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arXiv 2022年

作者： Zhao, Qile Zhao, Xianhong Ma, Xu Chen, Xudong Arce, Gonzalo R. The School of Optics and Photonics Key Laboratory of Photoelectronic Imaging Technology and System of Ministry of Education of China Beijing Institute of Technology Beijing100081 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) from one or several two-dimensional projection measurements. However, fewer projection measurements or more spectral channels leads to a severly ill-posed problem, in which case regularization methods have to be applied. In order to significantly improve the accuracy of reconstruction, this paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors (Fama-SDIP) of natural images. By integrating deep image prior (DIP) into the principle of compressive sensing (CS) reconstruction, the proposed algorithm can achieve state-of-the-art results without any training dataset. Extensive experiments show that Fama-SDIP method significantly outperforms prevailing leading methods on simulation and real HSI datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Hyperspectral imaging

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Fea Study on the Tsv Copper Filling Influenced by the Additives and Electroplating Process

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SSRN 2023年

作者： Zhao, Zifeng Liu, Ziyu Chen, Lin Sun, Qingqing Liu, Hongye Sun, Yabin School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China Jiashan Fudan Institute Zhejiang Province Jiaxing314100 China Department of Electrical Engineering East China Normal University Shanghai200241 China

Abstract Through-Silicon Via (TSV) is a key technology of three-dimensional (3D) integration. However, it is easy to have voids in the Cu filling process of TSV. Additives and optimized filling process are used to solve this problem. However, the comprehensive analysis of these affecting parameter needs many experiments and cost much time. Thus, the finite element analysis (FEA) for the mass transferring of electroplating is carried out by the COMSOL software in the work. The filling processes with different inhibitor concentrations are first studied and the most suitable density of 64mol/m2 is obtained. However, high inhibitor concentration will lead to much lower plating speed. Thus, the effect of filling process such as current density and plating bath flow velocity on the voids are investigated under the optimized concentrations above. The best filling quality is obtained at the current density of 1.4 ASD and the bath stirring velocity of 0.4 m/s. The underlying growth mechanism of these influencing factors are also analyzed and revealed. © 2023, The Authors. All rights reserved.

关键词： Copper

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Towards Energy-Efficiency: Integrating MATD3 Reinforcement Learning Method for Computational Offloading in RIS-Aided UAV-MEC Environments

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IEEE Internet of Things Journal 2025年

作者： Wu, Liangshun Zhang, Cong Zhang, Bin Du, Jianbo Qu, Junsuo Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai201804 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai201210 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China Xi'an Key Laboratory of Advanced Control and Intelligent Process School of Automation Xi'an University of Posts & Telecommunications Xi'an710061 China Xinjiang University of Political Science and Law Tumushuke832003 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430079 China Department of Computer City University of Hong Kong 999077 Hong Kong School of Communications and Information Engineering Xi'an University of Posts & Telecommunications Xi'an710061 China

With the proliferation of IoT devices, there is an escalating demand for enhanced computing and communication capabilities. Mobile Edge Computing (MEC) addresses this need by relocating computing resources to the network edge, thereby delivering swifter and more efficient services. This paper introduces a computation offloading and energy consumption optimization framework that leverages Reconfigurable Intelligent Surfaces (RIS), Unmanned Aerial Vehicles (UAVs), and MEC. The scheme aims to maximize energy efficiency through the optimization of task allocation, RIS phase shifts, and UAV trajectories. By employing the Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) reinforcement learning algorithm, the paper further refines UAV trajectories and RIS configurations. The simulation results indicate that the proposed method surpasses the traditional Concave-Convex Procedure (CCCP) algorithm in both UAV trajectory control and RIS configuration, demonstrating quicker convergence and enhanced stability. The method proves to be adaptable to diverse environments and tasks, showcasing notable benefits in RIS-assisted interference suppression, particularly with large RIS, thereby enhancing UAV data reception rates. Additionally, MATD3 exhibits faster and smoother convergence for extended task durations and smaller RIS scenarios. Simulation results reveal that UAVs tend to move closer to RIS, with energy efficiency falling as IoT tasks increase, affirming the proposed algorithm's high energy efficiency and effectiveness. © 2025 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Regularized Hypothesis-Induced Wasserstein Divergence for Unsupervised Domain Adaptation

