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2023 China Semiconductor technology International Conference, CSTIC 2023

作者： Ouyang, Keqing Peng, Minqiang Wang, Shuai Zhou, Guohua Wang, Kai State Key Laboratory of Mobile Network and Mobile Multimedia Technology Guangdong Shenzhen China Sanechips Technology Co. Ltd. Dept.of Back-end Design Guangdong Shenzhen China

ISBN: (纸本)9798350311006

Scan chains have emerged as an indispensable component of very-large-scale-integration (VLSI) circuits, commonly utilized for the purposes of testing and diagnosis. However, despite their crucial role as internal test circuits, scan chains are not immune to faults, similar to other logic. In fact, it has been reported that scan chain faults are responsible for as much as 50% of chip failures [1], particularly in advanced process nodes. Therefore, it is of utmost importance to accurately locate and diagnose faults within the scan chains to improve manufacturing yield. In this work, a novel structure-based scan chain diagnosis technique aiming to identify and isolate scan chains' faults with high accuracy and efficiency is proposed. During the implementation of this innovative approach, the performance and reliability of VLSI circuits are significantly enhanced, ensuring optimal yield and customer satisfaction. © 2023 IEEE.

关键词： Customer satisfaction

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Towards Routing in Low-Altitude Drone networks: A Physical Sensing-Aided Intelligent Forwarding Mechanism with Deep Learning

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IEEE Internet of Things Journal 2025年

作者： Chong, Jingzheng Jia, Xibei Yang, Zhihua Harbin Institute of Technology Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen518055 China Peng Cheng Laboratory Network Communication Department Shenzhen518055 China

In recent years, self-organizing networks composed of drones have received more attention due to their ability to expand coverage and improve mission efficiency. However, in GPS-denied complex low-altitude environments, typical routing protocols, as the cornerstone of drone communications, are greatly restricted or even in failures by numerous obstacles around, which can cause frequent None Line of Sight (NLOS) links leading to sharp declines in communication performance or even interruptions. Therefore, in this work, we propose a Physical Sensing-aided Intelligent Forwarding (PSIF) mechanism for Low-altitude Drone network (LDNET), which could enhance the forwarding capability between drones by integrating a Long Short-Term Memory (LSTM) based multi-feature link prediction with a Deep Q-network (DQN) enabled forwarding decision. Simulation results indicate that PSIF can efficiently facilitate packet forwarding in LDNET, resulting in enhanced system performance with regards to delay, packet loss ratio, throughput, and power consumption. © 2025 IEEE.

关键词： Drones

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Endogenous Security Through AI-Driven Physical-Layer Authentication for Future 6G networks

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ZTE communications 2025年第1期23卷 18-29页

作者： MENG Rui FAN Dayu XU Xiaodong LYU Suyu TAO Xiaofeng State Key Laboratory of Networking and Switching Technology Bei-jing University of Posts and TelecommunicationsBeijing 100876China Department of Broadband Communication Peng Cheng LaboratoryShenzhen 518066China School of Information Science and Technology Beijing University of TechnologyBeijing 100124China National Engineering Laboratory for Mobile Network Technologies Bei-jing University of Posts and TelecommunicationsBeijing 100876China

To ensure the access security of 6G,physical-layer authentication(PLA)leverages the randomness and space-time-frequency uniqueness of the channel to provide unique identity signatures for ***,the introduction of artificial intelligence(AI)facilitates the learning of the distribution characteristics of channel fingerprints,effectively addressing the uncertainties and unknown dynamic challenges in wireless link *** paper reviews representative AI-enabled PLA schemes and proposes a graph neural network(GNN)-based PLA approach in response to the challenges existing methods face in identifying mobile *** results demonstrate that the proposed method outperforms six baseline schemes in terms of authentication ***,this paper outlines the future development directions of PLA.

