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A Novel Multipurpose Fuel sensor for Efficient Estimation of Fuel-Level, Dual-Axis Inclination, and Two-Component Fuel Composition

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IEEE sensors JOURNAL 2025年第7期25卷 12109-12118页

作者： Narayanan, P. P. Vijayakumar, Sreenath Indian Inst Technol IIT Palakkad Elect Engn Dept Palakkad 678623 India

Modern automotive technologies necessitate monitoring of a wide variety of physical parameters, such as fuel level, dual-axis vehicle inclination, and fuel composition. Traditionally, this is achieved through the incorporation of numerous sensors, resulting in increased complexity, expense, and bulkiness. This article proposes a novel solution by designing a single capacitive-based sensor system capable of simultaneously monitoring fuel level, dual-axis vehicle inclination, and fuel mixture composition. In contrast to current fuel sensing architectures, the proposed sensor eliminates the need for movable parts and is immune to fuel type. This makes it compatible with any gasoline-powered combustion engine automobiles. The proposed sensor incorporates a dual-axis inclination measurement, which is essential for accurate level measurement, and ensuring vehicle safety. In addition, due to the current availability of a wide variety of fuel mixtures in the market, knowledge of fuel composition is critical in any vehicle for optimizing engine performance. Implementation of all these features through a single sensor eliminates the requirement for supplementary components, resulting in a reduction in both hardware implementation expenses and system complexity. This is the first of its kind such a single sensing scheme is reported. The prototype sensor demonstrates high linearity of 0.43% and 0.88% for level and inclination measurements, respectively. The sensor prototype was also tested with ethanol-petrol fuel mixtures and exhibits a worst case linearity error of 2.43% for concentrations ranging from E0-petrol to E100-petrol. The developed sensor promises a feasible and economically efficient solution for integration into contemporary automobiles.

关键词： sensors Fuels Liquids Capacitance Capacitive sensors Pollution measurement sensor systems Permittivity measurement Fuel storage Permittivity Capacitive sensor dual-axis inclination fuel level two-component fuel composition

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Machine learning-enhanced electrochemical sensors for food safety: Applications and perspectives

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TRENDS IN FOOD SCIENCE & TECHNOLOGY 2025年 156卷

作者： Pervaiz, Wajeeha Afzal, Muhammad Hussnain Feng, Niu Peng, Xuewen Chen, Yiping Huazhong Agr Univ Coll Food Sci & Technol 1 Shizishan St Wuhan Hubei Peoples R China Dalian Polytech Univ Acad Food Interdisciplinary Sci Sch Food Sci & Technol Dalian 116034 Liaoning Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Biomat & Med Protect Mat Sch Chem & Chem Engn & Serv Failure Sch Chem & Chem EngnKey Lab Mat Chem Energy Conve 1037 Luoyu Rd Wuhan Peoples R China

Background: Food safety is a critical global concern that directly impacts human health and well-being. Electrochemical sensors have garnered considerable interest for detecting contaminants in food due to their sensitivity and selectivity;however, issues such as sensor instability and electrode fouling limit their effectiveness. The integration of machine learning (ML) into electrochemical sensing offers a transformative approach, enhancing sensor performance, stability, and data processing capabilities while enabling real-time monitoring. Scope and approach: This review succinctly explores the use of ML-enhanced electrochemical sensors specifically for food safety applications. Initially, various ML algorithms applicable to electrochemical sensor technology for food safety monitoring are discussed. The review then highlights the application of ML-enhanced sensors in detecting food-related contaminants, such as pesticides, pharmaceutical residues, heavy metals, microorganisms, artificial dyes, and phenolic compounds. Finally, it addresses the challenges and future prospects in advancing electrochemical sensors for food safety, emphasizing the potential of appropriate ML algorithms to improve insitu food safety monitoring. Key findings and conclusions: The integration of ML with electrochemical sensors improves their sensitivity, selectivity, and stability, addressing issues like electrode fouling. ML algorithms such as support vector machines, artificial neural networks, and random forests effectively detect food contaminants like pesticides, heavy metals, and microorganisms. ML also enables real-time data processing for quick, accurate detection of trace-level contaminants. However, challenges remain in sensor calibration, data reliability, and the need for highquality training datasets. Future research should focus on enhancing sensor robustness, refining ML models for improved accuracy, and advancing the commercialization of ML-enhanced sensors for food safety m

关键词： Electrochemical sensors Machine learning Food contaminants Food safety monitoring Real-time monitoring

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A Piezoelectric-Piezoresistive Coupled Electric Field sensor for Large Dynamic Range Measurement From DC to 1.5 kHz

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IEEE sensors JOURNAL 2025年第4期25卷 7095-7106页

