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The integration of Machine Learning in Microwave Dielectric Sensing: From Design to Postprocessing

IEEE MICROWAVE MAGAZINE 2025年第3期26卷 60-77页

作者： Seyyedmasoumian, Sevda Chavoshi, Maede Mertens, Marie Schreurs, Dominique Katholieke Univ Leuven B-3001 Leuven Belgium Polytech Montreal Montreal PQ H3T 1J4 Canada

Microwave sensors are popular for characterizing materials non-invasively. Traditionally, designing these sensors and processing the data obtained from them requires a lot of trial and error and complex processing algorithms. Machine learning (ML) can offer a faster and more efficient way to improve the material characterization process through optimized sensor shapes, improved sensitivity, and more efficient and accurate material properties extraction. This article reviews how ML techniques are used in microwave dielectric sensing to improve sensor design, data processing, and measurement reliability. It also explores current challenges and opportunities, highlighting how ML can help sensors adapt to changing conditions and optimize performance in various situations. Overall, the research shows the important role ML can play in making microwave dielectric sensing more efficient, accurate, and scalable.

关键词： Microwave measurement Accuracy Sensitivity Machine learning sensor phenomena and characterization Microwave theory and techniques Reliability engineering Dielectrics Microwave sensors

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Unveiling the impact of 2-D materials on the gas sensing properties of metal oxides: A review

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JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING 2025年第2期13卷

作者： Bisht, Sakshi Dhariwal, Neeraj Yadav, Preety Chahar, Meenu Singh, Devender Kumar, Vinod NSUT Dept Phys Mat Anal & Res Lab New Delhi 110078 India Chaudhary Ranbir Singh Univ Dept Phys Jind 126102 Haryana India Maharshi Dayanand Univ Dept Chem Rohtak 124001 Haryana India

The integration of 2D materials with metal oxides has emerged as a promising strategy to enhance gas sensing properties, offering significant improvements in sensitivity, selectivity, and response times. This review thus critically discusses the improvements on the gas sensor technologies enabled by integration of 2D materials like MoS2, g-C3N4, Mxene, rGO, CNT, PANI and Black Phosphorus into different metal oxide materials. Several synthesis techniques such as sol-gel process, hydrothermal process, chemical vapour deposition, sputtering and electrospinning have been presented with emphasis on their effects sensor characteristics. Creating heterojunctions and utilizing properties of 2D materials in the structure of the composite sensors enables them to display a high sensitivity to gas molecules, including their low concentrations and ambient temperature. These hybrid nanostructures offer improved surface area, active sites, and electronic properties, enabling the detection of low gas concentrations at room temperature. This paper offers a background for the current state, emerging prospects, and obstacles, as well as future advances regarding hybrid nanostructures, demonstrating the great opportunity they offer in the field of gas sensors for environmental and health concerns, and safety and industrial applications. The findings reveal their superior performance over conventional sensors, addressing key challenges in the field.

关键词： Metal oxide Mox 2-D materials Gas sensor Heterojunction Thin film

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Design of Electronic Nose Based on MOS Gas sensors and Its Application in Juice Identification

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sensors 2025年第4期25卷 1205-1205页

作者： Zhang, Yafei Zhao, Yongli Jiang, Feiyang Lai, Rongjie Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Due to its advantages of fast response, low cost, low power consumption, and easy integration, Metal Oxide Semiconductor (MOS) gas sensor is widely used in the electronic nose system (E-nose). However, the MOS sensor has cross-sensitivity to different gases, which can impair the performance of the E-nose. Another key factor affecting the E-nose performance is the extraction method of gas features. In order to overcome the above shortcomings, an E-nose system that can modulate the operating temperature of gas sensors during the gas detection was designed in this paper, and a new gas feature extraction algorithm named Boruta-Recursive Feature Elimination (Boruta-RFE) was proposed based on the designed system. In order to verify the effectiveness of the designed system and the gas feature extraction algorithm, they were applied to the identification of different categories of apple juice. The experimental results show that more gas features can be obtained by modulating the operating temperature of the gas sensors, and the Boruta-RFE algorithm can effectively reduce the dimensionality of the original gas feature dataset, and can quickly select the key gas features, so as to effectively improve the identification accuracy of the E-nose system.

