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16th International Conference on Signal Processing Systems, ICSPS 2024

作者： Lyu, Wenqi Ke, Wei Sheng, Hao Ma, Xiao Liu, Lixue Faculty of Applied Sciences Macao Polytechnic University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781510689251

Light field cameras hold significant value in applications such as depth estimation, 3D video acquisition, and image super-resolution. Compared to traditional single-image super-resolution methods, light field images can capture object information from multiple spatial and angular perspectives, which is more conducive to capturing detail information. However, current advanced light field super-resolution models still exhibit issues like blurriness, jaggedness, and dimness in 4x light field super-resolution. To address this, we propose a novel light field super-resolution framework that utilizes the Retinex decomposition concept to solve common problems such as ghosting and overlap, thereby improving image quality. By decomposing the light field image into illumination and reflection components and processing them separately, our model significantly enhances detail preservation and color consistency. Experimental results on multiple datasets demonstrate that our model exhibits outstanding performance, both subjectively and objectively, highlighting the effectiveness of this framework and its broad application potential in the field of computational imaging. © 2025 SPIE.

关键词： Image acquisition

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Amend to Alignment: Decoupled Prompt Tuning for Mitigating Spurious Correlation in Vision-Language Models 41

Amend to Alignment: Decoupled Prompt Tuning for Mitigating S...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Jie Ma, Xiaosong Guo, Song Li, Peng Xu, Wenchao Tang, Xueyang Hong, Zicong Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Cyber Science and Engineering Xi'an Jiaotong University Shaanxi China

Fine-tuning the learnable prompt for a pre-trained vision-language model (VLM), such as CLIP, has demonstrated exceptional efficiency in adapting to a broad range of downstream *** prompt tuning methods for VLMs do not distinguish spurious features introduced by biased training data from invariant features, and employ a uniform alignment process when adapting to unseen target *** can impair the cross-modal feature alignment when the test data significantly deviate from the distribution of the training data, resulting in a poor out-of-distribution (OOD) generalization *** this paper, we reveal that the prompt tuning failure in such OOD scenarios can be attribute to the undesired alignment between the textual and the spurious *** a solution, we propose CoOPood, a fine-grained prompt tuning method that can discern the causal features and deliberately align the text modality with the invariant ***, we design two independent contrastive phases using two lightweight projection layers during the alignment, each with different objectives: 1) pulling the text embedding closer to the invariant image embedding and 2) pushing the text embedding away from the spurious image *** have illustrated that CoOPood can serve as a general framework for VLMs and can be seamlessly integrated with existing prompt tuning *** experiments on various OOD datasets demonstrate the performance superiority over state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Visual languages

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Enhancing Adversarial Example Transferability via Regularized Constrained Feature Layer

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computers, Materials & Continua 2025年第4期83卷 157-175页

作者： Xiaoyin Yi Long Chen Jiacheng Huang Ning Yu Qian Huang School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Chongqing Key Laboratory of Public Big Data Security Technology Chongqing401420China School of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of EducationChongqing400065China Artificial Intelligence and Big Data College Chongqing Polytechnic University of Electronic TechnologyChongqing401331China

Transfer-based Adversarial Attacks(TAAs)can deceive a victim model even without prior *** is achieved by leveraging the property of adversarial *** is,when generated from a surrogate model,they retain their features if applied to other models due to their good ***,adversarial examples often exhibit overfitting,as they are tailored to exploit the particular architecture and feature representation of source ***,when attempting black-box transfer attacks on different target models,their effectiveness is *** solve this problem,this study proposes an approach based on a Regularized Constrained Feature Layer(RCFL).The proposed method first uses regularization constraints to attenuate the initial examples of low-frequency *** are then added to a pre-specified layer of the source model using the back-propagation technique,in order to modify the original adversarial ***,a regularized loss function is used to enhance the black-box transferability between different target *** proposed method is finally tested on the ImageNet,CIFAR-100,and Stanford Car datasets with various target models,The obtained results demonstrate that it achieves a significantly higher transfer-based adversarial attack success rate compared with baseline techniques.

关键词： Adversarial examples black-box transferability regularized constrained transfer-based adversarial attacks

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Correlation Fusion with Instance-level Auto-weighted for Multi-view Graph Clustering 25

Correlation Fusion with Instance-level Auto-weighted for Mul...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Xuan Chen, Zhikui Yao, Liang Ma, Shubin Li, Peng Yu, Shuo School of Software Dalian University of Technology Da'lian China School of Electrical Engineering Anhui Polytechnic University Wu'hu China School of Computer Science and Technology Dalian University of Technology Da'lian China

