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作者： Yida Li Xintao Wu Huifang Chen Lei Xie College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China Zhoushan Ocean Research Center Zhejiang University Zhoushan China

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Underwater localization is critical for in the marine exploration. However, the harsh underwater conditions, like the multipath effect, signal attenuation and Doppler effect, introduces numerous difficulties for the underwater localization. In this paper, we propose a channel-estimation-aided underwater acoustic localization method in the underwater acoustic integrated communication and positioning (UWA-ICP) system. In the UWA-ICP system, a UWA-ICP device with a four-element hydrophone array and a transducer is equipped on each underwater node for acoustic positioning and communication. In order to mitigate the influence of multipath effect on the localization performance, an enhanced orthogonal matching pursuit algorithm is presented to perform the channel estimation. The estimated channel impulse response is used to compensate the received signal in order to mitigate the multipath effect. Numerical results show that the proposed localization method can effectively improve the localization performance.

关键词： Location awareness Performance evaluation Transducers Oceans Channel estimation Matching pursuit algorithms Sea measurements Estimation Underwater acoustics Multipath channels

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SDP-Net: Scene Flow Based Real-Time Object Detection and Prediction from Sequential 3D Point Clouds 15th

SDP-Net: Scene Flow Based Real-Time Object Detection and Pre...

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhang, Yi Ye, Yuwen Xiang, Zhiyu Gu, Jiaqi College of Information and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou China

ISBN: (纸本)9783030695248

Robust object detection in 3D point clouds faces the challenges caused by sparse range data. Accumulating multi-frame data could densify the 3D point clouds and greatly benefit detection task. However, accurately aligning the point clouds before the detecting process is a difficult task since there may exist moving objects in the scene. In this paper a novel scene flow based multi-frame network named SDP-Net is proposed. It is able to perform multiple tasks such as self-alignment, 3D object detection, prediction and tracking simultaneously. Thanks to the design of scene flow and the scheme of multi-task, our network is capable of working effectively with a simple network backbone. We further improve the annotations on KITTI RAW dataset by supplementing the ground truth. Experimental results show that our approach greatly outperforms the state-of-the-art and can perform multiple tasks in real-time. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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SGNet: Semantics Guided Deep Stereo Matching 15th

SGNet: Semantics Guided Deep Stereo Matching

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Chen, Shuya Xiang, Zhiyu Qiao, Chengyu Chen, Yiman Bai, Tingming College of Information and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou China

ISBN: (纸本)9783030695248

Stereovision has been an intensive research area of computer vision. Based on deep learning, stereo matching networks are becoming popular in recent years. Despite of great progress, it’s still challenging to achieve high accurate disparity map due to low texture and illumination changes in the scene. High-level semantic information can be helpful to handle these problems. In this paper a deep semantics guided stereo matching network (SGNet) is proposed. Apart from necessary semantic branch, three semantic guided modules are proposed to embed semantic constraints on matching. The joint confidence module produces confidence of cost volume based on the consistency of disparity and semantic features between left and right images. The residual module is responsible for optimizing the initial disparity results according to its semantic categories. Finally, in the loss module, the smooth of disparity is well supervised based on semantic boundary and region. The proposed network has been evaluated on various public datasets like KITTI 2015, KITTI 2012 and Virtual KITTI, and achieves the state-of-the-art performance. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Bird’s-Eye-View-Based LiDAR Point Cloud Coding For Machines

Bird’s-Eye-View-Based LiDAR Point Cloud Coding For Machines

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IEEE Visual communications and Image processing (VCIP)

作者： Xiang Gao Zhiwei Zhu Lu Yu Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China

Recently, there has been a growing interest in image/video coding tailored for intelligent analysis tasks, such as Image/Video Coding for Machines (ICM/VCM). These approaches have shown remarkable results when compared to human perception-based coding methods. However, point cloud coding methods for machine intelligence have not been extensively studied. Inspired by current LiDAR point cloud intelligent analysis methods which convert point cloud into bird’s-eye-view (BEV) perspective, we propose an end-to-end learnt point cloud coding framework for 3D machine intelligent tasks with BEV representation, named PC4M. Specifically, the PC4M system consists of a LiDAR encoder, a learnt BEV feature codec and a BEV region proposal network with a task-specific head. To achieve better rate-distortion performance for analysis tasks, we propose an efficient Res-NeXt fusion block with powerful multi-scale modeling ability to compress sparse BEV features, and design a long-distance adaptive attention module by using VanAtten block. Experimental results demonstrate that our method outperforms the state-of-the-art MPEG standard Geometry-based Point Cloud Coding (G-PCC) on the object detection and BEV map segmentation by 83.12% and 85.32% of BD-rate gain on nuScenes, respectively. To the best of our knowledge, this is the first end-to-end learnt task-oriented point cloud codec.

