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4th International Computational Imaging Conference, CITA 2024

作者： Tang, Guitao Li, Dongqing Zhou, Xinghong Yi, Duo Liu, Fei College of Physics and Optoelectronic Engineering Shenzhen Key Laboratory of Sensor Technology Shenzhen University Shenzhen518060 China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China

ISBN: (纸本)9781510688834

The current research relating to the fiber optic seismic sensor (FOSS) mainly focuses on optimizing its steady-state characteristic parameters including sensitivity, cross-sensitivity and operating bandwidth etc., while there are few studies on its transient response characteristics. In this study, we discuss the transient response improvement of the FOSS in both laboratory platform and oilfield. In the laboratory platform, the ball-falling process is used to simulate the perforation shot signal during down-hole seismic monitoring, and the transient response between the FOSS and the commercial piezoelectric sensor has been compared in both time and frequency domain. Besides, two different types of balls (steel and cotton) are used to verify the transient response performance of the presented FOSS when retrieving different types of signals. The experimental results demonstrate that correlation coefficients as high as 90% are obtained within the frequency range 0~3000 Hz for two ball-falling processes. Finally, the presented FOSS has been used for detecting the perforation shot signal in the oilfield, the comparative analyses based on the key parameters including taking off time and attenuation constant also confirm the transient response optimization of the presented sensor, which is beneficial to improve the acquisition accuracy of seismic signal. © 2025 SPIE.

关键词： Gas industry

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IPT-ILR: Image Pyramid Transformer Coupled With Information Loss Regularization for All-in-One Image Restoration

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IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4341-4356页

作者： Yang, Sai Hu, Bin Liu, Fan Wu, Xiaoxin Ding, Weiping Zhou, Jun Nantong University School of Electrical Engineering and Automation Nantong226019 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Hohai University College of Computer Science and Software Engineering Nanjing210098 China Griffith University School of Information and Communication Technology NathanQLD4111 Australia

All-in-one image restoration has recently developed to be a new research trend in the low-level computer vision field, aiming to tackle multiple image degradation types simultaneously in a unified model. As a typical multi-task learning, existing approaches focus on modeling either the specificity or commonality among different image restoration tasks. To exploit the unique strengths of both worlds, we propose a method of Image Pyramid Transformer coupled with Information Loss Regularization (IPT-ILR), in which the multi-scale architecture structure can excavate more information for multiple restoration tasks concurrently, while the learning strategy can identify the difference among multiple restoration tasks depending on the degree of information loss in each restoration task. Specifically, it first establishes a new Image Pyramid Transformer Network (IPT-Network) to accommodate multiple image restoration tasks. Given original degraded images, the IPT-Network exploits the image pyramid technique to establish a series of images with different scales, which are then restored by transformer-like auto-encoders. Moreover, the restored image on a low-level scale is referenced to assist restoring the degraded image on a high-level scale. Next, Information Loss Regularization (ILR) is presented to optimize the IPT-Network. ILR calculates the average distance between degraded images and their clean counterparts as the weights, which automatically implement different penalties for different image restoration tasks, thus avoiding the short-cut phenomenon for the easy task while encouraging the hard task. Extensive experiments have been conducted with 6 image restoration tasks in the all-in-one setting. The results show our method performs favorably against numerous state-of-the-art methods across most tasks, including image denoising, image deblurring, image dehazing, image deraining, image desnowing, as well as low-light enhancement. © 1991-2012 IEEE.

关键词： Restoration

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Toward next-generation networks: A blockchain-based approach for core network architecture and roaming identity verification

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Digital communications and Networks 2025年第2期11卷 326-336页

作者： Yi Gong Boyuan Yu Lei Yang Fanke Meng Lei Liu Xinjue Hu Zhan Xu School of Information and Communication Engineering Beijing Information Science&Technology University Xi'an University of Posts and Telecommunications Guangzhou Institute of Technology Xidian University Intelligent Transportation Systems Research Center Wuhan University of Technology State Key Laboratory of Maritime Technology and Safety

With the evolution of next-generation communication networks, ensuring robust Core Network(CN) architecture and data security has become paramount. This paper addresses critical vulnerabilities in the architecture of CN and data security by proposing a novel framework based on blockchain technology that is specifically designed for communication networks. Traditional centralized network architectures are vulnerable to Distributed Denial of Service(DDoS) attacks, particularly in roaming scenarios where there is also a risk of private data leakage, which imposes significant operational demands. To address these issues, we introduce the Blockchain-Enhanced Core Network Architecture(BECNA) and the Secure Decentralized Identity Authentication Scheme(SDIDAS). The BECNA utilizes blockchain technology to decentralize data storage, enhancing network security, stability, and reliability by mitigating Single Points of Failure(SPoF). The SDIDAS utilizes Decentralized Identity(DID) technology to secure user identity data and streamline authentication in roaming scenarios, significantly reducing the risk of data breaches during cross-network transmissions. Our framework employs Ethereum, free5GC, Wireshark, and UERANSIM tools to create a robust, tamper-evident system model. A comprehensive security analysis confirms substantial improvements in user privacy and network security. Simulation results indicate that our approach enhances communication CNs security and reliability, while also ensuring data security.

