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2nd International conference on advanced Nanomaterials and applications, ICANA 2024

2nd International Conference on Advanced Nanomaterials and A...

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2nd International conference on advanced Nanomaterials and applications, ICANA 2024

ISBN: (纸本)9789819608270

The proceedings contain 10 papers. The special focus in this conference is on advanced Nanomaterials and applications. The topics include: GO/ZnO—Intensified Hybrid Nanomaterial as an Electrochemical sensor for Formaldehyde Detection in Environmental applications;synthesis of 4-Substituted 2,2′;6′,2″-Terpyridine Copper Complexes and Study of Their Anti-cancer Activities;synthesis of Cobalt Complexes and Study of Their Catalytic Activities in Various Organic Transformations;Recent Developments in Doped TMO and TMO-Based Composites for Energy Harvesting and Pollutant Removal systems;recent Progress in Multiferroics and Its Composites;dilute Magnetic Semiconductors and Its applications—An Overview;study of Melting Phenomenon of a Palladium Nanocluster Using Molecular Dynamics Simulations.

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Design and Simulation of Surface Acoustic Wave sensor in COMSOL Multiphysics for Detection of Acetone

Design and Simulation of Surface Acoustic Wave Sensor in COM...

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2025 International conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Borra, Praneeth Korapati, Prasanti Sravanam, Vamsi Bala, Shanmuk Tikkisetty, Dikishitha Sree Velagapudi Ramakrishna Siddhartha Engineering College Department of Electronics and Instrumentation Engineering Vijayawada India

ISBN: (纸本)9798331509828

The increasing demand for efficient and reliable gas sensors has propelled the exploration of novel materials and advanced simulation techniques. This study presents the design and development of a gas sensor that detects acetone using molybdenum disulfide (MoS2) as the sensing material. Acetone is a major volatile organic compound that has applications in a variety of industries, including healthcare and environmental monitoring. Acetone gas is a respiratory symptom that indicates diabetes. The high sensitivity, stability and cost of MoS2 make it a good sensor material. The sensor design involves the integration of MoS2 thin films onto a substrate, with interdigitated electrodes facilitating the detection mechanism. COMSOL Multiphysics, a versatile simulation software, is employed to model the sensor's performance under different operating conditions and geometrical configurations. The simulation framework incorporates electrochemical processes, mass transport phenomena, and electrical properties to accurately represent the sensor's *** was found that the resistance of the sensor layer was reduced in the presence of acetone gas. The simulation was carried out using COMSOL Multiphysics. © 2025 IEEE.

关键词： Molybdenum disulfide

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Application of Wireless sensor Technology in Urban Energy Management 24

Application of Wireless Sensor Technology in Urban Energy Ma...

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8th International conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Yajuan Chen, Jie Gong, Zhijia College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou China

ISBN: (纸本)9798400710094

The rapid growth of urbanization has led to an increasing need for efficient energy management systems to optimize energy consumption and reduce environmental impacts. Wireless sensor technology emerges as a pivotal solution, providing advanced capabilities in real-time monitoring, control, and management of urban energy systems. This paper explores the practical applications of wireless sensor networks (WSN) in urban energy management, highlighting their role in smart grids, intelligent building systems, smart street lighting, public transportation, water management, and renewable energy integration. Additionally, it addresses the prominent challenges such as data security, sensor durability, network scalability, data management, energy consumption, and infrastructure integration. [1-2] The paper further discusses optimization pathways to enhance the effectiveness and sustainability of WSNs in urban energy management. By overcoming these challenges and leveraging the potential of wireless sensor technology, cities can achieve significant energy savings, operational efficiencies, and contribute to broader environmental sustainability goals. © 2024 Copyright held by the owner/author(s).

关键词： Green buildings

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Comparison of Actual Performance between CDMA and DDMA Waveforms in Vehicle Radar Application 16

Comparison of Actual Performance between CDMA and DDMA Wavef...

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16th International conference on Signal Processing systems, ICSPS 2024

作者： Wang, Wanbin Zou, Lin School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781510689251

The Millimeter-wave radar has become an important sensor for advanced driver assistance systems due to its advantages such as environmental adaptability, low cost and detection accuracy. Most of current vehicle radars use Multiple Input Multiple Output (MIMO) technology to obtain better angular resolution. Among all MIMO radar waveforms, both CDMA and DDMA meet the requirements of next-generation high-performance radars and may become the mainstream radar waveforms in the future. Performance comparisons of the two waveforms are scattered in a large number of literatures and they are all from a theoretical point of view. At present, there is no literature comparing the practical performance of CDMA and DDMA. AWR6843 is a high-precision millimeter-wave radar sensor widely used in automotive safety, environmental monitoring and other applications. This SOC chip has been adopted in numerous automotive radar products. Thus, based on the data collected by a TI's AWR6843 radar evaluation board, this paper compares the performance of CDMA and DDMA in terms of waveform orthogonality, parameter estimation capability, and processing complexity. In addition, the paper discusses the merits and demerits of each technology and the limitations of its practical application, which provides an important reference for the optimization and technical route selection of future radar systems. © 2025 SPIE.