关键词： physical-layer authentication artificial intelligence wireless security intelligent authentication

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DPText-DETR: Towards Better Scene Text Detection with Dynamic Points in Transformer 37

DPText-DETR: Towards Better Scene Text Detection with Dynami...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Ye, Maoyuan Zhang, Jing Zhao, Shanshan Liu, Juhua Bo, Du Tao, Dacheng Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University China The University of Sydney Australia JD Explore Academy National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China

ISBN: (纸本)9781577358800

Recently, Transformer-based methods, which predict polygon points or Bezier curve control points for localizing texts, are popular in scene text detection. However, these methods built upon detection transformer framework might achieve sub-optimal training efficiency and performance due to coarse positional query modeling. In addition, the point label form exploited in previous works implies the reading order of humans, which impedes the detection robustness from our observation. To address these challenges, this paper proposes a concise Dynamic Point Text DEtection TRansformer network, termed DPText-DETR. In detail, DPText-DETR directly leverages explicit point coordinates to generate position queries and dynamically updates them in a progressive way. Moreover, to improve the spatial inductive bias of non-local self-attention in Transformer, we present an Enhanced Factorized Self-Attention module which provides point queries within each instance with circular shape guidance. Furthermore, we design a simple yet effective positional label form to tackle the side effect of the previous form. To further evaluate the impact of different label forms on the detection robustness in real-world scenario, we establish an Inverse-Text test set containing 500 manually labeled images. Extensive experiments prove the high training efficiency, robustness, and state-of-the-art performance of our method on popular benchmarks. The code and the Inverse-Text test set are available at https://***/ymy-k/DPText-DETR. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Efficiency

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All-Fiber Switch based on Plasmonic FP Cavity

All-Fiber Switch based on Plasmonic FP Cavity

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2023 Opto-Electronics and communications Conference, OECC 2023

作者： Qiu, Ying Li, Zhen Jiang, Tian Cai Wu, Yonghui Tao, Jin China Information and Communication Technology Group Co. Ltd National Key Laboratory of Optical Communication Technology and Network Wuhan China Geely Automobile Research Institute Co. Ltd Ningbo China Shenzhen China

ISBN: (纸本)9781665462136

关键词： Gold nanoparticles

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Diverse Co-saliency Feature Learning for Text-Based Person Retrieval

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5465-5477页

作者： You, Shuai Chen, Cuiqun Feng, Yujian Liu, Hai Ji, Yimu Ye, Mang Nanjing University of Posts and Telecommunications (NJUPT) School of Internet of Things Nanjing China Anhui University School of Computer Science and Technology Hefei China South China Normal University school of computer Guangdong China NJUPT School of Computer Science Nanjing China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan 430072 China

Text-based Person Retrieval (TPR) plays a pivotal role in video surveillance systems for safeguarding public safety. As a fine-grained retrieval task, TPR faces the significant challenge of precisely capturing highly discriminative features across image and text modalities. Existing methods primarily focus on establishing modality-shared feature spaces to bridge cross-modal discrepancies. However, these methods are prone to disturbances from irrelevant information, such as background noises in the visual modality, and often over-emphasize specific local regions while neglecting the capture of diverse discriminative modal features, thereby limiting the robustness of cross-modal matching. In this paper, we introduce a novel framework, termed the Diverse Co-saliency Feature Learning network (DCFL), which mines the co-saliency information between image and text modalities and enhances the diversity of cross-modal discriminative features while mitigating the interference of noise. Specifically, to construct cross-modal co-saliency features, we devise the Intra-modal Saliency Feature Learning (ISFL) and Cross-modal Saliency Feature Matching (CSFM) modules. ISFL employs a weighted mask mechanism to guide the model in reducing the impact of noise information in both modalities. Complementing ISFL, CSFM establishes consistent relationships between saliency features across modalities, leveraging text descriptions to align pedestrian-relevant visual regions. Furthermore, we propose the Diverse Co-saliency Feature Mining (DCFM) to bolster the diversity of discriminative co-saliency features across both image and text modalities. This module integrates a diversity regularization term, enabling the extraction of varied visual cues and capturing comprehensive features of the target individual. Extensive benchmark experiments demonstrate a substantial superiority of our approach over the state-of-the-art methods. The code will be released publicly. © 2005-2012 IEEE.

