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IEEE Conference on Wireless communications and networking

作者： Yuzhi Yang Zhaoyang Zhang Yuqing Tian Zhaohui Yang Richeng Jin Lei Liu Chongwen Huang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Lab of Information Processing Communication and Networking (IPCAN) Hangzhou China Zhejiang Laboratory Hangzhou China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Recently , over-the-air computation (OAC) has shown potential in realizing computation tasks over wireless transmission. Through proper transmit and receive beamforming design, multiple-input multiple-output (MIMO)-based OAC systems can even realize partial functions of neural networks (NNs). In this paper, we propose an OAC-NN with reconfigurable intelligent surface (RIS)-aided MIMO, in which the NN computation task can be realized through updating the RIS reflection matrix. In the proposed structure, the communication system can complete the overall simple NN-based tasks only through multiple rounds of transmissions without introducing any additional computing resources. Numerical results reflect the effectiveness of the proposed scheme and the tradeoff between communication costs and computing performance.

关键词： Performance evaluation Costs Wireless networks Artificial neural networks Machine learning Reconfigurable intelligent surfaces Reflection

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Multi-Task Lycium barbarum Recognition and Pedicel Localization Network Based on Improved YOLOv8

Multi-Task Lycium barbarum Recognition and Pedicel Localizat...

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International Conference on Image, Vision and Computing (ICIVC)

作者： Fen Li Dongxiao Li Le Huang Ming Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China Jinhua Institute of Zhejiang University Jinhua China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Given the unique indeterminate inflorescence trait of Lycium barbarum, traditional indiscriminate harvesting techniques like vibration, comb-brush, or airflow methods, fail to selectively harvest mature fruits, leading to an unsatisfactory picking rate and considerable damage. While a vision system might provide assistance for precise picking, the intricate task of harvesting Lycium barbarum remains challenging due to aspects like their diminutive size, compact distribution, and the presence of various complex occlusions. The three-stage method proposed in this paper facilitates precise object recognition and semantic segmentation of Lycium barbarum and provides individually targeted segmentation of each stem on branches with mature fruits, achieved by organically arranging three operational components based on improved YOLOv8. Moreover, the proposed method has been validated with the LyciumSeg dataset, yielding respective mean average precision (mAP) scores of $\mathbf{9 7. 8 \%}$ for Lycium barbarum recognition and $\mathbf{9 9. 5 \%}$ for pedicel segmentation.

关键词： Vibrations Performance evaluation Semantic segmentation Lighting Network architecture Multitasking Robustness Object recognition Optimization Testing

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Fast Decoding of Sequence Rate-1 or SPC Nodes for Polar Codes

Fast Decoding of Sequence Rate-1 or SPC Nodes for Polar Code...

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IEEE International Conference on communications (ICC)

作者： Yang Lu Ming-Min Zhao Ming Lei Min-Jian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) China

Due to the sequential nature of the successive-cancellation (SC) algorithm, the decoding of polar codes suffers from significant decoding latencies. Fast SC decoding is able to speed up the SC decoding process, by implementing parallel decoders at the intermediate levels of the SC decoding tree for some special nodes with specific information and frozen bit patterns. To further improve the parallelism of SC decoding, this paper present a new class of special nodes composed of a sequence of rate one or single-parity-check (SR1/SPC) nodes, which can be typically found in high-rate polar codes and is able to envelop a wide variety of existing special node types. Then, we analyse the parity constraints caused by the frozen bits in each descendant node, such that the decoding performance of the SR1/SPC node can be preserved once the parity constraints are satisfied. Finally, a generalized fast decoding algorithm is proposed to decode SR1/SPC nodes efficiently, where the corresponding parity constraints are taken into consideration. Simulation results show that the proposed decoding algorithm of the SR1/SPC node can nearly achieve maximum-likelihood (ML) performance, and the overall SC decoding latency can be reduced by 43.8% as compared to the state-of-the-art fast SC decoder.

