Detected Research Articles

Experimental Verification of GFRP Bridge Deck Panels Using an Integrated Distributed Fiber Optic Sensing System

Experimental Verification of GFRP Bridge Deck Panels Using an Integrated Distributed Fiber Optic Sensing System

A Review of Advanced Sensor Technologies for Aquatic Products Freshness Assessment in Cold Chain Logistics.

The evaluation of the upkeep and freshness of aquatic products within the cold chain is crucial due to their perishable nature, which can significantly impact both quality and safety. Conventional methods for assessing freshness in the cold chain have inherent limitations regarding specificity and accuracy, often requiring substantial time and effort. Recently, advanced sensor technologies have been developed for freshness assessment, enabling real-time and non-invasive monitoring via the detection of volatile organic compounds, biochemical markers, and physical properties. The integration of sensor technologies into cold chain logistics enhances the ability to maintain the quality and safety of aquatic products. This review examines the advancements made in multifunctional sensor devices for the freshness assessment of aquatic products in cold chain logistics, as well as the application of pattern recognition algorithms for identification and classification. It begins by outlining the categories of freshness criteria, followed by an exploration of the development of four key sensor devices: electronic noses, electronic tongues, biosensors, and flexible sensors. Furthermore, the review discusses the implementation of advanced pattern recognition algorithms in sensor devices for freshness detection and evaluation. It highlights the current status and future potential of sensor technologies for aquatic products within the cold chain, while also addressing the significant challenges that remain to be overcome.

Application of Sensor Technology in Wheelchair Sports for Real-Time Data Collection during Training and Competition and for Assessment of Performance Parameters: A Systematic Review and Future Directions

This review reports on the use of sensors in wheelchair sports to monitor and analyze performance during match and training time. With rapid advancements in electronics and related technologies, understanding performance metrics in wheelchair sports is essential. We reviewed nine studies using various sensor types, including electric motors, inertial measurement units, miniaturized data loggers with magnetic reed switches, and smartphones with inbuilt accelerometers and gyroscopes, operating at frequencies from 8 Hz to 1200 Hz. These studies measured parameters such as angular and translational velocities, distance, number of starts/pushes, and other performance indicators in sports such as basketball, rugby, tennis, and racing. Despite differences in sport types and methodologies, most studies found sensor-derived data effective for assessment of performance. Future developments and research in this field should focus on multi-sensor systems that could provide real-time match analysis and deeper insights into performance metrics. Overall, sensor technologies show significant potential for improving wheelchair sport performance diagnostics, contributing to better athlete training and future wheelchair design, and enhancing competitive outcomes. This review emphasizes the need for continued innovation and standardization in applying sensor technologies in wheelchair sports.

The Embedded System of Battery Charging with Constant Current System using Microcontroller and IC LM338

The battery has the function of an electrical energy storage system. However, charging the battery is prone to damage. Damage to the battery is often caused by overcharging. The time used during the battery charging process is an important factor that must be considered when charging. thus, efficient battery charging is important and very necessary to avoid damage. An another important aspect of a battery charging method is the current and voltage values supplied during its charging process. The presence of inappropriate values damages and reduce battery life, while the maintenance of a constant current is an important charging process step that extends service life. This research is expected to create a safe and optimal battery charging system with a simple low-cost circuit based on constant current by utilizing the LM388 Integrated Circuit (IC) regulator. This study discusses a 12 V battery charging system with monitored and controlled constant current using an R-Shunt sensor. Voltage monitoring while charging was carried out with voltage sensors using a constant current of 0.1 to 1.3 Ampere. Furthermore, voltages and currents were displayed through a Liquid Crystal Display (LCD) screen controlled with Arduino Uno during battery charging. The measurement results showed that the circuit reached voltages and currents of 14.07 V and 0.19 A respectively for a charge duration of 8 hours.

REVOLUTIONIZING MOBILITY: A COMPREHENSIVE EVALUATION OF THE IMPACT OF AI ON AUTOMATED VEHICLES

Automated vehicles (AVs) represent a game-changing advancement in transportation, utilizing cutting-edge technologies to travel without human intervention. These vehicles, which range from advanced driver assistance systems (ADAS) to completely autonomous versions, rely on artificial intelligence (AI) to perceive and make decisions. Recent advances in sensor technology and machine learning have improved AV capabilities, but understanding how AI responds to various distance metrics is critical for assuring safe journeys. Hypothesizing that the more the distance the car is traveling, the more AI may be able to provide a safe journey for the passengers,this study seeks to investigate the complex relationship between distance and AIs ability to ensure AV safety. Investigating the physical, geographical, and temporal characteristics of distance can help optimize AV performance and reduce safety issues.

