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IEEE Annual conference on Wireless and Microwave Technology (WAMICON)

作者： Samira Balbach Christian Dorn Lena Schindler-Gmelch Matthias Berking Amelie Hagelauer School of Computation Information and Technology Technical University of Munich Germany Chair of Clinical Psychology and Psychotherapy Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Fraunhofer Institute for Electronic Microsystems and Solid State Technologies EMFT Germany

ISBN: (数字)9798331509606

ISBN: (纸本)9798331509613

Today's electromyography (EMG) sensor systems usually require complicated setups for data acquisition and processing. Especially for facial muscle sensing, small and portable systems are required to allow longer and more realistic measurements. Research in the area of affective states and distinct emotions requires larger studies with extended recording times. Therefore, the acceptance of the sensor by the subjects is crucial. In collaboration with a team of clinical psychologists, this work presents a Bluetooth Low Energy 5.4 enabled wireless surface EMG sensor with edge pre-processing and post-processing on a PC for data evaluation.

关键词： Wireless communication Wireless sensor networks Protocols Psychology Electromyography Facial muscles sensor systems sensors Recording Bluetooth Low Energy

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IoT Innovations for Enhanced Agricultural Practices and Sustainable Farming

IoT Innovations for Enhanced Agricultural Practices and Sust...

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International conference on Inventive Computation Technologies (ICICT)

作者： Chhinder Kaur Dhaliwal Bharti Sharma Samrity Sandeep Kaur Richa Chandel Department of Computer Applications Chandigarh Business School Of Administration Landran Mohali Punjab India Department of Computer Applications Chandigarh College Of Technology Landran Mohali Punjab India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Smart farming represents a forward-thinking transition by incorporating information and communication technology into agriculture. This refers to the use of equipment, machineries and sensors within network based hi-tech farming systems. The introduction of innovative technologies like IoT and cloud computing is expected to usher in robotics and artificial intelligence, challenging traditional approaches while presenting various hurdles. This paper explores tools and equipment in wireless sensor applications for IoT agriculture, addressing potential challenges when merging technology with conventional farming, offering technical insights applicable across the entire crop lifecycle, including sowing, harvest, packing, and transportation.

关键词： Wireless communication Smart agriculture Wireless sensor networks Technological innovation Crops Transportation Robot sensing systems sensor systems intelligent sensors Farming

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Robust Self-Localization of Wireless Acoustic sensor networks

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IEEE Internet of Things Journal 2025年

作者： Wang, Xu Hu, De Liu, Rui Bao, Feilong Inner Mongolia University College of Computer Science Hohhot010021 China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot 010021 China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot 010021 China

Wireless acoustic sensor networks (WASNs), or the so-called internet of audio things (IoAuTs), have attracted increasing attention in the IoT community. As the geometry structure of WASNs is required in audio/speech processing tasks like source localization or acoustic beamforming, automatic self-localization of sensors is necessary. However, most of the existing approaches suffer from poor stability, as their constructed cost functions involve non-convex programming. To address this issue, we investigate the robust self-localization (or geometry calibration) of WASNs in this paper. Specifically, a rough self-localization (RSL) method is first presented based on measurements including time-difference-of-arrivals (TDoAs), direction-of-arrivals (DoA), and energy-rates (ERs), and its closed-form solution is further derived. As ER estimates are sensitive to acoustic environments, the performance of the RSL method is somewhat limited. Therefore, a precise self-localization (PSL) method is then developed by building a weighted (and non-convex) TDoA-DoA cost function, after regarding the RSL approach as an initialization step. As the RSL offers better initial values compared with existing initialization strategies, the combination of RSL and PSL methods (named as RSL-PSL method) shows stronger robustness and stability. In addition, computational complexity of both RSL and PSL methods are analyzed in details. Finally, the Cramér-Rao Bound (CRB) of the PSL method is derived to show its theoretical lower bound. The proposed RSL-PSL method outperforms the state-of-the-arts in terms of stability and accuracy, which is confirmed by numerical real-world and simulation experiments. © 2014 IEEE.

