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arXiv 2023年

作者： Li, Depeng Wang, Tianqi Xu, Bingrong Kawaguchi, Kenji Zeng, Zhigang Suganthan, Ponnuthurai Nagaratnam The School of Artificial Intelligence and Automation The Institute of Artificial Intelligence Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China The School of Automation Wuhan University of Technology Wuhan430070 China The School of Computing National University of Singapore 117417 Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated how to design an Innately Forgetting-Free Network (IF2Net) for continual learning context. This study proposed a straightforward yet effective learning paradigm by ingeniously keeping the weights relative to each seen task untouched before and after learning a new task. We first presented the novel representation-level learning on task sequences with random weights. This technique refers to tweaking the drifted representations caused by randomization back to their separate task-optimal working states, but the involved weights are frozen and reused (opposite to well-known layer-wise updates of weights). Then, sequential decision-making without forgetting can be achieved by projecting the output weight updates into the parsimonious orthogonal space, making the adaptations not disturb old knowledge while maintaining model plasticity. IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time by integrating the respective strengths of randomization and orthogonalization. We validated the effectiveness of our approach in the extensive theoretical analysis and empirical study. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Robust 3D reconstruction of rib cage bones in computed tomography images, by combining knowledge from radiological, machine learning, and innovative graph guidance

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Research on Biomedical Engineering 2019年第1期35卷 45-56页

作者： Sais, Barbara Teixeira Vital, Daniel Aparecido Moraes, Matheus Cardoso Laboratory of Image and Signal Processing of the Institute of Science and Technology Federal University of São Paulo – UNIFESP 330 Talim St. room 108 Jardim Aeroporto CEP São José dos CamposSP12231-280 Brazil

Purpose: More than 70 million computed tomography scans are made per year. A great number of them aim at the thoraxic region, due to the number of organs and structures within it. The 3D visualization of these structures, including the bone, can lead to a more precise medical diagnosis. There are a number of works regarding 3D bone reconstruction, but most fail to present a quantitative evaluation of their assessment or have not achieved an assessment close to 100%. We present an automatic method of bone segmentation followed by 3D reconstruction that approaches these current limitations. Methods: The proposed methodology has three blocks: (1) Preprocessing, whereby a median filter was applied to images that presented a high level of noise;(2) feature extraction procedure, in which (i) the images intensity levels were converted to attenuation coefficients and (ii) a (MLP) neural network was used to populate the Space of Attributes with the corresponding feature vectors;and (3) 3D structural construction, whereby a red-and-black tree with graph guidance combined the regarding clustered feature vectors with their spatial neighbors. To evaluate the results, the accuracy between the 2D-segmented images and their corresponding gold standards was calculated. Results: The material is composed of a set of 90 CT chest volumes. We obtained high accuracy parameters, such as Overlap Dice (%) = 98.77 ± 0.58 and False Negative (%) = 0.13 ± 0.026. Conclusion: The proposed methodology presented significant accuracy values and processing time, having a large database to test the methodology, besides being a fully automatic method of 3D bone reconstruction. © 2019, Sociedade Brasileira de Engenharia Biomedica.

关键词： image reconstruction

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Multiple sclerosis segmentation method in magnetic resonance imaging using fuzzy connectedness, binarization, mathematical morphology, and 3D reconstruction

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Research on Biomedical Engineering 2020年第3期36卷 291-301页

作者： de Arruda, André Luiz Costa Vital, Daniel Aparecido Kitamura, Felipe Campos Abdala, Nitamar Moraes, Matheus Cardoso Laboratory of Image and Signal Processing of the Institute of Science and Technology of Federal University of São Paulo UNIFESP 330 Talim St. room 108-Jardim Aeroporto CEP São José dos CamposSP12231-280 Brazil Department of Imaging Diagnosis of Federal University of São Paulo UNIFESP 800 Napoleão de Barros St. Vila Clementino CEP São PauloSP04024-002 Brazil

