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IEEE International Conference on Consumer electronics-Asia (ICCE-Asia)

作者： Dat Vuong Van Vu Tran Van Thanh Tran Huynh Anh Nhat Tran Pham Hoang Hao Le Tran The Son Faculty of Computer Engineering and Electronics Vietnam-Korea University of Information and Communication Technology Da Nang Vietnam

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Fires are considered a risk that causes human death and property loss. To reduce fire damage, fires must be detected and warned promptly. This paper aims to design and experimentally implement a fully-function fire detection and warning system. The developed system is based on a Wireless Sensor Network (WSN) that includes various nodes distributed across the city. Each node has two main blocks which are the image processing and communication blocks. The image processing block applies the EfficientN et B0 model to detect the fire and smoke, whereas the communication block uses the Module 4G to contact the house owner over a mobile phone network and LoRa gateway to send the signal warning to the fire center over the a LoRaWAN. The Module 4G is responsible for sending captured images of suspected fire if any to the house owner's device for getting his/her acknowledgement. Then an urgent alarm message consisting of fire position and risk level are sent to the fire center if the fire is acknowledged by the house owner, otherwise, the alarm is ceased. Besides, the proposed system is installed the gas sensor, smoke sensor, water spray system and light-sound alarm to augment the reliability of fire detection. The proposed system is experimentally demonstratedand achieved positive results: Delay time sending from the proposed fire detection system to the user is recorded as 5 seconds and from the user to the fire center is recorded as 10 seconds, within a LoRaWAN coverage of1.5km.

关键词： Wireless sensor networks Image processing Urban areas LoRaWAN Optimization methods Alarm systems Logic gates Mobile handsets Internet Reliability

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An Efficient and Intelligent IoT-Based Security Model for Enhanced Protection Using Motion Detection and Cloud Storage Optimization 2

An Efficient and Intelligent IoT-Based Security Model for En...

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2nd IEEE International Conference on Networks, Multimedia and information Technology, NMITCON 2024

作者： Gangadharaswamy, H.B. Kumar, Narendra Sharmila, N. Shilpa, M. Mallikarjunaswamy, S. Shivaji, R. Basavaraju, N.M. Balachandra, P. Komala, M. Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Rns Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350372892

The proposed IoT-based Smart Security Solution (IoT-SSS) for CCTV and network nodes addresses the challenges of high data volume management, power consumption, and cost inefficiency in traditional security systems. Leveraging Python's image processing for motion detection via passive infrared (PIR) sensors, IoT-SSS intelligently stamps video recordings and sensor data, activating only when necessary. This method ensures data is initially saved on local storage, significantly reducing the need for continuous recording and, thereby, minimizing power usage and storage demands. When data hits a pre-defined threshold, influenced by location sensitivity, it's automatically uploaded to the cloud, further optimizing storage needs and eliminating manual intervention. This smart approach not only conserves energy by powering down inactive devices but also slashes maintenance costs. Time, date, and sensor information are embedded directly onto each video frame, facilitating easy review without excessive data overload. Simulation results demonstrate that IoT-SSS outperforms traditional security systems in terms of data storage efficiency and power consumption, offering a cost-effective and energy-efficient alternative for modern security needs. © 2024 IEEE.

关键词： Cloud upload Data storage efficiency IoT-based Smart Security Solution (IoT-SSS) Motion detection Power consumption

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer Systems Science & engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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IoT and Machine Learning for Traffic Monitoring, Headlight Automation, and Self-parking: Application of AI in Transportation 3rd

IoT and Machine Learning for Traffic Monitoring, Headlight A...