关键词： Co-saliency features modeling Diverse Co-saliency features Mining Text-based person retrieval

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ALFC-Point: Adaptive Laplacian Feature Convolution network for 3D Point Cloud Understanding 41st

ALFC-Point: Adaptive Laplacian Feature Convolution Network f...

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41st Computer Graphics International Conference, CGI 2024

作者： Liao, Junjie Yin, Mengxiao Li, Ming Zhu, Congyang Yang, Zhiqiang Zhan, Feng School of Computer and Electronics Information Guangxi University Guangxi Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Nanning China

ISBN: (纸本)9783031820236

To capture 3D geometric details, point cloud understanding primarily employs convolution, dynamic map, or transformer methods to build complex local geometry extractors. When dealing with sparse and noisy point clouds in real-world environments, the structural design of these methods tends to be complex and perform poorly. In this paper, we propose a lightweight point cloud understanding framework based on Laplacian feature convolution, which has high accuracy on normal-sized point cloud datasets and performs well when dealing with sparse and noisy point cloud inputs. The adaptive Laplacian convolution module combined with the vector attention mechanism can effectively capture the local details of point cloud geometry. The framework employs a multi-scale feature fusion mechanism between modules to reduce the number of sampling points step by step, reducing redundant computations and achieving a lightweight effect. In addition, we propose a parameter-free module for interpolation to enhance the training part. Experimental results on object-level datasets show that the lightweight method proposed in our paper has similar accuracy to the current best method in terms of accuracy (Acc) and mean intersection over union (mIoU), and has better robustness in handling sparse and noisy point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Laplace transforms

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Neural network Accelerator Architecture Designed for Next Generation communication Systems 10

Neural Network Accelerator Architecture Designed for Next Ge...

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10th International Conference on Computer and communications, ICCC 2024

作者： Cai, Wenhao Xiong, Xiankui Wang, Chengchen Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China

In the future 6G wireless communication networks, the artificial intelligent (AI) technology is a promising way to fundamentally reconstruct the future communication system. However, the AI method always requires a large number of parameter loading and calculation operations, which impose strict requirement on computing capability and data bandwidth of the system. Aiming to implement the existing deep learning algorithms for wireless communication more efficiently, in this paper, we propose an novel AI accelerator architecture, which is driven by data streams and separates computing and control. An affinity compiler and a supporting emulator are designed for this architecture, which meets most of the neural network inference acceleration requirements. Simulation results show that various neural network for wireless communication can be accelerated efficiently. © 2024 IEEE.

关键词： Bandwidth

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Advanced Recursive Algorithm Based Electronic Polarization Tracking for PDM-DQPSK Differential Detection Optical Transmission Systems

Advanced Recursive Algorithm Based Electronic Polarization T...

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2023 Asia communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Tian, Zhongxing Jiang, Chenxu Chai, Xudong Wang, Xiaozhou Huang, Huan Zhou, Jun Cai, Yi Jiangsu Hengxin Semitech Co. Ltd Suzhou China Soochow University Suzhou Key Laboratory of Advanced Optical Communication Network Technology Suzhou China

ISBN: (纸本)9798350312614

We propose a differential-detection receiver architecture and a novel recursive algorithm for electronic polarization tracking in polarization-division-multiplexed differential quadrature-phase-shift-keying (PDM-DQPSK) systems. Simulation results demonstrate that the proposed system approaches the performance of coherent-detection-based PDM-DQPSK systems. © 2023 IEEE.

关键词： differential quadrature-phase-shift-keying electronic polarization tracking optical fiber communication polarization-division-multiplexing

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Neural network Accelerator Architecture Designed for Next Generation communication Systems

Neural Network Accelerator Architecture Designed for Next Ge...

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International Conference on Computer and communications (ICCC)

作者： Wenhao Cai Xiankui Xiong Chengchen Wang State Key Laboratory of Mobile Network and Mobile Multimedia Technology ZTE Corporation Shenzhen China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

关键词： Wireless communication Deep learning Simulation Neural networks Loading AI accelerators Inference algorithms Streams Next generation networking Hardware acceleration

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