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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Long, Sian Zhao, Na Qin, Chaojun Luo, Yudong He, Zhipeng Shen, Yantao Dalian Maritime University Department of Computer Science and Technology Liaoning Dalian116026 China School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China University of Nevada Department of Electrical and Biomedical Engineering RenoNV89557 United States

ISBN: (纸本)9781665481090

Model Predictive Control (MPC) is commonly used to solve flight control problems in quadrotors due to its ability to handle multivariate and practical constraints. Considering its computational problem, which may lead to untimely control signal updates and thus affect flight stability, we adopt cascade control, where an MPC controller is used for attitude control in the inner loop, and a PD controller is used for position control in the outer loop to reduce the computational load. In the inner loop, physical constraints are considered, and an augmented MPC is constructed to reduce the steady-state error. Finally, we build a co-simulation environment based on Matlab and Coppeliasim to verify the proposed control scheme. © 2024 IEEE.

关键词： Predictive control systems

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Integrated Grading Framework for Histopathological Breast Cancer: Multi-level Vision Transformers, Textural Features, and Fusion Probability Network 27th

Integrated Grading Framework for Histopathological Breast C...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Balaha, Hossam Magdy Ali, Khadiga M. Mahmoud, Ali Ghazal, Mohammed El-Baz, Ayman Department of Bioengineering J.B. Speed School of Engineering University of Louisville LouisvilleKY United States Pathology Department Faculty of Medicine Mansoura University Mansoura Egypt Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9783031781032

Breast cancer (BC) remains a significant global health concern, necessitating accurate and efficient diagnostic approaches. In this study, we propose a comprehensive framework that integrates feature extraction, selection, and classification using Support Vector Machines (SVM) along with hyperparameter optimization. Additionally, we employ multi-level Vision Transformers (ViTs) for patch classification, aiming to capture both local and global information from histopathological slides. Moreover, we introduce a Fusion Probability Network (FPN) to combine the outputs of SVM and ViTs. Through this multi-faceted approach, we aim to improve diagnostic performance and contribute to more effective BC diagnosis. Sensitivity analyses and ablation studies across various sample sizes confirm the framework’s effectiveness. Results show high accuracy (up to 96.50%), precision (up to 93.33%), recall (up to 93%), specificity (up to 97.67%), F1 score (up to 92.99%), and balanced accuracy (up to 95.33%). Ablation studies highlight the significance of the feature extraction pipeline in enhancing the framework’s effectiveness and robustness, as well as its adaptability to diverse patch morphologies. Overall, our study offers promising avenues for improving BC grading, with potential implications for enhancing clinical decision-making and patient outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diseases

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Multi-agent Architecture for Passive Rootkit Detection with Data Enrichment 1

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International Conference on computer Science, Electronics, and Industrial engineering, CSEI 2022

作者： Trinks, Maickel Gondim, João Albuquerque, Robson Professional Post-Graduate Program in Electrical Engineering - PPEE - Department of Electrical Engineering University of Brasília DF Brasília Brazil Department of Computer Science Universidade de Brasília DF Brasília Brazil

ISBN: (数字)9783031305924

ISBN: (纸本)9783031305917

The added value of the information transmitted in a cybernetic environment has resulted in a sophisticated malicious actions scenario aimed at data exfiltration. In situations with advanced actors, like APTs, such actions use obfuscation techniques of harmful activities as persistence assurance on strategic targets. The MADEX and NERD architectures proposed flow analysis solutions to detect rootkits that hide network traffic;however, it presents some operational cost, either in traffic volume or due to lack of aggregated information. In that regard, this work changes and improves user flow analysis techniques to eliminate impacts on network traffic, with data enrichment on local and remote bases, detection of domains consulted by rootkits and aggregation of information to generate threat intelligence, while maintaining high performance. The results show the possibility of aggregating information to data flows used by rootkits in order to have effective cyber defense actions against cybernetic threats without major impacts on the existing network infrastructure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Information-Theoretic Design for High-Dimensional Computational Imaging

Information-Theoretic Design for High-Dimensional Computatio...

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Computational Optical Sensing and Imaging, COSI 2024 - Part of Optica Imaging Congress

作者： Markley, Eric Kabuli, Leyla Chien, Tiffany Pinkard, Henry Waller, Laura UCB/UCSF Joint Graduate Program in Bioengineering University of California Berkeley United States Department of Electrical Engineering and Computer Sciences University of California Berkeley United States

We present a technique for information-theoretic optimization of computational imaging systems demonstrated in snapshot 3D microscopy. By directly evaluating measurement quality and decoupling optimization from downst... 详细信息

关键词： Decoding

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COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPERATURE PREDICTION IN VANADIUM REDOX FLOW BATTERIES

COMPARATIVE PERFORMANCE OF MACHINE LEARNING MODELS FOR TEMPE...