关键词： advanced driver assistance systems

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Design, Fabrication and Calibration of an Embroidery Textile Tactile sensor Array 25th

Design, Fabrication and Calibration of an Embroidery Text...

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25th Annual conference on Towards Autonomous Robotic systems, TAROS 2024

作者： Hou, Ningzhe Pontin, Marco Costi, Leone Maiolino, Perla Oxford Robotics Institute University of Oxford Oxford United Kingdom University of Genoa Liguria Genoa Italy

ISBN: (纸本)9783031720581

This paper presents the development and evaluation of an embroidery textile tactile sensor, designed for advanced pressure mapping applications. Utilizing a unique embroidery technique with conductive threads and incorporating a Velostat piezoresistive layer, the sensor array is configured in an 8×8 grid, offering multiple contact detection over large areas. The sensor’s performance is validated through several experiments, including quasi-static loading and pressure mapping with various weights and hand gestures. This research proposes a flexible, scalable solution for tactile sensing that is both cost-effective and high-performing. The findings indicate significant potential for the sensor in real-world applications, promising advancements in how tactile information is captured and utilized. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mapping

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A Predictive Maintenance Platform for a Conveyor Motor sensor System Using Recurrent Neural Networks 5th

A Predictive Maintenance Platform for a Conveyor Motor Senso...

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5th International conference on Neural Computing for advanced applications (NCAA)

作者： Kiangala, Kahiomba Sonia Wang, Zenghui Univ South Africa UNISA Dept Elect & Smart Syst Engn ZA-1709 Johannesburg Gauteng South Africa

ISBN: (纸本)9789819770007;9789819770014

Since the effusion of Industry 4.0 (I40) and smart manufacturing, predictive maintenance (PdM) has become critical to prevent severe system breakdowns and costly production downtime in various industries. Several state-of-the-art Artificial Intelligence (AI) approaches, such as machine learning models (ML), empower the PdM design concept to produce more accurate outcomes. In this study, we propose the development of a PdM platform that effectively detects future possible warnings and failures in the vibration speed sensor of a conveyor motor. We build the platform backend using recurrent neural networks (RNN) regression ML models to predict vibration speed values and their trends. We train our RNN models using univariate time-series historical vibration sensor data recorded over the years. The experimental results demonstrate the robustness of our platform built on RNN models compared to other traditional regression machine learning models such as Extreme Gradient Boosting (XGboost). We compare the effectiveness of the PdM platform with two RNN models: a basic RNN model and a long short-term memory (LSTM) model. Unlike other regression PdM systems focusing on predicting devices Remaining Useful Life (RUL), our PdM platform forecasts and generates new vibration sensor data based on the desired future time range. It also pinpoints set warning and failure values and indicates when these faults are more likely to occur. Factories can utilize this feature for analytics purposes.

关键词： Conveyor Motor sensor Predictive Maintenance (PdM) Recurrent Neural Networks (RNN) Regression Models time-series data

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Flexible PEDOT:PSS/PVA Temperature sensor for Cost-Effective Wearable Medical Devices 5

Flexible PEDOT:PSS/PVA Temperature Sensor for Cost-Effective...

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5th National and the 1st International conference on Applied Research in Electrical Engineering, AREE 2025

作者： Teymourmoghadam, Jaefar Chegini, Ebrahim Hezare Sevom Co. 7-Niloofar Sq. 1533-874-417 Tehran Iran Shahid Sattari Aeronautical University of Science and Technology Electrical Engineering Department Tehran Iran

ISBN: (纸本)9798350377170

The development of flexible and biocompatible temperature sensors is critical for wearable healthcare applications, enabling continuous and non-invasive body temperature monitoring. This paper presents a temperature sensor based on a PEDOT:PSS/PVA composite fabricated using a straightforward drop-casting method encapsulated with polydimethylsiloxane (PDMS) for enhanced durability and biocompatibility. The device is based on the negative temperature coefficient (NTC) charateristics of the fabricated composite polymer, facilitating accurate measurement of temperature alterations. The fabricated sensor demonstrates consistent performance across a physiological temperature range (32°C-40°C), achieving a temperature coefficient of resistance (TCR) of 0.68%°. Measurements show a stable decrease in resistance when the sensor is in touch with human skin, reflecting its sensitivity to skin temperature changes. Experimental results show that the resistance returned to its initial baseline;highlighting the repeatability of the sensor and its reliability for dynamic monitoring. The encapsulation with PDMS further enhances the flexibility of the sensor and protects it against environmental hazards while maintaining its electrical and mechanical performance intact. Compared to existing flexible temperature sensors, this device offers a practical balance of sensitivity, flexibility, and ease of fabrication. Its simple design and scalable fabrication make it suitable for various wearable medical applications, including fever detection, thermal mapping, and integration into advanced health monitoring IoT systems. This study contributes to the advancement of flexible electronics for personalized healthcare solutions. © 2025 IEEE.

