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C-HTT for throughput Maximization in Time-Varying RF-Powered Systems

C-HTT for Throughput Maximization in Time-Varying RF-Powered...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Ziyan Tian College of Engineering Yanbian University Yanji City China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

RF energy harvesting offers a promising solution for addressing energy limitations in wireless devices, particularly in IoT and sensor networks, by enabling autonomous operation and reducing reliance on batteries. However, the power of RF signals can fluctuate due to channel fading, interference, and mobility, posing challenges for energy harvesting and data transmission. In multi-device scenarios, competition for limited RF energy resources exacerbates these challenges. To address this, the Cooperative Harvest-then-Transmit (C-HTT) method is proposed, allowing devices to cooperate in energy harvesting and data transmission. By sharing RF power information and coordinating schedules, devices can optimize energy use, avoid interference, and ensure fairness. the C-HTT method involves four steps: initialization, energy harvesting, data transmission, and cooperation, where devices adjust their schedules based on shared RF power conditions. Simulations comparing C-HTT to time-sharing and random access methods show significant improvements in throughput, energy efficiency, and fairness in multi-device, time-varying RF-powered systems.

关键词： Radio frequency Wireless communication Schedules Wireless sensor networks Interference throughput Energy efficiency Energy harvesting Data communication Time-varying systems

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Integrated Smart Water Management System for Urban Sustainability 5th

Integrated Smart Water Management System for Urban Sustainab...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Rayangoudar, Tejas K. Patil, Shahak Shahapur, Salma S. Kangralkar, Chandan P. Daddi, Dayanand V. Dhamanekar, Aditya A. Walaginamath, Balayya Jain College of Engineering VTU Karnataka Belagavi India

ISBN: (纸本)9789819788606

theWaterlogging and flooding. this study proposes an innovative prototype combining electronic devices, sensors, tanks, a natural sump, and “instant water filter” for efficient rainwater recycling. the system employs multiple tanks for water filteration, beginning with rainsensor activating the entire system upon rainfall. Rainwater is collected in the first tank, where an ultrasonic sensor monitors water levels. In cases of rain without a corresponding rise in tank water levels, a GSM module alerts the municipality, signaling potential blockages in the entry path. Manual cleaning addresses surface impurities, while colloidal impurities are treated with potash alum. the water undergoes sedimentation before passing through a sump filter, consisting of glass wool, activated charcoal, ceramic rocks, purified sand, and cellulose, effectively removing maximum impurities. Purified water accumulates in the second tank, equipped with turbidity and LDR sensor.. the turbidity sensor assesses water hardness and softness, while the LDR sensor checks water clarity. An Instant filter, boasting 98% efficiency, is employed for instant water filtering at the second tank's outlet valve, ensuring removal of remaining colloidal and soluble impurities, making the water fit for drinking. the treated water is suitable for drinking, irrigation, and various other purposes, contributing to urban water management. In addition to rainwater recycling, the study addresses sewage treatment, emphasizing three stages: primary, secondary (Activated Sludge Process), and tertiary treatment. Primary treatment involves screening, filtration, sedimentation, and coagulation for the removal of coarse, suspended, and floating solids. Secondary treatment focuses on organic matter removal through biological treatment. the sludge formed is removed, and the secondary effluent is treated with chlorine or UV radiation before discharge into running water or reuse for watering plants. Tertiary treatment is initiate

关键词： LDR sensor Rain water sensor Sump water filter Ultrasonic sensor Turbidity sensor Waterlogging

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SHGNN: A Substructure-Aware and High Expressive Graph Neural Network for Graph Classification 5th

SHGNN: A Substructure-Aware and High Expressive Graph Neural...

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5th international conference on Neural Computing for Advanced Applications (NCAA)

作者： Sun, Jinyong Dong, Zhiwei Sun, Zhigang Wen, Yimin Zhao, Xiang Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Image & Graph Intelligent Proc Guilin 541004 Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Graph Neural networks (GNNs), with their remarkable capabilities in learning graph features, have achieved success in graph classification tasks. However, the Message Passing GNNs (MP-GNNs), sharing the feature aggregation mechanism with the 1-Weisfeiler-Leman (1-WL) graph isomorphism test, suffer from limited expressiveness in downstream tasks, such as graph classification. To enhance the expressive power of GNNs, we propose a substructure-aware and high expressive graph neural network model. Firstly, Ego substructures are extracted from a graph and fed into a MP-GNN to obtain substructure-level features. then, a count-sensitive neighborhood hashing method is introduced to encode the structural characteristics of Ego substructures, which are then injected into the corresponding substructure-level features to enhance the model's expressiveness. Next, an Ego-substructure-aware Transformer is utilized to extract global features from the graph, which use a token to fuse the interaction information among graphs' nodes, thereby generating graph-level feature vector. Finally, we apply a Multi-Layer Perceptron (MLP) followed by a softmax layer to classify graphs. theoretical analysis demonstrates that the proposed SHGNN ismore powerful than 1-WL test. Experimental results show that, compared to the state-of-the-art graph deep learning methods, our model not only outperforms 1-WL in expressiveness, but also achieves higher classification accuracy.

