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Evaluation of Transmission Performance for Streaming CGH Video based on computer Holography and Electro-Holography 38

Evaluation of Transmission Performance for Streaming CGH Vid...

引用

38th International conference on Information Networking (ICOIN)

作者： Yamaguchi, Kazuhiro Onishi, Misato Sakamoto, Yuji Suwa Univ Sci Grad Sch Engn & Management Chino Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido Japan

ISBN: (纸本)9798350330946;9798350330953

Holography is an ideal three-dimensional display technology, and it can reconstruct natural-looking three-dimensional objects with sufficient depth. In this paper, we propose a system model that uses computer holography and electro holography to stream holographic videos. The holographic video is generated by synthesizing computer-generated holograms calculated by using light waves from a virtual object designed on a computer. The holographic video is streamed from a transmitter to a receiver with display devices via networks, and three-dimensional objects are optically reconstructed by using electroholography with a spatial light modulator. The transmission performances of streaming holographic video over wired LAN (1000BASE-T), wireless LAN (ieee 802.11ax), and local 5G network were evaluated.

关键词： computer holography electro-holography streaming system transmission performance

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Person Re-Identification Based on local Salient Feature Embedding Network 5

Person Re-Identification Based on Local Salient Feature Embe...

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5th International conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Duan, Wenjian Wang, Zhaohui Hainan University Faculty of Computer Science and Technology Haikou China

ISBN: (纸本)9798350373820

Person re-identification is an important branch in the field of computer vision. In recent years, convolutional neural networks-based models, especially for the residual network, have made significant progress with the excellent ability to extract deep feature representation. However, these methods are not good at focusing on the local salient features related strongly to the target class effectively, which may limit the identification accuracy. To handle this issue, this paper proposes a local salient feature embedding network which contains a residual feature extractor, a local strong response pooling layer, a global average pooling layer, and a feature embedding module. Based on the deep residual features of pedestrians, the local salient features, which are crucial and special for different people, are obtained by the local strong response pooling. To increase the gap between inter-class variance and intra-class variance effectively, the feature embedding module integrates the local salient features and global semantic features and generate the low-dimension robust feature representation. Experimental results on the Market1501 and DukeMTMC-reID data sets demonstrate the effectiveness of the proposed model. © 2024 ieee.

关键词： Semantics

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Federated Learning Based on Feature Fusion 4

Federated Learning Based on Feature Fusion

引用

4th International conference on Neural networks, Information and Communication Engineering, NNICE 2024

作者： Cao, Junjie Chen, Zhiyu Changchun University of Technology School of Computer Science & Engineering Changchun130102 China

ISBN: (纸本)9798350394375

Federated learning is a distributed machine learning method that has important research significance in areas such as privacy, data heterogeneity, communication efficiency, and incentive mechanisms. Data imbalance is a key challenge faced by federated learning. Due to the non iid nature of the data provided by each client in federated learning, the client is unable to obtain a good local model during client training. The iterative process of uploading, aggregating, and downloading the model hinders its ability to make accurate predictions. In order to solve the problem of data imbalance, this paper constructs multiple auxiliary branches in the local model and fuses their features to improve the prediction accuracy of the model in the case of data imbalance. The experimental results show that the model has improved performance in both scenarios of simulating 100 and 500 clients. © 2024 ieee.

关键词： Iterative methods

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DBCTfuse: A Dual-Branch CNN-Transformer feature extraction network for Infrared and Visible Image Fusion 17

DBCTfuse: A Dual-Branch CNN-Transformer feature extraction n...

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17th ieee International conference on Signal Processing, ICSP 2024

作者： Li, Yujing Ma, Xiaole Chen, Xiaoxuan Hu, Shaohai Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China

ISBN: (纸本)9798350387384

The process of fusing infrared and visible images necessitates integrating thermal radiation information from infrared image with the edge and texture detail captured by visible images. Most current fusion methods are based on convolutional neural networks, which can effectively extract local features from source images but are somewhat lacking in global features. To address this, we propose a dual-branch feature extraction fusion network based on convolutional neural network and Transformer - DBCTfuse, to effectively synthesize both local details and global contextual information from source images. Comprehensive experiments conclusively demonstrate the superiority of our DBCTfuse method in infrared and visible image fusion, surpassing contemporary state-of-the-art algorithms in both qualitative and quantitative assessments. © 2024 ieee.