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Vsgnet: Visual Saliency Guided Network for Skin Lesion Segmentation

SSRN

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SSRN 2023年

作者： Cai, Zhefei Fan, Yingle Fang, Tao Wu, Wei Laboratory of Pattern Recognition and Image Processing Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang310058 China

The accuracy of skin lesion segmentation is of great significance for the subsequent clinical diagnosis. In order to improve the segmentation accuracy, some pioneering works tried to embed multiple complex modules, or used the huge Transformer framework, but due to the limitation of computing resources, these type of large models were not suitable for the actual clinical environment. To address the coexistence challenges of precision and lightweight, we propose a visual saliency guided network (VSGNet) for skin lesion segmentation, which generates saliency images of skin lesions through the efficient attention mechanism of biological vision, and guides the network to quickly locate the target area, so as to solve the localization difficulties in the skin lesion segmentation tasks. VSGNet includes three parts: Color Constancy module, Saliency Detection module and Ultra Lightweight Multi-level Interconnection Network(ULMI-Net). Specially, ULMI-Net uses a U-shaped structure network as the skeleton, including the Adaptive Split Channel Attention (ASCA) module that simulates the parallel mechanism of biological vision dual pathway, and the Channel-Spatial Parallel Attention (CSPA) module inspired by the multi-level interconnection structure of visual cortices. Through these modules, ULMI-Net can balance the efficient extraction and multi-scale fusion of global and local features, and try to achieve the excellent segmentation results at the lowest cost of parameters and computational complexity. To validate the effectiveness and robustness of the proposed VSGNet on three publicly available skin lesion segmentation datasets (ISIC2017, ISIC2018 and PH2 datasets). The experimental results show that compared to other state-of-the-art methods, VSGNet improves the Dice and mIoU metrics by 1.84% and 3.34%, respectively, and with a 196× and 106× reduction in the number of parameters and computational complexity. This paper constructs the VSGNet integrating the biological vision m

关键词： Complex networks

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Deep Reinforcement Learning for Multi-Functional RIS-Aided Over-the-Air Federated Learning in Internet of Robotic Things

Deep Reinforcement Learning for Multi-Functional RIS-Aided O...

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IEEE International Conference on communications (ICC)

作者： Xinran Zhang Hui Tian Wanli Ni Zhaohui Yang State Key laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electronic Engineering Tsinghua University Beijing China Zhejiang Lab Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Lab of Information Processing Communication and Networking (IPCAN) Hangzhou China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

To facilitate edge intelligence in Internet of Robotic Things (IoRT), over-the-air federated learning (AirFL) is a communication-efficient enabler by virtue of its high spectrum efficiency and low transmission latency. Supported by multi-functional reconfigurable intelligent surface (MF-RIS), the model aggregation process of AirFL can be facilitated thanks to full-space signal amplification. However, the learning performance of AirFL may be degraded by the uncertainty of wireless channels originated from inaccurate channel estimation and robot mobility. In this paper, we investigate the model aggregation problem of MF-RIS-aided AirFL in a dynamic IoRT system with imperfect channel state information (CSI). We aim to minimize the long-term mean square error (MSE) of AirFL model aggregation by jointly optimizing MF-RIS coefficients and transceiver beamforming. To enable online decision-making in the dynamic system, we propose a novel deep reinforcement learning (DRL)-based double-agent algorithm, where one agent first decides operating modes of MF-RIS elements, and then the other agent devises transceiver beamforming and other MF-RIS coefficients referring to the mode switching strategy. Numerical results unveil the effectiveness and robustness of the proposed DRL-based algorithm in suppressing long-term MSE under hostile CSI.

关键词： Array signal processing Federated learning Heuristic algorithms Atmospheric modeling Wireless networks Channel estimation Transceivers

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communication-Efficient Task Scheduling for Real-Time Distributed Computing 13

Communication-Efficient Task Scheduling for Real-Time Distri...

