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

共 8138 条记录，以下是401-410 订阅

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the Creation of Intelligent Surfaces for the Purpose of Next-Gen Wireless networks 4

The Creation of Intelligent Surfaces for the Purpose of Next...

引用

4th IEEE international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Sowmiya, S. Senthilkumar, P. Kuppuraj, T. Mishra, Anshoo Muralidharan, P. Rosaline, S. Prince Shri Venkateshwara Padmavathy Engineering College TN India Nehru Institute of Engineering and Technology Tamil Nadu T.M.Palayam India Karpagam Academy of Higher Education TN India Ies Institute of Technology and Management Ies University M.P Bhopal India Christ University Bangalore India R.M.K.Engineering College TamilNadu Kavaraipettai India

ISBN: (纸本)9798350360165

In preparation of the changing environment of 5th wave (5G) and prospective networks of cells, this study explores new methods to meet challenges that result from the erratic character of the communication medium. Traditionally viewed as a chance factor, the relationship between broadcast radio waves with surrounding factors lowers signal quality in modern times of wireless communications. this paper performs a full literature review on customizable autonomous surfaces (RISs) alongside their uses, stressing the chance for network managers to control radio wave features and minimize environmental spread problems. RISs allow effective control over waveform parameters, including the amplitude, phase, number, and polarization, that without needing complex encoder, decoder, or radio wave processing methods. Leveraging technical developments, metasurfaces, reflectarrays, phase shifts, and liquid crystals appear as potential options for RIS application, placing them as pioneers in the realization of 5G as well as subsequent networks. the study dives into current actions in the RIS-operated mobile phone network area and covers core research issues that deserve exploration to feed unlocking the full promise of RISs at wireless communication networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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Leveraging Ensemble Deep Learning Model for Fruit Image Recognition with Explainable AI 5

Leveraging Ensemble Deep Learning Model for Fruit Image Reco...

引用

5th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Wang, Yaoqing Yu, Chang Lin, Xiyue Mgbejime, Goodness Temofe Nneji, Grace U. Monday, Happy N. University of Electronic Science and Technology School of Electronic Science and Engineering China Chengdu University of Technology Chengdu China Intelligent Computing Lab Hace Softtech Lagos102241 Nigeria

ISBN: (纸本)9798331506612

Precise classification of fruit images is crucial for a range of agricultural and retail purposes. Although there have been improvements, current methods have limits when it comes to dealing with complex feature extraction and ensuring model resilience. this paper introduces a new framework for categorizing fruits using a combination of advanced deep learning models, namely AlexNet, ResNet50, and EfficientNet. the collection of 43,912 photos across 44 fruit classifications underwent thorough pre-processing and data augmentation procedures. Grad-CAM, SHAP, and LIME were employed to achieve model interpretability, offering valuable insights into the decision-making process of the ensemble model. the experimental results indicate that the ensemble model surpasses the performance of individual models, obtaining 96.9% accuracy, 96.2% precision, 94.5% recall, and 95.4% F1-score. the proposed ensemble model provides a highly efficient solution for automated fruit classification, with substantial implications for agricultural technology and retail automation. © 2024 IEEE.

关键词： Convolutional neural networks

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Wavelet-Based Time Series Forecasting for Wireless sensor networks 5th

Wavelet-Based Time Series Forecasting for Wireless Sensor Ne...

引用

5th international conference on Data Science, Machine Learning and Applications, ICDSMLA 2023

作者： Murugan, R. Arya, Rakesh Kokila, S. Mishra, Awakash Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Wavelet-primarily based time series forecasting has been an effective device for Wi-Fi sensor network (WSN) packages. this approach has proven to provide correct forecasts for a selection of situations whilst enabling strength performance through decreased statistics storage and transmission bandwidth necessities. By means of extracting the vital time-related functions from sensor facts streams, records compression is executed at the same time as retaining the accuracy of the forecasts. Furthermore, with the aid of taking advantage of the multi resolution decomposition belongings of wavelets, this method can appropriately seize the temporal dynamics of the authentic signal. this permits better approximation of the underlying signal’s temporal correlations while as compared to other kinds of forecasting strategies. Additionally, wavelet-primarily based methods permit robust and green adaptation of the prediction fashions to changing sensor records, for this reason adapting the overall performance of the forecasts to exclusive situations. All these homes make wavelet-based time series forecasting an appealing choice for WSN programs. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless sensor networks

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Energy-Efficient Transmission in Wireless sensor Network Using Compressive Sensing 5th

Energy-Efficient Transmission in Wireless Sensor Network Us...

