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检索条件"任意字段=IEEE Conference on Local Computer Networks"

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Distributed Learning by local Training ADMM 63

Distributed Learning by Local Training ADMM

引用

63rd ieee conference on Decision and Control, CDC 2024

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Kth Royal Institute of Technology 2School of Electrical Engineering and Computer Science and Digital Futures Stockholm Sweden Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Aalborg University Department of Electronic Systems Denmark University of Trieste Department of Engineering and Architecture Trieste Italy

ISBN: (纸本)9798350316339

In this paper, we focus on distributed learning over peer-to-peer networks. In particular, we address the challenge of expensive communications (which arise when e.g. training neural networks), by proposing a novel local training algorithm, LTADMM. We extend the distributed ADMM enabling the agents to perform multiple local gradient steps per communication round (local training). We present a preliminary convergence analysis of the algorithm under a graph regularity assumption, and show how the use of local training does not compromise the accuracy of the learned model. We compare the algorithm with the state of the art for a classification task, and in different set-ups. The results are very promising showing a great performance of LT-ADMM, and paving the way for future important theoretical developments. © 2024 ieee.

关键词： Peer to peer networks

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Experimental Evidence for local Fading Memory Effects in TaOx ReRAM Cells 3

Experimental Evidence for Local Fading Memory Effects in TaO...

引用

3rd ieee International conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2024

作者： Schmitt, N. Messaris, I. Demirkol, A.S. Ntinas, V. Prousalis, D. Tetzlaff, R. Ascoli, A. Corinto, F. Gilli, M. Zhang, S. Menzel, S. Rana, V. Chua, L.O. Tu Dresden Electrical and Computer Engineering Dresden Germany Politecnico di Torino Department of Electronics and Telecommunications Turin Italy Peter Grünberg Institut 7 FZJGmbH Julich Germany Peter Grünberg Institut 10 FZJGmbH Julich Germany University of Berkeley Department of Electrical Engineering and Computer Sciences BerkeleyCA United States

ISBN: (纸本)9798350378009

This contribution presents experimental verification of bistability, a special form of local fading memory, in a physical TaOx memristor. System-theoretic methods are applied to the physics-based model of the ReRAM cell under consideration to explore the origin of the local fading memory and its robustness against the intrinsic cycle-to-cycle variability of the device. Multiple real-world measurements are performed to confirm the existence of one separatrix in the transient behavior of the device as a fingerprint of bistability. © 2024 ieee.

关键词： Memristors

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RADIANT: Better rPPG estimation using signal embeddings and Transformer 23

RADIANT: Better rPPG estimation using signal embeddings and ...

引用

23rd ieee/CVF Winter conference on Applications of computer Vision (WACV)

作者： Gupta, Anup Kumar Kumar, Rupesh Birla, Lokendra Gupta, Puneet Indian Inst Technol Indore Indore India

ISBN: (纸本)9781665493468

Remote photoplethysmography can provide non-contact heart rate (HR) estimation by analyzing the skin color variations obtained from face videos. These variations are subtle, imperceptible to human eyes, and easily affected by noise. Existing deep learning-based rPPG estimators are incompetent due to three reasons. Firstly, they suppress the noise by utilizing information from the whole face even though different facial regions contain different noise characteristics. Secondly, local noise characteristics inherently affect the convolutional neural network (CNN) architectures. Lastly, the CNN sequential architectures fail to preserve long temporal dependencies. To address these issues, we propose RADIANT, that is, rPPG estimation using Signal Embeddings and Transformer. Our architecture utilizes a multi-head attention mechanism that facilitates feature subspace learning to extract the multiple correlations among the color variations corresponding to the periodic pulse. Also, its global information processing ability helps to suppress local noise characteristics. Furthermore, we propose novel signal embedding to enhance the rPPG feature representation and suppress noise. We have also improved the generalization of our architecture by adding a new training set. To this end, the effectiveness of synthetic temporal signals and data augmentations were explored. Experiments on extensively utilized rPPG datasets demonstrate that our architecture outperforms previous well-known architectures. Code: https://***/Deep-Intelligence-Lab/***

关键词： Heart rate Image color analysis Estimation computer architecture Transformers Feature extraction Convolutional neural networks

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An Adaptive Backoff Algorithm Based on Topology Status for Flying Ad hoc networks 6

An Adaptive Backoff Algorithm Based on Topology Status for F...

