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

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Development of a Universal Integrated Monitoring System for Communication networks

Development of a Universal Integrated Monitoring System for ...

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Krasen Angelov Panagiotis Kogias Nikolay Manchev Stanimir Sadinov Dept. of Communications Equipment and Technologies Technical University – Gabrovo Gabrovo Bulgaria Dept. of Physics Democritus University of Thrace Kavala Greece

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Monitoring systems are a set of technical tools that continuously monitor and acquire information in a communication network, based on the analysis of statistical data, in order to identify faulty or malfunctioning nodes and processes and to alert the responsible persons. the main objective of this paper is to present the development of a hardware integrated system, mounted in a chassis for embedding in a 19" communications cabinet, which allows the monitoring of the server room in which it is located, by means of built-in sensors, as well as the monitoring of external networks and systems of different types (wireless, optical, etc.) and with different purposes. the applicability and functionality of the developed integrated system for monitoring communication networks is demonstrated through several use cases presented as examples in the paper.

关键词： Performance evaluation Wireless communication Wireless sensor networks Hardware sensor systems Communication networks Optical sensors Monitoring System implementation Standards

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RATNUS: Rapid, Automatic thalamic Nuclei Segmentation Using Multimodal MRI Inputs

RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation Using ...

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Workshop on Longitudinal Disease Tracking and Modeling with Medical Images and Data, LDTM 2024, 5th international Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th international conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

作者： Feng, Anqi Bian, Zhangxing Dewey, Blake E. Colinco, Alexa Gail Zhuo, Jiachen Prince, Jerry L. Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health BaltimoreMD21224 United States Department of Neurology Johns Hopkins School of Medicine BaltimoreMD21287 United States Department of Diagnostic Radiology and Nuclear Medicine University of Maryland School of Medicine BaltimoreMD21218 United States

ISBN: (纸本)9783031845246

Accurate segmentation of thalamic nuclei is important for better understanding brain function and improving disease treatment. Traditional segmentation methods often rely on a single T1-weighted image, which has limited contrast in the thalamus. In this work, we introduce RATNUS, which uses synthetic T1-weighted images with many inversion times along with diffusion-derived features to enhance the visibility of nuclei within the thalamus. Using these features, a convolutional neural network is used to segment 13 thalamic nuclei. For comparison with other methods, we introduce a unified nuclei labeling scheme. Our results demonstrate an 87.19% average true positive rate (TPR) against manual labeling. In comparison, FreeSurfer and thOMAS achieve TPRs of 64.25% and 57.64%, respectively, demonstrating the superiority of RATNUS in thalamic nuclei segmentation. the source code and pretrained models are available at: https://***/ANQIFENG/RATNUS. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Sine Cosine Algorithm based Optimal Cluster Head Selection in Wireless sensor networks

Sine Cosine Algorithm based Optimal Cluster Head Selection i...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： B. Pitchaimanickam P. Muthuvel R. Rajasekar Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India Department of CSE School of Computing Kalasalingam Academy of Research Kalasalingam Academy of Research Krishnankoil Tamil Nadu India

the sensor nodes are dispersed all through the environment to track changes in the physical and environmental conditions. Information from the environment is gathered with the help of sensors and transmitted to the Base Station (BS). the sensor node operates its sensing, computing, and communication processes using battery power. these procedures shorten the lifetime of the network and demand extra energy. the lifespan of the network problem can be effectively solved using the clustering strategy. In this paper, the Sine Cosine Algorithm (SCA) is recommended in the LEACH-C algorithm to discover efficient Cluster Head (CH). the proposed algorithm is developed by using the mathematical sine and cosine functions for finding the best solution. the proposed methodology finds the minimum distance and residual energy. this algorithm is implemented in Ns-2 for measuring the alive nodes and energy depletion. this technique minimizes energy utilization and maximizes network lifespan.

