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Forest Fire Prevention and Detection System

Forest Fire Prevention and Detection System

引用

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

作者： Lasitha Jayakody Chamathka Edirisinghe Tharush Senasinghe Shaminda Dissanayake Narmada Gamage Dinithi Pandithage Jaliya L. Wijayaraja Dept. of Computer Systems Engineering Sri Lanka Institute of Information Malabe Sri Lanka

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires.

关键词： Temperature sensors Technological innovation Wildfires Prevention and mitigation Forestry Sensor phenomena and characterization Sensor systems Real-time systems Gas detectors Resilience

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FPGA Implementation of QC-LDPC Decoder with Rapid Cyclic Shifter for CV-QKD systems

FPGA Implementation of QC-LDPC Decoder with Rapid Cyclic Shi...

引用

Communication, Image and Signal processing (CCISP), International conference on

作者： Zeyuan Jin Yinghua Xie Xue-Qin Jiang Genlong Chen School of Information Science and Technology Donghua University Shanghai China

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In continuous variable quantum key distribution (CV-QKD) systems, low-density parity check (LDPC) decoders have gained widespread adoption due to their robust error correction capabilities and good adaptability to parallel processing hardware designs. We introduce a syndrome-based normalized min-sum algorithm (S-NMSA) and implement the decoder on FPGAs for high throughput and minimal resource consumption. The decoder utilizes a semi-parallel architecture. The proposed rapid cyclic shifter (RCS) can complete the shift of any dimension matrix in one clock cycle and realizes a significant reduction in hardware resource consumption. The semi-parallel decoding structure attains a 68.5 Mbps processing throughput on the condition that the system clock of the quasi-cyclic (QC) LDPC decoder is 250 Mhz.

关键词： Phase change materials Signal processing algorithms Throughput Parity check codes Hardware Decoding Quantum key distribution Resource management Field programmable gate arrays Clocks

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Implementation and Performance Evaluation of Collective Communications Using CIRCUS on Multiple FPGAs

Implementation and Performance Evaluation of Collective Comm...

引用

2023 International conference on High Performance Computing in Asia-Pacific Region Workshops, HPC Asia 2023

作者： Kikuchi, Kohei Fujita, Norihisa Kobayashi, Ryohei Boku, Taisuke Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba Japan Center for Computational Sciences University of Tsukuba Tsukuba Japan

ISBN: (纸本)9781450399890

In the high-performance computing domain, Field Programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphics processing Unit (GPU). Recent advanced high-end FPGA is equipped with multiple channels of high speed optical link up to 100Gbps performance for each. This is a crucial feature when we construct PC clusters with FPGAs as accelerators, however it is not easy to utilize from user kernels because this feature is implemented in low level and simple direct communication between neighboring FPGAs. In order to provide the communication feature between FPGAs for accelerated PC clusters, we developed a communication system named CIRCUS which implies a user-friendly API from OpenCL and is equipped with routing function over multi-hop communication on multi-dimensional torus network of FPGAs. However, current CIRCUS only provides a point-to-point communication between source and destination FPGAs. In ordinary parallel processing environment such as MPI, the user program the message passing with various collective communication functions for parallel algorithm, for instance Allreduce, Allgather, etc. In this paper, we implement the collective communication function over CIRCUS for user-friendly programming of ordinary parallel algorithms on FPGAs. As the first target, we implement Allreduce function which is the most essential and important function. The paper describes the CIRCUS system briefly followed by the design, implementation and preliminary performance evaluation on Intel Stratix10 FPGAs. © 2023 ACM.

关键词： Field programmable gate arrays (FPGA)

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A configurable short-range Radar for Indoor and Outdoor Personnel Monitoring

A configurable short-range Radar for Indoor and Outdoor Pers...

引用

IEEE Asia-Pacific conference on Antennas and Propagation (APCAP)

作者： Jiabao Hua Wei Kang Chenyang Du School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

In this paper, we propose a configurable radar scheme based on a flexible RF front-end and signal processing methods, which achieves effective personnel monitoring in both indoor and outdoor scenarios. The indoor monitoring scheme can distinguish between the echo signals of stationary human bodies and other clutter signals indoors, and when combined with the MUSIC algorithm, it can provide precise location information of individuals. In outdoor scenes, the radar system can monitor real-time personnel changes within the monitoring area by calculating the correlation coefficient between real-time data and reference environmental data.

关键词： Radio frequency Surveillance Signal processing algorithms Radar detection Radar antennas Real-time systems Radar signal processing Safety Personnel Security

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Research on Global Performance Evaluation Method for Metering devices Based on Customer Load Characteristics

Research on Global Performance Evaluation Method for Meterin...

