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WiFi Channel State Information (CSI)-based human action recognition using convolutional neural networks (CNNs) has emerged as a promising approach for non-intrusive activity monitoring. However, the integrity and reliability of the reported performance metrics are susceptible to data leakage, wherein information from the test set inadvertently influences the training process, leading to inflated accuracy rates. In this paper, we conduct a critical analysis of a notable IEEE Sensors Journal study on WiFi CSI-based human action recognition, uncovering instances of data leakage resulting from the absence of subject-based data partitioning. Empirical investigation corroborates the lack of exclusivity of individuals across dataset partitions, underscoring the importance of rigorous data management practices. Furthermore, we demonstrate that employing data partitioning with respect to humans results in significantly lower precision rates than the reported 99.9% precision, highlighting the exaggerated nature of the original findings. Such inflated results could potentially discourage other researchers and impede progress in the field by fostering a sense of complacency. [ABSTRACT FROM AUTHOR]

Bridges are designed and built to be safe against failure and perform satisfactorily over their service life. Bridge structural health monitoring (BSHM) systems are therefore essential to ensure the safety and serviceability of such critical transportation infrastructure. Identification of structural damage at the earliest time possible is a major goal of BSHM processes. Among many developed damage identification techniques (DITs), vibration-based techniques have shown great potential to be implemented in BSHM systems. In a vibration-based DIT, the response of a bridge is measured and analyzed in either time or space domain for the purpose of detecting damage-induced changes in the extracted dynamic properties of the bridge. This approach usually requires a comparison between two structural states of the bridge—the current state and a reference (intact/undamaged) state. In most in-situ cases, however, data on the bridge structural response in the reference state are not available. Therefore, researchers have been recently working on the development of DITs that eliminate the need for a prior knowledge of the reference state. This paper thoroughly explains why and how the reference state can be excluded from the damage identification process. It then reviews the state-of-the-art reference-free vibration-based DITs and summarizes their merits and shortcomings to give guidance on their applicability to BSHM systems. Finally, some recommendations are given for further research. [ABSTRACT FROM AUTHOR]

The ground-based augmentation system (GBAS) is a regional system supporting navigation and ensuring the integrity of aircraft near airports during precision approaches. Standardized at the international level, GBAS Approach Service Types (GASTs) C and D, which are defined for the GPS L1 signal, support CAT I and II/III precision approaches with decision heights of 200 and 50 ft, respectively. However, the future GBAS, GAST E, which utilizes dual-frequency and multi-constellation signals, and the GAST D1, defined for both GPS L1 and Galileo E1 signals, require the establishment of standards. To define the continuity requirement, the number of critical satellites must be considered. Currently, there is a lack of analysis on the number of critical satellites for various GBAS service types available to the public. This paper aims to evaluate the number of critical satellites for future GBAS service types, employing optimized GPS and Galileo constellations and assessing all potential protection levels worldwide. The methodology to model the difference of position solutions using the 30 s and 100 s smoothing filters is presented in detail to compute the protection level for GASTs D and D1. The resulting number of critical satellites can be used to define the continuity allocation of future GBAS. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT's layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases. [ABSTRACT FROM AUTHOR]

Real-world rotordynamic systems exhibit inherent uncertainties in manufacturing tolerances, material properties, and operating conditions. This study presents a Monte Carlo simulation approach using MSC Adams View and Adams Insight to investigate the impact of these uncertainties on the performance of a Laval/Jeffcott rotor model. Key uncertainties in bearing damping, bearing clearance, and mass imbalance were modeled with probabilistic distributions. The Monte Carlo analysis revealed the probabilistic nature of critical speeds, vibration amplitudes, and overall system stability. The findings highlight the importance of probabilistic methods in robust rotordynamic design and provide insights for establishing manufacturing tolerances and operational limits. [ABSTRACT FROM AUTHOR]

Gait and balance have emerged as a critical area of research in health technology. Gait and balance studies have been affected by the researchers' slow follow-up of research advances due to the absence of visual inspection of the study literature across decades. This study uses advanced search methods to analyse the literature on gait and balance in older adults from 1993 to 2022 in the Web of Science (WoS) database to gain a better understanding of the current status and trends in the field for the first time. The study analysed 4484 academic publications including journal articles and conference proceedings on gait and balance in older adults. Bibliometric analysis methods were applied to examine the publication year, number of publications, discipline distribution, journal distribution, research institutions, application fields, test methods, analysis theories, and influencing factors in the field of gait and balance. The results indicate that the publication of relevant research documents has been steadily increasing from 1993 to 2022. The United States (US) exhibits the highest number of publications with 1742 articles. The keyword "elderly person" exhibits a strong citation burst strength of 18.04, indicating a significant focus on research related to the health of older adults. With a burst factor of 20.46, Harvard University has made impressive strides in the subject. The University of Pittsburgh displayed high research skills in the area of gait and balance with a burst factor of 7.7 and a publication count of 103. The research on gait and balance mainly focuses on physical performance evaluation approaches, and the primary study methods include experimental investigations, computational modelling, and observational studies. The field of gait and balance research is increasingly intertwined with computer science and artificial intelligence (AI), paving the way for intelligent monitoring of gait and balance in the elderly. Moving forward, the future of gait and balance research is anticipated to highlight the importance of multidisciplinary collaboration, intelligence-driven approaches, and advanced visualization techniques. [ABSTRACT FROM AUTHOR]

Conventional DC-DC boost converters have played a vital role in electric vehicle (EVs) powertrains by enabling the necessary voltage to increase to meet the needs of electric motors. However, recent developments in high-gain converters have introduced new possibilities with enhanced voltage amplification capabilities and efficiency. This study discusses and evaluates the state-of-the-art high-gain DC-DC converters for EV applications based on the Quadratic Boost Converter (QBC). Research into innovative topologies has increased in response to the increasing demand for efficient and high-performance power electronic converters in the rapidly expanding EV industry. Due to its ability to provide more significant voltage gains than conventional boost converters, the QBC has become a viable option for meeting the unique requirements of EV power systems. This survey focuses on the efficiency, power density, and overall performance parameters of QBC-based high-gain converters. The literature review provides a foundation for comprehending power electronics converters' trends, challenges, and opportunities. The acquired knowledge can enhance the design and optimization of high-gain converters based on the QBC, thereby fostering more sustainable and efficient power systems for the expanding electric mobility industry. In the future, the report suggests that investigating new high-gain converter design methodologies will reduce component stress and enhance the intact system efficiency. [ABSTRACT FROM AUTHOR]

Satisfying a context consumer's quality of context (QoC) requirements is important to context management platforms (CMPs) in order to have credibility. QoC indicates the contextual information's quality metrics (e.g., accuracy, timeliness, completeness). The outcomes of these metrics depend on the functional and quality characteristics associated with all actors (context consumers (or) context-aware applications, CMPs, and context providers (or) IoT-data providers) in context-aware IoT environments. This survey identifies and studies such characteristics and highlights the limitations in actors' current functionalities and QoC modelling approaches to obtain adequate QoC and improve context consumers' quality of experience (QoE). We propose a novel concept system based on our critical analysis; this system addresses the functional limitations in existing QoC modelling approaches. Moreover, we highlight those QoC metrics affected by quality of service (QoS) metrics in CMPs. These recommendations provide CMP developers with a reference system they could incorporate, functionalities and QoS metrics to maintain in order to deliver an adequate QoC. [ABSTRACT FROM AUTHOR]