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Deep Neural Network Based Matched-Field Geoacoustic Inversion

Deep Neural Network Based Matched-Field Geoacoustic Inversio...

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International Conference on Wireless communications and Signal processing (WCSP)

作者： Ruopeng Xu Hongnan Chen Ting Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking Hangzhou China

Acoustic field inversion offers an efficient approach for estimating geoacoustic parameters and acquiring valuable geoacoustic characteristics. The matched-field inversion technique aligns the measured acoustic field with a copy acoustic field generated by an acoustic propagation model. By employing optimization algorithms in a high-dimensional parameter space, the technique searches for the best matching acoustic parameters, and Bayesian theory is then applied to further determine the parameter uncertainties. However, the traditional global optimization process and posterior probability density sampling in matched-field inversion require multiple calls of the acoustic propagation model, resulting in a considerable computational time cost. Although using a surrogate model can enhance time efficiency, it often compromises inversion accuracy. To address this challenge, we propose intergrating a deep learning-based pre-screening process into the conventional matched-field inversion. This innovative approach significantly reduces the number of calls, thereby reducing time costs without sacrificing accuracy. In this paper, we conduct experiments on matched-field inversion based on Deep Neural Networks with simulated data and measured data to validate its efficacy.

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Adaptive HARQ Design for Semantic Image Transmission

Adaptive HARQ Design for Semantic Image Transmission

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IEEE Conference on Vehicular Technology (VTC)

作者： Haiqian Liu Ming-Min Zhao Ming Lei Liyan Li Yunlong Cai Min-Jian Zhao College of Information Science and Electronic Engineering 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

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Semantic communication is a promising framework for the next generation communication systems, which generally adopts deep learning based joint source and channel coding and has been verified to offer superior efficacy. A key ingredient in augmenting the reliability of this framework is the incorporation of hybrid automatic repeat request (HARQ) techniques. However, existing semantic HARQ architectures, such as fixed-length HARQ or chase combining HARQ (CC-HARQ), utilize predefined retransmission code lengths, lacking the flexibility to adjust to different channel signal-to-noise ratio (SNR) conditions. To address this issue, this paper develops an adaptive HARQ scheme by leveraging the double deep Q-network (DDQN) to determine the retransmission code lengths. Specifically, we first propose a basic model which consists of an image reconstruction module and a performance estimation module. The performance estimation module replaces the conventional error detection method like cyclic redundancy check (CRC) to estimate the structural similarity index measure (SSIM) of the reconstructed image at the receiver. Building on this basic model, our proposed HARQ scheme works by feeding back an NACK signal and an appropriate code length determined by the proposed DDQN algorithm to the semantic transmitter for the next transmission, if the estimated SSIM performance of the previous transmission does not exceed a predefined threshold. Experimental results demonstrate that our HARQ scheme is able to achieve the same SSIM performance as the existing semantic HARQ schemes, but with significantly reduced communication cost.

关键词： Adaptation models Vehicular and wireless technologies Codes Transmitters Image communication Semantics Estimation Reliability Image reconstruction Signal to noise ratio

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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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Improved information-Theoretic Bound for Multiple-Access Integrated Sensing and communication Systems

Improved Information-Theoretic Bound for Multiple-Access Int...

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IEEE Conference on Global communications (GLOBECOM)

作者： Yao Liu Min Li An Liu Lawrence Ong Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China The University of Newcastle Australia

Integrated sensing and communication (ISAC) is a promising technology for future 6G networks that enables the joint utilization of hardware and spectrum resources for sensing and communication systems. However, the co-sharing of resources leads to a fundamental tradeoff between sensing and communication performance, which is not well understood in multiple-access ISAC scenarios with perfect or imperfect channel state information at the receiver (CSIR). In this paper, we address this challenge by considering a state-dependent multiple access channel model that accounts for correlated sensing and channel states, as well as imperfect CSIR. We propose an achievable scheme that combines message cooperation and joint compression via distributed Wyner-Ziv coding at each user, resulting in unified cooperative communication and sensing. Our scheme always achieves a communication-rate-distortion region which includes that achieved by state-of-the-art coding scheme. In addition, a numerical example is provided to demonstrate strict inclusion. It is found that the compressed information not only enhances communication (especially in scenarios with imperfect CSIR) but also improves sensing performance.