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13th International Conference on Wireless communications and Signal processing, WCSP 2021

作者： Pang, Jiannan Wang, Wei Zhang, Zhaoyang College of Information Science and Electronic Engineering Zhejiang University Zhejiang Key Lab of Information Processing Communication and Networking Hangzhou310027 China

ISBN: (纸本)9781665407854

In the distributed big data real-time computing platform, the task scheduling has significant impact on the real-time performance. In this paper, we first reveal the inefficiency of the conventional real-time streaming processing scheduling algorithm in the well-known Storm framework. Based on our observations, we propose a communication-efficient task scheduling algorithm taking both physical cluster's computation and communication capacities into consideration. Our goal is to minimize the communication cost under taking full advantage of cluster computing capacity. Due to the non-convex constraints in our problem, we relax the binary integer to a continuous interval so that the condition can meet to handle the l_communication-norm constraint. By introducing an auxiliary variable and constructing penalty function, l_communication-norm can be transformed to obtain a solution for the relaxed problem. In view of the bipartite graph property of our model, we round the fractional solution of the relaxed problem to the integer solution in its sub-graphs to approach the solution of the original problem. Finally, to evaluate the proposed method, we compare it with two conventional scheduling algorithms, which demonstrates that the proposed algorithm outperforms the other two baseline schemes. © 2021 IEEE.

关键词： Scheduling algorithms

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Hybrid Coded MapReduce for Latency-Constrained Tasks with Straggling Servers

Hybrid Coded MapReduce for Latency-Constrained Tasks with St...

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IEEE International Conference on communications (ICC)

作者： Ruxue Mei Wei Wang Nannan Zhang Zhaoyang Zhang Zhejiang Key Lab of Information Processing College of Information Science and Electronic Engineering Communication and Networking Zhejiang University Hangzhou P.R. China

Coded distributed computing (CDC) can efficiently improve the performance of Map Reduce by mitigating the effects of straggling servers and decreasing the communication load simultaneously. In this paper, for the latency-constrained computation tasks, we use hybrid coded MapReduce and optimize the hybrid coded scheme to minimize the total latency, including the computation latency in Map phase and the communication latency in Shuffle phase. For the practical scenarios where the number of straggling servers is relatively small compared with the number of all servers, we reformulate the original optimization problem into a non-convex optimization problem. Then we use the successive convex approximation (SCA) algorithm to approximate the non-convex optimization problem into the iterations of a convex optimization problem. We prove the effectiveness and convergence of the proposed iterative SCA algorithm. Besides, the proposed SCA-based algorithm shows the effective convergence and outperforms the enumeration algorithm of the original problem with the approximated total latency and the short execution time via numerical experiments. Furthermore, the hybrid coded scheme achieves significant performance improvement in terms of decreasing the total latency compared with baseline schemes.

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CMVAE: Causal Meta VAE for Unsupervised Meta-Learning

arXiv

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arXiv 2023年

作者： Qi, Guodong Yu, Huimin College of Information Science and Electronic Engineering Zhejiang University China ZJU-League Research & Development Center China State Key Lab of CAD&CG Zhejiang University China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking China

Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks. However, existing approaches may be misled by the context-bias (e.g. background) from the training data. In this paper, we abstract the unsupervised meta-learning problem into a Structural Causal Model (SCM) and point out that such bias arises due to hidden confounders. To eliminate the confounders, we define the priors are conditionally independent, learn the relationships between priors and intervene on them with casual factorization. Furthermore, we propose Causal Meta VAE (CMVAE) that encodes the priors into latent codes in the causal space and learns their relationships simultaneously to achieve the downstream few-shot image classification task. Results on toy datasets and three benchmark datasets demonstrate that our method can remove the context-bias and it outperforms other state-of-the-art unsupervised meta-learning algorithms because of bias-removal. Code is available at https://***/GuodongQi/CMVAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Toys

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Design and Optimization of Cooperative Sensing With Limited Backhaul Capacity

arXiv

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arXiv 2024年

作者： Li, Wenrui Li, Min Liu, An Han, Tony Xiao College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Huawei Techologies Co. Ltd. Shenzhen518129 China

This paper introduces a cooperative sensing framework designed for integrated sensing and communication cellular networks. The framework comprises one base station (BS) functioning as the sensing transmitter, while several nearby BSs act as sensing receivers. The primary objective is to facilitate cooperative target localization by enabling each receiver to share specific information with a fusion center (FC) over a limited capacity backhaul link. To achieve this goal, we propose an advanced cooperative sensing design that enhances the communication process between the receivers and the FC. Each receiver independently estimates the time delay and the reflecting coefficient associated with the reflected path from the target. Subsequently, each receiver transmits the estimated values and the received signal samples centered around the estimated time delay to the FC. To efficiently quantize the signal samples, a Karhunen-Loève Transform coding scheme is employed. Furthermore, an optimization problem is formulated to allocate backhaul resources for quantizing different samples, improving target localization. Numerical results validate the effectiveness of our proposed advanced design and demonstrate its superiority over a baseline design, where only the locally estimated values are transmitted from each receiver to the FC. Copyright © 2024, The Authors. All rights reserved.

关键词： Time delay

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