关键词： electronic nose feature extraction classification recognition juice identification

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Bioinspired Artificial Infrared Neural System Based on Single-Crystal Thin Films for Spatiotemporal Fusion Imaging

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IEEE sensors JOURNAL 2024年第9期24卷 15065-15072页

作者： Zhao, Zebin Luo, Wenbo Pan, Xinqiang Xie, Qin Wang, Jiejun Xie, Yiduo Shuai, Yao Wu, Chuangui Zhang, Wanli Univ Elect Sci & Technol China Sch Integrated Circuit Sci & Engn Chengdu 610054 Peoples R China Univ Elect Sci & Technol China State Key Lab Elect Thin Films & Integrated Devic Chengdu 610054 Peoples R China

Artificial sensory memory is a novel way to solve the contradiction between the information explosion in the era of the Internet of Things (IoT) and the high demand for hardware resources by artificial intelligence, which achieves the integration of sensory and memory by mimicking biological neural system. Here, we innovatively propose an artificial infrared neural system (AINS) based on single-crystal thin films, consisting of artificial receptors, artificial afferent fibers, and artificial synapses. It is implemented by a pyroelectric sensor based on lithium tantalate (LT) thin film, a threshold-based signal processing module, and a memristor based on lithium niobate (LN). After demonstrating the detection ability of the pyroelectric sensor and the short-term plasticity (STP) and time coding ability of the memristor, we successfully coupled them together to achieve the conversion of pyroelectric current (PEC) signal to postsynaptic current (PSC). Based on the above characteristics, the output of an AINS-based visual array for multiple dynamic hand-waving action was further simulated. The results showed that our AINS can achieve intra-recognition of historical events via spatiotemporal fusion imaging. The proposed AINS realizes sensing, memorizing, and processing of sensor information in the analog domain, opening a novel avenue for sensor signal processing.

关键词： sensors Memristors sensor phenomena and characterization Synapses Infrared sensors Temperature sensors Pyroelectric devices Artificial sensory memory memristor pyroelectric sensor single-crystal thin film spatiotemporal fusion imaging

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A Skin-Inspired PDMS Optical Tactile sensor Driven by a Convolutional Neural Network

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IEEE sensors JOURNAL 2024年第6期24卷 8651-8660页

作者： Yue, Shichao Xu, Minzhi Che, Zifan Southeast Univ Dept Mech Engn Nanjing 211100 Jiangsu Peoples R China KTH Royal Inst Technol Dept Intelligent Syst S-10044 Stockholm Sweden KTH Royal Inst Technol Sch Elect Engn & Comp Sci S-10044 Stockholm Sweden

Tactile sensors play a crucial role in enhancing the integration of automation, robotics, and biomedical equipment, particularly in perceptual functions. Optical fiber-based tactile sensors have gained significance due to their robustness and immunity to electromagnetic interference. However, existing optical fiber-based tactile sensors face limitations related to bio-imitation, scalability, and precise data processing algorithms. This study introduces a novel skin-inspired polydimethylsiloxane (PDMS)-manufactured tactile sensor utilizing a structured light source with low-cost light-emitting diodes and a multimode optical fiber, coupled with tactile information processing through a trained convolutional neural network (CNN). Specklegram images captured from the optical fiber are analyzed for force amplitude and tactile location. The CNN is trained, validated, and tested, achieving accuracies of 99.6%, 99.5%, and 99%, respectively. The tactile sensor demonstrates a spatial resolution of 2 mm and a force-sensing range up to 3 N. The confusion matrix, based on classification results, reveals only three misclassifications out of 315 tests, indicating a mean absolute error (MAE) of 0.95%. The spatial resolution and force-sensing capabilities, coupled with the machine learning approach of the proposed tactile sensor, showcase promising potential for future applications in tactile embodiment.