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DQN based Anti-blocking Routing Algorithm for IRS-assisted MANET

DQN based Anti-blocking Routing Algorithm for IRS-assisted M...

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IEEE Conference on Vehicular Technology (VTC)

作者： Wenkai Cai Ming-Min Zhao Ming Lei Zijian Chen Min-Jian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China

Mobile ad-hoc networks (MANETs) have garnered significant interest in various specific scenarios owing to their capability to provide flexible and decentralized communication. However, in MANETs, link failures caused by obstacles, traffic surges and inefficient routing algorithms, are commonly en-countered. To address these issues, we propose an intelligent reflecting surface assisted anti-blocking routing (IRS-ABR) algorithm that incorporates the deep Q-network (DQN) for dynamic obstacles avoidance and traffic control. Moreover, by employing IRSs as intermediate nodes in the network, the proposed algorithm can achieve enhanced path routing. The simulation results validate the effectiveness of the proposed algorithm, as it achieves a 50% higher packet delivery rate compared to the traditional algorithm, while also reducing the transmission delay and energy consumption by 34% and 12%, respectively, through the utilization of IRS.

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Neural Adjusted Min-Sum Decoding for LDPC Codes

Neural Adjusted Min-Sum Decoding for LDPC Codes

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IEEE Conference on Vehicular Technology (VTC)

作者： Haochen Yu Ming-Min Zhao Ming Lei Min-Jian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China

In this work, we propose a neural adjusted min-sum (NAMS) decoder for low-density parity-check (LDPC) codes. In particular, we improve the traditional normalized min-sum (NMS) decoder by introducing a selection mechanism to adjust the check-node update step, where either the min-sum rule or the belief propagation (BP) rule is selected. Besides, we unfold the modified decoder into a model-driven neural network, where layer-dependent trainable parameters are introduced as weights in the Tanner graph and optimized by gradient descent-based methods during network training. Simulation results demonstrate that the proposed NAMS decoder is able to provide superior error-correction performance as compared to the neural NMS decoder, with only slightly increased computational complexity. Moreover, in certain circumstances, the proposed NAMS decoder even outperforms the neural BP decoder, with much lower computational complexity.

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IRS-Aided JSDM for mmWave Multiuser MISO Systems: A Low Overhead Scheme

IRS-Aided JSDM for mmWave Multiuser MISO Systems: A Low Over...

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IEEE Conference on Vehicular Technology (VTC)

作者： Zijian Chen Ming-Min Zhao Min Li Ming Lei Min-Jian Zhao College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking (IPCAN) Hangzhou China

In this paper, we combine two-timescale beamforming and multi-IRS aided joint spatial division and multiplexing (JSDM) in a mmWave multiuser system. Specifically, all the users are first divided into different groups and each group is associated with an IRS. Then, we propose a novel two-stage grouping-based randomized beamforming (TS-GRB) scheme, where the analog beamformer is designed based on the statistical CSI (S-CSI) in the first stage, and the short-term digital beamformer at the BS and long-term passive beam pattern control policy at the IRSs are jointly optimized in the second stage with both S-CSI and dimension-reduced effective I-CSI. In particular, in the first stage, the analog beamformer is designed to reduce the inter-group interference (IGI) and effective channel dimension, while in the second stage, a two-timescale randomized joint beamforming (TRJB) algorithm is proposed to maximize the proportional fairness utility (PFU). We show that through two-timescale beamforming, JSDM and proper problem reformulation, the pilot overhead of our TS-GRB scheme is significantly lower than existing schemes. Finally, simulation results are presented to illustrate the effectiveness of the proposed TS-GRB scheme.

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