关键词： Convolutional neural networks

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A Hierarchical Vision Transformer Using Overlapping Patch and Self-Supervised Learning

A Hierarchical Vision Transformer Using Overlapping Patch an...

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International Joint conference on Neural networks (IJCNN)

作者： Ma, Yaxin Li, Ming Chang, Jun Wuhan Univ Natl Engn Res Ctr Multimedia Software Sch Comp Sci Wuhan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China

ISBN: (纸本)9781665488679

Transformer-based network architectures have gradually replaced convolutional neural networks in computer vision. Compared with convolutional neural networks, Transformer is able to learn global information of images and has better feature extraction capability. However, due to the lack of inductive bias, vision Transformers require a large amount of data for pre-training, such as ViT. local-based Transformers effectively reduce the computational complexity, but could not establish long-range dependencies and do not perform as well on small-scale datasets. In response to these problems, OPSe Transformer is proposed. A global attention calculation module is designed to be added behind each stage of the vision Transformer, using a slightly larger and overlapping key patch and value patch to enhance the exchange of information between two adjacent windows and to aggregate global information in the local Transformer. In addition, a self-supervised learning proxy task is added to the architecture, corresponding to the loss function of the proxy task to constrain the training of the model on the dataset, so that the vision Transformer can learn spatial information within an image and improve the training effect of the network. Comparative experiments are conducted on the tiny-ImageNet, CIFAR-10/100, and other datasets, and the experimental results show that compared with the baseline algorithm, our model improves the accuracy by up to 3.91%.

关键词： computer Vision Image Classification Vision Transformer Self-supervised learning

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An Online Spike Pattern Learning Algorithm Based on local Variable Driven STDP 15

An Online Spike Pattern Learning Algorithm Based on Local Va...

引用

15th ieee Annual Computing and Communication Workshop and conference, CCWC 2025

作者： Liu, Jiahua Lin, Xianghong Zhang, Zhengyi Li, Chun College of Computer Science and Engineering Northwest Normal University Lanzhou China

ISBN: (纸本)9798331507695

Online learning algorithms adjust the synaptic weights when the neurons fire spikes during the learning process of spiking neural networks, which are shown to be more suitable and effective for processing spatio-temporal information in the real world. This paper presents an online spike pattern supervised learning algorithm for spiking neural networks based on local variable driven STDP (LVDSTDP), which combines low-pass filtering of spike trains fired by pre- and postsynaptic neurons to adjust synaptic weights in real time. The LVDSTDP algorithm has been successfully applied to spike trains learning tasks, and the effects of synaptic parameters are analyzed in detail. Experimental results show that the proposed algorithm can effectively adjust the synaptic weights to improve learning performance. This demonstrates effective for solving complex pattern learning problems using the LVDSTDP rule, and many further optimizations are possible. We also applied the proposed method in training SNNs, and obtained comparably high recognition accuracies on the MNIST datasets. © 2025 ieee.

关键词： Contrastive Learning

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Mushroom Recognition Based on ResNet-VIT Feature Fusion Network 10

Mushroom Recognition Based on ResNet-VIT Feature Fusion Netw...

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10th International conference on Systems and Informatics, ICSAI 2024

作者： Dai, Xinjie Lu, Xuanzhao Wang, Lijin Wu, Qingshou College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Fujian Province University Key Laboratory of Smart Agriculture and Forestry Fuzhou350002 China School of Mathematics and Computer Science Wuyi University Fujian Wuyishan354300 China

ISBN: (纸本)9798331510138

To address the challenge of rapid mushroom identification in natural environments, this study proposes a mushroom identification model based on a feature fusion network, using nine types of mushrooms as research objects. The proposed model effectively integrates the advantages of traditional convolutional neural networks focusing on local features and Transformer networks emphasizing global features, realizing the simultaneous use of local and global features in image classification tasks. By improving the adaptive self-attention head count, the model's classification performance is further enhanced. The findings of this study hold considerable application value. When combined with deployment on mobile devices, they can contribute to significantly reducing the risk of food poisoning due to accidental mushroom ingestion. © 2024 ieee.

关键词： Convolutional neural networks

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On Slicing Weighted Energy-Harvesting Wireless Sensing networks with Transmission Range Uncertainty 47

On Slicing Weighted Energy-Harvesting Wireless Sensing Netwo...

