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International Congress on Image and Signal Processing, BioMedical engineering and Informatics (CISP-BMEI)

作者： Dandan Wu Zhiyi Ma Xiaodong Yang Guangkang Jiang Chen Yan Lu Ma School of Computer Science and Technology China University of Mining and Technology Xuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Suzhou Vocational and Technical College Suzhou China

Visibility graph algorithm is a widely used time series complex network transformation algorithm. It has been mainly applied to one-dimensional or multidimensional time series, while many real-life objects, such as images, cannot be represented by sequences. In this paper, we focus on two-dimensional images and propose a novel method of image visibility graph network (IVGN) with redefined node-degree features. We use the staining images of corneal ulcer of eye as the research object and demonstrate the application of IVGN to medical images. Our method consists of the following steps: (1) We construct a novel IVGN by treating each pixel as a node and connecting the visible pixels. We compute the average node degree of the IVGN for ocular pathology image analysis. (2) Based on the IVGN, we extract the node-degree features of the visible images and map them to a new degree topology, which is used for image enhancement. (3) By doing so, we build a gray co-occurrence matrix based on the degree topology and extract its four parameters, showing the role of the degree topology in image texture extraction. (4) As a result, we use the proposed contrast-based descriptors to achieve effective detection of eye staining images with different categories and severity levels.

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Neural Rendering and Flow-assisted Unsupervised Multi-view Stereo for Real-time Monocular Tracking and Scene Perception

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IEEE Transactions on Automation Science and engineering 2025年

作者： Tong, Kevin W. Cai, Yandong Jie, Yu-Wen Duan, Ya Hou, Yuhong Wu, Edmond Q. Nanjing University of Posts and Telecommunications College of Automation College of Artificial Intelligence Nanjing210023 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology Dalian116024 China Shanxi University School of Automation and Software Engineering Shanxi 237016 China Flight Test Technology and Engineering Center of the Chinese Flight Test Establishment Xian710089 China China Civil Aviation Shanghai Aircraft Airworthiness Certification Center Shanghai200335 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

The existing camera tracking and perception methods mainly rely on sparse SLAM, which limits the dense perception ability of the scene and affects the reliability of auxiliary decision-making. Different from this, this work proposes a real-time tracking and unsupervised dense sensing framework. Firstly, the dense depth value of the scene is predicted by unsupervised multi-view stereo to remove the dependence on labeled data. Then, the quality of synthetic pseudo-reference image is quantified according to the predicted depth map and used as a weighted guidance to train the unsupervised model, thus reducing the ambiguity of feature matching in areas such as specular reflection. Moreover, the sparse optical flow of the keyframes is solved by real-time and robust ORB feature matching operator, which assists the high-precision training of unsupervised depth inference model. To increase the prediction accuracy of occluded area, a novel rendering consistency loss via neural radiance fields is designed to constrain the geometric characteristics of object surface. Finally, dense direct image alignment is performed from a global model to improve the tracking robustness, which is incrementally constructed from dense depth prediction. Extensive experiments on synthetic datasets and real datasets validate the effectiveness and practicability of the proposed work, which is an effective supplement to the existing SLAM work. © 2025 IEEE.

关键词： Stereo image processing

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Amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS electrodes

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Science China(Information Sciences) 2022年第4期65卷 48-63页

作者： Linxia XIAO Caizi LI Yanjiang WANG Weixin SI Hai LIN Doudou ZHANG Xiaodong CAI Pheng-Ann HENG College of Control Science and Engineering China University of Petroleum (East China) Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Department of Neurosurgery Shenzhen Second People's Hospital School of Medicine Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong

Parkinson's disease(PD) is treated effectively by deep brain stimulation(DBS) of the subthalamic nucleus(STN), using an electrode inserted into the head of a PD patient. The electrode has multiple electrical contacts along its length, so the best may be chosen for selectively stimulating the STN. Neurosurgeons usually determine the optimal stimulated contact via the clinical experience of the neurosurgeon and the motor improvement of PD patients. This is a time-consuming and labor-intensive trial-and-error process. The selection of optimal stimulated contact highly depends on the locations of sweet spots, which are manually identified by the characteristic features of microelectrode recordings(MERs). This paper presents an amplitude-frequency-aware deep fusion network for optimal contact selection on STN-DBS *** method first obtains the amplitude-frequency fusion features by combining the MERs time sequence features and the amplitude sequence features, and then uses the convolutional neural network(CNN) with convolutional block attention module(CBAM) to identify both the border of the STN and the sweet spots to implant the electrode. The optimal stimulated contact can be selected according to the distribution of the sweet spots. Experimental results indicate that, for successful surgeries, neurosurgeons and the proposed AI solution selected the same optimal contacts. Furthermore, the proposed method outperforms the state-of-the-art methods for STN and sweet spot identification. The proposed method shows great potential for optimal contact selection to improve the efficiency of STN-DBS surgery and reduce the dependence on clinicians' experience.