作者： Wang, Xiaobo Zhang, Rongbo Zhu, Yuliang Luo, Bofeng Luo, Chengtao Han, Tao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China China Southern Power Grid Digital Grid Res Inst Guangzhou 510000 Peoples R China

The electric field intensity is a critical parameter in electrical power systems, providing significant insights into the system's condition. Electric field sensors (E-sensors) are essential for monitoring variations in electric field strength. The current challenge facing E-sensors is to achieve the detection of dc electric fields while ensuring a wide dynamic range of field strengths. This article proposes a novel E-sensor that combines piezoelectric-piezoresistive (PE-PR) elements in its sensitive structure. This article proposes a novel E-sensor that combines PE-PR elements in its sensitive structure. Shielding plates are positioned on both the upper and lower sides of the PE-PR sensitive element. The conductive states of the two shield plates are controlled through a switching circuit, enabling the modulation and testing of dc electric fields. This design overcomes the incompatibility between dc electric field testing and a wide dynamic range of electric field strengths in E-sensors. This passive sensor can perform electric field measurements within a dc-1.5-kHz bandwidth from 0 to 150 kV/m, with a linearity of less than 0.92%, a field resolution up to 50 V/m, sensitivity reaching 1.06 mV/kV/m (@ 1500 Hz), 128 mV/kV/m (@ dc), and hysteresis error less than 2%. The specifications can be customized according to requirements. Compared to existing research, this E-sensor meets basic measurement requirements and offers the advantages of a simplified fabrication process and smaller dimensions. The relatively small sensor allows for integration into most power electrical equipment.

关键词： Electric fields sensors Testing Strain measurement Electric variables measurement sensor systems Optical sensors Linearity Sensitivity High dynamic range DC testing electric field sensor (E-sensor) high linearity large dynamic range piezoelectric-piezoresistive (PE-PR) coupling

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SDCBM: A Secure Data Collection Model With Blockchain and Machine Learning integration for Wireless sensor Networks

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IEEE sensors JOURNAL 2025年第4期25卷 7457-7466页

作者： Raj, P. V. Pravija Khedr, Ahmed M. Univ Sharjah Dept Comp Sci Sharjah 27272 U Arab Emirates

Wireless sensor networks (WSNs) often struggle with managing extensive data volumes, given their resource-constrained nature. Deployed in unattended areas, they face significant security risks and attacks. This study introduces the secure data collection model with blockchain and machine learning integration for WSNs (SDCBM), designed to identify intrusions and ensure secure data collection and storage for WSN applications. SDCBM employs an extreme learning machine (ELM) model, a fast single-layer feedforward neural network (NN), and integrates techniques for balancing the data distribution and selecting relevant features to enhance real-time detection of malicious attacks. Data is preprocessed and balanced utilizing the synthetic minority oversampling technique (SMOTE) and Tomek-Links combination method. To enhance the feature selection process, the Harris Hawk optimization (HHO)-based method is proposed. The blockchain module manages network node registration, authentication, node revocation, and secure storage of data hashes and node credentials. Simulation results demonstrate the efficacy of the proposed SDCBM method in detecting malicious nodes and enhancing secure data collection, thereby strengthening the security of WSNs.

关键词： Wireless sensor networks Blockchains Security Data models Feature extraction Accuracy Authentication Data collection sensors Radio frequency

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Innovative integration of machine learning and colorimetry for precise potential of hydrogen monitoring in printed hydrogel sensors

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 146卷

作者： Sakr, Abdelrahman El Shamy, Ahmed R. Butt, Haider Khalifa Univ Sci & Technol Dept Mech & Nucl Engn POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Dept Elect Engn POB 127788 Abu Dhabi U Arab Emirates

Proper potential of hydrogen (pH) monitoring finds wide applications in environmental monitoring, clinical diagnostics, and a variety of industrial processes. However, traditional pH sensors normally present several challenges related to adaptability, portability, and environmental compatibility. In addition, the recently developed hydrogel-based sensors have manifested several advantages due to the flexibility and biocompatibility of the material in a wide variety of applications. While much advancement has been made in integration techniques, further advances need improvement in precision and reliability. The present work describes a novel methodology of pH sensing through integration of hydrogel-based sensors with machine learning algorithms. pHsensitive dye-impregnated hydrogel sensors have been fabricated using three-Dimensional (3D) printing technology, whereby colorimetric data analysis is combined with five machine learning models, namely Decision Trees, eXtreme Gradient Boosting, K-Nearest Neighbours, Random Forests, and Neural Networks, in the classification of pH based on Red, Green, Blue (RGB) data. The sensor designed can detect pH between 4 and 10 pH with high speed, stability, and reversibility. With precision, recall, and F1-scores all above 99%, this shows how efficient the classification approach is based on RGB and gives weight to the potential of the developed sensors for real-time applications in monitoring and diagnostics, hence making a big contribution to the evolution of pH sensing and paving the way for smarter, more adaptable sensor solutions.

