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Abnormalities Detection in Chest CT Scan Images using Federated and Deep Learning

Abnormalities Detection in Chest CT Scan Images using Federa...

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International Conference on Emerging Technologies, ICET

作者： Mashab Ali Javed Syed Jawad Hussain Aqsa Yasmeen Amaad Khalil Muhammad Bilal Rafaqat Department of Electrical and Computer Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan School of ICT University of Tasmania Hobart Australia

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Lung cancer, which accounts for an estimated 2.20 million reported cases and leading to 1.80 million fatalities in the year 2020, represents a significant global cause of mortality. Early detection during Computed Tomography (CT) chest scans can improve survival rate of patients. To address the growing use of CT scan, a robust automated model is required to eliminate tedious manual procedures and reduce variability in diagnosis. Moreover, there are challenges associated with medical research such as data privacy, collaboration, regulatory compliance, and data diversity. In this study, we propose two machine learning (ML) methods and subsequently conduct a comparative analysis. In the first approach, we presented a deep learning technique to achieve high image classification accuracy by using multi-layer CNN and two pre-trained models, namely ResNet50 and DenseNet201. In the second approach, we presented a Federated Learning (FL) technique, which entails the utilization of all three models for classifying abnormalities in chest CT scans. Our results illustrate that the FL-based approach can assist in the diagnosis of anomalies in chest CT scan, while ensuring the privacy of patient data. By exploring the delicate balance between privacy and accuracy in medical image classification, our study will contribute to the advancing realm of research.

关键词： Deep learning Data privacy Privacy Accuracy Federated learning Computed tomography Data models Medical diagnostic imaging Residual neural networks Image classification

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Differentially Private Online Federated Learning with Correlated Noise

Differentially Private Online Federated Learning with Correl...

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IEEE Conference on Decision and Control

作者： Jiaojiao Zhang Linglingzhi Zhu Mikael Johansson Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Systems Engineering and Engineering Management Chinese University of Hong Kong

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an $(\epsilon, \delta)$ DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm.

关键词： Privacy Federated learning Heuristic algorithms Noise Signal processing algorithms Servers

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Robust and Safe Task-Driven Planning and Navigation for Heterogeneous Multi-Robot Teams with Uncertain Dynamics

Robust and Safe Task-Driven Planning and Navigation for Hete...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Tianyang Pan Christos K. Verginis Lydia E. Kavraki Department of Computer Science Rice University Department of Electrical Engineering Division of Signals and Systems Uppsala University Ken Kennedy Institute Rice University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Task and motion planning (TAMP) can enhance intelligent multi-robot coordination. TAMP becomes signifi-cantly more complicated in obstacle-cluttered environments and in the presence of robot dynamic uncertainties. We propose a control framework that solves the motion-planning problem for multi-robot teams with uncertain dynamics, addressing a key component of the TAMP pipeline. The principal part of the proposed algorithm constitutes a decentralized feedback control policy for tracking of reference paths taken by the robots while avoiding collision and adapting in real time to the underlying dynamic uncertainties. The proposed framework further leverages sampling-based motion planners to free the robots from local-minimum configurations. Extensive experimental results in complex, realistic environments illustrate the superior efficiency of the proposed approach, in terms of planning time and number of encountered local minima, with respect to state-of-the-art baseline methods.

关键词： Uncertainty Tracking Robot kinematics Heuristic algorithms Dynamics Robot sensing systems Planning Trajectory Sensors Collision avoidance

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Generative Adversarial Networks for Imputing Sparse Learning Performance 27th

Generative Adversarial Networks for Imputing Sparse Learnin...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Liang Yeasin, Mohammed Lin, Jionghao Havugimana, Felix Hu, Xiangen Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States Centre for Learning Analytics Monash University MelbourneVIC3800 Australia Department of Applied Social Sciences Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783031781711

Learning performance data, such as correct or incorrect responses to questions in Intelligent Tutoring systems (ITSs) is crucial for tracking and assessing the learners’ progress and mastery of knowledge. However, the issue of data sparsity, characterized by unexplored questions and missing attempts, hampers accurate assessment and the provision of tailored, personalized instruction within ITSs. This paper proposes using the Generative Adversarial Imputation Networks (GAIN) framework to impute sparse learning performance data, reconstructed into a three-dimensional (3D) tensor representation across the dimensions of learners, questions and attempts. Our customized GAIN-based method computational process imputes sparse data in a 3D tensor space, significantly enhanced by convolutional neural networks for its input and output layers. This adaptation also includes the use of a least squares loss function for optimization and aligns the shapes of the input and output with the dimensions of the questions-attempts matrices along the learners’ dimension. Through extensive experiments on six datasets from various ITSs, including AutoTutor, ASSISTments and MATHia, we demonstrate that the GAIN approach generally outperforms existing methods such as tensor factorization and other generative adversarial network (GAN) based approaches in terms of imputation accuracy. This finding enhances comprehensive learning data modeling and analytics in AI-based education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Cross-Modality Image Transformation Using Generative Adversarial Network 24th

Cross-Modality Image Transformation Using Generative Adversa...

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24th Conference on Man-Machine-Environment System engineering, MMESE 2024

作者： Liu, Haoting Yu, Shiqi Hou, Qingwen Chen, Shuai Guo, Kuiyuan Wang, Xu Li, Qing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing100083 China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China CATARC Technology Co. Ltd. Shenzhen518118 China

ISBN: (纸本)9789819771387

A cross-modality image transformation method using the Generative Adversarial Network (GAN) is proposed in this paper. First, the preprocessing methods are performed to the original image data. The computational steps include adjusting image size and suppressing image noises. Second, a kind of GAN is constructed. Both the generator network and discriminator network are created. Third, the GAN is trained and the back propagation optimization is performed continuously. Finally, the trained GAN can be used for data enhancement. To test the effects of computational steps above, the image transformation from the visible light image to other noise-contaminated image is performed. Lots of experiments have illustrated the correctness of our method. © Beijing KeCui Man-Machine-Environment System engineering Technology Research Academy 2024.

