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检索条件"任意字段=5th International Conference on Informational Processing in Sensor Networks"

共 8045 条记录，以下是1-10 订阅

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ISSNIP 2009 - Proceedings of 2009 5th international conference on Intelligent sensors, sensor networks and Information processing

ISSNIP 2009 - Proceedings of 2009 5th International Conferen...

引用

2009 5th international conference on Intelligent sensors, sensor networks and Information processing, ISSNIP 2009

ISBN: (纸本)9781424435180

the proceedings contain 81 papers. the topics discussed include: delivery packets reliably and efficiently over error prone channel of WSNs;compensating for the latency of data acquisition for localization in mobile wireless sensor networks;bounded degree energy aware topology discovery in ad hoc wireless sensor networks;resource allocation in adhoc networks using Markov random fields;resource allocation in adhoc networks using Markov random fields;formation control of robotic swarms based on sonar sensing;control of unmanned aerial vehicles performing multiple target passive detection and tracking;evolving discriminative features robust to sensor displacement for activity recognition in body area sensor networks;an adaptive algorithm for sensor activation in renewable energy based sensor systems;sensor scheduling for bearings-only tracking with a single sensor;and an improved cluster-head selection approach in wireless sensor networks.

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Designing 802.15.6 MAC Protocols for WBAN (Wireless Body Area) networks 5th

Designing 802.15.6 MAC Protocols for WBAN (Wireless Body Are...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Venkateshulu, B. Kishore, G. Krishna Kavya, T. G Narayanamma Inst Technol Sci Women Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

WBA networks support various medical applications consisting of heterogeneous requirements. For reliable data transfer, an effective media access control protocol must be used. Here in this project, a dynamic MAC protocol based on Super frame structure is proposed by extending the standard principles from IEEE 802.15.6 which is a standard protocol, a mechanism for allocating reserved slots with prioritization is used which is known to be Importance criteria through Inter-criteria Correlation, to assign a slot allocation for each sensor device. the sensor device values are calculated based on various sensor parameters using the CRITIC method. Here, we compare our proposed work with the IEEE 802.15.6 MAC standard and other MAC protocols. the simulation shows that our proposed MAC protocol performed better in reliability, throughput, energy efficiency as well as packet delivery delay. the results show that data transmission reliability is increased by more than 60% compared to the IEEE 802.15.6 MAC standard.

关键词： Wireless Body Area networks (WBAN) Criteria Importance through Inter-criteria Correlation (CRITIC)

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Intrusion Detection in Wireless sensor networks Using Histogram Gradient Boosting Classifier 5th

Intrusion Detection in Wireless Sensor Networks Using Histog...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Supriya, M. Adilakshmi, T. JNTUH Swami Vivekananda Inst Technol Dept CSE DS & IOT Secunderabad Telangana India Vasavi Coll Engn Dept CSE Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Intrusion Detection Systems (IDS), are an essential component of Wireless sensor networks (WSN) since they monitor the integrity of the network and report any suspicious or illegal activity. WSNs are susceptible to a wide variety of security risks, some of the most common of which being malicious attacks, data manipulation, unauthorized access, and compromised nodes. Identifying and addressing these security issues is much easier with the assistance of an IDS. this paper presents a machine learning model for employing IDS in WSN using Histogram Gradient Boosting Classifier. the proposed model is able to capture intricate patterns and distributions in the data in an effective manner thanks to the use of histogram representations. In addition, since gradient boosting is an iterative process, the model is able to zero in on difficult-to-detect instances of infiltration, hence continually enhancing its detection skills. However, in order to obtain optimum performance, it is necessary to appropriately tune the hyperparameters and solve possible difficulties such as class imbalance or noisy data. the proposed IDS model classified different attacks with an accuracy of 93.7%.

关键词： Secure Intrusion Detection Systems (IDS) Wireless sensor networks (WSN) Histogram Gradient Boosting Classifier

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Training Neural networks Based on PSO Algorithm and BP Algorithm 5

Training Neural Networks Based on PSO Algorithm and BP Algor...

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5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Gao, Darong The Electrical Engineering College Northwest Minzu University Lanzhou China

ISBN: (纸本)9798331533694

Signal processing has made extensive use of neural networks. this study examines neural network training using the PSO and BP algorithms. To train the neural network, the PSO algorithm alone, the BP algorithm alone, and the PSO algorithm combined with BP algorithm are examined for the same signal. It is determined that the PSO algorithm in conjunction with the BP algorithm can enhance the network's learning efficiency and accelerate its convergence. © 2025 IEEE.

关键词： Signal modulation

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sensor Placement Based on Cellular Communication Systems 5

Sensor Placement Based on Cellular Communication Systems

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Yuan, Shaobo Chen, Jiaxu Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9798331533694

this paper presents a novel approach for optimizing sensor placement within a designated area, leveraging a combination of cellular communication systems and feed-forward neural networks to streamline the selection process for operators. Initially, a modified hill-climbing algorithm, a form of local search, is implemented to generate preliminary data for sensor positioning. However, to prevent the algorithm from converging on local optima, a simulated annealing algorithm is subsequently employed to validate and refine the results. During the construction phase of the cellular communication system, flaws were detected in the evaluation metrics when using Modal Assurance Criterion (MAC) and singular value ratios, which led to the adoption of a feed-forward neural network. this neural network is utilized for both further optimization and a more accurate assessment of the sensor distribution. the study highlights the seamless integration of cellular communication systems with local search methods and underscores the significant improvements in optimization achieved through the use of neural networks. Ultimately, the research successfully develops a method for determining the optimal sensor positions, providing a robust solution for the effective deployment of cellular sensors in complex environments. © 2025 IEEE.

