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iLIAC: An approach of identifying dissimilar groups on unstructured numerical image dataset using improved agglomerative clustering technique

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Multimedia Tools and Applications 2024年第39期83卷 86359-86381页

作者： S, Sreedhar Kumar Ahmed, Syed Thouheed Fathima, Afifa Salsabil Mathivanan, Sandeep Kumar Jayagopal, Prabhu Saif, Abdu Gupta, Sachin Kumar Sinha, Garima Department of Computer Science and Engineering School of Engineering and Technology CMR University Karnataka Bengaluru India Department of Electrical Engineering Indian Institute of Technology Hyderabad Telangana Kandi India School of Computer Science and Engineering REVA University Bangalore India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Communication and Computer Engineering Faculty of Engineering and IT Taiz University Taiz Yemen Katra India Department of Computer Science Engineering Jain University Bangalore India

Unstructured Numerical Image Dataset Separation (UNIDS) method employing an enhanced unsupervised clustering technique. The objective is to delineate an optimal number of distinct groups within the input grayscale (G-S) image content, utilizing the improved limited iteration agglomerative clustering (iLIAC) clustering technique for the separation and enhancement of picture elements. The UNIDS method is structured into two primary stages: partitioning and validation. In the partitioning stage, the UNIDS method identifies an appropriate number of discrete clusters within the grayscale image using the iLIAC technique, eliminating the need for predetermined procedures. Subsequently, the method evaluates the similarity and deviation among data elements within the same group in the resultant image dataset. Additionally, it assesses the proximity and inters severance among clusters in the outcome of the image dataset through the partitioning process. Empirical results indicate that the UNIDS system excels in the spontaneous identification of an optimal number of discrete clusters within the input G-S image. The system demonstrates superior thickness, reduced deviation among data elements within the same cluster, increased inter-separation, and diminished inter-closeness between cluster elements. Furthermore, empirical analysis establishes the superior performance of the UNIDS approach compared to existing clustering techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Iterative methods

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FDCPNet:feature discrimination and context propagation network for 3D shape representation

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虚拟现实与智能硬件(中英文) 2025年第1期7卷 83-94页

作者： Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.

关键词： 3D shape representation Mesh model MeshNet Feature discrimination Context propagation

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Behaviour recognition based on the integration of multigranular motion features in the Internet of Things

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Digital communications and Networks 2024年第3期10卷 666-675页

作者： Lizong Zhang Yiming Wang Ke Yan Yi Su Nawaf Alharbe Shuxin Feng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China Beijing Institute of Remote Sensing Equipment Beijing100039China Applied College Taibah UniversityMedina42353Saudi Arabia

With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices,crowdsensing systems in the Internet of Things(IoT)are now conducting complicated video analysis tasks such as behaviour *** applications have dramatically increased the diversity of IoT ***,behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal *** recognition may even rely more on the modeling of temporal information containing short-range and long-range motions,in contrast to computer vision tasks involving images that focus on understanding spatial ***,current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in *** this paper,we propose a novel behaviour recognition method based on the integration of multigranular(IMG)motion features,which can provide support for deploying video analysis in multimedia IoT crowdsensing *** particular,we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module(CSEM)and a cascaded long-term motion feature integration module(CLIM).We evaluate our model on several action recognition benchmarks,such as HMDB51,Something-Something and *** experimental results demonstrate that our approach outperforms the previous state-of-the-art methods,which confirms its effective-ness and efficiency.

关键词： Behaviour recognition Motion features Attention mechanism Internet of things Crowdsensing

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A Disentangled Representation-Based Multimodal Fusion Framework Integrating Pathomics and Radiomics for KRAS Mutation Detection in Colorectal Cancer

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Big Data Mining and Analytics 2024年第3期7卷 590-602页

作者： Zhilong Lv Rui Yan Yuexiao Lin Lin Gao Fa Zhang Ying Wang School of Computer Science and Technology Xidian UniversityXi’an 710071China School of Biomedical Engineering University of Science and Technology of ChinaHefei 230026China Department of General Surgery Beijing Chaoyang HospitalCapital Medical UniversityBeijing 100020China School of Medical Technology Beijing Institute of TechnologyBeijing 100081China Department of Pathology Beijing Chaoyang HospitalCapital Medical UniversityBeijing 100020China