关键词： Neural network optical fiber tactile sensor

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Flexible tactile sensors inspired by bio-mechanoreceptors

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BIOsensors & BIOELECTRONICS 2025年 267卷 116828页

作者： Ren, Miaoning Wu, Qiushuo Huang, Xian Tianjin Univ Sch Precis Instrument & Optoelect Engn 92 Weijin Rd Tianjin 300072 Peoples R China Tianjin Univ State Key Lab Precis Measuring Technol & Instrumen 92 Weijin Rd Tianjin 300072 Peoples R China

Mechanoreceptors in animals and plants play a crucial role in sensing mechanical stimuli such as touch, motion, stretch, and vibration. Learning from the mechanisms of mechanoreceptors may facilitate the development of bionic tactile sensors, leading to higher sensitivity, spatial resolution, and dynamic ranges. However, very little literature has comprehensively discussed the relevance of biological tactile sensing systems and machinelearning-based bionic tactile sensors. This review first introduces the structural features, signal acquisition and transmission mechanisms, and feedback processes of both plant and animal mechanoreceptors, and then summarizes the efforts to develop bionic tactile sensors by mimicking the morphologies and structures of mechanoreceptors in plants and animals. Additionally, the integration of artificial intelligence approaches with these sensors for data processing and analysis are demonstrated, followed by the perspectives on current challenges and future trends in bionic tactile sensors. This review addresses the challenges in developing high-performance tactile sensors by focusing on surface microstructures and biological mechanoreceptors, serving as a valuable reference for developing bionic tactile sensors with enhanced sensitivity and multimodal sensing capabilities. Furthermore, it may benefit the future development of smart sensing systems integrated with artificial intelligence for more precise object and texture recognition.

关键词： Mechanoreceptor Tactile sensor Flexible electronics Artificial Intelligence Bionic sensor

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Energy Optimization in Smart Agriculture Wireless sensor Networks Based on the RK4PSO Algorithm

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IEEE sensors JOURNAL 2025年第8期25卷 13797-13809页

作者： Wang, Zuoxun Wu, Wangyao Sui, Jinxue Zhao, Guojian Pang, Chuanzhe Xu, Liteng Chinese Acad Sci Beijing 100094 Peoples R China Shandong Technol & Business Univ Sch Informat & Elect Engn Yantai 264005 Shandong Peoples R China

Inspired by the particle swarm optimization-based low-energy adaptive hierarchy of clusters (PSO-LEACH) protocol, this article proposes a particle swarm optimization algorithm with fourth-order Runge-Kutta (RK4PSO) algorithm, aimed at optimizing cluster head (CH) selection within the PSO-LEACH protocol, thereby reducing network energy consumption (EC). First, a fourth-order Runge-Kutta numerical integration method is introduced to reconstruct the particle motion model, significantly improving the accuracy of particle position and velocity estimation, thus enhancing the global search capability of the algorithm. Second, a population evolution mechanism based on an elite retention strategy is designed, with dynamic integration of the global optimal solution to guide the population's iterative direction, thereby improving the algorithm's convergence speed. Furthermore, adaptive individual and social cognitive coefficients are employed to adjust the particle weight distribution, further improving its robustness. Finally, simulation results demonstrate that the RK4PSO-LEACH protocol significantly improves the compactness of the cluster structure, reduces intracluster communication distance and lowers communication EC. Under the condition of a packet size of 3500 bits, the network lifetime before the death of the first node is extended by 103% and 56.4% compared to PSO-LEACH and traditional LEACH protocols, respectively, thus validating the significant advantages of the proposed algorithm in optimizing energy efficiency in wireless sensor networks (WSNs).

关键词： Wireless sensor networks Energy consumption Heuristic algorithms Clustering algorithms Routing protocols Optimization Particle swarm optimization Energy efficiency Intelligent sensors Business Energy optimization fourth-order Runge-Kutta low energy adaptive hierarchy of cluster (LEACH) protocol particle swarm optimization (PSO) wireless sensor networks (WSNs)

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Triboelectric Pressure sensor With Microstructured PDMS for Human Motion and Gait Pattern Monitoring

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 89-97页

作者： Das, Partha Sarati Rondeau-Gagne, Simon Jalal Ahamed, Mohammed University of Windsor Department of Mechanical Automotive Materials Engineering WindsorONN9B 3P4 Canada University of Windsor Department of Chemistry and Biochemistry WindsorONN9B 3P4 Canada