关键词： Mobile telecommunication systems

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Social Media Hate Speech Detection Using Machine Learning Algorithms: Comparative Study 5th

Social Media Hate Speech Detection Using Machine Learning Al...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Dharani, P. Bagade, Nidhi Nittala, Sripriya Konkala, Sowmya Sasidhar, B. G Narayanamma Inst Technol & Sci Women Dept CSE AI&ML Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Cyberbullying on social media using hate speech in text is applying depreciatory dialect in message dispatches on online forums to abuse, defile, as well as ill-treat recipients. A report by the New Indian Express stated that 93% of Indian children were subordinated to cyberbullying out of which 45% were bullied by strangers and 48% were bullied by people known to them. the existing system uses feature extraction using count vectorizer with Support Vector Machine classifier to give an accuracy of 94.78%. the proposed system uses feature extraction using tokenization and padding with Artificial Neural networks Classifier to achieve an accuracy of 95.85%. the system examines the text content of social media dispatches using Natural Language processing through Artificial Neural networks.

关键词： Cyberbullying Feature extraction Count vectorizer Tokenization Padding Natural language processing Artificial neural networks

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A Review on Energy Efficiency in Software-Defined Wireless sensor networks for IoT Applications 5

A Review on Energy Efficiency in Software-Defined Wireless S...

引用

5th National and the 1st international conference on Applied Research in Electrical Engineering, AREE 2025

作者： Rahimifar, Atefeh Kavian, Yousef Seifi Karoon Institute of Higher Education Dept. Electrical Engineering Ahvaz Iran Shahid Chamran University of Ahvaz Dept. Electrical Engineering Ahvaz Iran

ISBN: (纸本)9798350377170

As the Internet of things (IoT) transforms the structure of our globalized society there is an urgent need for energy-efficient and reconfigurable network solutions. In this paper, we explore the software-defined wireless sensor networks (SDWSNs), with energy being one of the key driving forces that can greatly augment the network functioning whilst prolonging the life of sensor nodes in various application domains of IoT. the most current approaches that employ advanced developments towards energy efficiency, namely intelligent data aggregation and balancing techniques, sleep scheduling strategies, and routing protocols, were reviewed in this study. It also identifies significant ongoing issues and future research directions and addresses energy efficiency issues by synthesizing ideas from various studies. this survey provides excellent information about technology and insight that derives from the current state of methods and challenges for primary concern to scientific and industrial communities and professional stakeholders regarding SDWSNs. © 2025 IEEE.

关键词： Artificial Intelligence Energy Consumption Energy Efficiency Internet of things Software-Defined Wireless sensor networks

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A Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for Image processing 5th

A Comprehensive Analysis for Advancements and Challenges in ...

引用

5th international conference on Data Science, Machine Learning and Applications

作者： Ch, Ravikumar Chary, Kalvog Prakasha Srinivas, S. Bhavani, Tedla Veeranna Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India CVR Coll Engn Dept CSE Cyber Secur Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vardhaman Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Sri Indu Coll Engn & Technol Dept Informat Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in image processing. the rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of image processing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional Neural networks (CNNs), clarifying their theoretical foundations within the image processing context, opening the door for further research. CNNs are notably essential for image processing due to their ability to handle complex images effectively.

关键词： Deep learning Image processing Backpropagation algorithm Convolutional Neural networks (CNNs) Model structures

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Simulation Verification Method for Image sensor Interface processing Unit Based on UVM 5

Simulation Verification Method for Image Sensor Interface Pr...

引用

5th IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2025

作者： Ma, Wenji Beijing Sunwise Information Technology Ltd. Department of FPGA Verification Organization Beijing China

ISBN: (纸本)9798331533694

Image sensors are widely employed across various industries, including smart mobile devices, autonomous vehicles, and surveillance systems. the Image sensor Interface processing Unit is typically characterized by a high degree of complexity to support diverse image sensor interfaces. Consequently, the effective and efficient verification of this unit is essential for upholding project quality and adhering to timelines. this article presents a simulation verification methodology for the Image sensor Interface processing Unit utilizing the Universal Verification Methodology (UVM). It explores the features of different sensor interfaces, the architectural framework, core base classes, runtime mechanisms, and reusable components within the UVM verification platform. the study identifies functional verification points based on the specific characteristics and application contexts of the Image sensor Interface processing Unit and formulates testing incentives. By analyzing the simulation results, the approach ensures comprehensive coverage of all functional points, achieving a 100% functional coverage rate. the testing platform is systematically designed to provide robust portability while meeting fundamental functional verification requirements, thereby serving as a reference for the development of future module verification environments. © 2025 IEEE.

关键词： Reusability

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Research on the Integration of Artificial Intelligence and Signal processing: A Trend Analysis Based on Bibliometrics 24

Research on the Integration of Artificial Intelligence and S...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Ma, Guoxuan School of Computer Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9798400709999

To understand the recent advancements in the application of artificial intelligence in the field of signal processing, this paper employs VOSviewer and CiteSpace to conduct a quantitive analysis and visualization of relevant literature from 2020 to September *** analyzing the number of publications, citation patterns, and collaboration networks, this study elucidates the primary contributors, core research directions, and the global landscape of scientific collaboration in this *** research findings indicate that the integration of AI and signal processing has not only achieved theoretical breakthroughs but is also widely applied to solve practical problems, such as optimizing medical diagnoses and enhancing communication *** bibliometric visualization analysis, this paper provides a panoramic view of the current state of research in this area and offers targeted predictions and recommendations for future development trends. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Quantization (signal)

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