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information Technology,Big Data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Jiaming Zhang He He Ji Xiao Ke Zheng Shishun Tan Feng Zhou Ningtao Liu Xiaoyang Dong State Grid Chongqing Electric Power Company Marketing Service Center(Metrology Center) Chongqing China State Grid Chongqing Electric Power CO. Electric Power Research Institute Chongqing China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

In the context of power systems, the daily load curve from substations serves as a tool for classifying and synthesizing load characteristics. The typical daily load curve data, after being acquired and processed by the SCADA system, forms the foundation for load classification studies. To tackle the issue of original curve pattern overlapping, a signal processing technique grounded in Variational Mode Decomposition has been introduced. Additionally, addressing the specific challenge of feature weighting in the Euclidean distance within the Fuzzy C-Means clustering algorithm, a feature-weighted fuzzy clustering approach was employed to develop a feature-weighted VMD-FCM clustering algorithm. Demonstrations reveal that the VMD algorithm can efficiently break down the inherent modes of signals, enhancing both the convergence rate and clustering precision of the FCM function; the introduction of a weight factor based on Euclidean distance boosts the practicality and accuracy of the FCM algorithm. The findings indicate that this enhanced method can effectively differentiate between various load categories, offering guidance for load forecasting, setting pricing criteria, and influencing marketing strategies.

关键词： Performance evaluation Accuracy Load forecasting Clustering algorithms Signal processing algorithms Pricing Euclidean distance Classification algorithms Indexes Convergence

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Development of a system for monitoring health and patient care based on IoT technologies

Development of a system for monitoring health and patient ca...

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9th International conference on Digital Technologies in Education, Science and Industry, DTESI 2024

作者： Lisnevskyi, Rostyslav Oderiieva, Viktoriia Gladka, Myroslava Kravchenko, Olga Kolesnikov, Oleksii International Information Technology University 34/1 Manas St. Almaty Kazakhstan Taras Shevchenko National University of Kyiv 64/13 Volodymyrska St. Kyiv01601 Ukraine AirFlow LLP Almaty Kazakhstan

The paper proposes an innovative hybrid architecture of a patient monitoring system that combines cloud technologies with local data processing to improve the efficiency and reliability of health monitoring. This solution enables real-time processing of critical data locally, reducing latency, while large data and longterm analysis results are transferred to the cloud for further processing and storage. For successful implementation of such systems, it is necessary to consider issues of data protection, standardization, ethical aspects, reliability, and training of medical personnel. The paper analyzes existing health monitoring systems, their strengths and opportunities for improvement. The proposed methods for developing such systems are described, including the use of various sensors, real-time data analysis, and patient condition classification. Data transmission scenarios and system operation algorithms are developed. Particular attention is paid to the benefits of integrating IoT devices for continuous health monitoring and their impact on patients and healthcare workers. © 2023 Copyright for this paper by its authors.

关键词： Large datasets

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InsightSee: Advancing Multi-agent Vision-Language Models for Enhanced Visual Understanding

InsightSee: Advancing Multi-agent Vision-Language Models for...

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IEEE International conference on Mechatronics and Automation

作者： Huaxiang Zhang Yaojia Mu Guo-Niu Zhu Zhongxue Gan Academy for Engineering and Technology Fudan University Shanghai China

ISBN: (数字)9798350388077

ISBN: (纸本)9798350388084

Accurate visual understanding is imperative for advancing autonomous systems and intelligent robots. Despite the powerful capabilities of vision-language models (VLMs) in processing complex visual scenes, precisely recognizing obscured or ambiguously presented visual elements remains challenging. To tackle such issues, this paper proposes InsightSee, a multi-agent framework to enhance VLMs’ interpretative capabilities in handling complex visual understanding scenarios. The framework comprises a description agent, two reasoning agents, and a decision agent, which are integrated to refine the process of visual information interpretation. The design of these agents and the mechanisms by which they can be enhanced in visual information processing are presented. Experimental results demonstrate that the InsightSee framework not only boosts performance on specific visual tasks but also retains the original models’ strength. The proposed framework outperforms state-of-the-art algorithms in 6 out of 9 benchmark tests, with a substantial advancement in multimodal understanding.

关键词： Visualization Accuracy Mechatronics Text recognition optical character recognition information processing Benchmark testing

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14th International conference on Applications and Techniques in information Security, ATIS 2024

14th International Conference on Applications and Techniques...