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SSRN 2023年

作者： Si, Lingyu Dong, Hongwei Qiang, Wenwen Zheng, Changwen Yu, Junzhi Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Engineering Peking University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Unsupervised domain adaptation (UDA) aims to align the data distributions of source and target domains to learn domain-invariant feature representations. The performance of such representations heavily relies on the measurement of the discrepancy between the distributions. Recent UDA methods utilize the hypothesis-based measurement, also known as classifier-induced divergence, to quantify the difference between the two domain distributions, for instance, H∆H-divergence. Nevertheless, the classifier-induced divergences face several challenges, including the fake covariate alignment problem and the inconsistency problem, rendering them inadequate for discerning the dissimilarity between different categories of output by the classifier. This paper proposes a novel method called Regularized Hypothesis-Induced Wasserstein Divergence-Based Representation Learning for Unsupervised Domain Adaptation (RHWD) to address these issues. Specifically, RHWD introduces a new hypothesis-induced Wasserstein divergence (HWD) to effectively quantify the difference between the two distributions, which aims to overcome the fake covariate alignment problem. Furthermore, RHWD includes a consistency regularization term to mitigate the inconsistency problem. We train RHWD adversarially in three iterative steps. Additionally, we provide empirical evaluations on standard benchmarks, which demonstrate the effectiveness of our proposed © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical Image Segmentation

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Xin Xu Wenjing Lu Jiahao Lei Peng Qiu Hong-Bin Shen Yang Yang Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China Department of Vascular Surgery Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual labeling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise Label Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods.

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection 39

Look Inside for More: Internal Spatial Modality Perception f...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

ISBN: (纸本)157735897X

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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FIRE: a dataset for feedback integration and refinement evaluation of multimodal models 24

FIRE: a dataset for feedback integration and refinement eval...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Pengxiang Li Zhi Gao Bofei Zhang Tao Yuan Yuwei Wu Mehrtash Harandi Yunde Jia Song-Chun Zhu Qing Li Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology and State Key Laboratory of General Artificial Intelligence BIGAI State Key Laboratory of General Artificial Intelligence BIGAI and State Key Laboratory of General Artificial Intelligence Peking University State Key Laboratory of General Artificial Intelligence BIGAI Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology and Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University Department of Electrical and Computer System Engineering Monash University Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University and Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology State Key Laboratory of General Artificial Intelligence BIGAI and State Key Laboratory of General Artificial Intelligence Peking University and Department of Automation Tsinghua University

ISBN: (纸本)9798331314385

Vision language models (VLMs) have achieved impressive progress in diverse applications, becoming a prevalent research direction. In this paper, we build FIRE, a feedback-refinement dataset, consisting of 1.1M multi-turn conversations that are derived from 27 source datasets, empowering VLMs to spontaneously refine their responses based on user feedback across diverse tasks. To scale up the data collection, FIRE is collected in two components: FIRE-100K and FIRE-1M, where FIRE-100K is generated by GPT-4V, and FIRE-1M is freely generated via models trained on FIRE-100K. Then, we build FIRE-Bench, a benchmark to comprehensively evaluate the feedback-refining capability of VLMs, which contains 11K feedback-refinement conversations as the test data, two evaluation settings, and a model to provide feedback for VLMs. We develop the FIRE-LLaVA model by fine-tuning LLaVA on FIRE-100K and FIRE-1M, which shows remarkable feedback-refining capability on FIRE-Bench and outperforms untrained VLMs by 50%, making more efficient user-agent interactions and underscoring the significance of the FIRE dataset.

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Mass flow rates effect on the performance of PV/T bi-fluid hybrid collector (single and simultaneous modes) 2nd

Mass flow rates effect on the performance of PV/T bi-fluid h...

