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Enhanced emotion recognition in an IoMT platform: leveraging data augmentation and the random forest algorithm for ECG-based E-health

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International Journal of Information Technology (Singapore) 2024年 1-11页

作者： Mokhtari, Bilal Merizig, Abdelhak Zerdoumi, Hadjer Department of Computer Science Mohamed Khider University Biskra 0700 Algeria Laboratoire d’Applications des mathématiques à l’informatique et à l’électronique: LAMIE Batna 2 University N 3 RN3 Batna Route de Fesdis 05000 Algeria LINFI Laboratory Mohamed Khider University Biskra 07000 Algeria

Advancement of technologies has enabled the creation of smart solutions to improve healthcare. The primary objective is to help healthcare practitioners make efficient and fast decisions and diagnoses. Vital signs provide meaningful information while monitoring patient health, which allows assessing patients’ emotional well-being and adjust therapy. This study introduces an AI-powered patient monitoring system and healthcare platform. The proposed healthcare platform uses Internet of Medical Things (IoMT) and machine learning to monitor and classify emotional states using ECG signals. Thus, a label transformation and Random Forest classifier are used to identify patient emotions as positive or negative based on selected features from Heart Rate Variability (HRV) provided by ECG sensor. Moreover, a data augmentation technique is used to expand the primary used dataset, DREAMER which is afterward integrated with SWELL and WESAD datasets to increase data variety. The model presents emotion identification on the platform with an accuracy of 99.89%, outperforming most existing related works. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Data augmentation E-health ECG Emotion recognition and classification IMoT Internet of Things Machine learning

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A Survey on Indoor Visible Light Positioning Systems: Fundamentals, Applications, and Challenges

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1656-1686页

作者： Zhu, Zhiyu Yang, Yang Chen, Mingzhe Guo, Caili Cheng, Julian Cui, Shuguang Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering and Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China The University of British Columbia Faculty of Applied Science School of Engineering KelownaBCV1V 1V7 Canada 518172 Hong Kong

The growing demand for location-based services in areas like virtual reality, robot control, and navigation has intensified the focus on indoor localization. Visible light positioning (VLP), leveraging visible light communications (VLC), becomes a promising indoor positioning technology due to its high accuracy and low cost. This paper provides a comprehensive survey of VLP systems. In particular, since VLC lays the foundation for VLP, we first present a detailed overview of the principles of VLC. Then, we provide an in-depth overview of VLP algorithms. The performance of each positioning algorithm is also compared in terms of various metrics such as accuracy, coverage, and orientation limitation. Beyond the physical layer studies, the network design for a VLP system is also investigated, including multi-access technologies, resource allocation, and light-emitting diode (LED) placements. Next, the applications of the VLP systems are overviewed. Finally, this paper outlines open issues, challenges, and opportunities for the research field. In a nutshell, this paper constitutes the first holistic survey on VLP from state-of-the-art studies to practical uses. © 1998-2012 IEEE.

关键词： Resource allocation

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Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

arXiv

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arXiv 2025年

作者： Chen, Weilin Cai, Ruichu Qiao, Jie Yan, Yuguang Hernández-Lobato, José Miguel School of Computer Science Guangdong University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Department of Engineering University of Cambridge Cambridge United Kingdom

Estimating causal effects under networked interference is a crucial yet challenging problem. Existing methods based on observational data mainly rely on the networked unconfoundedness assumption, which guarantees the identification of networked effects. However, the networked unconfoundedness assumption is usually violated due to the latent confounders in observational data, hindering the identification of networked effects. Interestingly, in such networked settings, interactions between units provide valuable information for recovering latent confounders. In this paper, we identify three types of latent confounders in networked inference that hinder identification: those affecting only the individual, those affecting only neighbors, and those influencing both. Specifically, we devise a networked effect estimator based on identifiable representation learning techniques. Theoretically, we establish the identifiability of all latent confounders, and leveraging the identified latent confounders, we provide the networked effect identification result. Extensive experiments validate our theoretical results and demonstrate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

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Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers 46

Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy ...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

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A Dual-Band MIMO Monopole Antenna with Enhanced Isolation for Sub 6 GHz 5G Applications

A Dual-Band MIMO Monopole Antenna with Enhanced Isolation fo...

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2024 International Conference on Advances in Electrical and Communication Technologies, ICAECOT 2024

作者： Hamrioui, Fatma Zohra Bouaza, Abdelmaoula Touhami, Rachida Yagoub, Mustapha C.E. Azrar, Arab Electronic Department 10 Rue des Frères OUDEK El Harrach Algiers16200 Algeria Usthb university Bp32 Instrumentation laboratory Electronics and Informatics faculty El Alia Bab Ezzouar Algiers Algeria University of Ottawa Element laboratory School of Electrical Engineering and Computer Science Ottawa Canada Institute of Electrical and Electronic Engineering University M'Hamed BOUGARA-Boumerdes Signals and Systems Laboratory Electronics Department Boumerdes Algeria

ISBN: (纸本)9798350353754

In this paper, a novel dual-band MIMO monopole antenna for 5G applications is presented. The proposed MIMO antenna is made up of two rings that are T-shaped, generating two distinguished bands: the N77 band and the 6 GHz 5G band. A Tshaped ground branch with a bottom slit is integrated into the ground plane to mitigate the mutual coupling at lower and higher operating bands by redistributing the current, thus improving the isolation enhancement. A reversed T-shaped grounded parasitic element with extra grounded parasitic elements on the partial ground is inserted on top of the substrate, between the MIMO elements. The grounded process is introduced by inserting two metallic vias in each branch of the parasitic element. These parasitic elements effectively reduce the coupling between the MIMO antennas by restricting the current to the respective antenna, thus improving the isolation at the higher band. To verify the design principle of the suggested MIMO antenna, this latter is manufactured and tested. It exhibits excellent impedance matching and high isolation characteristics, and a compact footprint of 29 mm × 42.5 mm. Thus, it is attractive for 5G MIMO systems. © 2024 IEEE.