关键词： graph classification graph neural networks substructure-aware neighborhood hashing Transformer

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Physiological Signal Monitoring Based on Frequency Modulated Continuous Millimeter-Wave Radar

Physiological Signal Monitoring Based on Frequency Modulated...

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sensors and Information Technology (ICSI), international conference on

作者： Xun Shen Chunfeng Yang School of Biological Science and Medical Engineering Southeast University Nan Jing China

ISBN: (数字)9798331542887

ISBN: (纸本)9798331542894

the demand for real-time health monitoring devices is growing due to an aging population and unhealthy lifestyles. Traditional wearable or contact-based sensors have limitations for certain groups and may pose risks. Non-contact millimeter-wave radar-based devices have emerged as a promising alternative. this paper reviews traditional signal processing methods for extracting respiratory and heartbeat signals from radar data, highlighting their limitations. We propose an innovative approach by extending heart rate analysis to higher harmonics, introducing an optimal fundamental-harmonic group method to accurately extract heartbeats despite respiratory harmonic interference. Additionally, we integrate convolutional neural networks (CNN) and long short-term memory (LSTM) deep learning techniques with spectral analysis, significantly improving accuracy and opening new avenues for the advancement of health monitoring technologies.

关键词： Accuracy Heart beat Signal processing algorithms Millimeter wave radar Harmonic analysis Convolutional neural networks Biomedical monitoring Monitoring Power harmonic filters Long short term memory

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A large number of optical turbidimeter designs 5

A large number of optical turbidimeter designs

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5th international conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Guo, Xingchen Gao, Yingming Dalian Polytechnic University Dalian 116000 China

ISBN: (纸本)9781510691667

Turbidity is an important index to evaluate water quality, and the measurement of water turbidity is of great significance to life and environmental protection. Turbidity reflects the content of suspended insoluble substances in water through the optical properties of water. Aiming at the problem of small range in existing turbidity measuring instruments, a new turbidity sensor is designed in this paper. through the combination of scattering method and transmission method, a new measuring device structure and signal circuit processing are designed to improve the measuring range of the equipment. In addition, according to the characteristics of turbidity detection, the signal transmission circuit is designed to eliminate the influence of noise. Finally, by cooperating with the single chip computer, the measurement data is calculated and presented on the computer, and the data can be observed in real time and recorded. © 2025 SPIE.

关键词： scattering method sensor transmission method Turbidity

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Research on the Application of Multimodal Sensing Technology in Surveying and Mapping Data Collection

Research on the Application of Multimodal Sensing Technology...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Liu Bo Weng Jianying School of Architectural Engineering Shandong University of Engineering and Vocational Technology Jinan Shandong China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

this paper proposes an innovative CNN and RNN fusion algorithm to optimize the application of multimodal sensing technology in surveying and mapping data collection. the algorithm combines the data of multiple sensors such as lidar, RGB camera and infrared sensor, uses CNN to extract the spatial features of the image, and uses RNN to analyze the temporal relationship between different modal data, so as to achieve efficient fusion of multimodal data. Experimental results show that the proposed algorithm improves the data accuracy by about 18% in multiple surveying and mapping scenarios compared with the traditional single-modal sensor method, and the processing efficiency is improved by 15%. In addition, the algorithm shows significant robustness in complex environments, can effectively cope with the challenges brought by environmental changes, and improves the accuracy and stability of data. the research in this paper provides technical support for the development of intelligent surveying and mapping systems and promotes the popularization of multimodal data fusion algorithms in practical applications.

关键词： Deep learning Solid modeling Accuracy Multimodal sensors Data integration Data collection sensor fusion Feature extraction Stability analysis thermal stability

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Breaking Barriers in Communication Using Long Short-Term Memory networks for Sign Language Recognition 5th

Breaking Barriers in Communication Using Long Short-Term Mem...

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5th international conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Gopale, Pratik R. Joshi, Sarang L. Kulkarni, Hrushikesh B. School of Mechatronics Engineering Symbiosis Skills and Professional University Maharashtra Pune India

ISBN: (纸本)9789819788606

Sign language recognition is pivotal in enhancing communication and accessibility for individuals with hearing impairments. this paper introduces an innovative system leveraging Long Short-Term Memory (LSTM) networks, a form of recurrent neural network renowned for capturing temporal dependencies in sequential data. the objective is to proficiently interpret and classify sign language gestures extracted from real-time video or image data. the proposed system employs a comprehensive methodology encompassing dataset selection, preprocessing, feature extraction, LSTM model architecture design, training, and evaluation. A diverse, well-annotated dataset is utilized, covering various sign language gestures with corresponding labels. the LSTM model is meticulously configured with appropriate architecture parameters, including the number of LSTM layers and memory cells. Experimental results underscore the robustness and efficiency of the proposed sign language recognition system. Real-time capabilities are achieved by integrating the trained LSTM model into a user-friendly interface for live video or image data processing. the system undergoes performance and usability evaluation through user studies, providing insights for future enhancements and applications. this LSTM-based sign language recognition system exhibits promising potential in enhancing accessibility and communication for individuals with hearing and speech impairments. Deep learning techniques and temporal modeling contribute to accurate interpretation and classification of sign language gestures, fostering improved inclusion and interaction within the deaf community. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accessibility Deep learning Inclusive communication Long Short-Term Memory (LSTM) networks Sign language recognition