Kirsten rat sarcoma viral oncogene homolog(namely KRAS)is a key biomarker for prognostic analysis and targeted therapy of colorectal ***,the advancement of machine learning,especially deep learning,has greatly promoted the development of KRAS mutation detection from tumor phenotype data,such as pathology slides or radiology ***,there are still two major problems in existing studies:inadequate single-modal feature learning and lack of multimodal phenotypic feature *** this paper,we propose a Disentangled Representation-based Multimodal Fusion framework integrating Pathomics and Radiomics(DRMF-PaRa)for KRAS mutation ***,the DRMF-PaRa model consists of three parts:(1)the pathomics learning module,which introduces a tissue-guided Transformer model to extract more comprehensive and targeted pathological features;(2)the radiomics learning module,which captures the generic hand-crafted radiomics features and the task-specific deep radiomics features;(3)the disentangled representation-based multimodal fusion module,which learns factorized subspaces for each modality and provides a holistic view of the two heterogeneous phenotypic *** proposed model is developed and evaluated on a multi modality dataset of 111 colorectal cancer patients with whole slide images and contrast-enhanced *** experimental results demonstrate the superiority of the proposed DRMF-PaRa model with an accuracy of 0.876 and an AUC of 0.865 for KRAS mutation detection.

关键词： KRAS mutation detection multimodal feature fusion pathomics radiomics

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DSCCNet for high-quality 4K computer-generated holograms

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Optics Express 2025年第6期33卷 13733-13747页

作者： Xu, Zhenqi Leng, Junmin Dai, Ping Wang, Chao Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100101 China Key Laboratory of the Ministry of Education for Optoelectronic Measurement Technology and Instrument Beijing Information Science and Technology University Beijing100101 China School of Information and Communications Engineering Beijing Information Science and Technology University Beijing100101 China

With the increasing demand for high-quality 3D holographic reconstruction, visual clarity and accuracy remain significant challenges in various imaging applications. Current methods struggle for higher image resolution and to resolve such issues as detail loss and checkerboard artifacts. To address these challenges, we propose the model Depthwise Separable Complex-valued Convolutional Network (DSCCNet) for phase-only computer-generated holography (CGH). This deep learning framework integrates complex-valued convolutions with depthwise separable convolutions to enhance reconstruction precision and improve model training efficiency. Additionally, the diffuser is employed to reduce checkerboard artifacts in defocused parts of 3D CGH. Experimental results demonstrate that DSCCNet can obtain 4K images reconstructed with more intricate details. The reconstruction quality of both 2D and 3D layered objects is enhanced. Validation on 100 images from the DIV2K dataset shows an average PSNR above 37 dB and an average SSIM above 0.95. The proposed model provides an effective solution for high-quality CGH applications. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： computer generated holography

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Learning DCT Subband using Kolmogorov-Arnold Network for Infrared Small Target Detection 2

Learning DCT Subband using Kolmogorov-Arnold Network for Inf...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Zhang, Zekai Zhao, Yingrui Zhou, Shichao Yang, Zihui Beijing Information Science and Technology University School of Information and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Deep learning-based infrared small target detection (IRSTD) methods typically exploit spatial domain cues to infer dim and weak infrared targets. However, relying solely on spatial domain information is sub-optimal due to the lack of structure and texture details of the target. Alternatively, we learn frequency priors to enhance targets in frequency domain, and propose a Frequency Domain Enhanced Network (FDENet). It contains a frequency domain discrete cosine transform enhancement (FDE) module with KAN-inspired DCT learning mechanism. Specifically, the FDE module uses multiple univariate nonlinear functions to combine continuous multivariate functions to directly map frequency-aware cues without spatial features. It then aligns the frequency-aware cues and with the spatial features to capture dimly weak targets. Generalization experiments and comparative studies on two real-world infrared single-frame image datasets fully demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： Frequency domain analysis

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Symmetrization of quasi-regular patterns with periodic tilting of regular polygons

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Computational Visual Media 2024年第3期10卷 559-576页