Introduction: Magnetic resonance imaging (MRI) is the most used medical modality for diagnosis and monitoring of multiple sclerosis (MS). A segmentation process is an important task to quantify lesion and its progression. However, manual segmentation of 3D images is tedious, time-consuming, and often not reproducible. The state of the art presents results with room for improvements. Consequently, a semiautomatic segmentation process is proposed and described in this study. Methods: The method consists on a 3D segmentation semiautomatic process for MS lesions in MRI. It initiates by firstly carrying out a preprocessing stage;thus, contrast adjustment is applied to enhance sclerosis regions from other brain information. Secondly, a feature extraction block based on fuzzy connectedness is performed so as to isolate sclerosis lesions from other brain regions. Finally, 3D brain reconstruction is executed along with sclerosis to provide a useful 3D information. Results: The robustness of this approach is demonstrated by high correlation between the results and their corresponding gold standard. The results were also obtained by computing parameters of accuracy of image segmentation, as well as overlap Dice. The proposed method reached true positive of 75.61%, false positive of 16.37%, and DICE of 78.23%. Conclusion: The high correlation between specialist and proposed approach outcome, a better monitoring of the disease, is provided;the specialist can understand the patient’s symptoms, thereby increasing the patient’s quality of life. © 2020, The Author(s).

关键词： Magnetic resonance imaging

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Remarks on the Design of First Digital Computers in Japan - Contributions of Yasuo Komamiya 17th

Remarks on the Design of First Digital Computers in Japan - ...

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17th International Conference on Computer Aided Systems Theory, EUROCAST 2019

作者： Stanković, Radomir S. Sasao, Tsutomu Astola, Jaakko T. Yamada, Akihiko Mathematical Institute of SASA Belgrade Serbia Department of Computer Science Meiji University KawasakiKanagawa214-8571 Japan Department of Signal Processing Tampere University of Technology Tampere Finland Computer Systems and Media Laboratory Tokyo Japan

ISBN: (纸本)9783030450922

This paper presents some less known details about the work of Yasuo Komamiya in development of the first relay computers using the theory of computing networks that is based on the former work of Oohashi Kan-ichi and Mochiori Goto at the Electrotechnical laboratory (ETL) of Agency of Industrial science and technology, Tokyo, Japan. The work at ETL in the same direction was performed under guidance of Mochinori Goto. © 2020, Springer Nature Switzerland AG.

关键词： Digital computers

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A Modified Omega-K Algorithm for Bistatic Forward-Looking SAR Data Imaging

A Modified Omega-K Algorithm for Bistatic Forward-Looking SA...

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IEEE International Conference on Radar

作者： Tinghao Zhang Yachao Li Xuan Song Tao Zhang Chunfeng Wu Ziyue Sun National Laboratory of Radar Signal Processing Xidian University Xi'an China Chengdu Aerospace Science and Technology Microelectronics System Research Institute Chengdu China University of Exeter BEng Electronic Enginnering Cornwall UK

ISBN: (纸本)9781665468893

Accurate two dimension frequency spectrum (2D FS) with acceleration, two-way range coupling terms, and spatial variant Doppler parameters are main problems to be solved in translational variant bistatic forward-looking SAR (TV BFSAR) with curved trajectory. For these issues, a modified Omega-K imaging algorithm is derived in this paper. The maximum usage of 2D FS based on method of series reversion (MSR) is achieved by linear range cell migration correction and 2D FS is linearized in bistatic range by using high-order polynomial fitting. Then, a method of azimuth resampling is introduced to implement compensation of spatial variant Doppler parameters. And the small-aperture data is focused in the azimuth frequency domain to avoid azimuth aliasing without padding zeros. Finally, the raw bistatic SAR data verifies the effectiveness of the proposed method

关键词： TV Azimuth Frequency-domain analysis Fitting Two dimensional displays Radar imaging Doppler radar

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A Recommender System for Metaheuristic Algorithms for Continuous Optimization Based on Deep Recurrent Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2020年第1期1卷 5-18页

作者： Tian, Ye Peng, Shichen Zhang, Xingyi Rodemann, Tobias Tan, Kay Chen Jin, Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei 230601 China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230601 China Honda Research Institute Europe Offenbach 63073 Germany Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong Hong Kong Department of Computer Science University of Surrey Guildford GU2 7XH United Kingdom Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110819 China