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International Conference on Green Energy, Computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Zhang, Anxiu Juwono, Filbert H. Li, Hongfei Purwanto, Erick Wong, W.K. Liu, Linfeng Telecommunications Engineering Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China Applied Mathematics Program Xi'an Jiaotong-Liverpool University Suzhou China Department of Computing Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia Sustainable Energy Technology Program Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (纸本)9781837242689

Vanadium Redox Flow Batteries (VRFB) are promising for large-scale energy storage due to their long life and environmental benefits. Accurate temperature prediction is key to optimizing VRFB performance and longevity. This study compares the performance of four machine learning models, i.e., 1D CNN, Particle Swarm Optimization - Support Vector Regressor (PSO-SVR), Decision Tree (DT), and K-Nearest Neighbors (KNN), using a publicly available dataset. Results show that KNN achieves the best results with test Root Mean Square Error (RMSE) of 0.0424 (average) and test R2 of 0.9746 (average), demonstrating strong predictive accuracy. 1D CNN, however, shows poor generalization. These findings suggest that non-parametric models like KNN and DT are highly effective for VRFB temperature prediction. © The Institution of engineering & Technology 2024.

关键词： machine learning prediction Vanadium redox flow battery

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Smart Cloud Computing Approach for Electronic based Health Record System 5

Smart Cloud Computing Approach for Electronic based Health R...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Kanna, R. Kishore Anitha, R. Sundaramoorthy, K. Salau, Ayodeji Olalekan Danquah-Amoah, Archibald Banappagoudar, Sudharani B. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Jerusalem College of Engineering Department of Information Technology Chennai India Afe Babalola University Department of Electrical/ Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Accra Technical University Medical Laboratory Technology and Biomedical Engineering Ghana Itm University School of Nursing Science MP Gwalior India

ISBN: (纸本)9798331542559

This study provides a comprehensive examination of the potential benefits of speed and substantial change from integrating health measurement with cloud-based electronic health records (cloud EHRs) to expedite health care Many challenges a traditional health record systems meet include data fragmentation, limited accessibility, security. There were some concerns. The use of cloud computing in healthcare, which integrates patient data and provides data security and universal access, addresses these issues. The healthcare industry has rapidly embraced cloud computing, dramatically changing the way patient care is delivered and delivered. This study looks at the benefits of using a cloud-based electronic health record (Cloud EHR) in conjunction with the generation of health metrics, providing a detailed analysis of how this integration leads to improved health care delivery increase The article also discusses how cloud EHR systems use context -the-art technologies such as data analytics and machine learning and provide relevant data for prediction and analysis. They also review the security measures used in cloud systems to ensure that confidential health information is accessible, intact and private. © 2024 IEEE.

关键词： Patient treatment

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High Gain 130-GHz Frequency Doubler with Colpitts Output Buffer Delivering Poutup to 8 dBm with 6% PAE in 55-nm SiGe BiCMOS

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IEEE Solid-State Circuits Letters 2021年 4卷 36-39页

作者： Pirbazari, Mahmoud M. Mazzanti, Andrea Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia27100 Italy

A mm-Wave frequency doubler in an SiGe BiCMOS technology is presented. The core of the circuit comprises a push-push pair, for second-harmonic generation, and a stacked common-collector Colpitts oscillator which works as a common-base injection-locked amplifier to boost the conversion gain and output power. The class-C operation of the transistor in the Colpitts buffer leads to a pulsed current shape with enhanced second-harmonic content. As a result, the power conversion gain of the frequency doubler is increased by up to 10 dB, compared to the push-push pair alone. Moreover, the common-collector configuration keeps separate the oscillator tank from the load, allowing independent optimization of the harmonic conversion efficiency and the load impedance for maximum power delivery. Realized in an SiGe BiCMOS technology with 330-GHz f _{\mathrm{ max}} , the proposed frequency doubler delivers P _{\mathrm{ out}} up to 8 dBm at 130 GHz with 13-dB conversion gain and 6.3% power added efficiency. A Figure-of-Merit is proposed to benchmark frequency doublers and the presented chip shows up to 3 times improvement compared to the previously reported designs in the same frequency range. © 2018 IEEE.