ISBN: (纸本)9798350330014

Multi-view graph clustering (MGC) has emerged as a hot research topic due to its effectiveness. Previous works mainly focus on the fusion of similarity graphs to exploit the complementary and consistent. However, these approaches suffer from three shortcomings: 1) The distance similarity estimate by the linear Euclidean metric may be incorrect in revealing the association relationship of heterogeneous multi-view samples. 2) Most of them often weight the whole view to balance the effect of different views, which ignores the importance of local samples. 3) They fail to preserve the inherent manifold structure between views via directly applying similarity matrix fusion to obtain a consistent graph. To fill these gaps, this paper proposed a novel framework called correlation fusion with instance-level auto-weighted for MGC (CFIMGC). In particular, the Spearman distance is leveraged to effectively characterize the versatile correlation matrix. The sample-level auto-weighted fusion mechanism is presented for flexible assigning weight values to different sample-wise relations in intra-view and inter-view, through which the complementary of numerous correlation matrices is appropri-ately explored to produce a consistent correlation matrix. What's more, a probability graph is adaptively learned based on the fusion correlation matrix for constructing a clustering-friendly similarity graph. Meanwhile, an alternating iteration algorithm is devised to address the non-convex joint optimization problem. Comprehensive experiment results on seven widely used but challenging multi-view datasets demonstrate that the proposed method yields superiority in clustering performance against six state-of-the-art counterparts. © 2023 IEEE.

关键词： Iterative methods

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Machine Learning-Driven Control and Optimization of Dual Band-Notched UWB Circular Monopole Antenna 12

Machine Learning-Driven Control and Optimization of Dual Ban...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Nikpoosh, Alireza Kapur, Aryaman Barón, Alejandra Palomo Soni, Gaurav Kumar Yadav, Dinesh School of Mathematics & Computer Science Iran University of Science and Technology Tehran Iran Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Polytechnic University of Madrid-ETSIAE Madrid Spain Department of ECE Manipal University Jaipur Rajasthan Jaipur India

ISBN: (纸本)9798350387315

As communication technologies continue to advance rapidly, there is a growing need for antennas that provide high performance while minimizing interference from other systems. This paper explores optimizing and controlling the performance of a dual band-notched Ultra-Wideband (UWB) circular monopole antenna with parasitic resonators using machine learning techniques. Building upon a previously proposed antenna design, we first employ various machine learning models—such as Gaussian Process Regression, Support Vector Regression, and Gradient-Boosted Trees—to precisely control band notches within specific frequency ranges, thereby preventing interference with any narrow-band systems in close vicinity. Following this, we leverage a new method for evaluating the performance of UWB antennas with band-notched characteristics and use similar models to optimize the antenna’s physical properties and enhance radiation characteristics. Validated through simulations in CST Studio Suite, the optimized antenna demonstrates significantly improved return loss, and our approach allows for accurate prediction of the physical properties required to achieve desired band notches within the 4–8 GHz and 6–9 GHz ranges, corresponding to each parasitic resonator. © 2024 IEEE.

关键词： Monopole antennas

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Integrating a novel irrigation approximation method with a process-based remote sensing model to estimate multi-years'winter wheat yield over the North China Plain

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Journal of Integrative Agriculture 2023年第9期22卷 2865-2881页

作者： ZHANG Sha YANG Shan-shan WANG Jing-wen WU Xi-fang Malak HENCHIRI Tehseen JAVED ZHANG Jia-hua BAI Yun Research Center for Space Information and Big Earth Data College of Computer Science and TechnologyQingdao UniversityQingdao 266071P.R.China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094P.R.China School of Surveying and Land Information Engineering Henan Polytechnic UniversityJiaozuo 454000P.R.China Department of Environmental Sciences Kohat University of Science and TechnologyKohat 26000Pakistan

Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food ***,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited ***,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing *** approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and ***,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the *** IPPs were determined using statistical yield data of reference years(2010–2015)over the *** findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),*** data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–*** addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city ***,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.

关键词： approximating irrigations process-based model remote sensing winter wheat yield North China Plain

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Spatiotemporal emotion recognition based on 3D time-frequency domain feature matrix

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The Journal of China Universities of Posts and Telecommunications 2022年第5期29卷 62-72页

作者： Chao Hao Lian Weifang Liu Yongli School of Computer Science and Technology Henan University of TechnologyJiaozuo 454000China China Mobile Communications Group Shanxi Co. Ltd.Xinzhou BranchXinzhou 034000China

The research of emotion recognition based on electroencephalogram(EEG)signals often ignores the related information between the brain electrode channels and the contextual emotional information existing in EEG signals,which may contain important characteristics related to emotional *** at the above defects,a spatiotemporal emotion recognition method based on a 3-dimensional(3 D)time-frequency domain feature matrix was ***,the extracted time-frequency domain EEG features are first expressed as a 3 D matrix format according to the actual position of the cerebral ***,the input 3 D matrix is processed successively by multivariate convolutional neural network(MVCNN)and long short-term memory(LSTM)to classify the emotional *** emotion recognition method is evaluated on the DEAP data set,and achieved accuracy of 87.58%and 88.50%on arousal and valence dimensions respectively in binary classification tasks,as well as obtained accuracy of 84.58%in four class classification *** experimental results show that 3 D matrix representation can represent emotional information more reasonably than two-dimensional(2 D).In addition,MVCNN and LSTM can utilize the spatial information of the electrode channels and the temporal context information of the EEG signal respectively.