关键词： Generative adversarial networks

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Adjustable Current Limiting Function with a Monolithically Integrated SiC Circuit Breaker Device

Adjustable Current Limiting Function with a Monolithically I...

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作者： Takamori, Taro Wada, Keiji Boettcher, Norman Erlbacher, Tobias Saito, Wataru Nishizawa, Shin-Ichi Tokyo Metropolitan University Department of Electrical Engineering and Computer Science Tokyo192-0397 Japan Fraunhofer Institute for Integrated Systems and Device Technology IISB Erlangen91058 Germany Kyushu University Research Institute for Applied Mechanics Fukuoka816-8580 Japan

This article proposes a current limiting function for a self-sensing and self-triggering monolithically integrated SiC circuit breaker device. The proposed function provides the device not only with a fast-response current breaking operation but also with the current limiting operation, including supplying a constant current to the load and preventing the detection of inrush currents. This function is suitable for replacing mechanical conductors in initial charging circuits within rectifiers and safety equipment in case of overcurrent phenomena. The proposed method achieves certain constant currents through a simple variation of the gate-drive circuit with the adjustable parameters of an additional MOSFET. The mechanism of the current limiting function is verified using TCAD simulations based on certain conditions. Consequently, experimental results verified that the circuit breaker device with the current limiting function reduces the inrush current by up to 76.9% using a 500 VDC distribution circuit system. © 1972-2012 IEEE.

关键词： Silicon carbide

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Multi-Stage Model Predictive Control with Enhanced Discrete-Time Models for Multilevel Inverters

Multi-Stage Model Predictive Control with Enhanced Discrete-...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Hoang Le Apparao Dekka Deepak Ronanki Abdul R. Beig Department of Electrical and Computer Engineering Lakehead University Thunder Bay Canada Department of Engineering Design Indian Institute of Technology Madras Chennai India Advanced Power and Energy Systems Electrical Engineering Department Khalifa University Abu Dhabi UAE

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Model accuracy, computational complexity, and weighting factor dependency are three primary challenges associated with multi-stage model predictive control (MS-MPC) of multilevel inverters (MLIs). Typically, existing MS-MPC employs the forward Euler integration method to discretize continuous-time models, due to the ease of implementation in real-time controllers. This approach faces a significant deterioration in computational accuracy as sampling period increases. To address this issue, this paper proposes a new formulation of MS-MPC for MLIs. Also, an enhanced discrete-time model of MLI by using Heun’s integration method is proposed to implement the proposed MS-MPC, resulting in a more precise formulation and indirect minimization of common-mode voltage (CMV). Unlike the existing methods, the proposed method does not employ a cost function with weighting factor or offline selection of voltage vectors (VVs) to minimize the CMV. Furthermore, it does not increase computational complexity compared to the existing MS-MPCs. Simulation studies are conducted on a four-level MLI system to validate the efficacy of the proposed MS-MPC, and its performance is compared with the existing methods.

关键词： Accuracy Computational modeling Predictive models Multilevel inverters Mathematical models Vectors Real-time systems Power electronics Computational complexity Predictive control

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Improving the Efficacy of Binary Communication Security in Communication Networks of Smart Cities

Improving the Efficacy of Binary Communication Security in C...

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3rd International Conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024

作者： Salama, Ramiz Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Mersin 10 Nicosia Turkey Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Mersin 10 Nicosia Turkey

ISBN: (纸本)9789819627660

Recently, smart cities have become more and more popular as a way to enhance society and the standard of living for its people. These are the results of the increased social and economic prospects that the advancement of information and communication technology (ICT) has brought about. But there are certain issues with our presumptions about privacy and security. The usage of cellphones and other technologies has already enhanced people’s engagement and sense of connection. Gadgets, smart energy meters, and security appliances are commonplace these days. The Internet of Things will soon be fully connected to homes, public spaces, healthcare systems, and other social institutions. In this work, we improve the security performance of communication networks in smart cities by implementing a novel framework and scheme to guarantee strong data secrecy and authentication. The smart city system can set up shared session key procedures and mutual authentication to secure a two-way communication channel between the control center and smart meters. Our comprehensive simulation was used to test and evaluate the security performance of the smart city communication network both with and without our suggested technique. Throughput, latency, load, and traffic received packets per second were all measured. The resources available in our suggested solution were further limited by the fact that we used a network intrusion detection system (NIDS) and a man-in-the-middle (MITM) attack to evaluate and validate the security criteria. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

Renderable Street View Map-Based Localization: Leveraging 3D...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

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Backstepping-Based Adaptive Cooperative Control For Multiple Euler-Lagrange systems Under Unknown Harmonic Disturbances

Backstepping-Based Adaptive Cooperative Control For Multiple...

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2023 China Automation Congress, CAC 2023

作者： Chen, Ying Zhao, Zi-Yi Sun, Peng Guo, Xiang-Gui Wang, Jian-Liang Coutinho, Daniel School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Hangzhou Innovation Institute of Beihang University Collaborative Flying Robotics HangZhou China Universidade Federal de Santa Catarina Department of Automation and Systems Florianópolis Brazil

ISBN: (纸本)9798350303759

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system. © 2023 IEEE.

关键词： Backstepping

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