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47th ieee conference on local computer networks (LCN)

作者： Abougamila, Salwa Elmorsy, Mohammed Elmallah, Ehab S. Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada MacEwan Univ Dept Comp Sci Edmonton AB T5J 4S2 Canada

ISBN: (数字)9781665480017

ISBN: (纸本)9781665480017

In this paper, we deal with a wireless sensor network (WSN) infrastructure management problem where a provider wants to partition a network into a given number of nodedisjoint subgraphs (called slices) for running different user applications. Nodes in the given infrastructure use energy harvesting for prolonged service time. The nodes manage fluctuations in their stored energy by adjusting their transmission range. We assume that each node is assigned an importance weight, and model the overall network using a probabilistic graph. In this context, we formalize a problem, denoted k-WBS-RU (for k weighted balanced slices with range uncertainty), to partition the network into k slices subject to some connectivity and operation constraints. We devise a solution to the problem, and present numerical results on the quality of the obtained slices. We also discuss an application of the proposed framework and solution when the assigned weights are derived from an area coverage application.

关键词： Wireless communication Wireless sensor networks Uncertainty Fluctuations Probabilistic logic computer networks Sensors

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Face Clustering via Graph Convolutional networks with Confidence Edges

Face Clustering via Graph Convolutional Networks with Confid...

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ieee/CVF International conference on computer Vision (ICCV)

作者： Wu, Yang Ge, Zhiwei Luo, Yuhao Liu, Lin Xu, Sulong Chinese Acad Sci Inst Automat Beijing Peoples R China JD COM Beijing Peoples R China Chinese Acad Sci SKLCS Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9798350307184

Face clustering is a method for unlabeled image annotation and has attracted increasing attention. Existing methods have made significant breakthroughs by introducing Graph Convolutional networks (GCNs) on the affinity graph. However, such graphs will contain many vertex pairs with inconsistent similarities and labels, thus degrading the model's performance. There are already relevant efforts for this problem, but the information about features needs to be mined further. In this paper, we define a new concept called confidence edge and guide the construction of graphs. Furthermore, a novel confidence-GCN is proposed to cluster face images by deriving more confidence edges. Firstly, local Information Fusion is advanced to obtain a more accurate similarity metric by considering the neighbors of vertices. Then Unsupervised Neighbor Determination is used to discard low-quality edges based on similarity differences. Moreover, we elaborate that the remaining edges retain the most beneficial information to demonstrate the validity. At last, the confidence-GCN takes the graph as the input and fully uses the confidence edges to complete the clustering. Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art. At the same time, comparable results are obtained on the fashion dataset.

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A Trustworthy Federated Learning Model: Client Selection for IoT Edge networks

A Trustworthy Federated Learning Model: Client Selection for...

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2024 Innovations in Intelligent Systems and Applications conference, ASYU 2024

作者： Telçeken, Merve Bozkaya-Aras, Elif Department of Computer Engineering National Defence University Istanbul Turkey Department of Computer Engineering National Defence University Turkish Naval Academy Istanbul Turkey

ISBN: (纸本)9798350379433

The interaction between the Internet of Things (IoT) and edge computing plays a critical role in processing and analyzing massive amounts of data. However, due to the malicious attacks of underlying IoT edge networks, the collected data can be untrustworthy, and designing a robust and trustworthy attack detection scheme for cloud-edge collaborative architecture becomes a challenge. In addition, malicious attacks also threaten data privacy. To address the data privacy concern and the need for cooperation between clients, federated learning is a cutting-edge machine learning solution, where local models are trained on clients and the global model is jointly constructed with local parameters. In this regard, we propose a trustworthy client selection scheme for IoT edge networks. Specifically, we first devise a trust management mechanism and calculate the trust scores of clients by using the Isolation Forest method. Then, a trust-based federated learning algorithm is proposed to protect data privacy. In this algorithm, trustworthy clients are selected to constitute the parameters of the global model. Extensive experiments are conducted to validate the trustworthy client selection model against malicious attacks. As a case study, Distributed Denial of Service (DDoS) attack is analyzed and system performance is compared with traditional benchmark methods in terms of accuracy, recall, precision, and F1 score. © 2024 ieee.

关键词： Federated learning

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