关键词： spatiotemporal emotion recognition model 3-dimensinal(3D)feature matrix time-frequency features multivariate convolutional neural network(MVCNN) long short-term memory(LSTM)

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Cross-Modality Disentangled Information Bottleneck Strategy for Multimodal Sentiment Analysis

Cross-Modality Disentangled Information Bottleneck Strategy ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Deng, Zhengnan Huang, Guoheng Zhong, Guo Yuan, Xiaochen Huang, Lian Pun, Chi-Man School of Computer Science and Technology Guangdong University of Technology Guangzhou510006 China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou510420 China Macao Polytechnic University Faculty of Applied Sciences 999078 China Guangdong Mechanical and Electrical College Department of Applied Electronics Guangzhou510515 China University of Macau Department of Computer and Information Science China

ISBN: (纸本)9781665410205

Multimodal Sentiment Analysis (MSA) has been a pivotal domain in current research area which utilizes diverse information carriers such as videos containing multiple modal-ities to understand the user's sentiment. With the success of multimodal fusion techniques, lots of fusion strategies have been proposed to obtain a favorable multimodal joint representation for MSA. However, existing studies hardly consider the problem of redundant information in unimodal, resulting in the joint representation may contain much redundant information from different modalities, thus limiting the accuracy of sentiment prediction. In this work, we propose a Cross-Modality Disentangled Information Bottleneck Strategy (CMDIBS), which consists of a Cross-Modality Knowledge Awareness (CMKA) module and a Multimodal Disentangled Information Bottleneck (MDIB) mechanism. Specifically, the CMKA module encourages in-teractions among different modalities to learn the sentiment embedding relevant to the predicted goals. In particular, MDIB mechanism aims to maximize the mutual information (MI) between the multimodal joint representation and the predicted label, and maximize the MI between the style embedding with the label and the input data while constraining the MI between the multimodal joint representation and the style embedding to obtain a succinct and efficient multimodal joint representation. Experimental results on the benchmark datasets, namely CMU-MOSI and CMU-MOSEI, indicated that the proposed method surpasses existing approaches and attains SOTA performance. © 2024 IEEE.

关键词： Benchmarking

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A Novel Circuit and Layout Design of SEU Tolerant SRAM in a 65nm CMOS Process 18

A Novel Circuit and Layout Design of SEU Tolerant SRAM in a ...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Lai, Xiaoling Guo, Yangming Zhang, Jian Ju, Ting Zhu, Qi Zhou, Guochang Northwestern Polytechnic University School of Computer Science Xi'an China Xi'an Branch of China Academy of Space Technology Xi'an China

ISBN: (纸本)9798350312201

In order to address the vulnerability of embedded SRAMs to single event soft errors due to high-energy particles in a space radiation environment, this paper proposes a novel circuit d layout design of SEU tolerant SRAM based on a DICE structure. A DICE SRAM design with a depth of 4096 and a bit width of 12 is completed based on a commercial 65nm bulk silicon CMOS process, implemented using 3 layers of metal, with an area only 1.6 times that of a non-DICE SRAM. Circuit and irradiation simulation results illustrate that the SRAM has an access time of less than 2.2ns and a single event upset cross section of 1.74×10-13 cm2/bit at an equivalent LET value of 55.3 MeV/mg/cm2 of particle incidence. The SRAM circuit's performance and resistance to single-particle soft errors can significantly reduce the area, wiring resources and timing overhead associated with hardening the embedded SRAM circuit in radiation hardened ASIC designs. © 2023 IEEE.

关键词： Heavy ions

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Fault detection method of power cable terminal based on infrared thermal imaging signal reconstruction 2

Fault detection method of power cable terminal based on infr...

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2023 2nd International Conference on Energy and Electrical Power Systems, ICEEPS 2023

作者： Li, Yan Gao, Yihang School of Transportation Information Jilin Communications Polytechnic Jilin Changchun130015 China College of Computer Science and Technology Heilongjiang University Heilongjiang Harbin150006 China

Considering the low efficiency of traditional fault detection methods, this paper proposes a power cable terminal fault detection method based on infrared thermal imaging signal reconstruction. The method includes the following steps: first, establish the thermal resistance network structure of the power cable terminal to obtain the structural relationship between terminal components;second, collect operational data of the power cable terminal and perform calculations;finally, use an infrared imager to capture the infrared radiation of the power cable terminal and obtain fault detection results based on the calculation results. Experiments show that the detected results of this method are completely consistent with the actual situation, with an accuracy rate of up to 99%, and can achieve high-precision detection of power cable terminal faults. © Published under licence by IOP Publishing Ltd.

关键词： Fault detection

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