关键词： Harmonic analysis

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Improved Time-Localization of Power System Forced Oscillations Using Changepoint Detection

Improved Time-Localization of Power System Forced Oscillatio...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Dosiek, Luke Hosur, Sanjay Union College Department of Electrical Computer and Biomedical Engineering SchenectadyNY12309 United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper explores the use of changepoint detection (CPD) for an improved time-localization of forced oscillations (FOs) in measured power system data. In order for the autoregressive moving average plus sinusoids (ARMA+S) class of electromechanical mode meters to successfully estimate modal frequency and damping from data that contains a FO, accurate estimates of where the FO exists in time series are needed. Compared to the existing correlation-based method, the proposed CPD method is based on upon a maximum likelihood estimator (MLE) for the detection of an unknown number changes in signal mean to unknown levels at unknown times. Using the pruned exact linear time (PELT) dynamic programming algorithm along with a novel refinement technique, the proposed approach is shown to provide a dramatic improvement in FO start/stop time estimation accuracy while being robust to intermittent FOs. These findings were supported though simulations with the minniWECC model. © 2022 IEEE.

关键词： Maximum likelihood estimation

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Smart Meter Application Analysis using PLS-SEM Deep Neural Network: A Case Study

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Journal of engineering Science and Technology Review 2023年第3期16卷 16-27页

作者： Thao, Nguyen Thi Phuong Duc, Minh Ly Bilik, Petr Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Faculty of Commerce Van Lang University Ho Chi Minh City Viet Nam Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB–Technical University of Ostrava 17. Listopadu2172/ 15 Ostrava708 00 Czech Republic

Smart meters measure, control, analyze, and predict the amount of electricity, water, and gas used. In developing countries, where there is no consensus to accept the use of smart meters, there are many possible risks when using smart meters. However, there are also many benefits of smart meters. This study conducts an overall assessment of the demand and impact of the smart meter in the southern region of Vietnam through a survey of 500 samples. This article examines information technology system (IS) related factors and engineering model-related factors according to technical readiness such as optimism, innovation, insecurity, and discomfort. Accompanying that is the expectation of smart meters, for Vietnamese people's intention to constantly use smart meters. Most of the previous studies on smart meter systems have focused on analyzing the impact of factors affecting the application using single-step structural equation modeling (SEM). In this study, it is proposed to use a 2-layer model between the research model of the multi-analysis method by combining the Partial Least Squares - Structural Equation Modeling (PLS-SEM) and Artificial Neural Networks (ANN) model was performed for additional analysis for the results of PLS-SEM, and ANN has higher predictive accuracy than PLS-SEM because ANN has the ability to perform well for both linear relational model and linear relationship model and non-linear with high prediction. First, the PLS-SEM model evaluates the factors affecting the intention to use the smart meter system. Second, the ANN ranks the impact factors of the critical predictors from the PLS-SEM model, and the Critical Performance Map Analysis (IPMA) analyzes the exact results for the critical performance of the variables elements. The results of this study show that the quality of information and quality of system factors of the IS model have a negative impact on users' intention to use smart meters. At the same time, factors of Optimization and innovati

关键词： Deep neural networks

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Prospector Heads: Generalized Feature Attribution for Large Models & Data 41

Prospector Heads: Generalized Feature Attribution for Large ...

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41st International Conference on Machine Learning, ICML 2024

作者： Machiraju, Gautam Derry, Alexander Desai, Arjun Guha, Neel Karimi, Amir-Hossein Zou, James Altman, Russ B. Ré, Christopher Mallick, Parag Department of Biomedical Data Science Stanford University United States Cartesia AI United States Department of Computer Science Stanford University United States Department of Electrical & Computer Engineering University of Waterloo Canada Department of Radiology Stanford University United States

Feature attribution, the ability to localize regions of the input data that are relevant for classification, is an important capability for ML models in scientific and biomedical domains. Current methods for feature attribution, which rely on "explaining" the predictions of end-to-end classifiers, suffer from imprecise feature localization and are inadequate for use with small sample sizes and high-dimensional datasets due to computational challenges. We introduce prospector heads, an efficient and interpretable alternative to explanation-based attribution methods that can be applied to any encoder and any data modality. Prospector heads generalize across modalities through experiments on sequences (text), images (pathology), and graphs (protein structures), outperforming baseline attribution methods by up to 26.3 points in mean localization AUPRC. We also demonstrate how prospector heads enable improved interpretation and discovery of class-specific patterns in input data. Through their high performance, flexibility, and generalizability, prospectors provide a framework for improving trust and transparency for ML models in complex domains. Copyright 2024 by the author(s)

关键词： Data assimilation

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