关键词： Cost functions

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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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Integration of Artificial Intelligence and BIM in intelligent Decision Support System for Water Conservancy Projects

Integration of Artificial Intelligence and BIM in Intelligen...

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Electronics, Energy Systems and Power Engineering (EESPE), IEEE International conference on

作者： Zekun Chang Shandong Vocational College of Industry Zibo China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

A kind of intelligent DSS based on AI and Architecture information Modeling (BIM) is put forward. The system is composed of several modules, including data collection, processing, intelligence decision-making, feedback and optimizing. The data acquisition and processing module collects environmental data, equipment status and water flow information of water conservancy projects in real time through a variety of sensors and remote sensing technologies; the intelligent decision module uses deep learning algorithms, especially GCN is to model and predict the collected data, so as to make effective risk assessment and scheduling decisions; the feedback and optimization module continuously optimizes the decision process according to real-time feedback information, and improves decision accuracy and response speed. Through a series of experimental simulations, this paper verifies the effectiveness of the system. The GCN-based model improves the forecasting precision by 15% compared with the conventional ANN, and the calculation efficiency is improved 20%. Furthermore, it is proved by experiments that the system has remarkable superiority in dealing with complicated data in real time, data fusion and decision-making.

关键词： Decision support systems Accuracy Graph convolutional networks Building information management Water conservation Predictive models Prediction algorithms Real-time systems Artificial intelligence Water resources

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Unlocking Eye Gestures with Earable Inertial Sensing for Accessible HCI

Unlocking Eye Gestures with Earable Inertial Sensing for Acc...

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International Communication Systems and networks and Workshops, COMSNETS

作者： Garvit Chugh Suchetana Chakraborty Sandip Chakraborty Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Eye gestures are widely used in many applications, including device control, biometrics, visual analytics, and health-care, like Alzheimer’s, accessibility, etc. The conventional method for eye gesture detection needs visual information processing devices or smart glasses to capture the movement patterns of the eyeballs. In this paper, we present a novel framework that utilizes inertial measurement unit (IMU) sensors embedded in earable devices for real-time eye gesture detection, which can work as a complementary to visual information processing devices. The framework translates specific eye movements into actionable commands, enabling hands-free interaction for users with limited mobility. Through extensive experimentation with 14 participants across diverse activities such as sitting, walking, running, and driving, the framework demonstrated high accuracy in eye gesture recognition, achieving a macro F1 score of 0.85 on our self-collected dataset.

关键词： Legged locomotion Vocabulary Measurement units Visual analytics information processing Gesture recognition Real-time systems Research and development intelligent sensors Smart glasses

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Digital Twin-Based Collaborative Management for Energy-Aware 6G IoT Systems

Digital Twin-Based Collaborative Management for Energy-Aware...

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IEEE conference on Wireless Communications and Networking

作者： Kubra Duran Berk Canberk School of Computing Engineering and The Built Environment Edinburgh Napier University UK BTS Group Istanbul Turkey

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Even though the emergence of 6G IoT systems has accelerated the deployment of hyper-connected networks, the inherent resource limitations of IoT sensors remain a significant problem. In addition, maintaining energy efficiency and low response times in such environments has become more challenging. However, the existing management methods often lack the real-time adaptability and intelligence to optimize energy consumption in 6G IoT networks. To tackle this, we propose a DT-based collaborative management consisting of a multi-agent twin layer, a collaboration protocol and a Reinforcement Learning (RL)-based learner model. In the multi-agent twin layer, each physical network sensor is modelled as an individual agent for enhanced granularity in the management. The collaboration protocol ensures information sharing among the sensors and, thus, lowers response times. Furthermore, in the learner model, we utilize a multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to optimise actions according to the novel energy-aware reward function. According to our simulation results, the proposed DT-based collaborative management surpasses the traditional method by 27 % for longer battery levels and 65 % more rapid responses.

关键词： 6G mobile communication Adaptation models Protocols Simulation Collaboration Reinforcement learning sensor systems Batteries Time factors intelligent sensors

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Advanced Flood Management and Rescue Strategies to Securing Lives from Flood Risks

Advanced Flood Management and Rescue Strategies to Securing ...

