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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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Enhancing NR-IQA Model Robustness through Simple Image Compression Techniques

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Liu, Yujia Yang, Chenxi Yu, Zhaofei Huang, Tiejun Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing School of Computer Science Beijing China Peking University School of Mathematical Sciences Beijing China China Institution for Artificial Intelligence Peking University National Engineering Research Center of Visual Technology State Key Laboratory of Multimedia Information Processing China

No-Reference Image Quality Assessment (NR-IQA) plays a crucial role in various real-world applications by predicting image quality scores without the need for reference images. Despite the impressive performance of deep learning-based NR-IQA models, they remain vulnerable to adversarial attacks, which introduce imperceptible perturbations to input images, causing significant changes in predicted scores. In this study, we explore the use of simple JPEG compression techniques, as well as their combination with norm regularization training, to defend against these adversarial attacks. Our results demonstrate that image compression is an effective method to enhance model robustness, and it can further improve the robustness of NR-IQA models when combined with appropriate training strategies. Since excessive compression may reduce performance on clean images, it is essential to strike a balance. This work provides valuable insights into designing effective image compression methods for NR-IQA models. © 1991-2012 IEEE.

关键词： Image compression

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Hybrid Optical-Electrical Transceiver Architecture for High-Speed Data Communications

Hybrid Optical-Electrical Transceiver Architecture for High-...

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Automation and Computation (AUTOCOM), International Conference on

作者： Suryakanta Panda Nitish Vashisht Srinivasa Rao K S Rajasekar. B Kalyan Acharjya Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Evaluating Design Tradeoffs in MMW Wave Arrays for High-Speed Wireless Data Communication and its Optimization over Fiber If you tear open your cloud, you will find a stream of is and Os at the heart of all this, with shrill screams from demanding applications like video, virtual reality, etc. pushing I believe close to their limits bandwidth comments here up purely optical links. We are reporting on highresolution computational imaging optimized for 3D surface reconstruction with tight constraints on operational uncertainty. Traditional electronic-only transceiver architecture is approaching its physical limits regarding data speed and power efficiency spurring the critical demand for more advanced approaches. Novel hybrid optical-electrical transceiver architecture has been discussed to overcome these limitations. It blends the fast speeds and broad bandwidths made possible by optical communication with the flexibility and scale of electrical circuits. The heart of this architecture is an electro-optic interface that converts electrical signals to optical. Enabling this interface is advanced materials like silicon photonics that permit optical and electronic components to be integrated into a single chip. This hybrid architecture combines optical communication for high-speed data transmission and enables data rates that are multiple orders of magnitude faster than traditional electronic-only architectures. Moreover, optical interconnects directly benefit the transceiver by lowering its power consumption, reducing total energy use and improving system performance.

关键词： Optical fibers Optical interconnections computer architecture Bandwidth Optical imaging Transceivers Optical noise Optical modulation Data communication High-speed optical techniques

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IoT-Enabled Traffic Management Systems using CNN-TransLSTM for Next-Generation Smart Cities

IoT-Enabled Traffic Management Systems using CNN-TransLSTM f...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Kumari Manswini Padhy Shouvik Chattopadhyay Neeru Malik Jyoti Prasad Patra Anvesh Perada N R Raghapriya Department of CSE Centurion University of Technology and Management R.Sitapur India Department of MBA Institute of Engineering and Management Kolkata & University of Engineering and Management Kolkata India School of Engineering and Technology Pimpri Chinchwad University Maharashtra India Department of Electrical Nigam Institute of Engineering and Technology NIET UG/PG Diploma Engineering Cuttack India Department of Electrical and Computer Engineering Drexel University Philadelphia Pennsylvania USA Department of Computer Science and Engineering Erode Sengunthar Engineering College Erode India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The rising quantity of vehicles has intensified traffic congestion, pollution, and road accidents. This paper introduces an IoT-enabled traffic management system utilizing CNN-TransLSTM, a hybrid model that combines convolutional neural networks, LSTM, and transformers for effective traffic prediction. Min-max normalization eliminates outliers and maintains data integrity, whereas feature extraction identifies essential variables such as weather, traffic density, and direction. The model attained a prediction accuracy of 91.25%, exceeding that of individual CNN, LSTM, and Transformer models. This method emphasizes the capacity of smart cities to utilize deep learning and IoT for superior traffic control, resulting in increased efficiency and sustainability.