Research trends and hot topics of wearable sensors in wound care over past 18 years: A bibliometric analysis

ObjectiveThis study determined the development trends, analyzed collaboration networks, and identified research hotspots in the field of wearable sensors for wound care from 2007 to 2024 using a rigorous bibliometric analysis approach. MethodsBibliometric and scientometric analyses were performed utilizing data sourced from the Web of Science Core Collection database. This study examined publication trends, contributions from various countries and institutions, author productivity, keyword prevalence, and citation patterns to discern research hotspots and potential future avenues in the application of wearable sensors for wound care. ResultsThis study included 1177 articles, which demonstrated a marked increase in publications since 2016 and underscores the burgeoning interest in wearable sensors for wound care. China and the United States have emerged as prominent contributors to the research field, exhibiting numerous international collaborations. An analysis of keywords and citation bursts highlighted wound healing, hydrogels, and sensors as the key research foci with recent trends shifting towards the integration of wearable technology with advanced materials and artificial intelligence for advanced wound management. The research landscape is characterized by a diverse network of international collaborations and an emphasis on interdisciplinary approaches that integrate materials science, sensor technology, and clinical applications. ConclusionThe utilization of wearable sensors in wound care constitutes a rapidly progressing area of research, garnering significant interest and promising avenues for future advances. The integration of wearable sensors with advanced materials and AI technologies presents a frontier of opportunity for innovating wound care methodologies, enhancing patient outcomes, and optimizing the allocation of healthcare resources.

Remote Sensing, Artificial Intelligence and Smart Agriculture Technology Trends of the Future

The efficient and sustainable operation of the agricultural sector has become increasingly important in light of the transformations brought about by the third and fourth industrial revolutions. Population growth, increasing food demand, rising input costs, and environmental pressures necessitate innovative approaches not only to ensure food security but also to mitigate the effects of climate change. The European Union (EU) emphasizes the role of digital technologies in supporting agricultural productivity and resilience by promoting a bio-based economy. Strategies such as Farm to Fork (F2F) initiative aim to reduce pesticide and nutrient inputs, thus preserving biodiversity and supporting ecosystem health. Artificial intelligence (AI) and predictive analytics, along with connected sensors, offer opportunities to optimize water and nutrient usage and increase crop yields. By utilizing AI, combining remote sensing technologies, and monitoring changes in land use, it is possible to reduce environmental risks associated with agricultural practices. Although there are challenges such as high investment costs and data control for the integration of digital technologies, ongoing research and development efforts promise to overcome these obstacles. In conclusion, the integration of digital technologies into agriculture presents unique opportunities to address urgent issues and achieve sustainability goals. This review discusses the applicability of fundamental technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Precision Agriculture (PA), and Machine Learning (ML) in making agriculture more efficient and sustainable, by enabling the perception, monitoring, collection, analysis, and extraction of meaningful insights from agricultural data.

Optimization of highly sensitive three-layer photonic crystal fiber sensor based on plasmonic

In this work, a photonic crystal fiber (PCF) refractive index (RI) sensor has been designed and optimized. The RIs range covered by the sensor is from 1.38 to 1.41. The proposed optical sensor has three layers of air holes with 24, 12 and 6 holes in each layer. The geometry parameters of the proposed sensor (the radius of the air holes and the thickness of the plasmonic layer) have been optimized using the Nelder-Mead algorithm and the FEM numerical with the objective of achieving the highest sensitivity. To achieve an optimized structure with minimal sensitivity to fabrication errors, the rotation angle of the hole layers has been analyzed. The results indicate that, due to the specific geometry of the proposed structure, variations in the rotation angle and displacement of the air holes have no significant impact on the outcomes. The results also indicate that the sensor’s maximum amplitude sensitivity (AS), maximum wavelength sensitivity (WS), and figure of merit (FOM) are 10000 (RIU−1), 23000 (nm/RIU) and 131 (RIU−1) respectively. The optimized design provides high sensitivity, a wide diagnostic range for the detection of the analytes’ RIs, and the advantages of a gold plasmonic layer, ensuring high stability in biological environments. This combination results in enhanced performance of the sensor for various applications particularly in biosensing and medical fields. The designed structural geometry also eliminates the effects of tolerances in manufacturing processes which makes the proposed PCF device a very efficient sensor.

