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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571-580 订阅

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Transmuting Wearable sensor Data: From Inertial to Electrical-Like Measurements of Heart Activity 1

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5th international conference on Deep Learning theory and Applications (DeLTA)

作者： Maiorana, Emanuele Roma Tre Univ Rome Italy

ISBN: (数字)9783031666940

ISBN: (纸本)9783031666933;9783031666940

Wearable sensor technology is revolutionizing several research fields, ranging from healthcare to fitness monitoring or biometric recognition, thanks to its many advantages against potential alternatives, such as non-invasiveness and long-term operation capabilities. In more detail, seismocardiography (SCG) and gyrocardiography (GCG) are emerging as useful tools for cardiovascular assessment, relying on inertial measurements of cardiac activity. However, the knowledge and confidence about these signals is still limited in many fields, including the medical one, where the use of electrical measurements, such as those obtained via electrocardiography (ECG), is largely preferred. this paper presents a pioneering study about the possibility of converting SCG and GCG data into ECG-like representations, with the aim of expanding the applicability of inertial wearable sensors to scenarios where their characteristics could provide relevant benefits, yet there could still be the need to exploit knowledge regarding electrical heart activity measurements. In more detail, the effectiveness of recurrent neural networks (RNNs) in performing such task is here investigated. Extensive experimentation on a public dataset demonstrates the feasibility and efficacy of the proposed method in generating signals that significantly resemble the desired ECG data. the capability of the proposed approach in reproducing relevant characteristics of ECG signals in the created data is evaluated considering two potential real-world applications, regarding heart rate estimation and ECG-based biometric recognition.

关键词： Electrocardiography (ECG) Seismocardiography (SCG) Gyrocardiography (GCG) Biometrics Wearable sensors Recurrent Neural networks (RNNs)

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An Improved Leach Algorithm Based on Hierarchical Clustering Approach for Wireless sensor Network Application 5

An Improved Leach Algorithm Based on Hierarchical Clustering...

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IEEE 5th international conference on Electronics and Communication Engineering (ICECE)

作者： Bhih, Amhmed Abushiba, Walid Elashheb, Adel Coll Elect Technol CET Tripoli Dept Commun Tripoli Libya Appl Sci Univ Bahrain Eker Bahrain Tech Coll Civil Aviat Meteorol Espiaa Dept Commun Espiaa Libya

ISBN: (纸本)9781665487894

Wireless sensor networks composed of a huge number of sensor node with the capability to sense, collect and process data in the physical environment. the sensors are usually battery-powered nodes which limit the network lifespan. Due to energy constraints, the deployment of WSNs required sophisticated techniques to prolong the network operation. A clustering based routing algorithm named Low-Energy Adaptive Clustering Hierarchy (LEACH) is proposed as an energy effective solution. However, in LEACH, the power of nodes in the network is quickly depleted and decreases the life of the network due to the clusters headers that are randomly selected without taking into account the remaining power and the position of the nodes. the enhanced LEACH algorithm, named LEACH-Kmeans, is proposed where the improvement is done in cluster head selection process. LEACH-Kmeans makes use of K-means clustering calculation for choosing optimal cluster heads. A comparison among LEACH, and LEACH-Kmeans clustering has been done. Simulation result shows that LEACH-Kmeans protocol can reduce energy consumption and improve the network lifetime.

关键词： wireless sensor networks hierarchical routing protocols LEACH K-means

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Dynamic-Feature-Based Object Tracking Using Real-Time Image processing 5th

Dynamic-Feature-Based Object Tracking Using Real-Time Image ...

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

作者： Mishra, Awakash Rengarajan Bhatt, Rahul Prince, P. Blessed Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & 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

ISBN: (纸本)9789819780426

this research explores the utility of today's real-time picture processing for dynamic-characteristic-primarily based object monitoring. Notably, this painting proposes a novel tracking method that combines an actual-time, place-primarily based convolutional neural community (R-CNN) with monitoring with the detection (TbD) technique. the proposed approach is for contemporary R-CNNs to constantly come across transferring objects in video frames and then modern-day the TbD technique to generate accurate item tracking information. the proposed system is evaluated on two benchmark datasets and compared to numerous tracking methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Power Optimization of Intelligent Reflecting Surface assisted NOMA for Wireless Communication networks 5

Power Optimization of Intelligent Reflecting Surface assiste...