关键词： Training Accuracy Smart cities Predictive models Feature extraction Transformers Convolutional neural networks Vehicle dynamics Long short term memory Meteorology

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Delving Into Quaternion Wavelet Transformer for Facial Expression Recognition in the Wild

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Yu Pei, Jialun Si, Weixin Qin, Jing Heng, Pheng-Ann Zhongnan University of Economics and Law School of Information Engineering Wuhan China The Chinese University of Hong Kong Department of Computer Science and Engineering ShaTin Hong Kong Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen China The Hong Kong Polytechnic University School of Nursing Kowloon Hong Kong

The Facial Expression Recognition (FER) technique has increasingly matured over time. However, recognizing facial expressions in wild environments poses great challenges in achieving promising performance. The main obstacles arise from various factors, such as illumination changes, head pose variations, and occlusions. To overcome interferences from external environments and improve recognition accuracy, we propose a novel Quaternion Wavelet TRansformer (QWTR) model for FER in the wild. Specifically, we present a Quaternion Value Transformer (QVT) network that combines quaternion multi-head attention with quaternion CNN to capture emotional cues from global and local perception. To preserve the color structure while enhancing image contrast and brightness, we introduce a Quaternion Histogram Equalization (QHE) representation to transform color images into quaternion matrices representation. After that, to alleviate the impact of head pose and occlusion together with feature redundancy, a Quaternion Wavelet Feature Selection (QWFS) scheme is designed to decompose quaternion features and select the most correlated signals. Extensive experiments have been conducted on four in-the-wild FER datasets and several specific FER benchmarks under various conditions. The qualitative and quantitative results demonstrate that QWTR outperforms other state-of-the-art methods in FER benchmarks, e.g., 68.37% vs. 66.31% accuracy on the AffectNet dataset. Code is available at https://***/zy197997312/QWTR-for-FER. © 1999-2012 IEEE.

关键词： Image enhancement

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K+ concentration-based NMR-fluorescence dual-functional senescence sensing using graphene quantum dots with crown ether structure

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Sensors and Actuators B: Chemical 2025年 431卷

作者： Chen, Xiaojing Tao, Quan Yang, Siwei Wang, Hang Wang, Xuelian Ding, Guqiao Gao, Xiumin Dong, Hui Rong, Liangliang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Key Laboratory of Superconductor Integrated Circuit Technology & National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Shanghai Jiaotong University School of Medicine Shanghai200025 China

Aging allows the occurrence and progression of cerebrovascular diseases, highlighting the importance of detecting senescent cells and tissues in the nervous system. Detecting senescent cells/tissues using nanoparticles as probes in the nervous system is desired with high sensitivity, high accuracy, and penetration of the blood-brain barrier. In response, we designed crown ether-based graphene quantum dots with fast proton transport channels to sense potassium ions, a significant differential indicator between healthy and senescent cells. Using the probe, we have achieved MRI-fluorescence dual-functional monitoring of potassium ion concentration in cells via fluorescent intensity and magnetic relaxation time. Importantly, we have further demonstrated the advantages of the crown ether-based graphene quantum dots in penetrating the blood-brain barrier, revealing their potential for diagnosing aging in the nervous system. © 2025 Elsevier B.V.

