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A review of hidden markov models and recurrent neural networks for event detection and localization in biomedical signals

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arXiv 2020年

作者： Khalifa, Yassin Mandic, Danilo Sejdić, Ervin Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering Imperial College LondonSW7 2BT United Kingdom Department of Bioengineering University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States

Biomedical signals carry signature rhythms of complex physiological processes that control our daily bodily activity. The properties of these rhythms indicate the nature of interaction dynamics among physiological processes that maintain a homeostasis. Abnormalities associated with diseases or disorders usually appear as disruptions in the structure of the rhythms which makes isolating these rhythms and the ability to differentiate between them, indispensable. computer aided diagnosis systems are ubiquitous nowadays in almost every medical facility and more closely in wearable technology, and rhythm or event detection is the first of many intelligent steps that they perform. How these rhythms are isolated? How to develop a model that can describe the transition between processes in time? Many methods exist in the literature that address these questions and perform the decoding of biomedical signals into separate rhythms. In here, we demystify the most effective methods that are used for detection and isolation of rhythms or events in time series and highlight the way in which they were applied to different biomedical signals and how they contribute to information fusion. The key strengths and limitations of these methods are also discussed as well as the challenges encountered with application in biomedical signals. Copyright © 2020, The Authors. All rights reserved.

关键词： Biomedical signal processing

Fast Network Recovery from Large-Scale Disasters: A Resilient and Self-Organizing RAN Framework

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arXiv 2024年

作者： Yagan, M. Yaser Kayraklik, Sefa Kesir, Samed Sumen, Gizem Hokelek, Ibrahim Basaran, Mehmet Alexandropoulos, George C. Basar, Ertugrul Cavdar, Cicek Arslan, Huseyin Gorcin, Ali Lab TÜBİTAK-BİLGEM Kocaeli Turkey The Department of Electronics and Telecommunications Engineering Istanbul Technical University Istanbul Turkey The 6GEN Laboratory Istanbul34854 Turkey The Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens15784 Greece The Department of Electrical and Computer Engineering University of Illinois ChicagoIL60601 United States Department of Electrical and Electronics Engineering Koç University Istanbul Sariyer Turkey The Department of Computer Science KTH Royal Institute of Technology Kista Sweden The Department of Electrical and Electronics Engineering Istanbul Medipol University Istanbul34810 Turkey

Extreme natural phenomena are occurring more frequently everyday in the world, challenging, among others, the infrastructure of communication networks. For instance, the devastating earthquakes in Turkiye in early 2023 showcased that, although communications became an imminent priority, existing mobile communication systems fell short with the operational requirements of harsh disaster environments. In this article, we present a novel framework for robust, resilient, adaptive, and open source sixth generation (6G) radio access networks (Open6GRAN) that can provide uninterrupted communication services in the face of natural disasters and other disruptions. Advanced 6G technologies, such as reconfigurable intelligent surfaces (RISs), cell-free multiple-input-multiple-output, and joint communications and sensing with increasingly heterogeneous deployment, consisting of terrestrial and non-terrestrial nodes, are robustly integrated. We advocate that a key enabler to develop service and management orchestration with fast recovery capabilities will rely on an artificial-intelligence-based radio access network (RAN) controller. To support the emergency use case spanning a larger area, the integration of aerial and space segments with the terrestrial network promises a rapid and reliable response in the case of any disaster. A proof-of-concept that rapidly reconfigures an RIS for performance enhancement under an emergency scenario is presented and discussed. © 2024, CC BY-NC-ND.

关键词： Radio access networks

Learning to entangle radio resources in vehicular communications: An oblivious game-theoretic perspective

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arXiv 2018年

作者： Chen, Xianfu Wu, Celimuge Bennis, Mehdi Zhao, Zhifeng Han, Zhu VTT Technical Research Centre of Finland Oulu Finland Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Centre for Wireless Communications University of Oulu Finland College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering University of Houston HoustonTX United States Department of Computer Science University of Houston HoustonTX United States

This paper studies the problem of non-cooperative radio resource scheduling in a vehicle-to-vehicle communication network. The technical challenges lie in high vehicle mobility and data traffic variations. Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUE-pairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued packets. The frequency allocation at the beginning of each slot by the RSU is regulated following a sealed secondprice auction. Each VUE-pair aims to optimize the expected long-term performance. Such interactions among VUE-pairs are modelled as a stochastic game with a semi-continuous global network state space. By defining a partitioned control policy, we transform the stochastic game into an equivalent game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium (MPE), which characterizes the optimal solution to the equivalent game. The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE policies. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Game theory

