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OTFS Communications in an Evolving Environments

OTFS Communications in an Evolving Environments

引用

Mechatronic Systems Supervision (IW_MSS), IEEE International Workshop on

作者： Amina Darghouthi Abdelhakim Khlifi Belgacem Chibani LR-MACS Laboratory: Modeling Analysis and Control of Systems University of Gabes Tunisia Innov'Com laboratory SUPCOM University of Carthage Tunis Tunisia

ISBN: (数字)9798350378382

ISBN: (纸本)9798350378399

The need for mobile communications networks to provide fast data rates and maintain a high level of quality of service (QoS) is crucial. This requires low latency and low power consumption. In the realm of 6G mobile networks, Orthogonal time-frequency space (OTFS) technology is emerging as the optimal solution for the physical layer that follows the fifth generation. This article delves into this topic, highlighting the advantages of OTFS for various schemes. Specifically, we will explore the SISO, SIMO, MISO, and MIMO configurations over fading channels. Through simulation results, we aim to identify potential enhancements for these configurations.

关键词： 6G mobile communication Time-frequency analysis Quadrature amplitude modulation Spectral efficiency Simulation Wireless networks Space technology Modulation Quality of service Massive MIMO

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Accelerated Degradation modeling of Power MOSFET Under Multiple Stresses 12

Accelerated Degradation Modeling of Power MOSFET Under Multi...

引用

12th International conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, QR2MSE 2022

作者： Ye, Xuerong Hu, Yifan Feng, Rui Chen, Cen Zhai, Guofu Tang, Loon Ching School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Shanghai Aerospace Control Technology Institute Shanghai China Department of Industrial and Systems Engineering & Management National University of Singapore Singapore

ISBN: (纸本)9781839538360

Power MOSFET is one of the most critical components in power electronic converters. However, it often performs a lower reliability index under multiple stresses. Conventional single-stress accelerated degradation test (ADT) and corresponding statistical inference are unable to evaluate its reliability accurately. In this article, a new accelerated degradation model is designed by combining multi-stress accelerated model and the generalized Wiener process model. This comprehensive model not only establishes the relationship between drift coefficient and multiple stresses but also take the nonlinearity and uncertainties existing in degradation process into consideration. Subsequently, an ADT with temperature, electrical and vibration stresses for power MOSFET is carried out to obtain the degradation data under different stress levels, which is employed for unknown parameter estimation. Comparative analysis demonstrates that the proposed model can provide a more accurate and reasonable reliability assessment result. © 2022 IEEE.

关键词： Power MOSFET

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ADHD-Net: A Robust Deep Learning-based Framework for Detecting ADHD Patients using EEG

ADHD-Net: A Robust Deep Learning-based Framework for Detecti...

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Communications and Information Technologies (GCCIT), Global conference on

作者： Sayan Mondal Debstuti Biswas Prithwijit Mukherjee Anisha Halder Roy Institute of Radio Physics and Electronics University of Calcutta Kolkata India Electrical and Computer Engineering University of Arizona Tucson AZ United States

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental condition that affects a significant proportion of the global population. In modern civilization, many individuals, including adults and children, are diagnosed with ADHD. ADHD can substantially affect the lives of individuals in diverse domains. The condition can make it challenging for a person to maintain focus, organizational skills, and impulse control, leading to difficulties in academic, social, and professional environments. It is much better to identify ADHD during the earlier stages in order to arrange tailored treatments. The goal of this study is to design a deep learning-based framework known as ADHD-Net for the precise diagnosis of ADHD from electroencephalogram (EEG) signals of the human brain. This study utilizes a publicly available dataset consisting of EEG data from 60 healthy controls and 61 ADHD patients aged 7-12 years. First, filtering of the EEG signal is performed to remove noise and artifacts. Subsequently, Independent Component analysis (ICA) is performed to remove the noise generated by muscle movements and eye blinking. Principal component analysis (PCA) is utilized to reduce the dimensionality of the preprocessed EEG data. A deep learning model called ‘ADHD-Net’ is designed for automated detection of ADHD patients by analyzing EEG signals. The PCA-enhanced EEG data obtained is used to train the ‘ADHD- Net’ model. The trained model can classify an EEG data sample into two classes: ‘ADHD Patient’ and ‘Healthy Person’, with a high classification accuracy of 97.96%. The novelty of this research lies in: i) designing a deep learning model named ADHD-Net for automated ADHD detection, and ii) incorporating the attention mechanism into the designed ADHD-Net model to enhance its efficacy.

关键词： Deep learning Visualization Accuracy Attention mechanisms Noise Brain modeling Transformers Electroencephalography data models Principal component analysis

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An analysis of Driver Drowsiness Detection Using Electromyography (EMG) Facial Muscles 7th

An Analysis of Driver Drowsiness Detection Using Electromyog...