关键词： optimal contact selection sweet spots microelectrode recordings amplitude-frequency feature deep fusion network

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Goods Anomaly Detection Based on Multi-Branch Feature Extraction and Foreground Mining

Goods Anomaly Detection Based on Multi-Branch Feature Extrac...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Zhiqian Jia Wei Li Zuoyong Li Zhipeng Mu Lihui Lin Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Xiamen Air Traffic Management Station of CAAC Xiamen China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Data Science MinjiangUniversity Fuzhou China Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry School of Mathematics and Computer Science Wuyi University Fujian China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

In recent years, significant advancements have been made in the field of anomaly detection. Expanding anomaly detection tasks to new scenarios has become a development avenue. Anomaly detection for the outer packaging of supermarket goods is a challenging task due to the diversity of goods’ appearances, which results in some detection methods suffering from limited sensitivity to anomalies and inadequate target mining. To address these issues, we propose a novel unsupervised anomaly detection method based on multi-branch feature extraction and foreground cosine structure mining strategy. Firstly, we use anomaly synthesis technique to enhance the model’s perception of anomalies and construct more precise decision boundaries in the feature space. Secondly, a multi-branch feature extractor extracts and integrates multi-level and multi-scale features, providing rich texture and semantic information to improve the model’s adaptability in real-world scenarios. Finally, the foreground cosine loss focuses on target mining while supplementing local structural information, further enhancing the model’s ability to detect subtle anomalies. To validate the effectiveness of the proposed method, extensive experiments were conducted on the GoodsAD dataset, sampled from supermarket scenarios. Experimental results show that this method achieves significant performance in image-level detection and pixel-level localization.

关键词： Location awareness Adaptation models Sensitivity Semantics Education Packaging Feature extraction Information technology Anomaly detection Biomedical imaging

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Multi-modal Speech Emotion Recognition via Feature Distribution Adaptation Network

arXiv

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arXiv 2024年

作者： Li, Shaokai Ji, Yixuan Song, Peng Sun, Haoqin Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China Business School Xuzhou University of Technology Xuzhou221018 China College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing210096 China

In this paper, we propose a novel deep inductive transfer learning framework, named feature distribution adaptation network, to tackle the challenging multi-modal speech emotion recognition problem. Our method aims to use deep transfer learning strategies to align visual and audio feature distributions to obtain consistent representation of emotion, thereby improving the performance of speech emotion recognition. In our model, the pre-trained ResNet-34 is utilized for feature extraction for facial expression images and acoustic Mel spectrograms, respectively. Then, the cross-attention mechanism is introduced to model the intrinsic similarity relationships of multi-modal features. Finally, the multi-modal feature distribution adaptation is performed efficiently with feed-forward network, which is extended using the local maximum mean discrepancy loss. Experiments are carried out on two benchmark datasets, and the results demonstrate that our model can achieve excellent performance compared with existing ones. Our code is available at https://***/shaokai1209/FDAN. Copyright © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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A novel high-through cell micronucleus detection algorithm based on improved YOLOv5

A novel high-through cell micronucleus detection algorithm b...

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Industrial IoT, Big Data and Supply Chain (IIoTBDSC), International Conference on

作者： Weiyi Wei Jingyu Li Xiaoqin Wu Hangjian Zhang College of Computer Science and Engineering Northwest Normal University Lanzhou China Radiology Department Gansu Provincial Center For Disease Control And Prevention Lanzhou China Business school Northwest Normal University Lanzhou China

ISBN: (数字)9798350341690

ISBN: (纸本)9798350341706

In response to the challenges associated with cell micronucleus image recognition, namely small target sizes, high class similarity, and limited sample data, A novel cell micronucleus detection algorithm named YOLOv5-ECA is proposed based on YOLOv5. The proposed algorithm gets hyper-accurate micronucleus identification through a series of improvements. To begin with, the anchor frame size is recalibrated based on a meticulously constructed dataset. This process serves to expedite convergence speed while bolstering accuracy. Subsequently, in C3 module, a coordinate attention (CA) is introduced, which enlarging the receptive field and enabling the model to allocate greater focus to crucial elements within processed images. Ultimately, a superficial detection layer is incorporated, thereby enhancing the precision of cell micronucleus identification through multi-scale detection. Comparative analysis with the original YOLOv5 which demonstrates a 2% increase in average precision, accuracy, and a 1.3% enhancement in recall for the YOLOv5-ECA algorithm based on experimental results.