As revealed by the no free lunch theorem, no single algorithm can outperform any others on all classes of optimization problems. To tackle this issue, methods for recommending an existing algorithm for solving given problems have been proposed. However, existing recommendation methods for continuous optimization suffer from low practicability and transferability, mainly due to the difficulty in extracting features that can effectively describe the problem structure and lack of data for training a recommendation model. This work proposes a generic recommender system to address the above two challenges. First, a novel method is proposed to represent an analytic objective function of a continuous optimization problem as a tree, which is directly used as the features of the problem. For black-box optimization problems whose objective function is unknown, a symbolic regressor is adopted to estimate the tree structure. Second, a large number of benchmark problems are randomly created based on the proposed tree representation, providing an abundant amount of training data with various levels of difficulty. By employing a deep recurrent neural network, a recommendation model is trained to recommend a most suitable metaheuristic algorithm for white- or black-box optimization, making a significant step forward towards fully automated algorithm recommendation for continuous optimization. Experimental results on 100 000 benchmark problems show that the proposed recommendation model achieves considerably better performance than existing ones, and exhibits high transferability to real-world problems. Impact Statement—Real-world optimization problems, such as aerodynamic design of turbine engines and automated trading, have been successfully solved by metaheuristics. However, practitioners are confronted with the challenge of how to choose an appropriate metaheuristic algorithm to solve a particular instance of these problems. This paper proposes a recommender system that can auto

关键词： Algorithm recommendation continuous optimization problem decision tree deep recurrent neural network metaheuristics symbolic regression

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Privacy-Preserving Brain-Computer Interfaces: A Systematic Review

arXiv

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arXiv 2024年

作者： Xia, Kun Duch, Wlodzislaw Sun, Yu Xu, Kedi Fang, Weili Luo, Hanbin Zhang, Yi Sang, Dong Xu, Xiaodong Wang, Fei-Yue Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The Department of Informatics School of Physics Astronomy and Informatics Neurocognitive Laboratory Center for Modern Interdisciplinary Technologies Nicolaus Copernicus University Torun Poland The Ministry of Education Key Laboratory for Biomedical Engineering Zhejiang University Hangzhou China The Zhejiang University School of Medicine National Clinical Research Center for Child Health Hangzhou China Department of Biomedical Engineering Zhejiang University Hangzhou China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The NetsUnion Clearing Corporation Beijing100045 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100049 China

A brain-computer interface (BCI) establishes a direct communication pathway between the human brain and a computer. It has been widely used in medical diagnosis, rehabilitation, education, entertainment, etc. Most research so far focuses on making BCIs more accurate and reliable, but much less attention has been paid to their privacy. Developing a commercial BCI system usually requires close collaborations among multiple organizations, e.g., hospitals, universities, and/or companies. Input data in BCIs, e.g., electroencephalogram (EEG), contain rich privacy information, and the developed machine learning model is usually proprietary. Data and model transmission among different parties may incur significant privacy threats, and hence privacy protection in BCIs must be considered. Unfortunately, there does not exist any contemporary and comprehensive review on privacy-preserving BCIs. This paper fills this gap, by describing potential privacy threats and protection strategies in BCIs. It also points out several challenges and future research directions in developing privacy-preserving BCIs. © 2024, CC BY.

关键词： Differential privacy

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Deep learning detection of anomalous patterns from bus trajectories for traffic insight analysis

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Knowledge-Based Systems 2021年 217卷 106833-106833页

作者： Zhang, Xiaocai Zheng, Yi Zhao, Zhixun Liu, Yuansheng Blumenstein, Michael Li, Jinyan Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia National Key Laboratory of Science and Technology on Blind Signal Processing ChengduSichuan610041 China College of Information Science and Engineering Hunan University ChangshaHunan410082 China Australian Artificial Intelligence Institute & Faculty of Engineering and IT University of Technology Sydney UltimoNSW 2007 Australia

