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Implementation of Non-intrusive intelligent sensor System and 5G Edge Computing Gateway for Smart Factory 4

Implementation of Non-intrusive Intelligent Sensor System an...

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4th IEEE Eurasia conference on IoT, Communication and Engineering, ECICE 2022

作者： Chuang, Shu-Min Chen, Chia-Sheng Wu, Eric Hsiaokuang National Central University Department of Computer Science and Information Engineering Chung-Li Taiwan

ISBN: (纸本)9781665482080

Smart factories integrate multiple advanced technologies to realize an efficient production line. Advanced technology is used to improve the manufacturing and production process including the technology of 5G, virtual reality, machine learning, augmented reality, and data analytics. These have been pushing manufacturing plants into smart manufacturing. The main goal of this research is to provide a non-intrusive intelligent smart sensor system and 5G edge computing gateway for the smart factory. The industrial IoT of data acquisition and processing is realized through nonintrusive intelligent sensors and a 5G edge computing gateway for the identification of mechanical faults and equipment health status on the manufacturing production line. The non-intrusive smart sensor system for data transmitters developed in this research does not require an external power supply and uses the data exchange through wireless communication architecture to collect data based on the power network and machine vibration and temperature. By using the intelligent smart sensor system to predict equipment health status for maintenance needs, the intelligent smart sensor system reduces the time and human resources for monitoring the equipment of the industrial control system. The predictive maintenance application uses timedomain signal features for fault diagnosis. It is estimated correctness of fault morphology diagnosis is 90 % and the maintenance cost can reach 15-40% of the production cost. © 2022 IEEE.

关键词： Internet of things

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Detecting Face-Touching Gestures with Smartwatches and Deep Learning networks

Detecting Face-Touching Gestures with Smartwatches and Deep ...

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International conference on Telecommunications and Signal processing (TSP)

作者： Sakorn Mekruksavanich Wikanda Phaphan Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Department of Applied Statistics Research Group in Statistical Learning and Inference Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention.

关键词： Deep learning Measurement units Prevention and mitigation Face recognition Signal processing sensor systems Telecommunications

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Poster Abstract: Enabling Non-contact, Low-Power Sensing using Tunnel Diodes

Poster Abstract: Enabling Non-contact, Low-Power Sensing usi...

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International Symposium on information processing in sensor networks (IPSN)

作者： Yuvraj Singh Bhadauria Lim Chang Quan Thaddeus C. Rajashekar Reddy Manoj Gulati Dhairya Shah Ambuj Varshney BITS-Pilani Goa Campus National University of Singapore

ISBN: (数字)9798350362015

ISBN: (纸本)9798350362022

Tracking movements in the environment of macroscopic objects enables numerous applications, from monitoring vital signs through body movements to inferring hand gestures. However, current systems overwhelmingly rely on contact-based sensors or energy-consuming radio frequency mechanisms that necessitate complex radio transceivers for receptions. We present ongoing research on a novel low-power sensor that leverages the unique characteristics of tunnel diodes. This sensor can detect minute changes in its vicinity and communicate these changes over radio waves, all while consuming under 150 microwatts of power consumption. Notably, the transmitted radio waves are processed using low-cost, off-the-shelf radio transceivers, resulting in low cost and power consumption. The sensor’s functionality stems from the sensitivity of the resonant frequency of the tunnel diode oscillators to changes in their electromagnetic surroundings. Our early work exhibits its potential for detecting a person’s breathing patterns, and hand gestures.

关键词： Radio transceivers Power demand Sensitivity Tracking Resonant frequency information processing sensor phenomena and characterization

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Blockchain-Enabled Precision Agricultural System Using IoT and Edge Computing 7th

Blockchain-Enabled Precision Agricultural System Using IoT a...

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7th International conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Sakthi, U. Thangaraj, K. Anuradha, M. Kirubakaran, M.K. Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu TN Chennai603203 India Department of Information Technology Sona College of Technology Junction Main Road TN Salem636 005 India Department of Computer Science and Engineering S.A. Engineering College Thiruverkadu TN Chennai77 India Department of Information Technology St. Joseph’s Institute of Technology TN Chennai119 India

ISBN: (纸本)9789819907687

With the improvement of Internet of Things (IoT) and edge computing, the smart agricultural system is driven by data produced by the different sensors and smart computing devices in the agricultural land. A new methodological paradigm high-performance edge computing is incorporated with blockchain implemented precision agriculture system to improve the data processing operation related with resource management. Edge computing nodes collect and analyze the sensor data locally without transforming to the remote centralized cloud server, which rises data processing speed and reduce the network latency. The blockchain technology is incorporated with machine learning algorithm to maintain secured and protected distributed database for storing smart farm details like pH, soil moisture, temperature, crop management, humidity, and water irrigation level. The proposed system improves the productivity of food items and performance of the smart agricultural system by providing useful information to the farmers to make time-based decision about the land and increase the profit. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Vibration Signals Modeling of Train Based on Distributed Optical Fiber Sensing

Vibration Signals Modeling of Train Based on Distributed Opt...

