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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561-570 订阅

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Apple Leaf Disease Recognition Based on Improved Convolutional Neural Network with an Attention Mechanism 5

Apple Leaf Disease Recognition Based on Improved Convolution...

引用

5th international conference on Natural Language processing, ICNLP 2023

作者： Zhao, Guangyuan Huang, Xu Xi'an University of Posts and Telecommunications School of Automation Xi'an China

ISBN: (纸本)9798350302219

Traditional plant disease recognition algorithms have a complicated approach, difficult feature extraction, and low recognition accuracy. Based on the improved EfficientNetV2 model, this research classifies images of apple leaf disease. this study collected images of seven common apple leaf disease categories and one healthy category to address the present needs of various complex disease recognition scenarios. the disease images not only contain the common laboratory background but also add the background of the field growth environment of apple trees. And different recognition scenarios are further enriched by image enhancement techniques. For the model part, the processing of spatial feature information was strengthened while focusing on the channel feature information to ensure that the model focuses more on the subtle disease spot information for different disease classifications. the experimental results show that the accuracy of the model training recognition is 97.49%. To better evaluate this study, comparison experiments were conducted with five other popular convolutional neural network classification models, such as ResNet-50, DenseNet-121, Xception, MobileNet, and EfficientNet-B3. the improved models enhance the recognition accuracy of complex scenes and improve the model parameters and training speed. It provides a reference for apple leaf disease recognition and the development needs of smart agriculture. © 2023 IEEE.

关键词： Convolutional neural networks

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Hardware Acceleration of Pre-trained networks for Colorectal Polyp Classification 5

Hardware Acceleration of Pre-trained Networks for Colorectal...

引用

5th international conference on Energy, Power, and Environment, ICEPE 2023

作者： Rana, Debaraj Balabantaray, Bunil Kumar Peesapati, Rangababu Nit Meghalaya Department of Ece Meghalaya Shillong India Nit Meghalaya Department of Cse Meghalaya Shillong India

ISBN: (纸本)9798350313123

the objective of the research is to identify a pre-trained Convolutional Neural Network (CNN) with faster inference time for classification of colorectal polyps on a Field Programmable Gate Array (FPGA) device. Basically, Deep Learning(DL) models are trained and executed using Graphical processing Units (GPUs) having high processing capacity, but those devices consume more power during execution. DL models that are appropriate for real-time miniature instruments must be implemented with low-power consumption. this paper evaluates the performance of relevant polyp classification models using FPGA (Avnet Ultra96-V2) and GPU (Quadro RTX 5000). the CNN models are trained on GPU and then deployed for inference on FPGA using the VITIS-AI platform and PYNQ framework. the MobileNetV2 performs classification with 88% accuracy and least inference time of 2.60 seconds at a throughput of 176 frames per second on FPGA. A precise comparison of the throughput and power consumption during execution on GPU and FPGA are provided. the FPGA device is found to perform 1 .25 × faster than GPU in terms of throughput while using 40 × less power with a negligible reduction in accuracy. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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A Comprehensive Analysis of Underwater Image processing based on Deep Learning Techniques 5

A Comprehensive Analysis of Underwater Image Processing base...

引用

5th international conference on Control, Communication and Computing, ICCC 2023

作者： Sree Vidhya, K.S. Deepthi, P.S. Computer Science and Engineering LBS Institute of Technology for Women Kerala Thiruvananthapuram India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram India

ISBN: (纸本)9798350334128

Underwater image processing has been an active research topic over the past few years as interest in marine observation and the use of ocean resources has increased. Different from conventional images, marine ecosystems are frequently subjected to challenging conditions such as underwater turbulence, low contrast, and high colour distortion as a result of the light's non-uniform attenuation as it passes through the water. To overcome these challenges, a good amount of work in conventional and deep learning based underwater image processing has been published over a period of time. Deep learning has demonstrated excellent performance improvement than the conventional approaches on the challenging vision tasks. In this survey, important underwater image processing methods using deep learning have been discussed. the major underwater metrics, common datasets, and challenges are also presented. © 2023 IEEE.

关键词： Generative adversarial networks

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Improving 3D Point Cloud Reconstruction with Dynamic Tree-Structured Capsules 5

Improving 3D Point Cloud Reconstruction with Dynamic Tree-St...

