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Eye-tracking is an important approach to collect evidence regarding some participants’ driving fatigue. In this contribution, the authors present a non-intrusive system for evaluating driver fatigue by tracking eye movement behaviours. A real-time eye-tracker was used to monitor participants’ eye state for collecting eye-movement data. These data are useful to get insights into assessing participants’ fatigue state during monotonous driving. Ten healthy subjects performed continuous simulated driving for 1–2 h with eye state monitoring on a driving simulator in this study, and these measured features of the fixation time and the pupil area were recorded via using eye movement tracking device. For achieving a good cost-performance ratio and fast computation time, the fuzzy K-nearest neighbour was employed to evaluate and analyse the influence of different participants on the variations in the fixation duration and pupil area of drivers. The findings of this study indicated that there are significant differences in domain value distribution of the pupil area under the condition with normal and fatigue driving state. Result also suggests that the recognition accuracy by jackknife validation reaches to about 89% in average, implying that show a significant potential of real-time applicability of the proposed approach and is capable of detecting driver fatigue.

An efficient VLSI architecture of a lossless ECG encoding circuit is proposed for wireless healthcare monitoring applications. To reduce the transmission and storage data, a novel lossless compression algorithm is proposed for ECG signal compression. It consists of a novel adaptive rending predictor and a novel two‐stage entropy encoder based on two Huffman coding tables. The proposed lossless ECG encoder design was implemented using only simple arithmetic units. To improve the performance, the proposed ECG encoder was designed by pipeline technology and implemented the two‐stage entropy encoder by the architecture of a look‐up table. The VLSI architecture of this work contains 3.55 K gate counts and its core area is 45987 µm2 synthesised by a 0.18 µm CMOS process. It can operate at 100 MHz processing rate with only 36.4 µW. The data compression rate reaches an average value 2.43 for the MIT‐BIH Arrhythmia Database. Compared with the previous low‐complexity and high performance techniques, this work achieves lower hardware cost, lower power consumption, and a better compression rate than other lossless ECG encoder designs.

Current camera traps use passive infrared triggers; therefore, they only capture images when animals have a substantially different surface body temperature than the background. Endothermic animals, such as mammals and birds, provide adequate temperature contrast to trigger cameras, while ectothermic animals, such as amphibians, reptiles, and invertebrates, do not. Therefore, a camera trap that is capable of monitoring ectotherms can expand the capacity of ecological research on ectothermic animals. This study presents the design, development, and evaluation of a solar-powered and artificial-intelligence-assisted camera trap system with the ability to monitor both endothermic and ectothermic animals. The system is developed using a central processing unit, integrated graphics processing unit, camera, infrared light, flash drive, printed circuit board, solar panel, battery, microphone, GPS receiver, temperature/humidity sensor, light sensor, and other customized circuitry. It continuously monitors image frames using a motion detection algorithm and commences recording when a moving animal is detected during the day or night. Field trials demonstrate that this system successfully recorded a high number of animals. Lab testing using artificially generated motion demonstrated that the system successfully recorded within video frames at a high accuracy of 0.99, providing an optimized peak power consumption of 5.208 W. No water or dust entered the cases during field trials. A total of 27 cameras saved 85,870 video segments during field trials, of which 423 video segments successfully recorded ectothermic animals (reptiles, amphibians, and arthropods). This newly developed camera trap will benefit wildlife biologists, as it successfully monitors both endothermic and ectothermic animals.

Introduction: This study aimed to estimate the criterion validity of functional movement and posture measurement using remote technology systems in people with and without Axial spondylarthritis (axSpA). Methods: Validity and agreement of the remote‐technology measurement of functional movement and posture were tested cross‐sectionally and compared to a standard clinical measurement by a physiotherapist. The feasibility of remote implementation was tested in a home environment. There were two cohorts of participants: people with axSpA and people without longstanding back pain. In addition, a cost‐consequence analysis was performed. Results: Sixty‐two participants (31 with axSPA, 53% female, age = 45(SD14), BMI = 26.6(SD4.6) completed the study. In the axSpA group, cervical rotation, lumbar flexion, lumbar side flexion, shoulder flexion, hip abduction, tragus‐to‐wall and thoracic kyphosis showed a significant moderate to strong correlation; in the non‐back pain group, the same measures showed significant correlation ranging from weak to strong. Conclusions: Although not valid for clinical use in its current form, the remote technologies demonstrated moderate to strong correlation and agreement in most functional and postural tests measured in people with AxSA. Testing the CV‐aided system in a home environment suggests it is a safe and feasible method. Yet, validity testing in this environment still needs to be performed. This article introduced and evaluated the validity of a new artificial intelligence‐driven method of automatically analysing a range of standard clinical functional tests. Remote technology systems in rehabilitation have the potential to reduce health inequality and improve cost and time effectiveness for both patients and the health system. In addition to demonstrating validity in most functional and postural measures, feasibility results show that using this computer vision‐aided system in a home environment is a safe and viable method that can widen accessibility and affordability.