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International conference on ent Computing and Control Systems (ICICCS)

作者： Manoranjitham E Kavin V Prasand S Praveen Kumar B Saravanan R Department of Electronics and Telecommunication Karpagam College of Engineering Coimbatore Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Both the locating of flood victims and the provision of continuous alarm information to the Disaster Rescue Force are accomplished by the Flood alarm and Rescue System. Additionally, it shows where the individuals who are trapped in the water are. Afterwards, a flood alarm system might be activated to notify the public. Contrarily, in an emergency, the Rescue System will direct the rescue workers to the people who need them. The individuals in need will activate the rescue operation by pressing the button that the government has given them. The "rescue team will use the signal processing device to receive and decode the encoded communication signal that it emits". Because of this, they will be able to find the individual far more quickly than was previously achievable with the technology at their disposal. This is the case since the top goals of the rescuing force are determined by our system. Our prototype is built with the idea that individuals may simply incorporate the gadget into their daily tasks when floods are in the area. The inclusion of a torch in our product ensures its continued viability and makes it suitable for usage in low-light situations, such as during floods.

关键词： Temperature sensors Wireless sensor networks Force Prototypes Pressing Signal processing Control systems Real-time systems Floods Testing

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AI Driven Rat Detection for Warehouse

AI Driven Rat Detection for Warehouse

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Ann Vincent Bhavesh Patil Aditya Murbade Hemalata Mote Shruti Mehata Dept. Elec and Telecomm. Engg. Don Bosco Institute of Technology Mumbai Mumbai India BharatGodam Solutions LLP Ahmedabad India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

The project’s goal is to create a system for controlling insect activity and monitoring of warehouses in sizable grain storage facilities. The idea behind the system uses neural networks, deep learning algorithms, and imagery processing methods to identify, monitor, track, and notify users of the activity of pests, notably rats, in real-time. By using algorithms that use deep learning for real-time object recognition and authentication, rats may be accurately identified. In addition, the sensors augment optical detection by using non-visual methods to detect motion and activity, providing full surveillance regardless of regions that are completely concealed. The system’s network of carefully positioned imaging devices and sensors allows for constant, continuous surveillance of the warehouse ambiance. The system activates and displays an indication once it recognises a rat, and it logs the precise time and place of the event. To ensure prompt action and response, warehouse administrators get updates in real time via email, SMS, or a mobile app. Through the application of artificial intelligence (AI), this initiative seeks to improve the security of food, reduce monetary damage, and revolutionize pest control at storage facilities for grain.

关键词： Deep learning Tracking Surveillance Neural networks Real-time systems sensor systems Optical sensors Object recognition Artificial intelligence intelligent sensors

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Stacking U-Nets in U-shape: Redesigning the information Flow in Model-based networks for MRI Reconstruction

Stacking U-Nets in U-shape: Redesigning the Information Flow...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Xiaoyu Qiao Weisheng Li Bin Xiao Yuping Huang Lijian Yang Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and Telecommunications Ministry of Education Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Model-based networks have shown convincing performance in MRI reconstruction. However, the unrolled cascades within the networks are constrained to solely obtain information from the preceding counterpart, resulting in potential error accumulation. Moreover, the linear structure fails to address the challenge of recovering fine-grained details. To tackle these problems, we propose to redesign the information flow in model-based networks. Our method features a large U-shaped network, where the nodes are built with unrolled cascades and U-Net-based regularizers. We design an input-level integration module to help the cascades acquire information from adjacent and skip-connected counterparts, building robust mappings to the target. We further design a coarse-to-fine feature-level integration module, aiming at guiding the network to progressively recover fine details. Intermediate reconstructions produced by subnetworks of different scales are integrated, enabling the extraction of complementary information to enhance the final performance. Compared with cutting-edge methods on different datasets, our method exhibits superior performances.

关键词： Magnetic resonance imaging Stacking Buildings Signal processing Feature extraction Acoustics sensors Data mining Speech processing

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