关键词： Wireless sensor networks Base stations Metaheuristics Clustering algorithms Energy measurement Heterogeneous networks sensors

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Enhanced Simulation Tool for ZigBee Security

Enhanced Simulation Tool for ZigBee Security

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Marieta Yordanova Aydan Haka Veneta Aleksieva Hristo Valchanov Faculty of Computer Sciences and Automation Technical University of Varna Varna Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

the significance of ZigBee security is on the rise because of the growing reliance on wireless sensor networks (WSNs) and the need for secure communication. Ensuring security in ZigBee networks involves elements like key management, encryption, and safeguarding data across various stack layers. Developing simulators to replicate and visually represent network security processes is essential for enhancing the security and understanding of these networks. Lately, numerous simulators have been created to assess the efficiency of Wireless sensor networks (WSNs). However, among the simulators examined, none focused on the communication security of ZigBee devices at the application layer of the ZigBee *** paper presents an enhanced simulator to help analyze and improve network security by showing the key generation and exchange process, along with application layer encryption. the simulator employs 128-bit AES encryption in accordance with ZigBee standards and shows a graphical depiction of the application layer key request, generation, and receipt sequence. this allows users to examine diverse security contexts and visualize the results of standard and non-standard operational approaches.

关键词： Training Wireless sensor networks Zigbee Network security Encryption Security Standards

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FIREnet 2.0: Advanced Neural Framework for Smart Fire Detection & Localization 2

FIREnet 2.0: Advanced Neural Framework for Smart Fire Detect...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Aruthaveni, R.M. Dhivya, B. Hariharan, M. Siva, R. Department of Computer Science Dr. SNS Rajalakshmi College of Arts and Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350340235

Fire safety plays an important role in smart environments, and traditional fire detection systems frequently demonstrate limits regarding reaction time and false alarms. the proposed study offers FIREnet 2.0, an innovative fire detection and localization system that harnesses the power of Edge AI and 5G connection. S uch a system is driven by the need for faster and more accurate fire detection in smart cities, industrial settings, and beyond. Existing fire detection systems usually suffer from high false alarm rates, slow reaction times, and restricted scalability. FIREnet 2.0, on the other hand, provides an innovative solution. the proposed study describes the system's novel technique: data collection, Edge AI processing with modified YOLO-based CNNs, 5G data transfer, fire localization, and reaction coordination. Extensive testing, with controlled, real-world fire tests, shows that FIREnet 2.0 delivers high accuracy, lowers false alarms, and considerably shortens reaction times compared to existing systems. the novel method of fire detection has the potential to revolutionize fire safety in smart environments and critical infrastructure, boosting safety and response times. © 2023 IEEE.

关键词： 5th generation connectivity Convolutional Neural networks Edge Artificial Intelligence Fire detection Fire Localization Smart environments

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Research on Outdoor Mobile Robot Localization and Mapping Algorithm Based on Multi-sensor Fusion

Research on Outdoor Mobile Robot Localization and Mapping Al...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Biao Liu Shuqin Xiang Guangzhou Gosuncn Robot Co.Ltd Guangdong China Chongqing University Chongqing China

An accurate localization and mapping method based on multi-sensor data fusion is proposed for mobile robots in outdoor open scenes. By collecting and pre-processing multi-sensor data, combining with LIO-SAM to build 3D point cloud maps and optimizing point cloud maps, it helps mobile robots to complete high-precision localization and mapping in outdoor environment. Finally, the mobile robot platform is built and combined with the robot operating system to verify the effectiveness and accuracy of the scheme in outdoor environments.

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A Case Study of Geoacoustic Inversion Based on Convolutional Neural Network Using Vertical Array Data 5

A Case Study of Geoacoustic Inversion Based on Convolutional...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Liu, Mingda Niu, Haiqiang Li, Zhenglin Guo, Yonggang Institute of Acoustics Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Acoustics Beijing China School of Ocean Engineering and Technology Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665485890

Geoacoustic inversion is regarded as a global or local optimization problem for the conventional matched-field inversion (MFI), while it is treated as a nonlinear regression problem in a machine learning (ML) framework in this paper. A case study is performed to evaluate the feasibility of these two inversion methods when the two-layer bottom is assumed to be a half space model in a shallow water environment. A convolutional neural network (CNN) with multi-task learning is used to estimate geoacoustic parameters simultaneously in shallow water. the network input is the normalized sample covariance matrices of the broadband data received by a vertical line array in frequency domain. the training data, validation data, and test data are generated by an acoustic propagation model. Localization and transmission loss (TL) for different typical bottom models are used to verify the performance of CNN and MFI. Simulation results demonstrate that the trained CNN is robust in geoacoustic inversion even on noisy test data with a moderate Signal-to-Noise Ratio (SNR) and achieve inversion performance comparable to the MFI. © 2022 IEEE.