With the acceleration of economic globalisation and the rapid development of network communication technology, remote monitoring and the management of ship fuel consumption have received extensive attention. Traditional fuel consumption monitoring methods are difficult to meet the growing management needs of the shipping industry due to problems such as large statistical errors and delayed information feedback. In order to better conduct energy management, equipment condition monitoring, and navigation analysis, the cyber-physical system (CPS) is deployed on ships to collect shipping data and communicate with remote monitoring centres. However, complex actual sailing conditions, sailing weather and other external factors tend to reduce the accuracy of fuel consumption data. In view of this challenge, a data-aware monitoring method for fuel consumption in ship-based CPS, named DMM, is proposed in this study. Technically, the fuel consumption index of ships is introduced firstly. Then, a fuel consumption model based on CPS is proposed, which improves the current fuel consumption model of the ship. Furthermore, the artificial neural network is employed to analyse a large amount of navigation data to get more accurate monitoring results of fuel consumption. Finally, experiments are conducted to verify the effectiveness of the authors’ proposed method.

To examine stress redistribution phenomena in bridges subjected to varying operational conditions, this study conducts a comprehensive analysis of three years of monitoring data from a 153-m double-deck road–rail steel arch bridge. An initial statistical comparison of sensor data distributions reveals clear temporal variations in stress redistribution patterns. XGBoost (eXtreme Gradient Boosting), a gradient-boosting machine learning (ML) algorithm, was employed not only for predictive modeling but also to uncover the underlying mechanisms of stress evolution. Unlike traditional numerical models that rely on extensive assumptions and idealizations, XGBoost effectively captures nonlinear and time-varying relationships between stress states and operational/environmental factors, such as temperature, traffic load, and structural geometry. This approach allows for the identification of critical periods and conditions under which stress redistribution becomes significant. Results indicate a clear shift of stress concentrations from beam ends toward mid-span regions following the commencement of metro operations, reflecting both structural adaptation and localized overstress near arch ribs. Furthermore, the model generates robust predictions of stress evolution, demonstrating potential applications in early warning systems and fatigue risk assessment. This work represents the first application of interpretable gradient-boosting techniques to stress redistribution modeling in double-deck bridges. In addition, a Stress Redistribution Index (SRI) is proposed, derived from this monitoring study and finite-element-based transverse load distributions, to quantify temporal stress shifts between midspan and edge beams. The results provide both theoretical contributions and practical guidance for the design, inspection, and maintenance of complex bridge structures.

A goal of runtime software-fault monitoring is to observe software behavior to determine whether it complies with its intended behavior. Monitoring allows one to analyze and recover from detected faults, providing additional defense against catastrophic failure. Although runtime monitoring has been in use for over 30 years, there is renewed interest in its application to fault detection and recovery, largely because of the increasing complexity and ubiquitous nature of software systems. We present taxonomy that developers and researchers can use to analyze and differentiate recent developments in runtime software fault-monitoring approaches. The taxonomy categorizes the various runtime monitoring research by classifying the elements that are considered essential for building a monitoring system, i.e., the specification language used to define properties; the monitoring mechanism that oversees the program's execution; and the event handler that captures and communicates monitoring results. After describing the taxonomy, the paper presents the classification of the software-fault monitoring systems described in the literature.

From voltage and current signals it is possible to obtain relevant information for solving some problems in several industrial and scientific applications as power quality (PQ) monitoring, monitoring and diagnosis of electrical machines, electric systems protection and control. At present, the PQ monitoring, measure through a set of PQ indices (PQI), is an important topic for the industrial sector since a poor PQ, characterised by the presence of harmonics in the power line, produces irregular or wrong operation of protection systems, excessive neutral currents in 3-phase four-wire systems, overheating of motors, transformers, capacitor banks and wiring in general. The PQI calculation is performed by many techniques proposed in the literature; however, they do not have either good performance for transient signals or the requirements for satisfying the power standards. This work proposes the assessment of the PQI-based in neural networks for transient or stationary signals in 3-phase power systems without losing the power standard requirements. Besides, this work contributes to the industrial application field by allowing the continuous and online monitoring of the PQI thanks to the field programmable gate array implementation of the proposed methodology.

This work builds an intelligent environmental monitoring system for industrial production and safety concerns. There are a number of smart monitoring systems developed for smart grids, bridges or machine system management, temperature/humidity monitoring and so on. As information technology advances at a continuous rapid pace, humans have become connected online and this connection has extended into the interactions between things and humans. Employing the FLAG-PSoC-1605A development board as the platform, all sensor data are transmitted via a ZigBee wireless module, a TCP/IP network module and a long range wireless communication GPRS/SMS module to a remote end PC for analysis. This constitutes a smart network for connections between humans and objects.

Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.