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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Shen, Bitao Shu, Haowen Xie, Weiqiang Chen, Ruixuan Liu, Zhi Zhang, Xuguang Wang, Yimeng Zhang, Yunhao Cheng, Buwen Yu, Shaohua Bowers, John E. Chang, Lin Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China

Massively parallel optical chaotic sources with large bandwidth are demonstrated based on chaotic microcombs in the AlGaAsOI platform. 30-channel parallel random bit generation with 30 Gbit/s per channel is realized b... 详细信息

ISBN: (纸本)9781957171258

关键词： Gallium compounds

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Research on Image Captioning Based on Vision-language Pre-trained Models

Research on Image Captioning Based on Vision-language Pre-tr...

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Shiwei Zou Yuxiang Xie Jie Yan Yingmei Wei Xidao Luan Department of System Engineering National University of Defense Technology Hunan China Key Laboratory of Big Data Department of System Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science Changsha University Hunan China

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively; 2) a multi-modal encoder to align the two modalities; and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance.

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Low-Complexity Chase Decoding of Elliptic Codes

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IEEE Transactions on Communications 2025年

作者： Wan, Yunqi Liang, Jiwei Chen, Li Zhang, Fangguo Sun Yat-sen University School of Electronics and Information Technology Guangzhou510006 China Sun Yat-sen University School of System Science and Engineering Guangzhou510006 China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou510006 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China

This paper proposes two low-complexity Chase (LCC) decoding algorithms for elliptic codes, which are realized by Kötter’s interpolation and the basis reduction (BR) interpolation, respectively. They are both developed from the perspective of computing the Gröbner bases of the interpolation modules. By identifying η unreliable symbols, 2η decoding test-vectors are formulated and the corresponding interpolation modules can be defined. The re-encoding transform (ReT) is further introduced to facilitate the interpolation. The LCC-Kötter decoding performs interpolation for the common elements, producing an intermediate outcome shared by all test-vectors. The desired Gröbner basis w.r.t. each test-vector can be obtained in a binary tree growing fashion. The new interpolation process can start from intermediate nodes of the previously interpolated paths, resulting in a low complexity. But the decoding latency cannot be contained. In contrast, the LCC-BR decoding performs the common computation in basis construction, which partly substantiates the bases for all interpolation modules. The subsequent basis construction and reduction can be performed in parallel. Besides a low complexity, it offers a latency advantage over the LCC-Kötter decoding. The decoding complexity and latency are analyzed and verified numerically. The LCC decoding performance are also presented, demonstrating their advantage over both the Guruswami-Sudan decoding and the algebraic soft decoding. Moreover, the performance advantage of elliptic codes over the Reed-Solomon (RS) codes is demonstrated. © 1972-2012 IEEE.

关键词： Interpolation

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A Novel Wideband CPW-Fed 5.8GHz RFID Tag Antenna

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Chinese Journal of Electronics 2023年第2期21卷 202-208页

作者： Huihui Li Xuanqin Mou Zhen Ji Hang Yu Yan Li Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Embedded System Design Shenzhen China

A novel wideband 5.8GHz CPW-fed antenna is presented for Radio frequency identification (RFID) tag. Four U-shaped and four L-shaped branches are used as additional resonators to achieve wideband operation. The proposed antenna was analyzed numerically using the Method of moment (MOM) and the Finite element method (FEM). With the antenna size limited to $30\times 30 \text{mm}^{2}$ , the −10dB bandwidth obtained by MOM is 3.235GHz (5.765∼9GHz) and the −9.5dB band-width obtained by FEM is 2.74GHz (5.32∼8.06GHz), corresponding to 55.7% and 47.2% of the center frequency 5.8GHz respectively. Moreover, the simulated results show that the proposed antenna has gain of more than 4.8dBi and the radiation pattern is nearly omnidirectional in the H-plane. The measured −10dB bandwidth is 2.68GHz (5.63GHz∼8.31GHz), 46.2% of the 5.8GHz frequency. Furthermore, there are three measured resonant frequencies at 1.34GHz, 3.23GHz and 5.8GHz with lower than −10dB return loss respectively. The measurement result achieves a wideband RFID tag antenna performance and is in good agreement with the calculated results.

关键词： Antenna measurements Resonant frequency Loss measurement Frequency measurement Finite element analysis Broadband antennas RFID tags

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IEEE T-BIOM Editorial Board Changes

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

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Geometric Constellation Shaping for Wireless Optical Intensity Channels: An Information-Theoretic Approach

arXiv

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arXiv 2024年

作者： Zhou, Suhua Li, Tianqi Fang, Zhaoxi Zhou, Jing Zhang, Wenyi School of Automation Beijing Information Science and Technology University Beijing100192 China National Computer System Engineering Research Institute of China Beijing100083 China Department of Computer Science and Engineering Shaoxing University Shaoxing312000 China Henan Key Laboratory of Visible Light Communications Zhengzhou450002 China Department of Electronic Engineering and Information Science University of Science and Technology of China Hefei230027 China

A simple geometric shaping method is proposed for optical wireless communication systems based on intensity modulation and direct detection (IM/DD) from an information-theoretic perspective. Constellations consisting of equiprobable levels with exponential-like distribution are obtained, which possesses asymptotic optimality in the sense that the high-SNR capacity of average-intensity constrained optical intensity channel can be approached by such constellations with increasing size. All 2b levels (b ∈ N) of the obtained constellation can be represented by a basic level and b + 2 bits, thereby reducing the required resolution of the digital-to-analog converter (DAC) without affecting the asymptotic optimality. Achievable information rate evaluations verify the asymptotic optimality. As an example, error performance results of a simple 16-level LDPC coded modulation scheme show that a shaping gain of 0.65 dB can be obtained by applying the proposed constellation design. This method can also be applied to more specific IM/DD channel models, since it only requires a near-optimal continuous input distribution. © 2024, CC BY-NC-ND.