关键词： Convolutional neural networks

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RPViT: Vision Transformer Based on Region Proposal 2022

RPViT: Vision Transformer Based on Region Proposal

引用

5th international conference on Image and Graphics processing, ICIGP 2022

作者： Ge, Jing Wang, Qianxiang Tong, Jiahui Gao, Guangyu School of Computer Science and Technology Beijing Institute of Technology China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China

ISBN: (纸本)9781450395465

Vision Transformers constantly absorb the characteristics of convolutional neural networks to solve its shortcomings in translational invariance and scale invariance. However, dividing the image by a simple grid often destroys the position and scale features in the image at the beginning of the network. In this paper, we propose a vision transformer based on region proposal, which obtains the inductive bias in a simple way. Specifically, RPViT achieves locality and scale-invariance by extracting regions with locality using a traditional region proposal algorithm and deflating objects of different scales to the same scale by a bilinear interpolation algorithm. In addition, to enable the network to fully utilize and encode diverse candidate objects, a multi-class token approach based on orthogonalization is proposed and applied. Experiments on ImageNet demonstrate that RPViT outperforms baseline converters and related work. © 2022 ACM.

关键词： Convolutional neural networks

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Domestic Solid Waste Classification Using Convolutional Neural networks 5

Domestic Solid Waste Classification Using Convolutional Neur...

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5th IEEE international conference on Image processing Applications and Systems (IPAS)

作者： Dookhee, Surajsingh Univ Technol La Tour Koenig Mauritius

ISBN: (纸本)9781665462198

the overwhelming amount of household solid waste generated daily is alarming, and this contributes to the rise in pollution and drastic climate change. In such a context, automated waste classification at the initial stage of disposal can be an effective solution to separate recyclable items. Convolutional Neural networks based on deep learning are often used for automated waste classification, but however, research works are limited to insufficient categories of waste such as the TrashNet dataset consisting of 2,527 images and 6 categories of waste. this dataset does not include other important categories such as battery, biological, and clothing items to reflect real-life environmental problems. therefore, in this paper, a larger dataset consisting of 15,515 images and 12 categories of common household solid waste was used to evaluate the performance of DenseNet121, DenseNet169, EfficientNetB0, InceptionV3, MobileNetV2, ResNet50, VGG16, VGG19, and Xception Convolutional Neural Network models. Data augmentation was applied to solve the problem of class imbalance, and findings of my first research showed that the Xception model compiled with Adam optimiser outperformed all other models with an accuracy of 88.77% and an F1-score of 0.89. the performance of the model was improved to 89.57% with an F1-score of 0.90 when compiled with Nadam optimiser. However, further experimentation showed that the model did not generalise well despite reaching an accuracy of 93.42% and an F1-score of 0.93 when trained without data augmentation. this demonstrates the feasibility of the proposed model for reallife environmental problems.

关键词： Automated Waste Classification Convolutional Neural Network Recyclable Waste Trash Classification

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Multi-scale damage identification method based on wireless sensor network 5

Multi-scale damage identification method based on wireless s...

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5th international conference on Mechatronics and Computer Technology Engineering, MCTE 2022

作者： Zhou, Zhuo Dai, YaWen School of Information Engineering Wuhan University of Technology Wuhan430070 China Faculty of Science Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9781510661073

Due to the threat of natural disasters such as earthquakes and floods, as well as the influence of the aging of civil engineering structures, the safety of long-term service structures cannot be guaranteed. In order to monitor the working status of structural engineering in real time and capture its damage information, a multi-scale damage identification method based on wireless sensor network is proposed in this paper. Autocorrelation analysis of time series data is carried out through nonlinear autoregressive network with exogenous inputs (NARX) neural network, the overall health of the structure is initially diagnosed locally at the node, and the sensor nodes are divided into different monitoring sub-networks according to the spatial location when the structure is damaged, and the empirical mode decomposition (EMD) method is used. the time-series data were preprocessed to further locate and quantify damage. Experiments show that the method can accurately identify and locate structural damage, and can visually represent the amount of structural damage. © 2022 SPIE.

关键词： Empirical mode decomposition

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