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Energy-Efficient Wireless sensor networks Optimization using Deep Q-networks for Smart Energy Applications

Energy-Efficient Wireless Sensor Networks Optimization using...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Satyanarayana Nimmala Nukala Sujata Gupta P Vasanth Sena K Koteswara Chari Md Aleem Pasha Bandi Rambabu Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of Computer Engineering Technology Chaitanya Bharati Institute of Technology (CBIT) Hyderabad India Department of CSE G. Narayanamma Institute of Technology and Science Hyderabad India Department of CSE Teegala Krishna Reddy Engineering College Hyderabad India Department of Mechanical Engineering Chaitanya Bharati Institute of Technology (CBIT) Hyderabad India Department of CSE CVR College of Engineering Hyderabad India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Energy efficiency poses a significant difficulty in Wireless sensor networks (WSNs), especially within smart energy applications like smart grids, energy monitoring, and predictive maintenance. this study presents an adaptive optimization approach based on reinforcement learning utilizing Deep Q-networks (DQN) to improve energy efficiency in Wireless sensor networks (WSNs) through the dynamic adjustment of transmission power and routing algorithms. the suggested model is trained and assessed utilizing the LEACH (Low-Energy Adaptive Clustering Hierarchy) dataset within a simulated NS-3 and MATLAB environment. the simulation configuration comprises 100 to 500 sensor nodes distributed inside a 100m x 100m region, each with differing energy limitations, utilizing the IEEE 802.15.4(Zigbee) communication protocol. the performance evaluation is carried out under varying traffic loads and network density, contrasting the proposed DQN-based optimization with classic LEACH, GA-based clustering, and Q-learning models. the results indicate a 27.5% enhancement in network longevity relative to LEACH, a 21.8% decrease in energy consumption per node, an 18.3% advancement in packet delivery ratio, and a 16.7% reduction in end-to-end latency, hence providing dependable data transfer. the findings underscore the efficacy of reinforcement learning in prolonging the lifespan of wireless sensor networks and enhancing energy efficiency, rendering it exceptionally appropriate for practical smart energy applications, including power grid monitoring, industrial IoT, and renewable energy management. the proposed DQN-based optimization dynamically adjusts transmission power and routing strategies, resulting in significant energy savings and improved network performance

关键词： Wireless sensor networks Adaptive systems Heuristic algorithms Telecommunication traffic Routing Energy efficiency Mathematical models Smart grids Optimization Monitoring

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Design and Implementation of a LoRa-Based Multi-Terminal Communication System

Design and Implementation of a LoRa-Based Multi-Terminal Com...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Kai Ding Jiawen Liang Ming Tao Renping Xie School of Computer Science and Technology Dongguan University of Technology Dongguan China Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

With the rapid development of the Internet of things (IoT) technology, there is an increasing demand for communication. Long Range (LoRa) communication, as an emerging wide-area network communication technology, has been widely used in many fields by virtue of its unique advantages of low power consumption and long distance. this paper describes the design and realization of LoRa -based data transmission system, which adopts microcontroller as the control core, LoRa module as the transmission core, and adopts a variety of data interfaces to collect sensor data and realize low power consumption through power control circuit. Perform overall testing of the completed design system. the results show that the system successfully realizes the data transmission between multiple terminal nodes and gateways while also providing the system with low-power sleep function.

关键词： Wireless communication Software testing Wireless sensor networks Power demand Microcontrollers Power control LoRa Logic gates Hardware Internet of things

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Experimental exploration of magnet motion effect on electromagnetic energy harvesting

Experimental exploration of magnet motion effect on electrom...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Amine Ben Alaya Charfeddine Mrad Ferid Kourda Laboratory of Electrical Systems (LSE) University of Tunis el Manar (UTM) National Engineering School of Tunis (ENIT) Tunis Tunisia Labo. of App. Mech. and Eng. (LMAI) University of Tunis el Manar (UTM) National Engineering School of Tunis (ENIT) Tunis Tunisia

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Energy harvesting systems for low power devices are becoming essential for different applications, such as wireless sensors, IoT devices, and autonomous systems. this study is about the exploration of the electrical energy harvested by electromagnetic effect, utilizing a mobile magnet into an electric coil. the magnet is mounted on a vibration exciter to command its motion. the motion is of different amplitudes and frequencies, using an amplitude amplifier and a frequency regulator. the magnet motion was found of a determinant effect on electromagnetic energy harvesting.

关键词： Vibrations Coils Wireless communication Wireless sensor networks Regulators Neodymium Nanogenerators sensor systems and applications Electromagnetics Magnetic circuits

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