Laminated composites have so far received little attention as a potential material for gear drive applications. In the presented study, the thermomechanical performance of a newly developed type of epoxy impregnated, autoclave-cured carbon fibre-reinforced polymer gear—running in pair with a steel pinion—was analysed, using a combination of experimental and numerical approaches. The employed metho

CO2-rich planetary atmospheres are continuously exposed to ionising radiation driving major photochemical processes. In the Martian atmosphere, CO2 clusters are predicted to exist at high altitudes motivating a deeper understanding of their photochemistry. In this joint experimental-theoretical study, we investigate the photoreactions of CO2 clusters (≤2 nm) induced by soft X-ray ionisation. We ob

The cross-layer approach presented in this article involves co-designing a feedback controller’s parameters together with the schedule of an Ethernet network used for communicating state information and control signals. The temporal properties of an Ethernet network depend significantly on the schedules and routes of the data flows along switches. Researchers have studied various design-space expl

Editor's notes: Non-Nyquist sampling-based event-triggered systems can enable adaptive sampling of IoT nodes resulting in large energy savings. This article reviews introductory concepts and building blocks of non-Nyquist sampling for cardiovascular monitoring systems. It further analyzes the performance of a knowledge-based adaptive sampling strategy applied to biophysiological signals such as el

Today, many control applications in cyber-physical systems are implemented on shared platforms. Such resource sharing may lead to complex timing behaviors and, in turn, instability of control applications. This paper highlights a number of anomalies demonstrating complex timing behaviors caused as a result of resource sharing. Such anomalous scenarios, then, lead to a dramatic increase in design c

Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over methods relying on hand-crafted features. However, neural networks are susceptible to confounding artifacts commonly present in EEG signals and

Adversarial examples have received a lot of attention over the past decade, particularly with the rise of deep neural networks. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences, involving patients' lives. In this article, we consider adversarial perturbations in the context of medical an

Epilepsy affects more than 50 million people and ranks among the most common neurological diseases worldwide. Despite advances in treatment, one-third of patients still suffer from refractory epilepsy. Wearable devices for real-time patient monitoring can potentially improve the quality of life for such patients and reduce the mortality rate due to seizure-related accidents and sudden death in epi

Increasing internet connectivity poses an existential threat for cyber-physical systems. Securing these safety-critical systems becomes an important challenge. Cyber-physical systems often comprise several control applications that are implemented on shared platforms where both high and low criticality tasks execute together (to reduce cost). Such resource sharing may lead to complex timing behavi

Today, the security of wearable and mobile-health technologies represents one of the main challenges in the Internet of Things (IoT) era. Adversarial manipulation of sensitive health-related information, e.g., if such information is used for prescribing medicine, may have irreversible consequences involving patients' lives. In this article, we demonstrate the power of such adversarial attacks base

Editor's notes: This article discusses self-awareness in wearable edge devices to enable real-time and long-term health monitoring. The authors use the notion of self-awareness to improve the battery life of edge wearable sensors for multimodal health and workload monitoring. This approach leads to a 27.6% lower energy consumption with less than 6% of performance loss. - Umit Y. Ogras, Arizona Sta

Monitoring stress and, in general, emotions has attracted a lot of attention over the past few decades. Stress monitoring has many applications, including high-risk missions and surgical procedures as well as mental/emotional health monitoring. In this paper, we evaluate the possibility of stress and emotion monitoring using off-the-shelf wearable sensors. To this aim, we propose a multi-modal mac

Epilepsy is one of the most common neurological disorders and affects over 65 million people worldwide. Despite the continuing advances in anti-epileptic treatments, one third of the epilepsy patients live with drug resistant seizures. Besides, the mortality rate among epileptic patients is 2 - 3 times higher than in the matching group of the general population. Wearable devices offer a promising

Today, wearable systems are facing fundamental barriers in terms of battery lifetime and quality of their results. The main challenge in wearable systems is to increase the battery lifetime, while maintaining the machine-learning performance of the system. A recently proposed concept for overcoming this challenge is self-awareness, which increases system's knowledge of itself and the surrounding e

The integration of wearable devices in humans' daily lives has grown significantly in recent years and still continues to affect different aspects of high-quality life. Thus, ensuring the reliability of the decisions becomes essential in biomedical applications, while representing a major challenge considering battery-powered wearable technologies. Transferring the complex and energy-consuming com

Today, it is common knowledge in the cyber-physical systems domain that the tight interaction between the cyber and physical elements provides the possibility of substantially improving the performance of these systems that is otherwise impossible. On the downside, however, this tight interaction with cyber elements makes it easier for an adversary to compromise the safety of the system. This beco

Epilepsy is one of the most prevalent paroxystic neurological disorders that can dramatically degrade the quality of life and may even lead to death. Therefore, real-time epilepsy monitoring and seizure detection has become important over the past decades. In this context, wearable technologies offer a promising solution to pervasive epilepsy monitoring by removing the constraints with respect to

Event detection and classification algorithms are resilient towards aggressive resource-aware optimisations. In this paper, we leverage this characteristic in the context of smart health monitoring systems. In more detail, we study the attainable benefits resulting from tailoring Support Vector Machine (SVM) inference engines devoted to the detection of epileptic seizures from ECG-derived features

Continuous monitoring of patients suffering from cardiovascular diseases and, in particular, myocardial infarction (MI) places a considerable burden on health-care systems and government budgets. The rise of wearable devices alleviates this burden, allowing for long-term patient monitoring in ambulatory settings. One of the major challenges in this area is to design ultra-low energy wearable devic