Hybrid Cnn-Bi-Lstm Model-Based Intention Decoding from Fingers of the Same Hand in High Demanding Boundary Voidance Task

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SSRN

SSRN 2023年

作者： Fu, Rongrong Wang, Zeyi Wang, Shiwei Mi, Ruifu Wen, Guilin Chen, Junxiang Wang, Yaodong School of Electrical Engineering Yanshan University Qinhuangdao China Jiangxi New Energy Technology Institute Xinyu China Department of Biomedical Informatics The University of Pittsburgh United States School of Computer Science Wuhan University Wuhan China

Capability for dexterous decoding of control intention involving electroencephalography (EEG) is of paramount significance to the research of cognitive perception and movement in computational neuroscience. A reasonable experimental task is a crucial prerequisite for evoking high-quality EEG signals. However, most EEG evoking tasks in computational neuroscience are low demanding sensory-motor tasks, which easily make the subjects fatigue and slack. In this study, a period of high concentration of users can be maintained by a high demanding boundary avoidance task. Meanwhile, instead of decoding control intention from large limbs’ movements, a finer and more challenging control intention decoding from fingers of the same hand was studied under this high demanding task. Specifically, EEG signals during control period of fingers within the same hand are collected under the boundary avoidance task. A novel hybrid model of CNN-Bi-LSTM is proposed for decoding dexterous EEG-based control intention. The experimental results from ten subjects assessed by 5-fold cross-validation method show that analyzing the EEG signals by the proposed model can enable accurate decoding of fingers control intentions within the same hand in this boundary avoidance task. © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

Comparative Analysis of Movie Recommendation Systems Using Filtering Techniques on IMDB and Rotten Tomatoes

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Comparative Analysis of Movie Recommendation Systems Using F...

International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Santosh Kumar Majhi Preetipadma Sahani Rosy Pradhan Biswaranjan Acharya Foteini Grivokostopoulou Andreas Kanavos Vassilis C. Gerogiannis Department of Computer Science and Information Technology Guru Ghasidas Vishwavidyalaya (Central University) Bilaspur India Department of Computer Science and Engineering Veer Surendra Sai University of Technology Burla Odisha India Department of Electrical Engineering Veer Surendra Sai University of Technology Burla Odisha India Department of Computer Engineering AI Marwadi University Rajkot India Computer Technology Institute and Press “Diophantus” Patras Greece Department of Informatics Ionian University Corfu Greece Department of Digital Systems University of Thessaly Larissa Greece

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Recommendation systems are pivotal in industries like entertainment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and Rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features; collaborative, which processes user behavior patterns; and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and Root Mean Square Error (RMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP©k of 0.271 and an RMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Sentiment analysis Filtering Reviews Soft sensors Data integrity Entertainment industry Motion pictures Reliability Root mean square Recommender systems

Enhanced Driver Stress Prediction from Multiple Biosignals via CNN Encoder-Decoder Model

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Enhanced Driver Stress Prediction from Multiple Biosignals v...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Vishal Singh Roha Maggie Ezzat Gaber Gendy Kaveti Pavan Thomas M. Deserno Nagarajan Ganapathy Mehmet R. Yuce Dept. of Electrical & Computer Systems Monash University Melbourne Australia Dept. of Biomedical Engineering Indian Institute of Technology Hyderabad Hyderabad India PLRI Lab for Medical Informatics Technical University of Braunschweig Brunswick Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this work, we present PhysioFuseNet, a novel framework designed to enhance driver stress state classification. PhysioFuseNet integrates a CNN-based encoder-decoder model with multimodal biosignal fusion. Using a driving simulator, different multimodal signals were acquired, namely electrocardiography, electrodermal activity, photoplethysmography, and respiration rate from (N = 25) healthy subjects. The experiment is of 35 minutes duration and contains different stress states (baseline (5 minutes), while normal, cognitive, and emotional sessions for 10 minutes). Multimodal features are extracted and employed in an encoder-decoder network. Extracted encoder features are combined through intermediate fusion and fed to support vector machine (SVM) and random forest (RF) classifiers. Experimental results demonstrate the efficacy of our approach, outperforming previous methods by achieving accuracies of 0.95 and 0.94 for SVM and RF, respectively. Notably, the framework excels in classifying emotional and cognitive stress states. In summary, the proposed framework could be useful in stress assessment in real-time and clinical conditions.

