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CVFNet: Real-time 3D Object Detection by Learning Cross View Features

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arXiv 2022年

作者： Gu, Jiaqi Xiang, Zhiyu Zhao, Pan Bai, Tingming Wang, Lingxuan Zhao, Xijun Zhang, Zhiyuan College of Information Science & Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou China China North Vehicle Research Institute Beijing China Ningbo Research Institute Zhejiang University Ningbo China

In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies. Although voxel or point based methods are popular in 3D object detection, they usually involve time-consuming operations such as 3D convolutions on voxels or ball query among points, making the resulting network inappropriate for time critical applications. On the other hand, 2D view-based methods feature high computing efficiency while usually obtaining inferior performance than the voxel or point based methods. In this work, we present a real-time view-based single stage 3D object detector, namely CVFNet to fulfill this task. To strengthen the cross-view feature learning under the condition of demanding efficiency, our framework extracts the features of different views and fuses them in an efficient progressive way. We first propose a novel Point-Range feature fusion module that deeply integrates point and range view features in multiple stages. Then, a special Slice Pillar is designed to well maintain the 3D geometry when transforming the obtained deep point-view features into bird's eye view. To better balance the ratio of samples, a sparse pillar detection head is presented to focus the detection on the nonempty grids. We conduct experiments on the popular KITTI and NuScenes benchmark, and state-of-the-art performances are achieved in terms of both accuracy and speed. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Exploring Channel Estimation and Signal Detection for ODDM-based ISAC Systems

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arXiv 2024年

作者： Wang, Dezhi Huang, Chongwen Liu, Lei Chen, Xiaoming Wang, Wei Zhang, Zhaoyang Yuen, Chau Debbah, Mérouane College of Information Science and Electronic Engineering Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore Department of Electrical Engineering and Computer Science the KU 6G Center Khalifa University Abu Dhabi127788 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Inspired by providing reliable communications for high-mobility scenarios, in this letter, we investigate the channel estimation and signal detection in integrated sensing and communication (ISAC) systems based on the orthogonal delay-Doppler multiplexing (ODDM) modulation, which consists of a pulse-train that can achieve the orthogonality with respect to the resolution of the delay-Doppler (DD) plane. To enhance the communication performance in the ODDM-based ISAC systems, we first propose a low-complexity approximation algorithm for channel estimation, which addresses the challenge of the high complexity from high resolution in the ODDM modulation, and achieves performance close to that of the maximum likelihood estimator scheme. Then, we employ the orthogonal approximate message-passing scheme to detect the symbols in the communication process based on the estimated channel information. Finally, simulation results show that the detection performance of ODDM is better than other multi-carrier modulation schemes. Specifically, the ODDM outperforms the orthogonal time frequency space scheme by 2.3 dB when the bit error ratio is 10−6 Copyright © 2024, The Authors. All rights reserved.

关键词： Signal detection

Automatic Sleep Staging Algorithm Based on Random Forest and Hidden Markov Model

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Computer Modeling in Engineering & Sciences 2020年第4期123卷 401-426页

作者： Junbiao Liu Duanpo Wu Zimeng Wang Xinyu Jin Fang Dong Lurong Jiang Chenyi Cai College of Information Science&Electronic Engineering Zhejiang UniversityHangzhouChina School of Communication Engineering Hangzhou Dianzi UniversityHangzhouChina Zhejiang Provincial Key Laboratory of Information Processing Communication and NetworkingHangzhouChina College of Information and Electric Engineering Zhejiang University City CollegeHangzhouChina School of Information Science and Technology Zhejiang Sci-Tech UniversityHangzhouChina Hangzhou Neuro Science and Technology Co. Ltd.HangzhouChina