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5th international conference on Computing and Network Communications, CoCoNet 2023

作者： Manuel, Ebin M. Darshana, A.P. Department of Electronics and Communication Rajiv Gandhi Institute of Technology Kottayam Kottayam India Department of Electronics and Communication College of Engineering Trivandrum Kerala Trivandrum India

ISBN: (纸本)9789819745395

Minimizing energy consumption and maximizing network longevity through efficient data transmission are open research problems in practical wireless sensor networks. the proposed work focuses on data transmission based on two methods: Compressive Sensing (CS) and Partial Canonical Identity matrix-based Compressive Sensing (PCI-CS). the performance of these two methods on different energy-efficient clustering and routing protocols, such as Low Energy Adaptive Clustering Hierarchy (LEACH), Stable Election Protocol (SEP), and threshold sensitive Energy-Efficient sensor Network (TEEN) is compared. the combination of CS or PCI-CS, along with these energy-efficient clustering and routing protocols, can immensely reduce energy consumption. Simulation results show that the PCI-CS method outperforms other methods in terms of energy efficiency as well as network longevity. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Intelligent vehicle classification system based on deep learning and multi-sensor fusion 5

Intelligent vehicle classification system based on deep lear...

引用

5th international conference on Computer Vision and Data Mining, ICCVDM 2024

作者： Li, Xinjin Yang, Yuanzhe Yuan, Yixiao Ma, Yu Huang, Yangchen Ni, Haowei Columbia University New YorkNY United States Diablo Valley College Pleasant HillCA United States Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781510682863

With the rapid development of Intelligent Transportation Systems (ITS), vehicle type classification, as a key link in Automatic Toll Collection systems (ATC), is of great significance in improving traffic efficiency and reducing economic losses. this study proposes an intelligent vehicle classification system based on deep learning and multi-sensor data fusion to address the accuracy issues existing in vehicle classification methods based on optical sensors (OS) and human observers. the system significantly improves the accuracy and robustness of vehicle classification by combining deep Convolutional Neural networks (CNN), LiDAR sensors, and machine learning algorithms. We first constructed a large-scale annotated dataset containing multiple vehicle types and complex traffic scenes to improve our model's capability to identify different vehicle characteristics. Next, CNN models based on different architectures were designed to extract global and local features of the vehicle, respectively. In addition, the LiDAR sensor was used to achieve the spatial structure architectures of the vehicle and combined with the output of the CNN model to improve classification performance under occlusion and complex scenes. © 2024 SPIE.

关键词： Convolutional neural networks

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Blockchain-Based Multi-target Distributed Passive Localization 5th

Blockchain-Based Multi-target Distributed Passive Localizati...

引用

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

作者： Song, Zijian Zhu, Jing Zheng, Hongyuan Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

this paper investigates the multi-target localization in distributed passive sensor system in terms of blockchain technology. In this distributed framework, each sensor captures and analyzes the angle measurement data of targets locally, and uploads the results onto blockchain network for coordination among nodes. Subsequently, smart contracts automatically associate data obtained from external sensors to establish an estimate of the target location. We deploy a novel position estimation smart contract using Remix on Ganache private blockchain. Meanwhile, we conduct simulations of the Electronic Support Measures (ESM) sensor and Angle of Arrival (AoA) measurement process using Matlab. Finally, Experimental simulations verify the effectiveness and feasibility of the proposed algorithm for target localization in distributed passive sensor networks leveraging blockchain technology.

关键词： Distributed Passive Localization AoA ESM sensor Blockchain Smart Contracts

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A real-time gait phase detection method for lower limb exoskeleton based on simple recurrent units 5

A real-time gait phase detection method for lower limb exosk...

引用

5th international conference on Communications, Information System and Computer Engineering, CISCE 2023

作者： Zhao, Yuxuan Wang, Yu Zhang, Chengyu Liao, Yibing Li, Jiaxuan Gao, Yifan School of Information Engineering Wuhan University of Technology Wuhan China Wuhan University of Technology Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks Wuhan China

ISBN: (纸本)9798350326796

A gait phase segmentation method integrating the motion characteristics of lower limb exoskeleton robots was proposed by analyzing their motion data. A neural network based on a simple recurrent unit was also presented for gait phase detection. this method achieves 98.53% accuracy in gait phase detection while meeting the real-time requirements of gait recognition. the proposed method provides a high-precision and real-time control basis for exoskeleton control systems, improving the coordination between the wearer and the exoskeleton robot during movement. © 2023 IEEE.