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

作者： Engelhardt, Chris Mittelberger, Jakob Peer, David Stabinger, Sebastian Rodriguez-Sanchez, Antonio Univ Innsbruck Intelligent & Interact Syst Innsbruck Austria

ISBN: (纸本)9781665462198

When applying convolutional neural networks to 3D point cloud reconstruction, these do not seem to be able to learn meaningful 2D manifold embeddings, suffer a lack of explainability and are vulnerable to adversarial attacks [20]. Except for the latter, these shortcomings can be overcome with capsule networks. In this work we introduce an auto-encoder based on dynamic tree-structured capsule networks for sparse 3D point clouds with SDA-routing. Our approach preserves the spatial arrangements of the input data and increases the adversarial robustness without introducing additional computational overhead. Our experimental evaluation shows that our architecture outperforms the current state-of-the-art capsule and CNN-based networks.

关键词： Point clouds Capsule networks routing algorithm

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conference Committee

Conference Committee

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

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Performance Analysis of User Authentication Schemes in Wireless sensor networks 5th

Performance Analysis of User Authentication Schemes in Wirel...

引用

5th international conference on Recent Innovations in Computing, ICRIC 2022

作者： Kumar, Ravi Singh, Samayveer Department of Computer Science & Engineering Dr. B R Ambedkar National Institute of Technology Jalandhar Punjab India

ISBN: (纸本)9789811998751

Wireless sensor networks (WSNs) have a broad range of applications in most of the sectors which require a high level of security. these networks provide sensor data to the external users on a real-time basis;thus, it is a good idea to provide them immediate access. User authentication is an essential requirement to access the services of sensor areas to prevent unauthorized access. the network and data security are key concerns in WSNs for secure and authentic data communication. thus, an analysis of a secure and efficient data transmission scheme is considered. In this paper, an analysis of existing user authentication schemes is discussed and provides comparative studies of user authentication schemes for network security. An explicitly simulated one of the user authentication schemes by the AVISPA tool is discussed, and its demonstration is also given which shows the resistant to the cryptographic attacks. the lower communication costs are one of the main parameters, i.e., considered to compared with the previous relevant techniques. Moreover, future directions are also discussed for the existing works. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： sensor nodes

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Detection of Dental Cavity using Wireless sensor based Technology 5

Detection of Dental Cavity using Wireless Sensor based Techn...

引用

5th IEEE international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Kumari, Rani Astya, Parmanand Kantha, Praveen Kaur, Damandeep Chitkara University School Of Engineering & Technology Chitkara University Himachal Pradesh India Sharda University Department Of Computer Science And Engineering Greater Noida India Chitkara University Institute Of Engineering & Technology Chitkara University Punjab India

ISBN: (纸本)9798350330861

Dental Cavities are most common persistent disorders across the lifespan and are mainly avoidable. Untreated tooth decay can develop in an abscess behind the gums, it's a type of infection that may be spread in to other regions of the mouth or between tooth and cause catastrophic, occasionally fatal consequences. Now days, dental caries is a common tooth decay issue and the people of all generations may be affected from this problem especially children. Because of its increasing incidence, successful intervention measures are required. To provide best care and early detection of cavities, wireless sensor technology has gained popularity in the dental industry. In an effort to avoid and diagnose dental caries in the first stage of cavity, this article suggests innovative and creative approach based on wireless sensors technology, cloud storage for patient data storage, telemedicine. this paper explain different types of sensors used in detection process of dental cavity. through this article, we suggest a sensor technology based dental cavity detection model and explain how we detect dental caries based on collected data by sensors. © 2023 IEEE.

关键词： Hospital data processing

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Research on simulation of human brain neural network and signal processing technology 5

Research on simulation of human brain neural network and sig...