In the field of medical informatics,sleep staging is a challenging and timeconsuming task undertaken by sleep *** to the new standard of the American Academy of Sleep Medicine(AASM),the stages of sleep are divided into wakefulness(W),rapid eye movement(REM)and non-rapid eye movement(NREM)which includes three sleep stages(N1,N2 and N3)that describe the depth of *** study aims to establish an automatic sleep staging algorithm based on the improved weighted random forest(WRF)and Hidden Markov Model(HMM)using only the features extracted from double-channel EEG *** WRF classification model focuses on reducing the bias of the imbalance data,while the HMM model focuses on improving the detection rate of sleep staging through the relationship between adjacent sleep *** particular,the improved weighted RF classification model can increase the recognition rate of the N1 *** addition,the method of removing features with low variance is used to select meaningful and contributing feature parameters for model *** is an innovative content of this *** sleep EEG data are first segmented into 30 s epochs,and the feature parameters of the epoch data are extracted from the double-channel by applying continuous wavelet packet transform(WPT).Each epoch is then segmented into 29 subepochs of 2 s long with 1 s overlap,and the frequency domain features and statistical features of each subepoch are *** performance of the proposed method is tested by evaluating the accuracy(AC),Kappa coefficient,Recall(R),Precision(P)and F1-score(F1).In the Sleep-EDF database,the overall AC and Kappa coefficient obtained by WRF are 93.20%and 0.890,respectively using the subject-non-independent *** the 10 sc*and 10 st*Sleep-EDF Expanded database,the overall AC and Kappa coefficient obtained by proposed method are 91.97%and 0.874,respectively using the subject-independent *** best AC and Kappa coefficient of single subject can reach 96.3

关键词： Sleep staging wavelet packet transform random forest HMM

Subjective evaluation of traditional and learning-based image coding methods

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arXiv 2021年

作者： Fang, Zhigao Zhang, Jiaqi Yu, Lu Zhao, Yin Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China Huawei Technologies Co. Ltd. Hangzhou China

We conduct a subjective experiment to compare the performance of traditional image coding methods and learning-based image coding methods. HEVC and VVC, the state-of-the-art traditional coding methods, are used as the representative traditional methods. The learning-based methods used contain not only CNN-based methods, but also a GAN-based method, all of which are advanced or typical. Single Stimuli (SS), which is also called Absolute Category Rating (ACR), is adopted as the methodology of the experiment to obtain perceptual quality of images. Additionally, we utilize some typical and frequently used objective quality metrics to evaluate the coding methods in the experiment as comparison. The experiment shows that CNN-based and GAN-based methods can perform better than traditional methods in low bit-rates. In high bit-rates, however, it is hard to verify whether CNN-based methods are superior to traditional methods. Because the GAN method doesn't provide models with high target bit-rates, we can't exactly tell the performance of the GAN method in high bit-rates. Furthermore, some popular objective quality metrics have not shown the ability well to measure quality of images generated by learning-based coding methods, especially the GAN-based one. Copyright © 2021, The Authors. All rights reserved.

关键词： Image coding

Sufficient-Statistic Memory AMP

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arXiv 2021年

作者： Liu, Lei Huang, Shunqi Yang, Yuzhi Zhang, Zhaoyang Kurkoski, Brian M. The Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Nomi923-1292 Japan

Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. While state evolution is a useful analytic tool, its convergence is not guaranteed. To solve the convergence problem of the state evolution of AMP-type algorithms in principle, this paper proposes a sufficient-statistic memory AMP (SS-MAMP) algorithm framework under the conditions of right-unitarily invariant sensing matrices, Lipschitz-continuous local processors and the sufficient-statistic constraint (i.e., the current message of each local processor is a sufficient statistic of the signal vector given the current and all preceding messages). We show that the covariance matrices of SS-MAMP are L-banded and convergent, which is an optimal framework (from the local MMSE/LMMSE perspective) for AMP-type algorithms given the Lipschitz-continuous local processors. Given an arbitrary MAMP, we can construct an SS-MAMP by damping, which not only ensures the convergence of the state evolution, but also preserves the orthogonality, i.e., its dynamics can be correctly described by state evolution. As a byproduct, we prove that the Bayes-optimal orthogonal/vector AMP (BO-OAMP/VAMP) is an SS-MAMP. As a result, we recover two interesting properties of BO-OAMP/VAMP for large systems in the literature: 1) the covariance matrices are L-banded and are convergent, and 2) damping and memory are not needed (i.e., do not bring performance improvement). As an example, we construct a sufficient-statistic Bayes-optimal MAMP (SS-BO-MAMP) whose state evolution converges to the minimum (i.e., Bayes-optimal) mean square error (MSE) predicted by replica methods when it has a unique fixed point. In addition, the MSE of SS-BO-MAMP is not worse than the original BO-MAMP. Finally, simulations are provided to support the theoretical results. © 2021, C