关键词： Real time control

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Tracking moving targets of interest-based deep learning 5

Tracking moving targets of interest-based deep learning

引用

2023 5th international conference on Information Science, Electrical, and Automation Engineering, ISEAE 2023

作者： Shangguan, Caoxi Gong, Zhenxuan Chen, Ruqi Jun, Li School of Physics and Telecommunications Engineering South China Normal University Guangzhou510006 China School of Electronics and Information Engineering South China Normal University Foshan528225 China

ISBN: (纸本)9781510667440

Tracking specific target is an important research area in machine vision. When tracking specific target encounters the same local features with light and dark variations, convolutional neural networks based on deep learning require large data sets to extract target feature information. To address these problems, this paper proposes a target of interest tracking scheme based on specific object imaging. the method allows for fast capture and processing of important information about the target of interest. In addition, small data sets are used to track the target and extract the target of interest efficiently. the target of interest can also be accurately tracked when the background and light changes. the results show that our method reduces the processing of redundant information, reduces the storage overhead, and tracks the target accurately. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image processing

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Revolutionizing Healthcare: Federated Learning for Large-Scale Image processing in IoT-Integrated Systems 5

Revolutionizing Healthcare: Federated Learning for Large-Sca...

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5th international conference on Smart Electronics and Communication, ICOSEC 2024

作者： Ravi, Kiran Chand Vadlamani, Subramanyam M Kumarbasu, Asim Kumar, Vivek Mallik, Satadal Sreelatha, K. Mlr Institute of Technology Department of Ece Hyderabad India Anurag Engineering College Department of Cse Kodad India The Neotia University West Bengal Kolkata India Department of Physics Andhrapradesh Eluru534003 India

ISBN: (纸本)9798331504403

the internet of things (IOT) devices integrated into federated learning (FL) is a revolutionary concept in the fast-paced world of healthcare, especially in medical image processing. In this paper, we propose a novel framework with the enablement of the Internet of things (IoT)-based medical devices and Federated Learning to autonomously handle large-scale medical images in an efficient and secure way. the approach adopts Cascaded Convolutional Neural networks with Spatial-Variant Convolution Kernel (CCSVCK) for improved feature extraction and classification performance. the framework de-centralizes the training process and thus allows cohering to patient privacy and other strict data protection regulations. We used a large dataset of mixed real and synthetic medical images after various preprocessing steps, such as normalization, augmentation, and anonymization, to implement and experiment on all stages of our approach. Training iterations were launched on each local node and contributed model updates to a central coordinator for aggregation. At this point in time, the model was synchronized across all nodes again and iteratively a global model was continuously computed. Evaluation metrics confirmed the robustness of the framework, yielding a commendable accuracy of 97.2%. this novel approach is a blend of not only providing better accuracy in diagnosis but also taking care of the privacy of the information, which has great potential to change health care diagnostics into IoT and Federated Learning. © 2024 IEEE.

关键词： Differential privacy

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Prediction of Quality of Eggs in Poultry Farming 5

Prediction of Quality of Eggs in Poultry Farming

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5th international conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Shrinivasan, Lakshmi Verma, Reshma Ramaswamy, Madhumitha Goudar, Vaishnavi B Rashmi, T. Dept. of Electronics and Communication Bengaluru India

ISBN: (纸本)9798331528539

the work proposes an innovative system to modernize chicken egg quality testing and prediction using advanced image processing and deep learning. Comprising three modules, it aims to revolutionize poultry industry standards. the first module uses controlled lighting to take high-resolution pictures of eggs. Using variables like air cell size, the image processing module extracts important information including crack detection, yolk quantity, and egg age prediction. Hardware improvements include the creation of specialized tools, such as controlled lighting systems and precise cameras, to ensure the best possible image acquisition. A graphical user interface (GUI) that is easy to use places emphasis on features such as clear controls, educational displays, and tools for modifying parameters, examining results, and providing feedback. the objective of this method is to optimize interactions and speed integration with current poultry industry operations. this system is also beneficial for small-scale operations, allowing individual customers to use the device for personal egg quality assessment. Future research may explore incorporating more quality parameters and the development of complete system automated devices and a mobile app. © 2024 IEEE.

关键词： Convolutional neural networks

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