Advanced Pressure-Sending Wristband for Disease Detection Based on Traditional Chinese Medicine Pulse Diagnosis

This paper explores the integration of Traditional Chinese Medicine (TCM) pulse diagnosis with modern sensor technology to develop a sophisticated wristband system for monitoring vital organ health. Leveraging the principles of TCM, the proposed system focuses on the “Three regions and nine indicators” method to accurately detect pulse variations at different wrist positions and depths. By utilizing a pressure sensor array that covers the Cun, Guan, and Chi pulse positions, the wristband aims to enhance diagnostic accuracy beyond conventional wearable technologies. The system incorporates advanced micro-electro-mechanical techniques to minimize size while maintaining functionality, enabling continuous health monitoring through a coplanar crossed sensing array. Additionally, the use of machine learning algorithms facilitates the analysis of pulse data to identify potential health issues related to the heart, lungs, liver, kidneys, and spleen. An accompanying mobile app provides real-time feedback and health insights, promoting proactive health management. This innovative approach merges TCM’s holistic diagnosis methods with cutting-edge technology to offer a powerful tool for early disease detection and health monitoring, potentially transforming the landscape of personal healthcare devices.

Pattern recognition of multiple metal ions using fluorescent Perylenyl nanoaggregates and application in food traceability

Metal ions (MIs) identification is essential for the safety assessment, traceability and authentication of food. Most current approaches for detecting MIs are difficult to reconcile the simplicity, sensitivity and stability simultaneously. In this work, we proposed a novel strategy for discriminating MIs based on fluorescent supramolecular nanoaggregates (SNAs). In the presence of MI, perylene diimide derivatives (PDI)-based SNAs could be formed through the multiple non-covalent interactions between them including electrostatic, coordination and π-π interactions. With the assistance of discriminant analysis (LDA), different MIs (Hg2+, Ag+, Cd2+, Fe3+, Cr3+, Fe2+, Zn2+, Cu2+ and Pb2+) were successfully identified at three different concentration levels. It featured good quantitative sensing abilities in buffer solutions and practical samples. Furthermore, a water-quality evaluation model was successfully constructed for the distinction of different sources of drinking water, and the fluorescence array sensor technology was applied for the first time to the geographical traceability of apples.

Applying Michelson-interferometer based optical fiber sensors for active control of road noise in a car

Active noise control (ANC) technology is a promising solution for reducing road noise in cars, where the quality of reference signals is critical for improving the performance. In this paper, an optical fiber sensor based on Michelson interferometer is proposed to obtain the vibration reference signal for active control of road noise in a car. First, the principle of this optical fiber sensor is introduced, then the feasibility of using optical fiber sensor for active control of sound radiation from a panel of an enclosure is verified, where the spectrum, coherence and impulse response of the optical fiber sensor are reported and compared with that of an accelerometer. Finally, the optical fiber sensor is applied in a car for active control of road noise and the simulation results based on the measured data are presented. The research demonstrates the potential of using the proposed optical fiber sensor for active control of road noise in a car by discussing its advantages and weakness.

FBG Interrogator Using a Dispersive Waveguide Chip and a CMOS Camera.

Optical sensors using fiber Bragg gratings (FBGs) have become an alternative to traditional electronic sensors thanks to their immunity against electromagnetic interference, their applicability in harsh environments, and other advantages. However, the complexity and high cost of the FBG interrogation systems pose a challenge for the wide deployment of such sensors. Herein, we present a clean and cost-effective method for interrogating an FBG temperature sensor using a micro-chip called the waveguide spectral lens (WSL) and a standard CMOS camera. This interrogation system can project the FBG transmission spectrum onto the camera without any free-space optical components. Based on this system, an FBG temperature sensor is developed, and the results show good agreement with a commercial optical spectrum analyzer (OSA), with the respective wavelength-temperature sensitivity measured as 6.33 pm/°C for the WSL camera system and 6.32 pm/°C for the commercial OSA. Direct data processing on the WSL camera system translates this sensitivity to 0.44 μm/°C in relation to the absolute spatial shift of the FBG spectra on the camera. Furthermore, a deep neural network is developed to train the spectral dataset, achieving a temperature resolution of 0.1 °C from 60 °C to 120 °C, while direct processing on the valley/dark line detection yields a resolution of 7.84 °C. The proposed hardware and the data processing method may lead to the development of a compact, practical, and low-cost FBG interrogator.