关键词： Machine learning classification Potential of hydrogen-sensitive hydrogel sensors Red Green Blue colorimetric analysis Three-dimensional printing fabrication

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A Survey on sensor Selection and Placement for Connected and Automated Mobility

IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS

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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2024年 5卷 692-710页

作者： Kiraz, Mehmet Sivrikaya, Fikret Albayrak, Sahin Tech Univ Berlin Chair Agent Technol D-10623 Berlin Germany IAV GmbH Intelligent Syst Funct Dept D-10587 Berlin Germany

The progress towards fully autonomous mobility is significantly impacted by the integration of evolving technologies in connected and automated mobility (CAM). Connected and automated vehicles (CAVs) have the potential to revolutionize our transportation system by improving efficiency, safety, and environmental sustainability. Automated shuttles and public buses, smart traffic signals, intelligent passenger cars, and smart roundabouts are just a few examples of technologies that are being planned and actively researched for integration into transportation systems. sensors are essential in making this possible. This article provides a structured overview of research on the selection and positioning of sensors on- and off-vehicle to achieve cooperative, connected, and automated mobility. The general integration and usage of sensors in vehicles and infrastructure is described, a detailed taxonomy of the examined research is provided, and future research directions are presented, involving solutions for quantification of sensor performance and addressing current trends. The findings of this article also highlight numerous challenges in creating a universal framework, the lack of application of novel machine learning methods, and the complexity of modeling multi-sensor settings.

关键词： Real-time systems Safety Roads Laser radar sensor placement Automobiles Transportation Cloud computing Image edge detection Clouds CAVs CAM ITS sensor placement sensor selection sensor location problem

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Hybrid Machine Learning-Based Fault-Tolerant sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment

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sensors 2025年第7期25卷 2146-2146页

作者： Desikan, Jayameena Singh, Sushil Kumar Jayanthiladevi, A. Bhushan, Shashi Rishiwal, Vinay Kumar, Manish Marwadi Univ Dept Comp Engn Rajkot 360003 Gujarat India Univ Teknol PETRONAS Comp & Informat Sci Dept Bandar Seri Iskandar 32610 Malaysia MJP RohilKhand Univ Dept CSIT Bareilly 243006 Uttar Pradesh India

In the oil and gas IIoT environment, fire detection systems heavily depend on fire sensor data, which can be prone to inaccuracies due to faulty or unreliable sensors. These sensor issues, such as noise, missing values, outliers, sensor drift, and faulty readings, can lead to delayed or missed fire predictions, posing significant safety and operational risks in the oil and gas industrial IoT environment. This paper presents an approach for handling faulty sensors in edge servers within an IIoT environment to enhance the reliability and accuracy of fire prediction through multi-sensor fusion preprocessing, machine learning (ML)-driven probabilistic model adjustment, and uncertainty handling. First, a real-time anomaly detection and statistical assessment mechanism is employed to preprocess sensor data, filtering out faulty readings and normalizing data from multiple sensor types using dynamic thresholding, which adapts to sensor behavior in real-time. The proposed approach also deploys machine learning algorithms to dynamically adjust probabilistic models based on real-time sensor reliability, thereby improving prediction accuracy even in the presence of unreliable sensor data. A belief mass assignment mechanism is introduced, giving more weight to reliable sensors to ensure they have a stronger influence on fire detection. Simultaneously, a dynamic belief update strategy continuously adjusts sensor trust levels, reducing the impact of faulty readings over time. Additionally, uncertainty measurements using Hellinger and Deng entropy, along with Dempster-Shafer Theory, enable the integration of conflicting sensor inputs and enhance decision-making in fire detection. This approach improves decision-making by managing sensor discrepancies and provides a reliable solution for real-time fire predictions, even in the presence of faulty sensor readings, thereby mitigating the fire risks in IIoT environments.