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2nd International Conference on Electronic engineering and Renewable Energy, ICEERE 2020

作者： El Manssouri, Oussama El Fouas, Chaimae Hajji, Bekkay Rabhi, Abdelhamid Tina, Giuseppe Marco Gagliano, Antonio Renewable Energy Embedded System and Data Processing Laboratory National School of Applied Sciences Mohamed First University Oujda Morocco Modelization Information and Systems Laboratory Picardie Jules Verne University Amiens France DIEEI: Department of Electric Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (纸本)9789811562587

A hybrid photovoltaic/thermal collector (PV/T) is used to produce simultaneously electrical and thermal energy from absorbed solar irradiation. The research to date has tended to focus on either bi-fluids (water and air) as the working fluid to supply energy needs for different applications. The purpose of this work is to test the performances of PV/T at different operating modes of fluid e.g. the air mode, the water mode, and the simultaneous mode (water& air). Furthermore the effect of mass flow rate as a key parameter for better electrical and thermal performances has been investigated. The PV/T performances were assessed based on a dynamic numerical model. An energy balance equations have been established for each layer, then implemented in MATLAB software. The results show that thermal efficiency in the simultaneous mode (air & water) is better compared to others modes. The thermal efficiencies for independently fluid condition have ranged from approximately 20 to 48%, and increased to a maximum efficiency of near to 68% for the case of the simultaneously fluids. This result indicates that the optimum mass flow rates for air and water are 0.035 kg/s and 0.007 kg/s respectively. Therefore, the theoretical model developed of the independently and simultaneously operational modes is validated, evidencing a good fit between simulation results and the experimental data available in literature experimental. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Air

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New Approach for Efficient Malicious Multiparty Private Set Intersection

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SSRN 2023年

作者： Lv, Siyi Wei, Yu Jia, Jingyu Li, Xinhao Liu, Zheli Chen, Xiaofeng Guo, Liang College of Cyber Science The College of Computer Science Key Laboratory of Data and Intelligent System Security Ministry of Education Nankai University Tianjin China Department of Computer Science Purdue University United States School of Cyber Engineering Xidian University Xi’an China Huawei Technology Co. Ltd Shenzhen China

The Multiparty Private Set Intersection (MPSI) protocol allows untrusted parties to compute the intersection of their sets without revealing any additional information. However, existing solutions face challenges in terms of security and efficiency. Some schemes are secure only against semi-honest adversaries and cannot effectively defend against malicious adversaries. Other schemes, while capable of withstanding malicious adversaries, exhibit efficiency problems, especially as the number of corrupt participants increases. A practical solution must maintain high security and efficiency, even when handling large-scale data and a majority of corrupted *** paper introduces a novel scheme named TH-PSI, built upon a new star-shaped topological network communication framework based on the trusted execution environment. TH-PSI has been rigorously proven to be secure within the standard UC framework against malicious adversaries. To demonstrate the efficiency of TH-PSI, we implement the TH-PSI scheme and tested it in both LAN and WAN settings, involving up to 30 participants and datasets as large as $2^{22}$. We compare TH-PSI with state-of-the-art schemes, ENOC21 and NTY21. Our experiments show that TH-PSI is an efficient solution capable of delivering results within a matter of seconds, even when dealing with data at the million-level and a majority of corrupted participants. © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Integrated Parallel Chaos driven LiDAR system with High Interference Tolerance

Integrated Parallel Chaos driven LiDAR System with High Inte...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Ruixuan Chen Bitao Shen Haowen Shu Lin Chang Weiqiang Xie Wenchao Liao Zihan Tao John Bowers Xingjun Wang State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara CA USA Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

We introduce a chaotic microcomb based high interference-tolerance parallel LiDAR. Parallel chaos with record-high noise spectra beyond 7 GHz leads to sub-cm-level ranging accuracy. The intrinsic orthogonality permits... 详细信息

ISBN: (纸本)9781957171258

关键词： Chaos Image resolution Lidar Range finding Ring resonators Three dimensional imaging

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