关键词： Blockchain Core network Privacy data protection Decentralized identity Roaming identity verification

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PAH2T-Former: Paired-Attention Hybrid Hierarchical Transformer for Synergistically Enhanced FMT Reconstruction Quality and Efficiency

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2025年 11卷 536-545页

作者： Zhang, Peng Liu, Xingyu Xue, Qianqian Shang, Yu Liu, Chen Chen, Ruhao Gao, Honglei Liang, Jiye Wang, Wenjian Zhang, Guanglei Shanxi University School of Computer and Information Technology Taiyuan030006 China Dongguan University of Technology School of Life and Health Technology Dongguan523808 China Beihang University School of Biological Science and Medical Engineering Beijing100191 China

Fluorescence molecular tomography (FMT) is a sensitive optical imaging technique that can achieve three-dimensional (3D) tomographic images at the molecular and cellular levels. However, reconstructing the internal 3D distribution of fluorescent targets from surface two-dimensional (2D) fluorescence projection data remains a challenging task. In recent years, deep learning-based FMT reconstruction has received considerable attention, demonstrating superior performance compared to conventional methods, particularly combined with Transformers. Unlike convolutional architectures that emphasize local context, Transformers leverage self-attention mechanisms to excel at capturing long-range dependencies, thereby enhancing FMT reconstruction accuracy. Nevertheless, the quadratic computational complexity of self-attention poses a bottleneck, particularly pertinent in 3D FMT reconstructions. This paper aims to propose a novel Transformer-based FMT reconstruction algorithm that not only delivers high-quality reconstruction accuracy but also maintains excellent performance in efficiency and inference speed. The key design involves introducing a novel Spatial-Channel Paired Attention Module (SC-PAM), which employs a pair of interdependent branches based on spatial and channel attention, thus effectively learn discriminative features in both spatial and channel domains, meanwhile exhibiting linear complexity relative to the input projection size. Furthermore, to facilitate data transmission between the spatial and channel branches, we share the weights of the query and key mapping functions, which provides a complementary paired attention without elevating complexity. Extensive evaluations through numerical simulations and in vivo experiments were performed to validate effectiveness of the proposed model. The results show that our PAH2T-Former method achieves the highest Dice while reducing model parameters and complexity. © 2015 IEEE.

关键词： Image reconstruction

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A Comprehensive Review on Deep Learning System Testing 24th

A Comprehensive Review on Deep Learning System Testing

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Ying Shan, Chun Liu, Zhen Liao, Shuyan Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9789819615476

Deep learning(DL) systems exhibit multiple behavioral characteristics such as correctness, robustness, and fairness. Ensuring that these behavioral characteristics function properly is crucial for maintaining the accuracy of DL systems’ outputs. As a specialized form of software, DL systems’ security testing techniques have increasingly become a focus of research in quality assurance. We analyze and organize the testing techniques for DL systems based on an investigation of the current state of the art both domestically and internationally. This paper categorizes existing approaches as component-oriented and attribute-oriented methods, providing a detailed review based on this classification. Additionally, we forecast the future development of testing techniques for DL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

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IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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BiEfficient: Bidirectionally Prompting Vision-Language Models for Parameter-Efficient Video Recognition 17th

BiEfficient: Bidirectionally Prompting Vision-Language Model...

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17th Asian Conference on computer Vision, ACCV 2024

作者： He, Haichen Liu, Weibin Xing, Weiwei School of Computer Science and Technology Beijing Jiaotong University Beijing China School of Software Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819609079

Vision-language models (VLMs) pre-trained on large-scale image-text pairs have shown great success in various image tasks. However, how to efficiently transfer such powerful VLMs into video domain is still an open problem. Given that full finetuning VLMs for video tasks could be computationally expensive, recent studies turn their focus on parameter-efficient finetuning (PEFT). The great potential of VLMs lies in leveraging the bidirectional semantic connections between the two modalities of vision and language. Nevertheless, most current PEFT methods use the vision-only framework and usually ignore the semantic connections between vision and language. In this paper, we propose a novel method called BiEfficient, which use bidirectional prompting schemes to efficiently transfer the VLM to video recognition task with a small number of tunable parameters: 1) Vision-to-Language: we propose two prompt mechanisms, Pre-Prompt and Post-Prompt, which act before and after the text encoder respectively to generate discriminative video-level text representation for each input video. 2) Language-to-Vision: we propose Word-Guided Visual-Prompt, which enhances the temporal modeling of videos using textual knowledge in an almost parameter-free manner. Experiments on Kinetics-400, UCF-101, HMDB-51 demonstrate that the proposed method can achieve comparable or even better performance to the full finetuning methods with much fewer tunable parameters across closed-set and zero-shot video recognition benchmarks. Code is available here: https://***/WbLiuBJTULab/BiEfficient. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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HEAT: Efficient Vision Transformer Accelerator With Hybrid-Precision Quantization