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3rd Innovative Product Design and Intelligent Manufacturing System, IPDIMS 2021

作者： Sinha, Anurag Singh, Abhishek Shubham, Prince Raj, Vivek Ramish, Md. Department of Information Technology Amity University Jharkhand Ranchi India Department of Computer Science and Engineering JIS College of Engineering West Bengal Kalyani India Department of Computer Science and Engineering Amity University Jharkhand Ranchi India Department of Electronics and Communication Engineering Amity University Jharkhand Ranchi India

ISBN: (纸本)9789811946059

Artificial intelligence is one of the technologies which are being applied in various domains. In transportation, managing the vehicles pass way parking and highway traffic is one of the challenging task. Maximum of the road accidents happen due to the high-beam trails on the highway and reflection gage. In this paper, we have studied the total accidents occurred during past years due to this same issue and proposed a conceptual solution of headlight beam reduction using automation of headlight system. We have also focused on the other emerging source of AI in transportation and briefly stated their application area. Overall, this paper is approach of proposing significant application area of AI and IoT in transportation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Headlights

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Edge-Driven Industrial Computing Power Networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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An Attention Mechanism for Forest Tree Segmentation based on Efficient Convolutional Neural Networks 8

An Attention Mechanism for Forest Tree Segmentation based on...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Jeevitha, K. Bindhu, A. Rayen, Sonia Jenifer Vidhyalakshmi, K. Rajalakshmi, D. Agnes Shifani, S. R.M.K. Engineering College Department of Electronics and Communication Engineering Chennai India Infant Jesus College of Arts and Science for Women Department of Computer Science Mulagumoodu Kanyakumari India Loyola Icam College of Engineering and Technology Department of Information Technology Chennai India Vel Tech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Jp College of Engineering Department of Electronics and Communication Engineering Tenkasi India

ISBN: (纸本)9798350347791

It is critical to have accurate semantic categorization of standing trees in order to extract relevant information about standing trees automatically and efficiently from digital photos. The effective categorization of several standing trees set against complicated backdrops is the goal of various classic approaches, however these methods suffer from drawbacks such as poor segmentation accuracy and the need for human intervention. This article presents Modified Convolutional Neural Network,which is based on MobileNet (CNNM), a lightweight network segmented model that is based on deep learning. The goal of this model is to accomplish correct categorization of standing tree photos in an efficient manner. For the purpose of performing multi-scale fusion of the convolutional features of standing trees in images, asemantic segmentation has been selected as the base framework. This decision was made in order to minimize the loss of image edge details that occurs during the standing tree segmentation process, as well as minimize the loss of feature data. MobileNet, which is a lightweight network, has been incorporated into the backbone network in order to minimize the complexity of the computations. The lightweight network segmentation model CNNM based on deep learning that was introduced in this study has the capability of segmenting numerous standing trees with a high degree of precision. © 2023 IEEE.

关键词： Semantic Segmentation

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Sparse Recovery Space Time Adaptive Processing Based on Log-Determinant 15

Sparse Recovery Space Time Adaptive Processing Based on Log-...

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15th International Conference on Signal Processing Systems, ICSPS 2023

作者： Chang, Yuqing Du, Xiaolin Jin, Biao Tian, Tuanwei Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China Ocean College Jiangsu University of Science and Technology Zhenjiang China School of Physics and Electronics Henan University Kaifeng China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510675056

Airborne radars usually face non-uniform clutter environments, and it is difficult to obtain enough independent and identical distributed (i.i.d.) training samples, which degrades the clutter suppression performance of space time adaptive processing (STAP). To address the problem, a log-determinant sparse rocovery based STAP (LSR-STAP) method is proposed in this paper. Through the sparse recovery theory of low-rank matrices and the prior knowledge of clutter covariance matrices, the corresponding problem is modeled using the log-determinant (LogDet) approximation of the rank function, and the solution of the resulting nonconvex optimization problem is derived in the framework of the symmetric alternating direction method of multipliers (S-ADMM). The simulation results show the superiority of the proposed method over similar algorithms. © 2024 SPIE.

关键词： Space time adaptive processing

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Advanced Deep Learning Framework for Automated Lung Cancer Detection Using DeepResNet 6

Advanced Deep Learning Framework for Automated Lung Cancer D...