引用

6th International conference on Communications, Information System and computer Engineering, CISCE 2024

作者： Li, Zhenghan Zhang, Yu Liu, Zhoujie Wang, Yiming School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (纸本)9798350353174

In recent years, the utilization of flying ad hoc networks (FANETs) has seen a gradual rise across diverse civilian and military applications. The high mobility of nodes in FANETs leads to highly dynamic topology, frequent disruptions in links, and heightened competition for channel access. Traditional adaptive backoff algorithms primarily adjust the backoff window based on wireless channel states, achieving only local network optimality and restraining network performance. This paper first proposes a network global backoff window optimization algorithm T-BEB based on global network topology status, and conducts algorithm performance analysis through BOBYQA. On this basis, this paper implements the algorithm through the link state routing protocol (NLSR) in the named data network (NDN) in ns-3, and simulates the network performance under different loads and node mobility. It is verified that when network traffic is saturated and nodes have high mobility, compared with the traditional BEB algorithm, the T-BEB algorithm can reduce packet delay and system packet loss rate, and improve system throughput. © 2024 ieee.

关键词： Ad hoc networks

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Modified Affinity Propagation for Key Node Identification Based on Intra-Class Distances 7

Modified Affinity Propagation for Key Node Identification Ba...

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7th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Yu School of Computer Engineering and Science Shanghai University Shanghai China

ISBN: (纸本)9798350350890

Identifying key nodes is vital to the research of information control in network science and multiagent systems. This paper presents an enhanced affinity propagation clustering algorithm designed to identify critical network nodes by leveraging local and global information. Specifically, we introduce a novel influence matrix, which serves as a standard representation of node relationships. This matrix considers factors such as the number of neighbors and the number of shortest paths, providing a comprehensive view of node importance within the network. The proposed algorithm combines matrix iteration with evaluating local and global network structures to identify key nodes. And intra-class distance is introduced to pinpoint a smaller group of key nodes in large-scale networks. The experimental results on six real networks of different sizes show the validity of identification using the Susceptible-Infected (SI) model and network efficiency. © 2024 ieee.

关键词： Multi agent systems

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Optimized Deployment of Intelligent Multi-Modal Sensor Network Based on Graph Transformer 7

Optimized Deployment of Intelligent Multi-Modal Sensor Netwo...

引用

7th ieee International conference on computer and Communication Engineering Technology, CCET 2024

作者： Wang, Wei Cui, Naiyu Gao, Hefei Wu, Jiaxin Du, Rongfang Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission Tianjin300387 China

ISBN: (纸本)9798350355673

To address the issues of node redundancy, uneven distribution, and high energy consumption caused by random deployment in intelligent sensor networks, this paper proposes an optimization deployment algorithm for intelligent multimodal sensor networks based on graph transformer. Firstly, a multidimensional graph construction algorithm based on spatiotemporal fusion is proposed to construct the raw sensing data of the multimodal sensor network into a multidimensional graph. On this basis, a graph transformer with overlapping local and global attention is designed to perform feature representation on the time series of each dimension of the graph. Additionally, a graph convolutional network and a multilayer perceptron are introduced to achieve contribution inference and fusion. The entire inference process is iteratively optimized under the constraints of feature reconstruction error and feature semantics. Experimental results show that the proposed algorithm effectively identifies important nodes and outperforms other graph neural network models across various metrics. © 2024 ieee.

关键词： Graph neural networks

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NL-FFC: Non-local Fast Fourier Convolution for Image Super Resolution

NL-FFC: Non-Local Fast Fourier Convolution for Image Super R...

引用

ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Space Applicat Ctr Signal & Image Proc Grp Ahmadabad India

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Deep neural networks have shown promising results in image super-resolution by learning a complex mapping from low resolution to high resolution image. However, most of the approaches learns to upsample by using convolution in spatial domain and are confined to local features. This results into restricting the receptive field of the network and therefore deteriorates the overall quality of the high-resolution image. To alleviate this issue, we propose an architecture that learns both local and global features, and fuses them together to generate high quality images. The network uses a non-local attention aided Fast Fourier Convolutions (NL-FFC) to widen the receptive field and learn long-range dependencies. The analyses further show that these Fourier features implicitly provide faster convergence on low frequency components only to learn prior for unobserved high frequency components. The model generalizes well to different datasets. We further investigate the role of non-local attention, and the ratio of local and global features to maximize the performance gain in the ablation study.

关键词： Deep learning Convolution Fuses Superresolution Neural networks Performance gain Pattern recognition

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StrideNET: Swin Transformer for Terrain Recognition with Dynamic Roughness Extraction

StrideNET: Swin Transformer for Terrain Recognition with Dyn...

