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International Conference on Biosignals, Images and Instrumentation (ICBSII)

作者： Sonal Patreena D’Almeida Surekha Kamath K.V Rajagopal K.N Manjunath Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Radiodiagnosis and Imaging Kasturba Medical College Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331513863

ISBN: (纸本)9798331513870

computer-aided diagnosis (CAD) is an efficient tool in analyzing medical images for the diagnosis and staging of rectal cancer. Magnetic Resonance Imaging (MRI) plays a crucial role in the detection and evaluation of rectal cancer due to its high-resolution imaging capabilities. This paper discusses the pre-processing methods for MR images of rectal cancer, which facilitate further image analysis for diagnosing the stage of cancer. Pre-processing is a critical step for MR images, aiding in subsequent steps of segmentation and classification. This paper investigates the impact of widely used pre-processing techniques. Initially, pelvic DICOM images are provided as input. By combining three filters—mean, median, and adaptive median filters—the noise is removed from the image. Image enhancement techniques such as Adaptive Histogram Equalization (AHE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to enhance the quality of the image. Statistical parameters like Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Structural Similarity Index Measure (SSIM) are used to evaluate performance. Results indicate that CLAHE provides superior outcomes.

关键词： Image quality Maximum likelihood detection PSNR Magnetic resonance imaging Noise Nonlinear filters computer aided diagnosis Medical diagnostic imaging Image enhancement Cancer

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

arXiv

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arXiv 2024年

作者： Sun, Li Huang, Zhenhao Wu, Hua Ye, Junda Peng, Hao Yu, Zhengtao Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois ChicagoIL United States

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Safe vehicle motion design with learning for moving in environment with uncertainties ⁎

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IFAC-PapersOnLine 2024年第4期58卷 228-233页

作者： Dénes Tompos Balázs Németh Tamás Hegedűs Vu Van Tan Péter Gáspár Institute for Computer Science and Control (SZTAKI) Hungarian Research Network (HUN-REN) Kende u. 13-17. 1111 Budapest Hungary Department of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Stoczek u. 2. 1111 Budapest Hungary Department of Automotive Mechanical Engineering Faculty of Mechanical Engineering University of Transport and Communications 3 Cau Giay Street 100000 Hanoi Vietnam

In this paper a motion profile design for unmanned aerial vehicles is proposed which method is able to guarantee safe collision-free motion. The motivations of the work are provided by the uncertainties of covered areas by the vehicles, and also the need of high performance fast vehicle motion. The uncertain information on the environment for detecting Conflict areas is processed through clustering and Mahalanobis-distance-based filtering methods. The resulted Conflict areas are involved in the motion design method, which is facilitated through reinforcement learning. This paper shows the application of the method on a drone that moves together with a mobile robot in the same environment. The safe and high performance motion of the drone is illustrated through simulation example.

关键词： Unmanned aerial vehicles safe motion clustering point clouds

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Multimodal MR Brain Image Segmentation using Hybrid Optimization Techniques for Earlier Prognosis of Tumor

Multimodal MR Brain Image Segmentation using Hybrid Optimiza...

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Advanced Computing and Communication Technologies for High Performance Applications

作者： Baiju Karun T. Arun Prasath M. Pallikonda Rajasekaran M Rakhee Department of Instrumentation and Control Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Biomedical Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science & Engineering School of Engineering Cochin University of Science and Technology Kerala India

Segmentation of medical images is the process of dividing an image into regions that have some characteristics that are homogeneous and consistent. Medical image segmentation for brain images is an important process in the early diagnosis of brain tumors, brain abnormalities, and their treatment planning. When performed by medical specialists, segmentation of Magnetic Resonance Imaging (MRI) takes a long time. This paper needs to propose some new hybrid approaches for image segmentation and classification for early detection and treatment of brain tumors. The conventional fuzzy c-means clustering (FCM) method is susceptible to artifacts; the local spatial information is frequently presented to an objective function to enhance the FCM method's reliability for segmenting the images. To overcome this limitation, the authors proposed the Fast and Robust FCM (FRFCM), an optimized FCM technique that utilizes morphological restoration (MR) and membership filtration which is considerably quicker and much more reliable than classical FCM. Following the clustering procedure, the new Social Spider Optimization (SSO) swarm algorithm relies on the co - operative features of the social spider is employed to obtain the best outcome. The novel integration of FRFCM and SSO has been affirmed through evaluating on numerous different BraTS datasets, yielding improved performance metric values.

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A Cyclic Small Phase Theorem

arXiv

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arXiv 2023年

作者： Chen, Chao Chen, Wei Zhao, Di Chen, Jianqi Qiu, Li KU Leuven Leuven3001 Belgium The Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex Systems Peking University Beijing100871 China The Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

This paper introduces a brand-new phase definition called the segmental phase for multi-input multi-output linear time-invariant systems. The underpinning of the definition lies in the matrix segmental phase which, as its name implies, is graphically based on the smallest circular segment covering the matrix normalized numerical range in the unit disk. The matrix segmental phase has the crucial product eigen-phase bound, which makes itself stand out from several existing phase notions in the literature. The proposed bound paves the way for stability analysis of a cyclic feedback system consisting of multiple subsystems. A cyclic small phase theorem is then established as our main result, which requires the loop system phase to lie between −π and π. The proposed theorem complements a cyclic version of the celebrated small gain theorem. In addition, a generalization of the proposed theorem is made via the use of angular scaling techniques for reducing conservatism. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear time-invariant system

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

arXiv

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arXiv 2024年

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. The Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Control Science and Engineering Tongji University Shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China The Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Engineering and Architecture University of Trieste Trieste34127 Italy

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multi-player non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensor nodes

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Toward an Interaction-Driven Framework for Modeling Big Data Visualization Systems

Toward an Interaction-Driven Framework for Modeling Big Data...