Existing data-driven methods for traffic anomaly detection are modeled on taxi trajectory datasets. The concern is that the data may contain much inaccuracy about the actual traffic situations, because taxi drivers often choose optimal routes to evade from the congestions caused by traffic anomalies. We use bus trajectory data in this work. Bus trajectories can capture real traffic conditions in the road networks without drivers’ preference, which are more objective and appropriate for accurately detecting anomalous patterns for a broad range of insight analyses on traffics. We proposed a deep learning-based feature visualization method to map 3-dimensional features into a red–green–blue (RGB) color space. A color trajectory (CT) is then derived by encoding a trajectory with the RGB colors. With the spatial and temporal properties extracted from the CT, spatio-temporal outliers are detected by a novel offline detection method. We then conduct GIS map fusion to obtain insights for better understanding the traffic anomaly locations, and more importantly the influences on the road affected by the corresponding anomalies. Extended from the offline detection, an online detection method is developed for real-time detection of anomalous patterns. Our proposed methods were tested on 3 real-world bus trajectory datasets to demonstrate the performance of high accuracies, high detection rates and relatively low false alarm rates. © 2021

关键词： Trajectories

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Restoration-enhanced reversible information steganography network for CT images in the Internet of Medical Things

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chen, Kai Chen, Yang Coatrieux, Gouenou Wu, Jiasong Coatrieux, Jean-Louis School of Cyber Science and Engineering Southeast University Nanjing210096 China Southeast University Nanjing210096 China IMT Atlantique Inserm LaTIM UMR1101 Brest29000 France Laboratoire Traitement du Signal et de L'Image Université de Rennes 1 RennesF-35000 France Centre de Recherche en Information Biomédicale Sino-francais Rennes35042 France National Institute for Health and Medical Research RennesF-35000 France Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Southeast University Nanjing210096 China Jiangsu Key Laboratory of Molecular and Functional Imaging Department of Radiology Zhongda Hospital Southeast University Nanjing210009 China Ministry of Education Nanjing210096 China Ministry of Education Nanjing210096 China

Smart health and emotional care powered by the Internet of Medical Things (IoMT) are revolutionizing the healthcare industry by adopting several technologies related to multimodal physiological data collection, communication, intelligent automation, and efficient manufacturing. The security of medical images containing private patient information distributed in IoMT is of paramount importance in social communication, and guaranteeing their reliability has attracted widespread attention. Some existing medical image steganography methods fail to guarantee the original image's integrity, authenticity, and lossless restoration. To solve this problem, a restoration-enhanced reversible adaptive information steganography network, MediSR-Net, is proposed. Specifically, MediSR-Net consists of an analysis module and an encoding module. The image degradation-restoration strategy is exploited by the analysis module to improve the effectiveness of information embedding and perceptual field to reduce the error of pixel prediction. An information distillation and attention machine-based approach are adopted to achieve degradation-restoration of CT medical images at any scale and focus more on the degradation-restoration effect with small magnification factors. The encoding module is responsible for the embedding and extraction of privacy information. Ten state-of-the-art are compared with the MediSR-Net method concerning steganographic effect, embedding rate, distortion analysis, and prediction error. The qualitative and quantitative results demonstrate the effectiveness and superiority of MediSR-Net. © 1963-2012 IEEE.

关键词： Steganography

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Multilevel-evolutionary population P systems and applications in tumor segmentations

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Applied Soft Computing 2025年 179卷

作者： Jie Xue Qi Li Bosheng Song Xiyu Liu Dengwang Li School of Business Shandong Key Laboratory of Medical Physics and Image Processing Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Shandong Normal University Jinan 250358 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China Shandong Key Laboratory of Medical Physics and Image Processing Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine School of Physics and Electronics Shandong Normal University Jinan 250014 China

Population P systems are a class of tissue-like P systems, inspired by natural computing and are applied in real-world problems. Although the model could dynamically reflect the changing state of cells in P systems, classical ones usually tree-based cells in multienvironments, ignoring the various evolutionary characteristics of elementary, sub population and population, which limit the evolutionary computing ability of population P systems. In this paper, we propose a new multilevel-evolutionary population P (MEPP) systems, containing three types of cells with corresponding new rules to describe different evolutionary mechanism. Furthermore, a special control variable is designed to accept nonimproving moves probabilistically to adaptively find the global optima. Multi-supervisions are also designed to constantly revises effective information. Besides, we applied the MEPP system to segment organs and tumors in CT/MR images. Experimental results also verify the effectiveness of MEPP system. The code of MEPP system is available at https://***/xuejUNC/Multilevel-evolutionary-Population-P-Systems .

关键词： Population P systems Multilevel-evolutionary Tumor/organ segmentation

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