关键词： Damping

Fundamental Limits of Multiple-Access Integrated Sensing and communication Systems

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arXiv 2022年

作者： Liu, Yao Li, Min Liu, An Ong, Lawrence Yener, Aylin The College of Information Science and Electronic Engineering The Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang University Hangzhou310027 China The School of Engineering University of Newcastle CallaghanNSW2308 Australia The Department of Electrical and Computer Engineering The Ohio State University ColumbusOH43210 United States

A memoryless state-dependent multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the sensing state sequences through echo signals. In particular, the sensing states are assumed to be correlated with the channel state, and the receiver has imperfect channel state information. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results. © 2022, CC BY.

关键词： Transmitters

Improved Incremental Freezing HARQ Schemes Using Polar Codes over Degraded Compound Channels 14th

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Improved Incremental Freezing HARQ Schemes Using Polar Codes...

14th EAI International Conference on communications and networking in China, ChinaCom 2019

作者： Hu, Tianze Xie, Lei Chen, Huifang Duan, Hongda Wang, Kuang College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Zhoushan Ocean Research Center Zhoushan316021 China

ISBN: (纸本)9783030411138

The error propagation problem in incremental freezing (IF) hybrid automatic repeat request (HARQ) scheme using Polar codes is studied. We propose two IF HARQ schemes using polar codes, namely the cyclic redundancy check (CRC)-aided IF HARQ scheme and the cumulative-path-metrics-based IF HARQ scheme. In the CRC-aided IF HARQ scheme, several CRC bits are added to each transmitted block. Using these CRC bits, the proposed IF HARQ scheme and the Chase Combining HARQ scheme can be combined to achieve a better error correction performance in the cost of a larger decoding delay. In the cumulative-path-metrics-based IF HARQ scheme, the successive joint decoder maintains multiple possible paths simultaneously, and the cumulative path metrics is used to represent the reliability of each surviving path in the decoding process. Moreover, a modified path splitting reduced successive cancellation list (SCL) decoding algorithm is presented to reduce the computational complexity and the memory requirement of cumulative-path-metrics-based IF HARQ scheme. Simulation results show that, using the Polar code constructed under long block length and high block error rate, the CRC-aided IF HARQ scheme has a higher system throughput. With the Polar code constructed under short block length and low block error rate, the cumulative-path-metrics-based IF HARQ scheme has a higher system throughput. In both situations, the system block error rate of the CRC-aided IF HARQ scheme performs well. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Freezing

Performance Analysis of Consensus-Based Distributed System Under False Data Injection Attacks 14th

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Performance Analysis of Consensus-Based Distributed System U...

14th EAI International Conference on communications and networking in China, ChinaCom 2019

作者： Zheng, Xiaoyan Xie, Lei Chen, Huifang Song, Chao College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou310027 China Zhoushan Ocean Research Center Zhoushan316021 China

ISBN: (纸本)9783030411138

This paper investigates the security problem of consensus-based distributed system under false data injection attacks (FDIAs). Since the injected false data will spread to the whole network through data exchange between neighbor nodes, and result in continuing effect on the system performance, it is significant to study the impact of the attack. In this paper, we consider two attack models according to the property of the injection data, the deterministic attack and the stochastic attack. Then, the necessary and sufficient condition for the convergence of distributed system under the attack are derived, and the attack feature making the system unable to converge is provided. Moreover, the convergence result under resource-limited attack is deviated. On the other hand, the statistical properties of the convergence performance under zero-mean and non-zero-mean stochastic attacks are analyzed, respectively. Simulation results illustrate the effects caused by FDIAs on the convergence performance of distributed system. © 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Electronic data interchange

VSGNet: visual saliency guided network for skin lesion segmentation

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Expert Systems with Applications 2025年

作者： Zhefei Cai Yingle Fan Tao Fang Wei Wu Laboratory of Pattern Recognition and Image Processing Hangzhou Dianzi University Hangzhou 310018 China Department of Biomedical Engineering University of Miami Florida 33124 USA Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Zhejiang 310058 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 vis

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