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sensors and information Technology (ICSI), International conference on

作者： Qingyuan Hu Jinyi Li Longting Huang Jiaqi Wang Zhengying Li School of Information Engineering Wuhan University of Technology Wuhan China LongZhong Laboratory Wuhan University of Technology Wuhan China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

This paper develops innovative train vibration analysis methods using Distributed Acoustic Sensing (DAS) for intelligent rail systems. While conventional systems face limited spatial coverage and electromagnetic interference, DAS enables full-range monitoring with immunity to interference and simplified installation, proving ideal for long-distance vibration detection. However, its high sensitivity introduces environmental noise, complicating signal extraction. To address this, we propose a framework that first separates signal-noise subspaces through matrix decomposition and source estimation, then enhances features via iterative singular value thresholding, and finally reconstructs principal frequency components using a multi- component sinusoidal model. This approach advances vibration processing while preserving DAS advantages, offering insights for intelligent rail development.

关键词： Vibrations Rails Technological innovation Working environment noise Estimation sensor phenomena and characterization Feature extraction Acoustics Matrix decomposition Iterative methods

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Thermal Aware Routing Protocols in WBAN 4

Thermal Aware Routing Protocols in WBAN

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4th International conference on Signal processing and information Security (ICSPIS)

作者： Kaur, Navneet Gupta, Deeksha Singla, Ripty Bharadwaj, Anuj Kavita Mansoor, Wathiq Chandigarh Univ Dept Comp Sci & Engn Mohali Punjab India MCMDAV Coll Women Dept Comp Sci & Applicat Chandigarh India Univ Dubai Dept Comp Sci & Engn Dubai U Arab Emirates

ISBN: (纸本)9781665437967

With the increasing usage of wireless technologies in communication and development of miniaturized smart bio medical sensors, Wireless Body Area Network (WBAN) has emerged as one of the crucial research field in medical applications and computer science. Routing is the most crucial and energy consuming activity of WBAN. During routing, the packets transmission may lead to rise in temperature of sensor nodes which in turns may have hazardous effects on human body cells. To evade overheating of nodes while keeping high throughput of the networks, numerous thermal aware routing protocols are designed. This paper presents an extensive review of the various thermal aware routing protocols of WBAN. Each routing protocol has been analysed in terms of its design techniques, routing approach, application, performance and pros and cons.

关键词： Thermal Aware Routing Wireless Body Area networks Biomedical sensors Wireless sensor networks

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Obstruction Detection Based on Light Field Image processing

Obstruction Detection Based on Light Field Image Processing

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intelligent Communications and Computing (ICC), International conference on

作者： Shijie Che Gujing Zhang Ning Wang School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Patent Examination Cooperation (Henan) Center of the Patent Office CNIPA Zhengzhou China School of Electrical and Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798331530365

ISBN: (纸本)9798331530372

Detection of foreign object intrusion on railway track is a critical research topic to railway information systems. Current imaging systems often suffer from a lack of spatial scene information due to the reliance on single visible-light sensors. A light-field imaging-based scheme for foreign object intrusion detection is proposed. By extracting saliency features from light field data, it leverages the capability of light field cameras to simultaneously capture both RGB image information and depth information. Firstly, traditional manifold sorting (MR) algorithm is employed to improve the accuracy of RGB image detection. Then the salient features of depth images are extracted as a supplement to RGB image processing. Finally, an effective data fusion method is proposed to further highlight the object contour and suppress the background, so as to improve the detection performance. The experimental results show that the proposed light-field-based scheme has the application ability to detect foreign objects in rail transit environment efficiently.

关键词： Rails Manifolds Intrusion detection Object detection Feature extraction Light fields Rail transportation sensor systems sensors Sorting

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Lie algebra-based multi-validation model for 3D human pose estimation

Lie algebra-based multi-validation model for 3D human pose e...

