RoSA: A Framework for Modeling Self-Awareness in Cyber-Physical Systems

The role of smart and autonomous systems is becoming vital in many areas of industry and society. Expectations from such systems continuously rise and become more ambitious: long lifetime, high reliability, high performance, energy efficiency, and adaptability, particularly in the presence of changing environments. Computational self-awareness promises a comprehensive assessment of the system state for sensible and well-informed actions and resource management. Computational self-awareness concepts can be used in many applications such as automated manufacturing plants, telecommunication systems, autonomous driving, traffic control, smart grids, and wearable health monitoring systems. Developing self-aware systems from scratch for each application is the most common practice currently, but this is highly redundant, inefficient, and uneconomic. Hence, we propose a framework that supports modeling and evaluation of various self-aware concepts in hierarchical agent systems, where agents are made up of self-aware functionalities. This paper presents the Research on Self-Awareness (RoSA) framework and its design principles. In addition, self-aware functionalities abstraction, data reliability, and confidence, which are currently provided by RoSA, are described. Potential use cases of RoSA are discussed. Capabilities of the proposed framework are showcased by case studies from the fields of healthcare and industrial monitoring. We believe that RoSA is capable of serving as a common framework for self-aware modeling and applications and thus helps researchers and engineers in exploring the vast design space of hierarchical agent-based systems with computational self-awareness

Confidence-Enhanced Early Warning Score Based on Fuzzy Logic

Cardiovascular diseases are one of the world’s major causes of loss of life. The vital signs of a patient can indicate this up to 24 hours before such an incident happens. Healthcare professionals use Early Warning Score (EWS) as a common tool in healthcare facilities to indicate the health status of a patient. However, the chance of survival of an outpatient could be increased if a mobile EWS system would monitor them during their daily activities to be able to alert in case of danger. Because of limited healthcare professional supervision of this health condition assessment, a mobile EWS system needs to have an acceptable level of reliability - even if errors occur in the monitoring setup such as noisy signals and detached sensors. In earlier works, a data reliability validation technique has been presented that gives information about the trustfulness of the calculated EWS. In this paper, we propose an EWS system enhanced with the self-aware property confidence, which is based on fuzzy logic. In our experiments, we demonstrate that - under adverse monitoring circumstances (such as noisy signals, detached sensors, and non-nominal monitoring conditions) - our proposed Self-Aware Early Warning Score (SA-EWS) system provides a more reliable EWS than an EWS system without self-aware properties.</p

Observation of a phononic quadrupole topological insulator

The modern theory of charge polarization in solids is based on a generalization of Berry’s phase. The possibility of the quantization of this phase arising from parallel transport in momentum space is essential to our understanding of systems with topological band structures. Although based on the concept of charge polarization, this same theory can also be used to characterize the Bloch bands of neutral bosonic systems such as photonic or phononic crystals. The theory of this quantized polarization has recently been extended from the dipole moment to higher multipole moments. In particular, a two-dimensional quantized quadrupole insulator is predicted to have gapped yet topological one-dimensional edge modes, which stabilize zero-dimensional in-gap corner states. However, such a state of matter has not previously been observed experimentally. Here we report measurements of a phononic quadrupole topological insulator. We experimentally characterize the bulk, edge and corner physics of a mechanical metamaterial (a material with tailored mechanical properties) and find the predicted gapped edge and in-gap corner states. We corroborate our findings by comparing the mechanical properties of a topologically non-trivial system to samples in other phases that are predicted by the quadrupole theory. These topological corner states are an important stepping stone to the experimental realization of topologically protected wave guides in higher dimensions, and thereby open up a new path for the design of metamaterials

Mobile Health Technology: From Daily Care and Pandemics to their Energy Consumption and Environmental Impact

Mobile health technology is a rapidly growing field with numerous promises to make substantial impact in our lives. To open this special issue, which brings to you many exciting research results in mobile health technology, we discuss two important aspects of this technology. One is how they can be integrated in our daily lives as important care devices, especially during periods such as the more and more frequent pandemics around the world. Having discussed their advantages, we calculate their estimated footprint in the energy consumption and dioxide carbon they produce globally. With that we raise awareness and invite researchers to work on reducing their energy consumption to ensure that they maintain a low footprint even if their numbers explodes in the near future. We finish this article with a brief teaser of the papers published in this special issue and wish you a good read