Ratiometric optical temperature sensing properties based on up-conversion luminescence of novel NaLaTi2O6:Yb3+,Er3+/Ho3+ phosphors

To satisfy the increasing necessity of contactless optical thermometry, utilizing both thermally coupled (TCLs) or non-thermally-coupled (NTCLs) energy levels of rare earth ions in novel phosphors has significant potential for devising the optical thermometers with high temperature sensitivity. Herein, a sequence of novel Yb3+,Er3+/Ho3+ doped NaLaTi2O6 (NLTO) phosphors were sintered using a high-temperature solid-state reaction approach. Structure, phase component and luminescence performance were identified in detail. Upon 980 nm near-infrared (NIR) excitation, the obtained materials presented typical Er3+/Ho3+ characteristic emission wavelengths in visible region, which include two green emission bands around 522 and 543 nm, as well as a weaker red band around 661 nm for Er3+ doped NLTO, a green emission band around 545 nm and a red band around 657 nm for Ho3+ doped NLTO. Besides, a unusual emission band around 757 nm of Ho3+ in visible edge region was also clearly found. The temperature sensing properties of representative NLTO:Yb3+,Er3+/Ho3+ samples were assessed based on the fluorescence intensity ratio (FIR) technique of TCLs or NTCLs energy levels. The maximal absolute sensitivity (Sa) and relative sensitivity (Sr) were determined to be 0.0374 K-1 (293 K) and 0.970% K-1 (293 K) by taking the FIR of NTCLs 2H11/2→4I15/2 and 4F9/2→4I15/2 transitions in NLTO:Yb3+,Er3+. Simultaneously, the maximal Sa and Sr were 0.0306 K-1 (573 K) and 0.776% K-1 (373 K) using the FIR of NTCLs 5F5→5I8 and 5F4,5S2→5I7 transitions in NLTO:Yb3+,Ho3+. Consequently, the temperature sensitivities above can be compared to reported results, which indicate that as-prepared materials have a promising application in optical temperature sensors field.

Cutting-Edge Hydrogel Technologies in Tissue Engineering and Biosensing: An Updated Review.

Hydrogels, known for their unique ability to retain large amounts of water, have emerged as pivotal materials in both tissue engineering and biosensing applications. This review provides an updated and comprehensive examination of cutting-edge hydrogel technologies and their multifaceted roles in these fields. Initially, the chemical composition and intrinsic properties of both natural and synthetic hydrogels are discussed, highlighting their biocompatibility and biodegradability. The manuscript then probes into innovative scaffold designs and fabrication techniques such as 3D printing, electrospinning, and self-assembly methods, emphasizing their applications in regenerating bone, cartilage, skin, and neural tissues. In the realm of biosensing, hydrogels' responsive nature is explored through their integration into optical, electrochemical, and piezoelectric sensors. These sensors are instrumental in medical diagnostics for glucose monitoring, pathogen detection, and biomarker identification, as well as in environmental and industrial applications like pollution and food quality monitoring. Furthermore, the review explores cross-disciplinary innovations, including the use of hydrogels in wearable devices, and hybrid systems, and their potential in personalized medicine. By addressing current challenges and future directions, this review aims to underscore the transformative impact of hydrogel technologies in advancing healthcare and industrial practices, thereby providing a vital resource for researchers and practitioners in the field.

Research on the Fabrication and Parameters of a Flexible Fiber Optic Pressure Sensor with High Sensitivity

In recent years, flexible pressure sensors have garnered significant attention. However, the development of large-area, low-cost, and easily fabricated flexible pressure sensors remains challenging. We designed a flexible fiber optic pressure sensor for contact force detection based on the principle of backward Rayleigh scattering using a single-mode optical fiber as the sensing element and polymer PDMS as the encapsulation material. To enhance the sensor’s sensitivity and stability, we optimized its structural design, parameters, and fabrication process and measured the fiber strain using an optical frequency domain reflectometer (OFDR). The results showed that the sensor achieved a high sensitivity of 6.93247 με/kPa with a PDMS concentration ratio of 10:1, a curing time of 2 h, and a substrate thickness of 5 mm. The sensor demonstrated excellent linearity and repeatability in static performance tests and was successfully used to monitor the plantar pressure distribution in real time. This flexible fiber optic pressure sensor can be developed via a simple fabrication process, has a low cost, and has high sensitivity, highlighting its potential applications in smart wearables and medical diagnostics.

Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study

Metal–semiconductor sulfide bilayer based SPR fiber optic sensors for the detection of methanol adulteration of ethanol using machine learning algorithms: an investigative study

A Review of Recent Techniques for Human Activity Recognition: Multimodality, Reinforcement Learning, and Language Models

Human Activity Recognition (HAR) is a rapidly evolving field with the potential to revolutionise how we monitor and understand human behaviour. This survey paper provides a comprehensive overview of the state-of-the-art in HAR, specifically focusing on recent techniques such as multimodal techniques, Deep Reinforcement Learning and large language models. It explores the diverse range of human activities and the sensor technologies employed for data collection. It then reviews novel algorithms used for Human Activity Recognition with emphasis on multimodality, Deep Reinforcement Learning and large language models. It gives an overview of multimodal datasets with physiological data. It also delves into the applications of HAR in healthcare. Additionally, the survey discusses the challenges and future directions in this exciting field, highlighting the need for continued research and development to fully realise the potential of HAR in various real-world applications.

Wearable Sensors for Healthcare of Industrial Workers: A Scoping Review

Background and Objectives: This scoping review evaluates the use of wearable sensor technologies for workplace safety and health monitoring in industrial settings. The aim is to synthesize evidence on the impact of these sensors and their application in high-risk environments. Materials and Methods: Following the PRISMA guidelines, a systematic search across four international electronic databases yielded 59 studies, of which 17 were included in the final review. The selection criteria involved studies that specifically utilized wearable sensors to monitor various health and environmental parameters relevant to industrial workers. Results: The analysis categorizes wearable technologies into five distinct groups based on their function: gas monitoring technologies, heart rate and physiological data collection, fatigue and activity monitoring, comprehensive environmental and physiological monitoring, and advanced sensing and data collection systems. These devices demonstrated substantial benefits in terms of early detection of health risks and enhancement of safety protocols. Conclusions: The review concludes that wearable sensor technologies significantly contribute to workplace safety by providing real-time, data-driven insights into environmental hazards and workers’ physiological status, thus supporting proactive health management practices in industrial settings. Further research is recommended to address the challenges of data privacy, sensor reliability, and cost-effective integration to maximize their potential in occupational health safety.

Complementary Metal-Oxide-Semiconductor-Based Magnetic and Optical Sensors for Life Science Applications.

Optical and magnetic sensing methods are integral to both research and clinical applications in biological laboratories. The ongoing miniaturization of these sensors has paved the way for the development of point-of-care (PoC) diagnostics and handheld sensing devices, which are crucial for timely and efficient healthcare delivery. Among the various competing sensing and circuit technologies, CMOS (complementary metal-oxide-semiconductor) stands out due to its distinct cost-effectiveness, scalability, and high precision. By leveraging the inherent advantages of CMOS technology, recent developments in optical and magnetic biosensors have significantly advanced their application in life sciences, offering improved sensitivity, integration capabilities, and reduced power consumption. This paper provides a comprehensive review of recent advancements, focusing on innovations in CMOS-based optical and magnetic sensors and their transformative impact on biomedical research and diagnostics.

Research on adjustable measuring range vibration sensors based on optical fiber tweezers

We presents a vibration sensor with an adjustable measuring range based on optical fiber tweezers. The sensor employs a tapered optical fiber to trap individual yeast cells in a liquid environment, forming a sensing structure. Vibration signals from the source are transmitted through the medium to the cells in the optical trap. By adjusting the input optical power, the maximum restoring force of the light trap is increased when the vibration amplitude exceeds the measuring range, thus achieving variable-range vibration sensing. Experimental results demonstrate that the measuring range of the vibration sensor increases linearly with trapping power, ranging from 0.5 to 12.5 mW. This method extends the measuring range while maintaining the original sensing structure, making the optical fiber tweezers vibration sensor more adaptable for multi-task measurements with varying measuring ranges.