引用

2024 ieee International conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Shelare, Maitreya Shigvan, Neha Satam, Atharva Sonar, Poonam Rajiv Gandhi Institute of Technology University of Mumbai India

ISBN: (纸本)9798350376876

The field of remote-sensing image classification has seen immense progress with the rise of convolutional neural networks, and more recently, through vision transformers. These models, with their self-attention mechanism, can effectively capture global relationships and long-range dependencies between the image patches, in contrast with traditional convolutional models. This paper introduces StrideNET, a dual-branch transformer-based model developed for terrain recognition and surface roughness extraction. The terrain recognition branch employs the Swin Transformer to classify varied terrains by leveraging its capability to capture both local and global features. Complementing this, the roughness extraction branch utilizes a statistical texture-feature analysis technique to dynamically extract important land surface properties such as roughness and slipperiness. The model was trained on a custom dataset consisting of four terrain classes - grassy, marshy, sandy, and rocky, and it outperforms benchmark CNN and transformer based models, by achieving an average test accuracy of over 99 % across all classes. The applications of this work extend to different domains such as environmental monitoring, land use and cover classification, disaster response and precision agriculture. © 2024 ieee.

关键词： Convolutional neural networks

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BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention networks

BEV-SAN: Accurate BEV 3D Object Detection via Slice Attentio...

引用

ieee/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Chi, Xiaowei Liu, Jiaming Lu, Ming Zhang, Rongyu Wang, Zhaoqing Guo, Yandong Zhang, Shanghang Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Univ Sydney Sydney NSW Australia AI2Robotics Beijing Peoples R China

ISBN: (纸本)9798350301298

Bird's-Eye-View (BEV) 3D Object Detection is a crucial multi-view technique for autonomous driving systems. Recently, plenty of works are proposed, following a similar paradigm consisting of three essential components, i.e., camera feature extraction, BEV feature construction, and task heads. Among the three components, BEV feature construction is BEV-specific compared with 2D tasks. Existing methods aggregate the multi-view camera features to the flattened grid in order to construct the BEV feature. However, flattening the BEV space along the height dimension fails to emphasize the informative features of different heights. For example, the barrier is located at a low height while the truck is located at a high height. In this paper, we propose a novel method named BEV Slice Attention Network (BEV-SAN) for exploiting the intrinsic characteristics of different heights. Instead of flattening the BEV space, we first sample along the height dimension to build the global and local BEV slices. Then, the features of BEV slices are aggregated from the camera features and merged by the attention mechanism. Finally, we fuse the merged local and global BEV features by a transformer to generate the final feature map for task heads. The purpose of local BEV slices is to emphasize informative heights. In order to find them, we further propose a LiDAR-guided sampling strategy to leverage the statistical distribution of LiDAR to determine the heights of local slices. Compared with uniform sampling, LiDAR-guided sampling can determine more informative heights. We conduct detailed experiments to demonstrate the effectiveness of BEV-SAN. Code will be released.

关键词： Autonomous driving

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LiCT-Net: Lightweight Convolutional Transformer Network for Multiclass Breast Cancer Classification

LiCT-Net: Lightweight Convolutional Transformer Network for ...

引用

2024 ieee Region 10 conference, TENCON 2024

作者： Babita Nayak, Deepak Ranjan Malaviya National Institute of Technology Dept. of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350350821

Classification of multiclass breast cancer through histopathological images is indispensable and poses daunting challenges due to color inconsistencies, high appearance variations, and large inter-class similarities. Regardless of the success of traditional convolutional neural networks (CNNs) and vision transformers (ViTs), they often fail to capture intricate patterns and local contextual information, respectively, while demanding high computational resources and data requirements. To mitigate these issues, this paper proposes a lightweight convo-lutional transformer network named LiCT-Net for multiclass breast cancer classification. The LiCT-Net introduces a local-global spatially-aware transformer layer and integrates it with a pre-trained FastViT model to effectively capture global and local contextual features, thereby facilitating learning fine-grained and intricate lesion patterns from histopathological images. The LiCT-Net is validated on a benchmark dataset and the experimental results and comparative analysis demonstrate its effectiveness. In specific, it achieves a higher accuracy of 96.16 %, 95.62 %, 95.25 %, and 94.21 % on 40×, 100×, 200×, and 400× magniflcations respectively, © 2024 ieee.

关键词： Convolution

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