关键词： Graphene quantum dots

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A 3D semantic segmentation network for accurate neuronal soma segmentation

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Journal of Innovative Optical Health sciences 2025年第1期18卷 67-83页

作者： Li Ma Qi Zhong Yezi Wang Xiaoquan Yang Qian Du School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan 430074P.R.China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan 430074 P.R.China Department of Electrical and Computer Engineering Mississippi State University Mississippi StateMS 39762USA

Neuronal soma segmentation plays a crucial role in neuroscience ***,the fine structure,such as boundaries,small-volume neuronal somata and fibers,are commonly present in cell images,which pose a challenge for accurate *** this paper,we propose a 3D semantic segmentation network for neuronal soma segmentation to address this *** an encoding-decoding structure,we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module(MSAW)after each encoding *** MSAW module can not only emphasize the fine structures via an upsampling strategy,but also provide pixel-wise weights to measure the importance of the multi-scale ***,a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different *** proposed MSAW-based semantic segmentation network(MSAW-Net)was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain,demonstrating the efficiency of the proposed *** achieved an F1 score of 91.8%on Fezf2-2A-CreER dataset,97.1%on LSL-H2B-GFP dataset,82.8%on Thy1-EGFP-Mline dataset,and 86.9%on macaque dataset,achieving improvements over the 3D U-Net model by 3.1%,3.3%,3.9%,and 2.3%,respectively.

关键词： Neuronal soma segmentation semantic segmentation network multi-scale feature extraction adaptive weighting fusion

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A Cross-Task Transfer SAR Target Recognition Method Based on AMFFNet and IACGAN

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wan, Xunyang Du, Xiaolin Cui, Guolong Chen, Xiaolong Zheng, Jibin Yantai University School of Computer and Control Engineering Yantai264005 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Naval Aviation University Yantai264001 China Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China

Convolutional neural networks (CNN) have shown excellent capability in synthetic aperture radar (SAR) target recognition. However, obtaining sufficient training samples for improving network recognition is difficult due to the deficiency of labeled SAR image data. To tackle the problem, a cross-task transfer method for SAR target recognition is proposed in this paper, which is based on a designed attention multi-scale feature fusion network (AMFFNet) and an improved auxiliary classifier generative adversarial network (IACGAN). In this method, firstly, a data augmentation method based on you only cut twice (YOCT) is devised for expanding the training samples. Secondly, the partial weight parameters of AMFFNet are initialized by the pre-trained IACGAN discriminator. Finally, AMFFNet is fine-tuned using the training samples. Additionally, on the network structure of AMFFNet, multi-scale features are extracted by adopting a multi-scale depth separable convolution module, which allows the model to acquire more informative features. Attention mechanisms are employed in this network to enhance its ability to capture important features while reducing the effects of noise. A multi-level feature fusion structure is used to further raise the representational capability of the model. Experimental results on multiple open-source real SAR datasets show that the proposed method can achieve better recognition performance under different percentages of training data, and the effectiveness and superiority of the method are confirmed by its robustness to noise. © 1965-2011 IEEE.

关键词： Convolutional neural networks

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Optical Cross-Connect Technologies for Flexible Optical Networks

Optical Cross-Connect Technologies for Flexible Optical Netw...

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Automation and Computation (AUTOCOM), International Conference on

作者： Prateek Aggarwal Sireesha Nanduri Poonguzhali S B.P. Singh Satya Ranjan Das Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India School of Mangement - PG Management JAIN (Deemed to be University) Bangalore Karnataka Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Various optical cross-connect technologies are being developed for flexible next-generation optical networks to ensure the efficiency of real-time optical network routing. Demand for larger bandwidth and faster data transmission continues to grow. Therefore, these networks are becoming more and more significant. Conventional optical cross-connects offer limited scalability, and they need to be more flexible by nature due to their traditional architecture, hindering fast adaptation to altering traffic patterns. A solution to this problem is the new OXC technologies, which allow dynamic and reconfigurable optical networks. These technologies use high-end optics and electronics, including wavelength-selective switches and coherent receivers, to offer routing and switching flexibility. One key benefit of flexible OXC technologies is that their capabilities to support different transmission formats and data rates cater well to specific service requisites. They also allow for greater implementation of software-defined networking principles, creating centralized control and management over the network. Moreover, flexible OXCs enable efficient tower best practices and O&M savings through reduced manual network intervention.

关键词： Optical fibers Optical switches Scalability Bandwidth Optical fiber networks Wavelength division multiplexing Optical receivers Real-time systems Network function virtualization Software defined networking

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