引用

14th International conference on Applications and Techniques in information Security, ATIS 2024

ISBN: (纸本)9789819797424

The proceedings contain 24 papers. The special focus in this conference is on Applications and Techniques in information Security. The topics include: optical Neural Networks – A Strategy for Secure Quantum Computing;guarding Against Quantum Threats: A Survey of Post-Quantum Cryptography Standardization, Techniques, and Current Implementations;cryptographic Distinguishers Through Deep Learning for Lightweight Block Ciphers;detection and Mitigation of Email Phishing;securing Digital Forensic Data Using Neural Networks, Elephant Herd Optimization and Complex Sequence Techniques;Design of Image Encryption Technique Using MSE Approach;Low Latency Binary Edward Curve Crypto Processor for FPGA Platforms;Augmenting Security in Edge devices: FPGA-Based Enhanced LEA Algorithm with S-Box and Chaotic Functions;machine Learning Approach for Malware Detection Using Malware Memory Analysis Data;DDOS Attack Detection in Virtual Machine Using Machine Learning algorithms;an Unsupervised Method for Intrusion Detection Using Novel Percentage Split Clustering;HATT-MLPNN: A Hybrid Approach for Cyber-Attack Detection in Industrial Control systems Using MLPNN and Attention Mechanisms;silent Threats: Monitoring Insider Risks in Healthcare Sector;Enhanced Deep Learning for IIoT Threat Intelligence: Revealing Advanced Persistent Threat Attack Patterns;Adaptive Data-Driven LSTM Model for Sensor Drift Detection in Water Utilities;Enhancing FGSM Attacks with Genetic algorithms for Robust Adversarial Examples in Remote Sensing Image Classification systems;GAN-Enhanced Multiclass Malware Classification with Deep Convolutional Networks;IOT Based Locker Access System with MFA & Remote Authentication;a Secure Authentication Scheme between Edge devices Using Hypergraph Hashing Technique in Iot Environment;enhancing Access Control and information Sharing in Cloud IoT with an Effective Blockchain-Based Authority System.

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Empowering the Disabled with Brain-Computer Interface

Empowering the Disabled with Brain-Computer Interface

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Software, systems and information Technology (SSITCON), International conference on

作者： To Geetharani Purushottama Lingadevaru Department of Electronics and Communication Engineering Siddaganga Institute of Technology Tumakuru India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

This work presents a Brain-Computer Interface (BCI) system designed to help persons suffering from severe motor function loss due to accidents or diseases. The system enables users to control and interact with their external environment through neural signals. An Ag/AgCl electrode cap is placed on the scalp to capture the brain’s electrical activity, while an RMS 32-channel Electroencephalogram (EEG) data acquisition system amplifies the signals. The recorded signals from multiple subjects are converted into Excel format using suitable software. These data files are then processed in MATLAB using a similarity matching algorithm to detect patterns in event-related potentials. Based on the processed brain signals, the system performs specific actions, such as switching a fan or light bulb on or off through relays. Control of these external devices is achieved using an Arduino microcontroller. The system shows promising accuracy of 95% in translating brain activity into actionable commands and controlling external devices, offering potential for improved independence and quality of life for affected individuals. The proposed BCI system offers a potential communication pathway for persons with limited mobility, assisting them to interact with their surroundings more effectively.

关键词： Electrodes Fans Accuracy Microcontrollers Signal processing algorithms Switches Motors Software systems Brain-computer interfaces Electroencephalography

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Dcha: Distributed-Centralized Heterogeneous Architecture Enables Efficient Multi-Task processing for Smart Sensing

Dcha: Distributed-Centralized Heterogeneous Architecture Ena...

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Design, Automation and Test in Europe conference and Exhibition

作者： Erxiang Ren Cheng Qu Li Luo Yonghua Li Zheyu Liu Xinghua Yang Qi Wei Fei Qiao School of Electronic and Information Engineering Beijing Jiaotong University Beijing China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China MakeSens AI Beijing China College of Science Beijing Forestry University Beijing China Department of Precision Instrument Tsinghua University Beijing China Department of Electronic Engineering Tsinghua University Beijing China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

The rapid development of artificial intelligence (AI) has accelerated the progression of IoT technology into the smart era. Integrating AI processing capabilities into IoT devices to create smart sensing systems holds significant promise. In this work, we propose a distributed-centralized heterogeneous architecture that enables efficient multitask processing for smart sensing. This architecture improves the operational efficiency of sensing systems and enhances the deployment scalability through collaborative computing across end, edge, and center nodes. Specifically, we partition the network in traditional centralized sensing systems into several parts and perform algorithm-hardware co-design for each part on its respective deployment platform. We developed a sample design to validate the proposed architecture. By implementing a lightweight image encoder, we achieved an 88x reduction in encoder parameters and up to 9873x energy gain, facilitating deployment on resource-constrained devices. Experimental results demonstrate that the proposed architecture effectively reduces overall energy consumption by 0.0573x to 0.0889x, while maintaining robust multitask inference capabilities. Moreover, energy consumption reductions of 2.88x to 3.22x on edge nodes and 6311.56x to 10037.23x on end nodes were observed.

关键词： Performance evaluation Energy consumption Soft sensors Scalability Collaboration Computer architecture Sensors Partitioning algorithms Internet of Things Artificial intelligence

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