关键词： Support vector machines Radio frequency Biological system modeling Anxiety disorders Predictive models Electrocardiography Feature extraction Photoplethysmography Real-time systems engineering in medicine and biology

Community-based security for the internet of things

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arXiv 2018年

作者： Zhu, Quanyan Rass, Stefan Schartner, Peter Department of Electrical and Computer Engineering New York University 2 Metrotech Center Brooklyn11201 United States Institute of Applied Informatics System Security Group Universitaet Klagenfurt Universitaetsstrasse 65-67 Klagenfurt9020 Austria

With more and more devices becoming connectable to the internet, the number of services but also a lot of threats increases dramatically. Security is often a secondary matter behind functionality and comfort, but the problem has already been recognized. Still, with many IoT devices being deployed already, security will come step-by-step and through updates, patches and new versions of apps and IoT software. While these updates can be safely retrieved from app stores, the problems kick in via jailbroken devices and with the variety of untrusted sources arising on the internet. Since hacking is typically a community effort these days, security could be a community goal too. The challenges are manifold, and one reason for weak or absent security on IoT devices is their weak computational power. In this chapter, we discuss a community based security mechanism in which devices mutually aid each other in secure software management. We discuss game-theoretic methods of community formation and light-weight cryptographic means to accomplish authentic software deployment inside the IoT device community. Copyright © 2018, The Authors. All rights reserved.

关键词： Internet of things

Attitude and Horizontal Speed Control System on Unmanned Aerial Vehicle Using LQR

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Attitude and Horizontal Speed Control System on Unmanned Aer...

International Conference on Science and Technology-computer (ICST)

作者： Andi Dharmawan Lazuardi Ichsan Hanif Baskoro Jazi Eko Istiyanto Ariesta Martiningtyas Handayani Electronics and Instrumentation Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Computer System and Networking Research Laboratory Universitas Gadjah Mada Yogyakarta Indonesia Department of Electrical and Informatics Engineering Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (数字)9781728123691

ISBN: (纸本)9781728123707

Unmanned Aerial Vehicles (UAV) or drones are expected to fly automatically, especially in performing basic maneuvers. The necessary maneuver is to fly straight and level, where no rotation occurs on the roll, pitch, and yaw axes that work on the plane. Also, to free from rotational motion, the aircraft needs to maintain its horizontal speed to prevent the vehicle from losing lift and free-fall. This research uses a Linear Quadratic Regulator (LQR) control method to obtain full-state feedback K gain from the control system. The gain of the full-state feedback is used to control the output, which is converted into Pulse Width Modulation to control the rotational speed of the brushless motor, and the angle of servo. The result of this research is that system response obtained is optimum with the desired specification.

关键词： Control systems Angular velocity Unmanned aerial vehicles Mathematical model Attitude control Force Torque

Computational experience with robust pole assignment algorithms

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Computational experience with robust pole assignment algorit...

IEEE International Conference on computer-Aided Design

作者： Vasile Sima Andre L. Tits Yaguang Yang National Institute for Research and Development in Informatics Bucharest Romania Electrical and Computer Engineering and the Institute for Systems Research University of Maryland College Park USA Guidance Navigation and Control Department Orbital Sciences Corporation Dalles VA USA

Two algorithms for robust pole assignment by state feedback, proposed by Kautsky, Nichols and Van Dooren (1985) and by Tits and Yang (1996) are briefly reviewed. MATLAB code implementations of these algorithms, place (from the MATLAB Control System Toolbox) and robpole (from SLICOT), are then numerically compared on randomly generated test data sets, as well as on examples from two benchmark collections, in terms of the robustness (insensitivity of poles to variations in plant parameters) of the closed-loop systems they produce. The functions place and robpole are also compared with each other, as well as with the (non robust) pole assignment code pass (from SLICOT) in terms of CPU time and accuracy of the pole assignment.

关键词： Robustness Eigenvalues and eigenfunctions Control systems State feedback Robust control MATLAB System testing Libraries Benchmark testing Linear feedback control systems