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5th international conference on Multimedia, Signal processing and Communication Technologies (IMPACT)

作者： Ul Amin, Mehboob Singh, G. Univ Kashmir Post Grad Dept Elect & Instrumentat Technol Srinagar India Univ Johannesburg Dept Elect & Elect Engn Sci Ctr Smart Informat & Commun Syst Johannesburg South Africa

ISBN: (纸本)9781665476478

In this paper, we have proposed an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) communication networks to achieve better quality of service in terms of improved Ergodic capacity and low transmission power. the sum rate of users is maximized by optimizing the transmit vector and IRS reflecting vector, satisfying the IRS reflecting constraint and successive interference cancellation (SIC) decoding condition. Taylor approximation is used to tackle the non-convex optimization problem and is used to convert it into convex one using successive convex approximation. Finally, Convex Optimization tool (CVX) is used to solve this optimization by iterative algorithm, verified on MATLAB software. Simulation result depict that IRS assisted NOMA performs better, when compared with NOMA system.

关键词： Non-orthogonal multiple access intelligent reflecting surfaces ergodic capacity outage probability

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Tracking of Fragmented Particles with Neural networks 5th

Tracking of Fragmented Particles with Neural Networks

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5th international conference on Information Technology in Geo-Engineering, ICITG 2024

作者： Zhu, Zhiren Wang, Jianfeng Department of Architecture and Civil Engineering City University of Hong Kong Hong Kong

ISBN: (纸本)9783031765278

In this paper, a novel particle tracking method is proposed to investigate the kinematics of Leighton Buzzard sand (LBS) particles exhibiting slight- and medium-level fragmentation by combining PointConv and PointNetLK networks. Firstly, a series of image processing algorithms were employed on the raw CT slices to facilitate reproduction of particle morphology. Subsequently, all particles were represented as point sets, down sampled and grouped, yielding substantial datasets for neural network training and testing. Additionally, Gaussian noise was generated and introduced into the particle point sets to enhance the network robustness. then, the PointConv network was implemented to efficiently match the particles under different strains. this success was attributed to the good preservation of morphological features of these particles and the excellent capacity of PointConv to capture morphological features. Next, the PointNetLK network was trained and incorporated with the particle correspondence obtained by PointConv, aiming to determine the optimal transformation matrix for the corresponding particles. Finally, the predictive results are evaluated based on the visualization of the transformed point cloud, and the particle kinematics is analyzed. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computerized tomography

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Message from the conference Chair

Message from the Conference Chair

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Image processing and Capsule networks (ICIPCN), international conference on

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Dynamic Transmit Awake Feature based Forwarder Election in Mobile WSN 5

Dynamic Transmit Awake Feature based Forwarder Election in M...

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5th international conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Juliet, P. Sudha Raman, Anantha Valarmathi, R. Brindha, G. Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Secunderabad India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665474672

Mobile Wireless sensor Network (MWSNs) is the collection of wireless mobile sensor nodes that are able to dynamically form a short-term network. Because of its dynamic nature, MWSN is self-configuring and obtaining the secure route is very challenging. the traditional routing efficiency approaches are not adequate for the protected routing of information from sender to receiver in MWSN since it necessitates refined hardware, software, great memory, speed of high processing, and transmission bandwidth. the major challenge of designing and developing WSNs for industrial supervising and control is fulfilling their strict stability necessities. Also, the network's stability is greatly affected and challenged in MWSN. In this paper, Dynamic Transmit Awake Feature (DTAF)-based forwarder election in mobile WSN is proposed. the DTAF approach aims to improve routing efficiency and minimize link failure in industrial applications. Here, the forwarder node is chosen along with the Transmit Awake Feature (TAF). this TAF is evaluated by the sensor node link weight, received signal strength, Expected Transmission count, and available throughput by applying the Cuckoo search (CS) algorithm. this approach improves the whole network's lifespan and connectivity. Furthermore, it increases the network stability and selects an efficient relay. the network simulator tool is used for verifying the DTAF performance. Simulations results evaluate the DTAF approach provides better-remaining energy, better throughput, and lesser packet delay. © 2023 IEEE.

关键词： sensor nodes

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GLCM-Based Feature Extraction and CNN Approach for Fruit Freshness Detection 5

GLCM-Based Feature Extraction and CNN Approach for Fruit Fre...