关键词： Monopole antennas

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IDTrust: Deep Identity Document Quality Detection with Bandpass Filtering

IDTrust: Deep Identity Document Quality Detection with Bandp...

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IEEE Winter Applications and computer Vision Workshops (WACVW)

作者： Musab Al-Ghadi Joris Voerman Mickaël Coustaty Olivier Lessard Nicolas Sidere Computer Science L3i Laboratory La Rochelle University France Information Technology Department Hashemite University Jordan IMDS SAS France

ISBN: (数字)9798331536626

ISBN: (纸本)9798331536633

The increasing use of digital technologies and mobile-based registration procedures highlights the vital role of personal identity documents (IDs) in verifying users and safeguarding sensitive information. However, the rise in counterfeit ID production poses a significant challenge, necessitating the development of reliable and efficient automated verification methods. This paper introduces IDTrust, a deep-learning framework for assessing the quality of IDs. IDTrust is a system that detects the quality of identity documents using a deep learning-based approach. This method eliminates relying on original document patterns for quality checks and pre-processing steps for alignment. As a result, it offers significant improvements in terms of dataset applicability. Using a bandpass filtering-based method, the system aims to detect and differentiate ID quality effectively. Comprehensive experiments on the MIDV-2020 and L3i-ID datasets identify optimal parameters, significantly improving discrimination performance, and effectively distinguishing between original and scanned ID documents.

关键词： Band-pass filters computer vision Accuracy Filtering Conferences Computational modeling Production Real-time systems Computational efficiency Reliability

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Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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Attentional Ptycho-Tomography(APT)for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

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Light(science & Applications) 2023年第6期12卷 1127-1140页

作者： Iksung Kang Ziling Wu Yi Jiang Yudong Yao Junjing Deng Jeffrey Klug Stefan Vogt George Barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Argonne National Laboratory LemontIL 60439USA Singapore-MIT Alliance for Research and Technology(SMART)Centre 1 CREATE WaySingapore 138602Singapore Present address:Department of Molecular and Cell Biology University of CaliforniaBerkeleyCA 94720USA Present address:Singapore-MIT Alliance for Research and Technology(SMART)Centre 1 CREATE WaySingapore 138602Singapore Present address:Center for Transformative Science ShanghaiTech University201210 ShanghaiChina

Noninvasive X-ray imaging of nanoscale three-dimensional objects,such as integrated circuits(ICs),generally requires two types of scanning:ptychographic,which is translational and returns estimates of the complex electromagnetic field through the IC;combined with a tomographic scan,which collects these complex field projections from multiple ***,we present Attentional Ptycho-Tomography(APT),an approach to drastically reduce the amount of angular scanning,and thus the total acquisition *** is machine learning-based,utilizing axial self-Attention for Ptycho-Tomographic *** is trained to obtain accurate reconstructions of the ICs,despite the incompleteness of the *** training process includes regularizing priors in the form of typical patterns found in IC interiors,and the physics of X-ray propagation through the *** show that APT with×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique(SART)with the original set of *** using the same set of reduced angles,then APT also outperforms Filtered Back Projection(FBP),Simultaneous Iterative Reconstruction Technique(SIRT)and *** time needed to compute the reconstruction is also reduced,because the trained neural network is a forward operation,unlike the iterative nature of these *** experiments show that,without loss in quality,for a 4.48×93.2×3.92µm^(3) IC(≃6×10^(8) voxels),APT reduces the total data acquisition and computation time from 67.96 h to 38 *** expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging,including materials science and biological imaging.

关键词： utilizing computation interior

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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More for Less: Integrating Capability-Predominant and Capacity-Predominant Computing

arXiv

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arXiv 2025年

作者： Zheng, Zhong Papka, Michael E. Lan, Zhiling Department of Computer Science University of Illinois Chicago United States Argonne National Laboratory University of Illinois Chicago United States University of Illinois Argonne National Laboratory Chicago United States

Capability jobs (e.g., large, long-running tasks) and capacity jobs (e.g., small, short-running tasks) are two common types of workloads in high-performance computing (HPC). Different HPC systems are typically deployed to handle distinct computing workloads. For example, Theta at the Argonne Leadership Computing Facility (ALCF) primarily serves capability jobs, while Cori at the National Energy Research Scientific Computing Center (NERSC) predominantly handles capacity workloads. However, this segregation often leads to inefficient resource utilization and higher costs due to the need for operating separate computing platforms. This work examines what-if scenarios for integrating siloed platforms. Specifically, we collect and characterize two real workloads from production systems at DOE laboratories, representing capability-predominant and capacity-predominant computing, respectively. We investigate two approaches to unification. Workload fusion explores how efficiently resources are utilized when a unified system accommodates diverse workloads, whereas workload injection identifies opportunities to enhance resource utilization on capability computing systems by leveraging capacity jobs. Finally, through extensive trace-based, event-driven simulations, we explore the potential benefits of co-scheduling both types of jobs on a unified system to enhance resource utilization and reduce costs, offering new insights for future research in unified computing. Copyright © 2025, The Authors. All rights reserved.

关键词： Computing power

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