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2024 International conference on Computer Application and information Security, ICCAIS 2024

作者： Wang, Zhenrong He, Zanic Huang, Jiang Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Guangdong Shenzhen618060 China Huamei International School Guangdong Guangzhou512520 China

ISBN: (纸本)9781510689312

We investigate 3D stereo view human posture estimation. We investigate a straightforward architecture that uses intermediate 2D pose predictions to reason, in contrast to many methods that attempt to directly predict 3D stance from picture data. Our strategy is founded on two important findings. (1) 2D posture estimation has been transformed by deep neural networks, which can now provide precise 2D predictions even for poses with self-occlusions. (2) 2D stereo vision is fruitful enough to reconstruct 3D vision, making it tempting to "lift" predicted 2D poses to 3D through stereo triangulation. In this paper, we propose to model skeleton parts as the trajectory in the Lie group SE(n)×...×SE(n). The 3D pose estimation is performed based on the resulting trajectories mapped to a vector space, the Lie algebra, where the 3D pose of the human body is obtained by pose construction in Lie algebra space from a series of raw images from a different view, thus making sense how it reaches good performance. Our method is compared with other stereo-vision-based methods and outperforms them. Especially, our method shows good performance compared with the Microsoft Kinect sensor. © The Authors.

关键词： Vector spaces

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Detection of gate skipping behavior based on machine vision and evolutionary neural network 7

Detection of gate skipping behavior based on machine vision ...

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7th International conference on intelligent Computing and Signal processing, ICSP 2022

作者： Ren, Fei Liu, Ye Ren, LiQun Xu, LiBing Devaraj, Madhavi Li, HongSheng School of Information Technology Mapua University Manila Philippines School of Automation Nanjing Institute of Technology Nanjing China Nanjing Super Control Electronic Technology Co. Ltd Technology Innovation Department Nanjing China College of Veterinary Medicine Yangzhou University Yangzhou China

ISBN: (纸本)9781665478571

Machine vision is more and more widely used in the field of intelligent transportation. In view of the deficiency of traditional gate sensor recognition system in detecting gate skipping events, an evolutionary neural network based on machine vision is proposed to detect gate skipping behavior(skip the gate to avoid buying tickets). This method extracts four eigenvalues based on the changes of picture pixel values and template matching rate at the gate when the gate skipping event occurs, and then classifies the eigenvalues based on the improved evolutionary neural network by using the global search ability of mind evolutionary algorithm and the strong generalization of BP neural network to determine whether the gate skipping event occurs. The experimental results show that the improved evolutionary neural network algorithm can effectively detect the gate skipping event. It has the advantages of good accuracy, no additional sensors and low cost, and has a good engineering application prospect. © 2022 IEEE.

关键词： Computer vision

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An Optimized sensors Associated Forest Fire Prediction and Reporting System Based on Internet of Things Technology

An Optimized Sensors Associated Forest Fire Prediction and R...

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Innovative Computing, intelligent Communication and Smart Electrical Systems (ICSES), International conference on

作者： P. Deepak Nidamanuri Srinu Benisha Maria Xavier R. Sravani Nandurkar Y Suha Afaneh Dept of Computer Science and Engineering Einstein Engineering college Tirunelveli Department of CSE QIS College of Engineering and Technology AndhraPradesh Department of ECE Jeppiaar Institute of Technology Kunnam Chennai Tamilnadu India Department of S&H QIS College of Engineering and Technology AndhraPradesh Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Department of cybersecurity faculty of information technology Zarqa university Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Forest fires pose a significant threat to the environment, leading to the destruction of ecosystems and contributing to climate change. This paper presents an optimized sensor-based forest fire prediction and reporting system leveraging Internet of Things (IoT) technology. The system integrates Wireless sensor networks (WSNs) and IoT-enabled devices to monitor environmental parameters, including temperature, CO2, and smoke levels. The system demonstrated high sensor accuracy, with temperature sensors showing a maximum deviation of ±0.5°C and gas sensors displaying an accuracy of ±2 ppm for CO2. Smoke detection accuracy was within ±0.05 ppm, making the system reliable for early fire detection. The system's response time improved significantly with increasing temperatures, detecting fire events within 10 seconds for temperatures above 100°C. The Wireless sensor Network maintained a packet delivery ratio (PDR) of over 99% and an average latency of 200ms under high node density, ensuring timely transmission of critical data. With power consumption optimized for long-term deployment, sensor nodes were estimated to operate for up to 24 months. Overall, this system offers a cost-effective, accurate, and efficient solution for real-time forest fire monitoring and response, with potential applications in large, remote forest areas.

关键词： Temperature sensors Temperature measurement Wireless sensor networks Accuracy Forestry Real-time systems Internet of Things Time factors Reliability Monitoring

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