关键词： YOLO Accuracy Heuristic algorithms Supply chains Real-time systems Detection algorithms Optimization

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Improved Multisignature Scheme for Authenticity of Digital Document in Digital Forensics Using Edward-Curve Digital Signature Algorithm

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Security and Communication Networks 2023年第1期2023卷

作者： Shankar, Gauri Ai-Farhani, Liwa H. Anitha Christy Angelin, P. Singh, Parvinder Alqahtani, Abdullah Singh, Abha Kaur, Gaganpreet Samori, Issah Abubakari Chandigarh University Mohali Punjab Mohali140413 India System Analysis Control and Information Processing Academy of Engineering RUDN University Moscow Russia PSNA College of Engineering and Technology Tamilnadu Dindigul624622 India Central University of Punjab Bathinda Punjab 151001 India Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Basic Science College of Science and Theoretical Study Dammam-Female Branch Saudi Electronic University Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Engineering Sciences University of Ghana Accra Ghana

At the moment, digital documents are just as important as paper documents. As a result, authenticity is essential, especially in legal situations and digital forensics. As technology advances, these digital signature algorithms become weaker, necessitating the development of digital authentication schemes capable of withstanding current security threats. This study proposed a scheme based on an asymmetric key cryptosystem and the user's biometric credentials to generate keys for digital signatures. A single document can be signed by multiple signatories at the same time under this scheme. The primary goal of this article is to create a safe and cost-effective multiignature scheme. To create keys for document signing and verification, the Edwards-curve Digital Signature Algorithm (EdDSA), especially Ed25519, is employed. The Edwards-curve Digital Signature Algorithm is used with blockchain technology to sign crypto wallets. The Python implementation of a scheme that enables platform independence. We performed performance, security, and comparative analysis to ensure maximum usability. The article's main findings are that the Ed25519 algorithm can be used in blockchain. © 2023 Gauri Shankar et al.

关键词： Digital forensics

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Machine Learning and Intelligent Network System based Covid 19 Health Care System

Machine Learning and Intelligent Network System based Covid ...

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International Conference on Power, Energy, control and Transmission Systems (ICPECTS)

作者： Amjath Ali Jakul Basha K. Saravanan T Mangayarkarasi C Balarengadurai RCC - ENGG Department Lecturer Computer Engineering Electrical and Electronic Section Department of Engineering Sultanate of Oman University of Technology and Applied Sciences Ibra Department of Information Technology R.M.D Engineering College Chennai Tamil Nadu India Department of Instrumentation and Control Engineering Sri Sairam Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru Karnataka India

ISBN: (纸本)9781665462761

COVID-19 has lately infected a big number of people worldwide. Medical service frameworks are strained as a result of the infection. The emergency unit, which is part of the medical services area, has experienced several challenges as a result of the low data quality offered by existing ICU clinical equipment. The Internet of Things has enhanced the capability for essential information mobility in medical services in the twenty-first century. Nonetheless, many of today's ideal models use IoT innovation to assess patients' well-being. As a result, executives lack understanding regarding the most effective method to apply such innovation to ICU clinical equipment. The IoT Based Paradigm for Medical Equipment Management Systems, a breakthrough IoT-based paradigm for successfully administering clinical hardware in ICUs, is introduced in this study. During the COVID-19 episode, IoT technology is used to boost the data stream between clinical hardware, executive frameworks, and ICUs, enabling the maximum level of openness and reasonableness in clinical equipment redistribution. IoT MEMS conceptual and functional features were painstakingly drawn. Using IoT MEMS expands the capacity and limits of emergency clinics, effectively easing COVID-19. It will also have a substantial impact on the nature of the data and will improve the partners' trust and transparency.

关键词： COVID-19 Micromechanical devices Biomedical equipment Technological innovation Medical services Production Hardware

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Regional Correlation Aided Mobile Traffic Prediction with Spatiotemporal Deep Learning

arXiv

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arXiv 2023年

作者： Park, JeongJun Mwasinga, Lusungu J. Yang, Huigyu Raza, Syed M. Le, Duc-Tai Kim, Moonseong Chung, Min Young Choo, Hyunseung Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon Korea Republic of Department of Superintelligence Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Department of IT Convergence Software Seoul Theological University Bucheon Korea Republic of

Mobile traffic data in urban regions shows differentiated patterns during different hours of the day. The exploitation of these patterns enables highly accurate mobile traffic prediction for proactive network management. However, recent Deep Learning (DL) driven studies have only exploited spatiotemporal features and have ignored the geographical correlations, causing high complexity and erroneous mobile traffic predictions. This paper addresses these limitations by proposing an enhanced mobile traffic prediction scheme that combines the clustering strategy of daily mobile traffic peak time and novel multi Temporal Convolutional Network with a Long Short Term Memory (multi TCN-LSTM) model. The mobile network cells that exhibit peak traffic during the same hour of the day are clustered together. Our experiments on large-scale real-world mobile traffic data show up to 28% performance improvement compared to state-of-the-art studies, which confirms the efficacy and viability of the proposed approach. © 2023, CC BY.

关键词： Long short-term memory

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DAGC: Data-Aware Adaptive Gradient Compression

DAGC: Data-Aware Adaptive Gradient Compression

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Rongwei Lu Jiajun Song Bin Chen Laizhong Cui Zhi Wang Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate School School of Software Technology Dalian University of Technology Harbin Institute of Technology Shenzhen College of Computer Science and Software Engineering Shenzhen University Guangdong Laboratory of Artificial Intelligence and Cyber Economics (SZ) Peng Cheng Laboratory

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers; This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios 1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication.

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