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5th international conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Abu Bakar, Mas Azza Zulkifli, Zalikha Mohamad, Masurah Ahmad, Samsiah Lumut Maritime Terminal Sdn Bhd Lot 1 Lumut Port Industrial Park Jalan Pelabuhan 4 Kg. Acheh Sitiawan Perak32000 Malaysia College of Computing Informatics & Mathematics Universiti Teknologi Mara Perak Branch Tapah Campus Perak Malaysia

ISBN: (纸本)9798331528553

the industry responsible for providing fresh fruits to the market plays a crucial role in ensuring the quality and safety of the produce. Classifying and grading the freshness of fruits is essential to maintain high standards. the presence of harmful bacteria in fruits poses a significant threat to the global agriculture sector. Recently, there has been an increased awareness of the vulnerability of fruits to various diseases, adding economic pressure on the agriculture sector worldwide. To address this issue, the proposed system employs a combination of image processing algorithms and deep learning models to analyze the visual characteristics of fruits and accurately determine their freshness. Addressing the challenges associated with subjective visual judgment and time-consuming manual inspection, the research introduces a methodology termed Machine Learning Life Cycle (MLLC), comprising six distinct phases from data gathering to the test model. Utilizing a carefully curated dataset sourced from Kaggle, featuring fresh and rotten apples, oranges, and bananas, the research involves training and classification process diagrams and algorithm implementation covering image pre-processing and Convolutional Neural Network (CNN) based model training. Evaluation metrics, including classification reports and confusion matrices, reveal assessed performance across fruits, with bananas demonstrating the highest accuracy which is 85%. the research aims to develop a web-based deep learning system for fruit freshness detection but has a limitation in optimal feature segmentation using the Gray-Level Co-occurrence Matrix (GLCM) for fruit freshness detection. Challenges include selecting relevant features across diverse fruit images, potential overfitting with deep convolutional layers, and balancing model complexity with data availability. Future works should explore advanced feature extraction techniques, mitigate overfitting risks, and enhance user interface inclusivity through

关键词： Convolutional neural networks

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AgileGCN: Accelerating Deep GCN with Residual Connections using Structured Pruning 5

AgileGCN: Accelerating Deep GCN with Residual Connections us...

引用

IEEE 5th international conference on Multimedia Information processing and Retrieval (MIPR)

作者： He, Qisheng Banerjee, Soumyanil Schwiebert, Loren Dong, Ming Wayne State Univ Comp Sci Detroit MI 48202 USA

ISBN: (数字)9781665495486

ISBN: (纸本)9781665495486

Deep Graph Convolutional networks (GCNs) with multiple layers have been used for applications such as point cloud classification and semantic segmentation and achieved state-of-the-art results. However, they are computationally expensive and have a high run-time latency. In this paper, we propose AgileGCN, a novel framework to compress and accelerate deep GCN models with residual connections using structured pruning. Specifically, in each residual structure of a deep GCN, channel sampling and padding are applied to the input and output channels of a convolutional layer, respectively, to significantly reduce its floating point operations (FLOPs) and number of parameters. Experimental results on two benchmark point cloud datasets demonstrate that AgileGCN achieves significant FLOPs and parameters reduction while maintaining the performance of the unpruned models for both point cloud classification and segmentation.

关键词： Point cloud compression Computational modeling Semantics Information processing Benchmark testing Task analysis

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A Brief Review of State-of-the-art Routing Methods in Wireless sensor Network 5

A Brief Review of State-of-the-art Routing Methods in Wirele...

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5th international conference on Contemporary Computing and Informatics, IC3I 2022

作者： Meena, Manoj Kumar Sharma, Kanika NITTTR Chandigarh Department of ECE Chandigarh India

ISBN: (纸本)9798350398267

Due to the widespread use in recent decades, wireless sensor networks (WSNs) have emerged as a top research area. However, the dynamic reconfiguration of WSNs offers a significant hurdle due to the fact that conventional methodologies conFigure WSNs statically. Clustering methods can be implemented in WSNs to overcome this difficulty. A plethora of researchers have proposed routing methods based on the concepts of clustering. In this paper, a brief review of state-of-the-art routing methods is presented. the focus of this work has been on the selection of Cluster Head (CH) adopted by recently proposed studies that use different optimization methods. Hence, this study will help the researchers to propose new parameters for selecting the CH that can enhance the network lifetime. © 2022 IEEE.

关键词： Wireless sensor networks

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