关键词： IIoT faulty sensors anomaly detection Dempster-Shafer machine learning sensor fusion

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Graduate Student Literature Review: The use of integrated sensor data for the detection of hyperketonemia in pasture-based dairy systems during the transition period

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JOURNAL OF DAIRY SCIENCE 2025年第1期108卷 568-578页

作者： Vallenari, Pia F. Benedetti Hunt, Ian Horton, Brian Rose, Michael Andrewartha, Sarah Univ Tasmania Tasmanian Inst Agr Launceston Tas 7248 Australia

This review evaluates research regarding the use of sensors to predict and manage hyperketonemia (HYK) in dairy cows during the transition period, with a focus on pasture-based systems. By doing so, we assessed the accuracy of HYK-detection models, noting that no studies thus far have produced models with sufficient accuracy for practical use. sensors have been validated for their use in dairy farming, proving they produce reliable and useful information. Research is beginning to focus on the analysis of multiple sensors together as a sensor system, discovering the potential for these technologies to be a valuable aid in decision making and farm management. Of the studies that use sensors to predict and manage disease in dairy cows, few studies use data integration (the process of combining data from multiple sensors which in turn improves model accuracy), highlighting a gap in the literature. Recently published research has focused on the detection of mastitis and lameness in pasture- based systems, with less focus toward the detection of metabolic disease. This is reflected in the lack of studies that report the prevalence of metabolic diseases, such as HYK, in pasture-based systems, especially in Australia and New Zealand. It is suggested that further research focuses on (1) determining the prevalence and effect of HYK in pasture-based systems;(2) exploring the use of sensors for HYK detection in pasture-based systems;(3) improving model accuracy with data integration;and (4) confirming the economic benefit of sensors to justify the cost of investing in sensor systems.

关键词： sensor dairy hyperketonemia pasture-based precision livestock technology

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Advanced Calibration and Compensation for Nonlinearities in Ball Joint-Integrated Hall sensors for Accurate Ride Height Measurement

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IEEE ACCESS 2024年 12卷 176425-176439页

作者： Jung, Minwoo Kim, Dae-Young Kyungpook Natl Univ Ctr Selforganizing Software Daegu 41566 South Korea Soonchunhyang Univ Dept Comp Software & Engn Asan 31538 South Korea

This study is motivated by the critical need for accurate and durable ride height sensors in modern automotive systems, particularly as these sensors play a crucial role in maintaining vehicle stability and safety. Current sensors' susceptibility to environmental damage and inherent nonlinearities highlight the necessity for improved sensor designs and nonlinearity correction algorithms. To address these issues, this study proposes a novel sensor system that integrates a ball joint and a Hall sensor to enhance the accuracy and durability of ride height sensors and develops a Real-time Nonlinearity Correction and Calibration (RNCC) Algorithm for real-time correction. The RNCC algorithm combines Model Reference Adaptive Control (MRAC), Kalman filtering, and nonlinear optimization techniques to correct sensor nonlinearity in real-time, enabling precise position measurement. Experimental results demonstrate that the RNCC algorithm can calibrate sensors more quickly and accurately than existing methods and is effective in handling outlier data. This research suggests the potential for the RNCC algorithm to be applied in various real-time control applications and contribute to improving automotive suspension systems.

关键词： sensors Magnetic sensors sensor systems Accuracy sensor phenomena and characterization Optical sensors Real-time systems Ultrasonic variables measurement Reliability Calibration Hall sensor calibration ball joint integration ride height measurement nonlinear compensation real-time nonlinear compensation and calibration (RNCC)

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Flexible piezoresistive sensors with high sensitivity and ultra-wide pressure range based on alkalized MXene

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SOLID STATE IONICS 2025年 419卷

作者： Yan, Shuo Li, Xiaopeng Wang, Shifeng Liu, Xing Hu, Xianjin Liang, Mengyu Li, Ting-Ting Chen, Jie Peoples Liberat Army Naval Med Ctr Shanghai 200433 Peoples R China Tiangong Univ Sch Text Sci & Engn Tianjin 300387 Peoples R China

Flexible pressure sensors, characterized by the high sensitivity and broad detection range, have gathered significant interest for applications in wearable electronics and human-machine interfaces. Despite their potential, the development of such sensors remains a formidable challenge. In this study, we report the fabrication of a flexible, highly sensitive piezoresistive sensor featuring a pleated architecture. This sensor was crafted from thermoplastic polyurethane (TPU) as the base material and a spun film serving as the flexible substrate, utilizing an electrospinning technique. The integration of MXene, which was subjected to structural optimization via alkaline treatment with sodium hydroxide (NaOH), was achieved through an impregnation and coating process onto the TPU film. The resulting MXene-composite sensor exhibits remarkable sensitivity (2.88 kP- 1), an extensive detection range (up to 300 kPa), rapid response time (100 ms), and superior stability over more than 5000 cycles. The sensor's versatility is demonstrated through its successful deployment in capturing a variety of physiological signals from the human body, such as pulse, respiration, and swallowing, as well as in monitoring full-body motion in real-time, including movements of the fingers, wrist, and sole of the foot. Furthermore, its application as a component of flexible electronic skin underscores its immense potential for integration into physiological analysis systems, humanoid robotics, and biomedical prosthetics.

关键词： Alkaline MXene Flexible piezoresistive sensor Wide detection range Electronic skin Human motion monitoring

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