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24th Eurographics Conference on Visualization, EuroVis 2022

作者： Benvenuti, D. Fiordeponti, G. Cheng, H. Catarci, T. Fekete, J.D. Santucci, G. Angelini, M. Battle, L. Department of Computer Control and Management Engineering Antonio Ruberti University of Rome "La Sapienza" Rome Italy University of Maryland College ParkMD United States Aviz Inria Saclay France Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States

ISBN: (纸本)9783038681854

Designing big data visualization applications is challenging due to their complex yet isolated development. One of the most common issues is an increase in latency that can be experienced while interacting with the system. There exists a variety of optimization techniques to handle this issue in specific scenarios, but we lack models for integrating them in a holistic way, hindering the integration of complementary functionality and hampering consistent evaluation across systems. In response, we present a framework for modeling the big data visualization pipeline which builds a bridge between the Visualization, Human-computer Interaction, and Database communities by integrating their individual contributions within a single, easily interpretable pipeline. With this framework, visualization applications can become aware of the full end-to-end context, making it easier to determine which subset of optimizations best suits the current context. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Data visualization

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Cross-Modality Person Re-Identification based on Shared Feature Attention Aggregation Algorithm 13

Cross-Modality Person Re-Identification based on Shared Feat...

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13th International Conference on Information science and Technology, ICIST 2023

作者： Wang, Jiong Zhang, Nian Xie, Linjuan Xiong, Jiang Song, XiaoLing Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Chongqing40044 China Department of Electrical and Computer Engineering University of the District of Columbia WashingtonDC20008 United States College of Mathematics and Statistics Chongqing Three Gorges University Chongqing40044 China College of Computer Science and Engineerings Chongqing Three Gorges University Chongqing40044 China

ISBN: (纸本)9798350313925

Person re-identification in the visible-infrared spectrum (VI-ReID) is a challenging task due to significant difference of modalities, making it difficult to extract salient features across modalities. To solve this problem, in this paper, we propose a novel Cross-Modality Shared Feature Attention Aggregation algorithm (cm-SFAA) for VI-ReID. In the cm-SFAA approach, we first design a Random Channel Alternation Erasure Network to better learn robust features against occlusions and noise, as well as diverse feature representations under different channel combinations. This enhances the generalization ability of the model. Furthermore, a Local Feature Aggregation module is introduced in cm-SFAA, which utilizes an adaptive weighting mechanism to aggregate features from different levels and domains, thereby improving the performance of the model. Extensive experiments on two mainstream datasets SYSU-MM01 and RegDB show that cm-SFAA outperforms several baseline methods and acquires state-of-the-art performance. The source code of our proposed cm-SFAA is available at https://***/wdqlndl/cm-SFAA. © 2023 IEEE.

关键词： computer vision

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Classification-Detection of Medical Images by Visualizing Hidden-Layer Features of a Deep Learning Approach 12th

Classification-Detection of Medical Images by Visualizing Hi...

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12th International Conference on computer engineering and Networks, CENet 2022

作者： Tong, Ziyu Deng, Xing Shao, Haijian Wang, Xia School of Computer Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing Jiangsu210096 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Deep learning algorithms, such as CNN, have been widely used on a variety of computer vision problems due to their powerful capacity to extract nonlinear features for medical picture analysis. However, due to the decreased identification rate for medical images, CNNs continue to fail to offer an interpretable depiction of the hidden-layers’ features. Furthermore, the enormous amount of redundant parameters from the convolutional to fully connected layers will surely add to the computational complexity. This work provides a set of fast deep learning architectures for visualizing and analyzing hidden-layer properties in medical images. The clustered feature map is used to simplify the model parameters and minimize redundancy parameters in order to meet the goal of a streamlined trade-off between model performance and architecture. The testing results on the medial images reveal that 41% of redundant channels may be removed while 100% of performance can be preserved. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Propagation Characteristics of Partial Discharges in an Oil-Filled Power Transformer

Propagation Characteristics of Partial Discharges in an Oil-...

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Compatibility in Power Electronics (CPE)

作者： Sayed Mohammad Kameli Shady S. Refaat Ali Ghrayeb Haitham Abu-Rub Jarosław Guziński Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Engineering School of Physics Engineering and Computer Science University of Hertfordshire Electrical and Control Engineering Faculty Gdansk University of Technology ul. Gabriela Narutowicza 11/12 Gdańsk Poland

Power transformers are among the most important assets in the power transmission and distribution grid. However, they suffer from degradation and possible faults causing major electrical and financial losses. Partial discharges (PDs) are used to identify the insulation health status and their degradation level. PDs are incipient, low-magnitude faults caused by localized dielectric breakdown. Those activities emit signals in many forms, including electrical, chemical, acoustic, electromagnetic, and optical, facilitating various detection methods. This paper provides a theoretical basis for the condition evaluation of an oil-filled power transformer and clarifies the relationship between the operating voltage, void location, and electric-field intensity within the void. This was achieved by investigating the propagation characteristics of partial discharge signals in an oil-filled power transformer using a 3D finite element method (FEM) based simulation. Moreover, the characterization of simulated PD sources at different positions is investigated in this paper. The simulation results are curried out to show that air voids near the windings are subject to greatest peak electric field intensity.

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