposition (ZOA).

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition.

Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study.

Respiration Disorders Classification With Informative Features for m-Health Applications.

Respiratory disorder is a highly prevalent condition associated with many adverse health problems. As the current means of diagnosis are obtrusive and ill-suited for real-time m-health applications, we explore a convenient and low-cost automatic approach that uses wearable microelectromechanical system sensor technology. The proposed system introduces the use of motion sensors to detect the changes in the anterior-posterior diameter of the chest wall during breathing function as well as extracting the informative respiratory features to be used for breathing disorders classification.

Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications.

A reliable long-term monitoring and diagnosis of breath disorders at an early stage provides an improvement of medical act, life expectancy, and quality of life while decreasing the costs of treatment and medical services. Therefore, a real-time unobtrusive monitoring of respiration patterns, as well as breath parameters, is a critical need in medical applications. In this paper, we propose an intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology.

A medical cloud-based platform for respiration rate measurement and hierarchical classification of breath disorders.

The measurement of human respiratory signals is crucial in cyberbiological systems. A disordered breathing pattern can be the first symptom of different physiological, mechanical, or psychological dysfunctions. Therefore, a real-time monitoring of the respiration patterns, as well as respiration rate is a critical need in medical applications.