引用

5th international conference on Signal processing and Computer Science, SPCS 2024

作者： Wang, Haochen Xuteli College Beijing Institute of Technology Beijing China

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

With the cross development of computational neuroscience and artificial intelligence, research on human brain neural network simulation and signal processing technology has become a hot topic of common concern in both academia and industry. this study used a multi-level and multi-scale computational model and high-precision algorithm to simulate the dynamic behavior of human brain neural networks, exploring the complex interactions between neurons and their impact on information processing capabilities. Real time dynamic simulation of billions of neurons was achieved on a simulation platform using ultra large-scale integrated circuit chips, simulating signal processing patterns similar to those of the human brain. By using an improved backpropagation algorithm to decode and reconstruct neural signals, the efficiency of the algorithm and the accuracy of signal processing have been improved. On this basis, combined with experimental data obtained from functional magnetic resonance imaging, the similarities and differences between neural network simulation results and actual human brain activity were compared and analyzed, revealing the potential connection between cognitive function and brain network activity patterns. this study not only achieved new breakthroughs in simulation technology and signal processing algorithms, but also provided a new quantitative tool and theoretical support for related neuroscience research, which is of great significance for the development of brain computer interfaces and intelligent information processing systems. In addition, the study also delved into the balance between ensuring model complexity and processing efficiency, as well as the challenges and opportunities brought by interdisciplinary collaboration in the field of neuroscience. through cross validation and error analysis of simulation experiments, the effectiveness of the model and the accuracy of prediction results were ensured. Based on this, feasible suggestions for opt

关键词： Magnetic resonance imaging

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A Wide-Narrow Hybird Interference Coordination Scheme for Multi-Hop Wide Band Access 5

A Wide-Narrow Hybird Interference Coordination Scheme for Mu...

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Zhai, Di Lu, Yang Lin, Chen State Grid Smart Grid Research Institute Co. Ltd Global Energy Interconnection Research Institute Co. Ltd Beijing102209 China

ISBN: (纸本)9781665485081

In smart grid, the internet of things (IoT) plays a crucial role, where wide band based wireless sensor networks (WSNs) are used to monitor the power lines. Different from traditional WSN, there exists serious interference in such a power line monitoring IoT (PL-IoT) network. In this paper, we proposed a wide-narrow hybrid interference coordination (IC) scheme considering the special characteristics of PL-IoT network. the proposed scheme allocates power and frequency resource for both sensors and access points (APs), unlike only for users in traditional IC scheme. Besides, due to the weak computing ability of APs, this scheme also proposes a allocation strategy of power and frequency resource without exchanging loading indicator (LI). Simulation shows our scheme can improve average signal to interference plus noise ratio (SINR) of whole system obviously compared to the full frequency reuse scheme. © 2022 IEEE.

关键词： Wireless sensor networks

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A Novel Approach for Duty Cycle Optimization in Designing Scalable Wireless sensor networks 5th

A Novel Approach for Duty Cycle Optimization in Designing Sc...

引用

5th international conference on Electrical and Electronics Engineering, ICEEE 2024

作者： De, Sudip Kumar Banerjee, Avishek Majumder, Koushik Department of Information Technology Asansol Engineering College WB Asansol India Department of Computer Science and Engineering MAKAUT WB Kolkata India

ISBN: (纸本)9789819791118

this paper aims to enhance the efficiency of wireless sensor networks (WSNs) within an area of interest (AOI) by optimizing duty cycles. Focusing on a specific AOI divided into clusters, each with a dynamically selected cluster head, the study emphasizes establishing a time-synchronized duty cycle optimization protocol in the communication between cluster heads and the base stations. Unlike conventional approaches that randomly select nodes for duty cycle optimization, this paper introduces a method to identify the optimal cluster head (CH) of each cluster based on maximum remaining energy and a shorter distance from the Base Station (BS) to maximize the durability of the network. By introducing a time-synchronized Duty Schedule, the proposed methodology ensured that nodes that are not in use will enter a sleep mode, conserving energy. To ensure effective communication between the Data Centre and multiple Base Stations (BSs) of AOIs, the Particle Swarm Optimization (PSO) has been also used. through simulation and analysis, the effectiveness of the proposed duty cycle optimization method is evaluated, demonstrating its potential to enhance WSN performance in terms of energy efficiency, network longevity, and communication. this research contributes to advancing the design and operation of WSNs, particularly in scenarios where energy conservation and data transmission are paramount concerns. the suggested duty cycle model assures a 20.44% increase in the network’s lifetime as compared to other available studies. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Energy efficiency

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