关键词： Polydimethylsiloxane

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Application of big data analysis in improving the efficiency of sensor reliability testing

Application of big data analysis in improving the efficiency...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Wan, Wangjun Zhao, Minghua Song, Weijie Dong, Chunfa Ling, Zhixiang State Grid Jinan Power Supply Company Shandong Jinan250000 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

In modern technological applications, the reliability of sensors plays a crucial role in ensuring the proper functioning of systems. Therefore, sensor reliability testing has become an important aspect of quality control and system maintenance. However, traditional testing methods often face challenges in terms of efficiency and accuracy, especially when dealing with large-scale sensor data. In order to address these challenges, this paper proposes an innovative approach to improving sensor reliability testing efficiency by leveraging big data analytics, particularly through the application of neural networks. The method involves extracting features from different scales and abstraction levels using a multi-scale neural network architecture. Subsequently, the extracted detailed information is fused with high-level semantic features to minimize information loss, enhance feature utilization, and obtain advanced features, thereby improving the performance of the network. Additionally, a spatial attention module is introduced into the network to extract critical information and enhance overall network performance, effectively detecting sensor faults. Through comprehensive experiments conducted on real-world sensor data, the effectiveness and efficiency of the proposed method have been demonstrated, highlighting its significant potential in enhancing sensor reliability testing processes. This research not only contributes to the advancement of sensor reliability testing but also underscores the broader potential of big data analytics in improving the performance and reliability of complex technological systems. © 2025 SPIE.

关键词： Data reliability

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Machine Learning for sensor-Based Handwritten Character Recognition: A Brief Survey 21st

Machine Learning for Sensor-Based Handwritten Character Reco...

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21st International conference on Distributed Computing and Intelligent Technology

作者： Singh, Shashank Kumar Chaturvedi, Amrita IIT BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

ISBN: (纸本)9783031814037;9783031814044

Handwriting recognition technologies have evolved significantly, with recent advancements incorporating wearable devices such as smartpens, smartwatches, and smartphones to capture motion and gesture data. This paper reviews various efficient handwriting recognition models that utilize inertial sensors, visual sensors, EMG sensors, and acoustic and Wi-Fi signals. Inertial sensor-based approaches often use accelerometers and gyroscopes to collect three-dimensional data during handwriting, with models achieving high accuracy in recognizing digits and alphabets. Visual sensor-based techniques, leveraging cameras and image processing, have been fundamental in developing Optical Character Recognition (OCR), further enhanced by deep learning algorithms. EMG sensors have been explored for their potential to capture muscle activity for gesture recognition. At the same time, acoustic and WiFi signal-based methods offer innovative ways to recognize handwriting through sound and signal variation. Despite the progress, challenges such as sensor drift, data generalization, and computational requirements persist. Integrating multi-modal sensors and advanced machine learning techniques holds promise for overcoming these limitations and improving the accuracy and usability of handwriting recognition systems across diverse applications.

关键词： Inertial sensor Machine Learning Visual sensors

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Real-Time Indoor Target Tracking Using IoT and Wireless sensor Networks 4

Real-Time Indoor Target Tracking Using IoT and Wireless Sens...

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4th International conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Katakwar, Sachin R. Jajulwar, Kapil K. Tadas, Surekha M. Tech Communication Engineering G H Raisoni College of Engineering Nagpur India G H Raisoni College of Engineering Department of Electronics and Telecommunication Engineering Nagpur India

ISBN: (纸本)9798331523923

Indoor target tracking presents significant challenges across various fields such as security surveillance, industrial automation, and healthcare. This research paper outlines the design and implementation of a reliable direction monitoring system aimed at achieving accurate indoor target tracking using IoT and wireless sensor networks. By utilizing advanced sensor technologies and sophisticated data processing algorithms, the proposed system enables real-time monitoring and precise localization of targets within indoor spaces. The combination of IoT devices and wireless sensor networks facilitates effective data collection, analysis, and visualization, thereby improving the overall performance of the monitoring system. The results indicate that the developed system is effective in accurately tracking and localizing targets, fulfilling the essential requirement for efficient indoor direction monitoring solutions. The paper concludes with a discussion on the practical implications and potential applications of this system, emphasizing its importance in enhancing indoor target localization across diverse sectors. © 2025 IEEE.

关键词： Indoor positioning systems

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