This work presents a low-cost, out-of-cleanroom method for fabricating microstructured polydimethylsiloxane (PDMS) films for triboelectric pressure sensors, using a tape mold replication process that eliminates the need for expensive equipment. A triboelectric nanogenerator (TENG)-based pressure sensor is developed with materials, including PDMS, polyimide (kapton) films, and copper electrodes. The TENG-based pressure sensors have been successfully applied to monitor various human motions, such as walking (via insole integration), tactile sensing (via cup integration), foot pressure detection, and tracking movements such as elbow and finger bending, as well as jumping. The flexible sensor demonstrated high linearity (R2 =0.9817), a quick response time (100 ms), and a reliable loading and unloading rate (10 Hz). The sensor showed stable output across diverse forces and frequencies, which is ideal for flexible and wearable applications. These findings highlight the potential of the proposed TENG fabrication method for applications in wearable pressure sensors, self-powered electronic skin (e-skin) for humanoid robots, and human - machine interaction systems. © 2022 IEEE.

关键词： Triboelectricity

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Facile Preparation of Carbon Nanotube-Based Skin-Like Pressure sensors

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SMALL 2025年第18期21卷 e2408795页

作者： Yu, Xiao Adronov, Alex McMaster Univ Brockhouse Inst Mat Res Dept Chem & Chem Biol 1280 Main St W Hamilton ON L8S 4M1 Canada

Flexible sensors have garnered significant interest for their potential to monitor human activities and provide valuable feedback for healthcare purposes. Single-walled carbon nanotubes (SWNTs) are promising materials for these applications but suffer from issues of poor purity and solubility. Dispersing SWNTs with conjugated polymers (CPs) enhances solution processability, yet the polymer sidechains can insulate the SWNTs, limiting the sensor's operating voltage. This challenge can be addressed by incorporating a self-immolative linker into the sidechain of a poly(fluorene-co-phenylene) polymer, facilitating the fast and clean removal of sidechains and enabling the generation of high-conductivity SWNT materials. In this work, the integration of this advanced material with polydimethylsiloxane (PDMS) to create skin-like ultra-wrinkled film surfaces in a simple, cost-effective, and highly reproducible manner is demonstrated. The sensors exhibit remarkable sensitivity (1,655 kPa(-)(1)) across a wide dynamic range (0.003-70.1 kPa, R2 = 0.9931) when the wrinkle axis is aligned perpendicularly to the interdigitated electrode fingers. The sensor shows an almost instantaneous pressure response and maintains excellent stability. This sensor can monitor various human motions, from low-intensity activities such as breathing, pulse, and voice vibrations to high-intensity actions like walking and jumping, highlighting their potential use in wearable human health monitoring systems.

关键词： carbon nanotube conjugated polymer pressure sensor self-immolative

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Improving Sewer Damage Inspection: Development of a Deep Learning integration Concept for a Multi-sensor System

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sensors 2024年第23期24卷 7786页

作者： Jung, Jan Thomas Reiterer, Alexander Univ Freiburg Dept Sustainable Syst Engn Georges Kohler Allee 10 D-79110 Freiburg Germany Fraunhofer Inst Phys Measurement Tech IPM Georges Kohler Allee 301 D-79110 Freiburg Germany

The maintenance and inspection of sewer pipes are essential to urban infrastructure but remain predominantly manual, resource-intensive, and prone to human error. Advancements in artificial intelligence (AI) and computer vision offer significant potential to automate sewer inspections, improving reliability and reducing costs. However, the existing vision-based inspection robots fail to provide data quality sufficient for training reliable deep learning (DL) models. To address these limitations, we propose a novel multi-sensor robotic system coupled with a DL integration concept. Following a comprehensive review of the current 2D (image) and 3D (point cloud) sewage pipe inspection methods, we identify key limitations and propose a system incorporating a camera array, front camera, and LiDAR sensor to optimise surface capture and enhance data quality. Damage types are assigned to the sensor best suited for their detection and quantification, while tailored DL models are proposed for each sensor type to maximise performance. This approach enables the optimal detection and processing of relevant damage types, achieving higher accuracy for each compared to single-sensor systems.

关键词： automated inspection damage detection sewer pipes artificial intelligence robotic inspection computer vision urban infrastructure 3D vision point cloud LiDAR

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