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IEEE Transactions on Circuits and Systems II: Express Briefs 2025年第5期72卷 758-762页

作者： Zhao, Pan Xue, Donghui Wu, Licheng Chang, Liang Tan, Haining Han, Yinhe Zhou, Jun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Quantization is an important technique for the acceleration of transformer-based neural networks. Prior related works mainly consider quantization from the algorithm level. Their hardware implementation is inefficient. In this brief, we propose an efficient vision transformer accelerator with retraining-free and finetuning-free hybrid-precision quantization. At the algorithm level, the features and weights are divided into two parts: normal values and outlier values. These two parts are quantized with different bit widths and scaling factors. We use matrix transformation and group-wise quantization policy to improve hardware utilization. At the hardware level, we propose a two-stage FIFO group structure and a hierarchical interleaving data flow to further improve the utilization of the PE array. As a result, the input and weight matrices are quantized to 5.71 bits on average with 0.526 % accuracy loss on Swin-T. The accelerator achieves a frame rate of 118.9 FPS and an energy efficiency of 43.58 GOPS/W on the ZCU102 FPGA board, better than state-of-the-art works. © 2004-2012 IEEE.

关键词： Particle accelerators

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Enhancing Federated Learning Performance on Heterogeneous IoT Devices Using Generative Artificial Intelligence With Resource Scheduling

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IEEE Internet of Things Journal 2025年第10期12卷 13286-13296页

作者： Meng, Zezhao Li, Zhi Hou, Xiangwang Xu, Minrui Xia, Yi Zhang, Zekai Song, Shaoyang Xidian University School of Electro-Mechanical Engineering Xi’an710071 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University School of Computer Science and Engineering Jurong West Singapore National University of Defense Technology College of Intelligence Science and Technology Changsha410073 China Tsinghua University School of Electronic Information Engineering Shenzhen518071 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China

The integration of federated learning (FL) with the Internet of Things (IoT) represents an advanced technological trend, combining the extensive connectivity of IoT with the powerful processing capabilities of FL to drive innovation and optimization across multiple domains. Given the heterogeneity of IoT devices and the variability in data distribution, developing strategies to enhance FL performance without overly burdening resource-constrained devices is crucial. This article proposes an FL algorithm based on generative artificial intelligence (GAI) for IoT devices with extreme heterogeneity in data and resources. The algorithm utilizes pretrained GAI models to generate new data, aligning the data distributions of individual IoT devices closer to independent and identically distributed (i.i.d.), thereby effectively reducing the heterogeneity of local data. Additionally, the proposed algorithm incorporates data synthesis and resource scheduling strategies to mitigate the heterogeneity of local device resources. Finally, we formulate a joint optimization problem aimed at minimizing total energy consumption while maximizing FL performance. Experimental results demonstrate that, under significant resource and data distribution disparities, most existing solutions struggle to converge, whereas the proposed method converges and achieves superior performance. Compared to existing GAI-based approaches, our method significantly reduces latency and energy consumption. © 2014 IEEE.

关键词： Federated learning

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Oxygen Plasma Treatment-Improved Source/Drain Contact Property for Achieving High-Performance ZnO Transistors

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 757-760页

作者： Yi, Tingchen Sun, Chuanlin Dong, Junchen Han, Dedong Bao, Lin Wang, Zongwei Cai, Yimao Zhang, Xing Peking University School of Integrated Circuits Beijing100871 China Beijing Advanced Innovation Center for Integrated Circuits Beijing100871 China Beijing Superstring Academy of Memory Technology Beijing100176 China Beijing Information Science and Technology University School of Information and Communication Engineering Beijing100101 China

Oxide transistors are of increasing interest inthe field of integrated circuits. In this work, we explore theeffects of oxygen plasma treatment on source/drain (S/D)region contact property of the ZnO transistors. Throughfilm characterization, we found that oxygen plasma treat-ment process not only helps to improve surface mor-phology of the S/D region but also increases the oxygenvacancies in the ZnO active layer of the device. This sug-gests the promotion of bonding between the ZnO activelayer and the Al S/D electrode, thus reducing the contactbarrier and contact resistance. With an oxygen plasmatreatment time of 2 min, a distinct decrease of 96.50%in the S/D resistance (Rc) is achieved, and electrical per-formance of the ZnO transistors obtains comprehensiveimprovement. The major properties of the optimized ZnOtransistors include a field-effect mobility of 39.56 cm2/Vs,an on-to-off current ratio of 2.89 × 107, a sub-thresholdswing of 103.01 mV/decade, and a threshold voltage of-0.50 V. Our work provides an effective strategy to fabricatehigh-performance ZnO transistors. © 1980-2012 IEEE.

关键词： Zinc oxide

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