A Study on the Effects of Orally Administered Copper Sulfate on Learning ‎and Spatial Memory of Wistar Rats

BACKGROUND AND OBJECTIVE: Copper is one of the main micronutrients in the human body. Malfunction in copper homeostasis results in Menkes syndrome and Wilson’s disease, which are associated with complications such as seizure and impairments in learning and memory. Use of high copper concentrations can cause permanent damage to the cells and neurons. The aim of this study was to examine the toxic effects of orally administered copper sulfate on rats’ learning in Morris water maze. METHODS: In this experimental study, 39 Wistar rats were divided into male (n=21) and female (n=18) groups. These two groups were each randomly divided into three sub-groups. The control group received distilled water, while the other two groups were administrated 1 and 1.5 mM of copper sulfate, dissolved in distilled water for a period of one month. After this period, the Morris water maze was incorporated to evaluate the spatial memory of rats. FINDINGS: In male rats, copper sulfate, which was added to drinking water, made no significant changes in the distance traveled to find the platform (24.09%±3.01 in the control group, 26.06%±2.95 in the 1 mm copper sulfate group, and 25.68%±1.82 in the 1.5 mM copper sulfate group), the time spent to find the platform (23.93±2.87 in the control group, 25.54±3.47 in the 1 mM copper sulfate group, and 25.33±1.92 in the 1.5 mM copper sulfate group), or the swimming speed. The comparison of female groups showed that 1 and 1.5 mM concentrations of copper sulfate could not cause any significant impairments in learning of rats. CONCLUSION: The results showed that the addition of copper sulfate to drinking water have no detrimental impacts on the memory or learning of male and female rats

Wireless Mobile Communication and Healthcare : 7th International Conference, MobiHealth 2017, Vienna, Austria, November 14–15, 2017, Proceedings

Early Warning Score (EWS) systems are a common practice in hospitals. Health-care professionals use them to measure and predict amelioration or deterioration of patients’ health status. However, it is desired to monitor EWS of many patients in everyday settings and outside the hospitals as well. For portable EWS devices, which monitor patients outside a hospital, it is important to have an acceptable level of reliability. In an earlier work, we presented a self-aware modified EWS system that adaptively corrects the EWS in the case of faulty or noisy input data. In this paper, we propose an enhancement of such data reliability validation through deploying a hierarchical agent-based system that classifies data reliability but using Fuzzy logic instead of conventional Boolean values. In our experiments, we demonstrate how our reliability enhancement method can offer a more accurate and more robust EWS monitoring system.</p

A multi-channel medium access control protocol for vehicular power line communication systems

In-vehicle communications are emerging to play an important role in the continued development of reliable and efficient X-by-Wire applications in new vehicles. Since vehicle devices, sensors, and the electronic control unit (ECU) are already connected to power wires, the advancement of power line communications (PLCs) can provide a very low cost and virtually free platform for in-vehicle communications. In this paper, we propose a medium access control (MAC) protocol for vehicular PLC systems, where multiple nodes are competing for transmission over the direct current (dc) power line. The proposed protocol uses a combination of time and frequency multiplexing and consists of two key features: 1) a distributed channel selection policy to arbitrate packet transmission across different channels and provide robustness against interference and noise and 2) a distributed collision resolution algorithm to allow efficient nodes completion over selected channels. Specifically, the collision resolution algorithm is optimized with respect to the channel policy such that the success probability of transmission in each channel is maximized. Numerical results are also supplemented to validate the performance of the proposed protocol and provide useful guidelines for developing a robust contention-based MAC protocol for vehicular PLC systems

2020 33rd International Conference on VLSI Design and 2020 19th International Conference on Embedded Systems (VLSID)

Modern battery powered Embedded Systems (ES) must provide a high performance with minimal energy consumption to enhance the user experience. However, these two are often conflicting objectives. In current ES resource management techniques, user behavior and preferences are only indirectly or not at all considered. In this paper, we present a novel user- and battery-aware resource management framework for multi-processor architectures that considers these conflicting requirements and dynamic unknown workloads at run-time to maximize user satisfaction. Proposed technique learns user’s habits to dynamically adjust the resource management schemes based on the data it collects regarding user’s plug-in behavior, battery charge status, and workloads variability at run-time. This information is used to improve the balance between performance and energy consumption, and thus optimize the Quality of Experience (QoE). Our evaluation results show that our framework enhances the user experience by 22% in comparison with the existing state-of-the-art.</p