关键词： Digital to analog conversion

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Validating SMT Solvers via Skeleton Enumeration Empowered by Historical Bug-Triggering Inputs

Validating SMT Solvers via Skeleton Enumeration Empowered by...

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International Conference on Software engineering (ICSE)

作者： Maolin Sun Yibiao Yang Ming Wen Yongcong Wang Yuming Zhou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Technology Huazhong University of Science and Technology China

SMT solvers check the satisfiability of logic formulas over first-order theories, which have been utilized in a rich number of critical applications, such as software verification, test case generation, and program synthesis. Bugs hidden in SMT solvers would severely mislead those applications and further cause severe consequences. Therefore, ensuring the reliability and robustness of SMT solvers is of critical importance. Although many approaches have been proposed to test SMT solvers, it is still a challenge to discover bugs effectively. To tackle such a challenge, we conduct an empirical study on the historical bug-triggering formulas in SMT solvers' bug tracking systems. We observe that the historical bug-triggering formulas contain valuable skeletons (i.e., core structures of formulas) as well as associated atomic formulas which can cast significant impacts on formulas' ability in triggering bugs. Therefore, we propose a novel approach that utilizes the skeletons extracted from the historical bug-triggering formulas and enumerates atomic formulas under the guidance of association rules derived from historical formulas. In this study, we realized our approach as a practical fuzzing tool HistFuzz and conducted extensive testing on the well-known SMT solvers Z3 and cvc5. To date, HistFuzz has found 111 confirmed new bugs for Z3 and cvc5, of which 108 have been fixed by the developers. More notably, out of the confirmed bugs, 23 are soundness bugs and invalid model bugs found in the solvers' default mode, which are essential for SMT solvers. In addition, our experiments also demonstrate that HistFuzz outperforms the state-of-the-art SMT solver fuzzers in terms of achieved code coverage and effectiveness.

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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MCB-CPPA: An Efficient Conditional Privacy-Preserving Authentication Scheme Based on Message Classification for VANETs

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IEEE Transactions on Vehicular Technology 2025年

作者： Tong, Jiawei Wang, Liangliang Liu, Zhiquan Zhang, Kai Luo, Yiyuan Chen, Kefei Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Jinan University College of Cyber Security Guangzhou510632 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Hangzhou Normal University Hangzhou310036 China

Vehicular ad hoc networks (VANETs) can enhance traffic conditions through real-time communication, but vehicle-to-infrastructure exchanges in open channels require higher security and privacy. Numerous conditional privacy-preserving authentication (CPPA) schemes have been designed to ensure message authenticity. However, most CPPA schemes still suffer from many shortcomings, such as redundant authentication of messages, lack of effective identity revocation mechanism, and over-reliance on the authentication center. In order to address these concerns, an efficient certificateless CPPA scheme based on message classification (MCB-CPPA) is introduced for VANETs, which aggregates authentication for the same message across different vehicles and performs batch verification for identical messages from different vehicles, thus reducing redundant message validation. Furthermore, the scheme separates the processes of identity authentication and traffic message verification relying on both roadside units and vehicles to reduce security dependence on the authentication center. Regarding the certificateless signature in the MCB-CPPA scheme, we demonstrate existential unforgeability in the random oracle model (ROM) against the adaptive chosen message attack. Upon evaluation, it is revealed that the transmission and batch authentication overheads of this proposed scheme have been reduced by approximately 47. 7% and 51. 6% compared to existing state-of-the-art solutions. As a result, the proposed scheme successfully reduces the computational overhead and communication cost while satisfying the safety requirements of VANETs, making it more adaptable to the VANETs environment. © 1967-2012 IEEE.

关键词： Privacy by design

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Identity-domain Removal for Robust EEG-based Emotion Recognition 25

Identity-domain Removal for Robust EEG-based Emotion Recogni...

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Proceedings of the 2025 International Conference on Multimedia Retrieval

作者： Wenchang Deng Shenghua Zhong Rongrong Lu Yi Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Rehabilitation Huashan Hospital Fudan University Shanghai China Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798400718779

Although deep learning models have shown promise in Electroencephalogram (EEG) - based emotion recognition, their precision and robustness are often limited by domain-specific bias and low signal-to-noise ratio of EEG. Among the challenges encountered, the disparities in EEG data between subjects have been observed to potentially exceed those observed between different states of the task. The integration of identity domain information with task domain information may cause the model's learning of subject-dependent information, thereby hindering the ability to discern differences between tasks. This paper proposes a novel deep learning framework that improves the performance of EEG-based emotion classification models through the removal of the identity domain. Specifically, the proposed framework utilizes Auto-encoder (AE) based model as denoising module to extract globally informative features from the EEG signals. The domain information extraction module extracts subject-specific features from EEG samples by minimizing the differences between the network's output and the subject's identity labels while ensuring consistency of intermediate features across samples from the same subject. By combining domain-specific and globally informative features, an additional domain removal network is introduced before the final classification network to mitigate domain-specific biases by stripping away identity domain information from globally informative features through pairwise contrastive learning. The proposed EEG-based emotion recognition framework, supported by extensive experimental evidence, shows that incorporating the suggested domain removal technique significantly improves the robustness of the classification model and achieves state-of-the-art results in emotion classification.

关键词： domain removal

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