作者： Zhengzheng Yin Yao Jin Zhijian Fang Yun Zhang Huaxiong Zhang Jiu Zhou Lili He School of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing 312000China School of Media Engineering Communication University of ZhejiangHangzhou 310018China

computer-generated aesthetic patterns arewidely used as design materials in various fields. Themost common methods use fractals or dynamicalsystems as basic tools to create various patterns. Toenhance aesthetics and controllability, some researchershave introduced symmetric layouts along with thesetools. One popular strategy employs dynamical systemscompatible with symmetries that construct functionswith the desired symmetries. However, these aretypically confined to simple planar symmetries. Theother generates symmetrical patterns under theconstraints of tilings. Although it is slightly moreﬂexible, it is restricted to small ranges of tilingsand lacks textural variations. Thus, we proposed anew approach for generating aesthetic patterns bysymmetrizing quasi-regular patterns using general kuniformtilings. We adopted a unified strategy toconstruct invariant mappings for k-uniform tilings thatcan eliminate texture seams across the tiling ***, we constructed three types of symmetriesassociated with the patterns: dihedral, rotational, andreﬂection symmetries. The proposed method can beeasily implemented using GPU shaders and is highlyefficient and suitable for complicated tiling with regularpolygons. Experiments demonstrated the advantages of our method over state-of-the-art methods in terms ofﬂexibility in controlling the generation of patterns withvarious parameters as well as the diversity of texturesand styles.

关键词： quasi-regular patterns(QRP) k-uniform tilings invariant mappings symmetry aesthetic patterns

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Faster Few-Shot Object-Detection: Two-Stage Fine Tuning Integrated with External Memories 2

Faster Few-Shot Object-Detection: Two-Stage Fine Tuning Inte...

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2nd IEEE International Conference on Signal, Information and Data Processing, ICSIDP 2024

作者： Zhang, Yunpu Wang, Zhuowei Zhou, Shichao Zhao, Yingrui Beijing Information Science & Technology University School of Information and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

Traditional object detection requires a large amount of annotated data to ensure model accuracy and generalization, but in practical scenarios, labeled samples are often limited. Few-shot Object Detection (FSOD) addresses this issue by enabling models to quickly learn and infer with minimal labeled data, which is crucial for real-time applications. Although various FSOD approaches, including feature representation, meta-learning, Generative Adversarial Networks (GANs), and self-supervised learning, have been proposed, challenges like overfitting and poor generalization persist. This paper introduces a two-stage fine-tuning model incorporating an External Memory Module. Through the use of two small, learnable, shared memory units that extract visual word representations from limited images, the model enhances generalization and reduces computational complexity. The external attention mechanism, optimized through back-propagation, improves the model's effectiveness and efficiency. © 2024 IEEE.

关键词： Generative adversarial networks

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A Learning Sentiment Database for Machine Learning 3

A Learning Sentiment Database for Machine Learning

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3rd International conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Zhang, Jing Hu, Siquan Shi, Zhiguo Han, Shumin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China

Learners' affective states play a crucial role in learning evaluation, and the external expressions that can directly reflect affect are facial expressions. However, the sample size of the database for the learning process of learners is limited, and most of the existing expression databases are the expressions of ordinary people, which is difficult to support an in-depth study of the emotional states of learners in learning scenarios. In order to better study the influence of learners' emotion on the learning state, we built a learning expression database for machine learning, and at the same time labeled the data with expression-emotion state, and finally formed a database of 8,000 expression images containing 36 learners, which is important for studying learners' emotion state under learning scenarios. © Published under licence by IOP Publishing Ltd.

关键词： Database systems

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Security Evaluation of RIS-Aided V2X communication Systems in Low-Speed Scenarios 4

Security Evaluation of RIS-Aided V2X Communication Systems i...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Bai, Yongqiang Han, Shuangshuang University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Key Laboratory of Ocean Observation Technology Mnr Tianjin China

ISBN: (纸本)9798350349252

The security of vehicle-to-everything (V2X) communication systems is of critical importance for intelligent transportation systems. This paper discusses the use of reconfigurable intelligent surface (RIS) technology to enhance the security of V2X systems in the presence of interference from potential eavesdropper. By precisely controlling the RIS, the enhancement of the legitimate user's signal and the interference of the eavesdropper's signal are achieved, which effectively improves the communication confidentiality. High-quality suboptimal solutions are obtained by utilizing alternate optimization and semidefinite relaxation techniques. The simulation results clearly demonstrate that the proposed scheme can markedly enhance the secure communication rate. In addition, the influence of factors such as base station transmit power, user position and RIS element number on the secrecy rate is analyzed, which provides a reference for the further optimization of the security evaluation of the V2X system. © 2024 IEEE.

关键词： Secure communication

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