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6th International Conference on Cybernetics, Cognition and Machine Learning Applications, ICCCMLA 2024

作者： Ahammed, Syed Zaheer Baskar, Radhika Nalinipriya, G. Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science & Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331505790

This study presents a sophisticated framework for automatically identifying lung cancer nodules from CT scans using a unique DeepResNet model. Our approach combines the segmentation abilities of UNet with the feature extraction capabilities of ResNet, resulting in accurate identification and classification of pulmonary nodules. The model's performance is assessed using the LIDC-IDRI dataset, showcasing exceptional metrics: an accuracy of 9 9. 8 9 %, an F1 score of 9 9. 8 3 2 %, a sensitivity of 100 %, a precision of 99.832 %, and a specificity of 9 9. 7 8 8 3 %. This blend deep learning architecture improves both the accuracy of diagnoses and the ability to detect subtle characteristics of nodules that are important for diagnosing early-stage lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Predictive Maintenance in Autonomous Systems using Improved Support Vector Machine 1

Predictive Maintenance in Autonomous Systems using Improved ...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Mamatha Bai, B.G. Divya, N. Sree Hamedshnain, Ammar Kumar, S. Senthil Vasumathi, T. Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bengaluru India Mahatma Gandhi Institute of Technology Department of Information Technology Hyderabad India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electrical and Electronics Engineering Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, rapid development in the domain of Predictive Maintenance have resulted in improved equipment lifetime span, decrease maintenance costs and reduced the unplanned downtime. Predictive maintenance is an approach that addresses any equipment or component failures in the systems proactively. However, integration of this approach in automated systems has several challenges such as ensuring the accuracy of fault predictions, management of large-scale data and maintenance of system reliability under various operational conditions. To overcome these challenges Improved Support Vector Machine (ISVM) is proposed. Initially, the data is collected from the Predictive Maintenance Dataset (PMD) and the preprocessing is performed through Inter Quartile Range (IQR) which enhances the quality of data and modeling efforts. Further relevant features are extracted using Recursive Feature Elimination (RFE). Finally, the prediction is performed using ISVM which achieved better outcomes compared to existing a One-Class Support Vector Machine (SVM) in terms of accuracy as well as recall by achieving 90.58%, 80.79% respectively. © 2025 IEEE.

关键词： Predictive maintenance

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Advanced deep learning approach for enhancing crop disease detection in agriculture using hyperspectral imaging

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Journal of Intelligent and Fuzzy Systems 2024年第2期46卷 3281-3294页

作者： Zekrifa, Djabeur Mohamed Seifeddine Lamani, Dharmanna Chaitanya, Gogineni Krishna Kanimozhi, K.V. Saraswat, Akash Sugumar, D. Vetrithangam, D. Koshariya, Ashok Kumar Manjunath, Manthur Sreeramulu Rajaram, A. Department of Bioinformatics Higher School of Food Science and Agri-Food Industry Ahmed Hamidouche Av Oued Smar Algiers Algeria Department of Computer Science & Information Technology School of Computing and Information Technology REVA University Rukmini Knowledge Park Kattigenahalli Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science Engineering Dr. K. N. Modi University Jaipur Rajasthan Newai India Department of Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Deemed to be University Coimbatore India Department of Computer Science and Engineering University Institute of Engineering Chandigarh University Punjab Mohali India Department of Plant Pathology School of Agriculture Lovely Professional University Punjab Phagwara India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamilnadu Nagapattinam India

Crop diseases pose significant challenges to global food security and agricultural sustainability. Timely and accurate disease detection is crucial for effective disease management and minimizing crop losses. In recent years, hyperspectral imaging has emerged as a promising technology for non-destructive and early disease detection in crops. This research paper presents an advanced deep learning approach for enhancing crop disease detection using hyperspectral imaging. The primary objective is to propose a hybrid Autoencoder-Generative Adversarial Network (AE-GAN) model that effectively extracts meaningful features from hyperspectral images and addresses the limitations of existing techniques. The hybrid AE-GAN model combines the strengths of the Autoencoder for feature extraction and the Generative Adversarial Network for synthetic sample generation. Through extensive evaluation, the proposed model outperforms existing techniques, achieving exceptional accuracy in crop disease detection. The results demonstrate the superiority of the hybrid AE-GAN model, offering substantial advantages in terms of feature extraction, synthetic sample generation, and utilization of spatial and spectral information. The proposed model’s contributions to sustainable agriculture and global food security make it a valuable tool for advancing agricultural practices and enhancing crop health monitoring. With its promising implications, the hybrid AE-GAN model represents a significant advancement in crop disease detection, paving the way for a more resilient and food-secure future. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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