引用

7th International conference on Electrical, control, and Computer Engineering, InECCE 2023

作者： Naim, Faradila Subramaniam, Ashvien Kumar Mustafa, Mahfuzah Sulaiman, Norizam Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia

ISBN: (纸本)9789819738502

Drowsiness during driving can lead to fatal vehicle crashes and deaths. Facial responses when drowsy such as eyes closures and yawning are useful signals to detect driver drowsiness. This project aims to analyze the effectiveness of using electromyography (EMG) signals from the facial muscles (Masseter and Orbicularis Orris) to detect driver drowsiness. 12 healthy male participants took part in the data collection process by driving for 1 hour in a driving simulator where the EMG electrodes were placed on the targeted facial muscles. 7-time domain features were extracted from the raw EMG and k-Nearest Neighbor (kNN) classifier was used as the signal processing model to detect driver drowsiness. The highest classification accuracy for two classes (drowsy and non-drowsy) problem achieved is 85.71%. This result acquired by setting these parameters;i) 70:30 training-test data ratio, ii) the number of neighbors, k values set to 2 or 4, iii) all seven-time domain features, iv) and both Masseter and Orbicularis Orris muscles. The study concludes that the facial muscles are a good predictor to detect drowsiness in drivers during simulated driving. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Automobile driver simulators

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A New Algorithm to Compute the Correlation Matrix Implied By a Confirmatory Factor analysis Model

A New Algorithm to Compute the Correlation Matrix Implied By...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Ebnou Abdem Seyid Abdellahi Zouhair El Hadri M'barek Iaousse Department of Mathematics Mohammed V University in Rabat Rabat Morocco Department of Mathematics Hassan II University of Casablanca Rabat Morocco

Confirmatory Factor analysis is an advanced sta-tistical technique that aims to explain the covariance structure of high-dimensional data by utilizing a small number of factors. The computation of the correlation matrix implied by the model is the main step in the entire modeling process, and it is critical for estimating the model's parameters and testing its validity. This computation typically involves a method that relies on certain assumptions. This paper introduces a new algorithm for computing the implied correlation matrix that is simpler, more flexible, provides more accurate results, and is less restrictive in terms of assumptions. An illustrative example of this proposed algorithm is presented. Finally, the paper discusses and illustrates theoretical and numerical comparisons between the classical estimation method and the proposed approach based on the proposed algorithm.

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BCC: Re-architecting Congestion control in DCNs

BCC: Re-architecting Congestion Control in DCNs

引用

IEEE Annual Joint conference: INFOCOM, IEEE Computer and Communications Societies

作者： Qingkai Meng Shan Zhang Zhiyuan Wang Tao Tong Chaolei Hu Hongbin Luo Fengyuan Ren Institute of Artificial Intelligence Beihang University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The nature of datacenter traffic is a high volume of bursty tiny flows and standing long flows, which forms the coexistence of transient and persistent congestion. Traditional congestion control (CC) algorithms have inherent limitations in reconciling fast response and high efficiency towards transients with stability and fairness during persistence. In this paper, we provide an insight that re-architects CC with two control laws, tailored to transient and persistent concerns, respectively. Armed with this key insight, we propose bimodal congestion control (BCC), which is founded on two core ideas: (i) Quaternary network state detection, which further distinguishes transient and persistent states in switches, and (ii) Bimodal control law, which is manifested as the transient controller and persistent controller at sources. The transient controller employs a precise control paradigm that pauses flows to drain backlogged packets and ramps down/up flow rates to bottleneck bandwidth directly, striving for high efficiency. The persistent controller grounds itself in traditional CC algorithms, inheriting stability and fairness. We implement BCC in the Linux kernel and P4-programmable switch. In our evaluation, compared to DCQCN, HPCC, PowerTCP, and Swift, BCC reduces flow completion times by 14% ~ 99%.

关键词： data centers Linux Computational modeling Bandwidth control systems Market research Stability analysis

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An Empirical Review of Dark Web data Classification Methods Using NLP, SVM, CNN, and GAN

An Empirical Review of Dark Web Data Classification Methods ...

引用

International conference on Computer, Communication, control and Information Technology (C3IT)

作者： Yogita H. Dhande Amol Zade Sonal P. Patil Computer Science & Engineering GHRU Amravati India Computer Science & Engineering GHRCEM Jalgaon India

ISBN: (数字)9798350389128

ISBN: (纸本)9798350389135

The requirement for effective data classification on the Dark Web has increased following the rising sophistication and spread of illicit operations over this secretive internet segment. This paper systematically reviews and compares the state-of-the-art Dark Web data classification methods that fall into text-based, image-based, and hybrid approaches. In the review process, this research outlines their strengths, weaknesses, and common challenges while also identifying gaps. The text-based approaches made extensive use of NLP, machine learning algorithms (such as SVM), and deep learning models, most notably, RNN and CNN. These techniques are good at information processing and understanding textual content, but high variability and obfuscation tactics make the communications hard to understand most of the time. Image-based classification leverages state-of-the-art computer vision techniques, for instance, CNN and GAN. These models are effective at detecting and classifying illicit images, such as goods or services that are illegal. However, it faces problems with the low quality or morphed images that are common on the Dark Web. Hybrid approaches incorporate both text and image in the analysis so that the information provided on the Dark Web is considered in an integrated form. These models use multimodal deep learning, which involves the use of CNN for image data and RNN for textual data samples. Hybrid methods combine text and image analysis to provide a more comprehensive understanding of Dark Web data samples. The approaches integrate multimodal deep learning models where CNNs are used for image data and RNNs for text data samples. Hybrid models show promise in improving classification accuracy by capturing the contextual information from both text and image samples. The present work outlines the necessity for developing highly adaptive and robust classification methods for Dark Web data samples. By assessing the strengths and limitations of existing methods, th

关键词： Support vector machines Deep learning Analytical models Adaptation models Dark Web Accuracy Reviews Computational modeling data models Random forests

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16th International conference on Acoustic and Vibration of Mechanical Structure, AVMS 2021

16th International Conference on Acoustic and Vibration of M...

引用

16th International conference on Acoustic and Vibration of Mechanical Structure, AVMS 2021

ISBN: (纸本)9783030967864

The proceedings contain 39 papers. The special focus in this conference is on Acoustic and Vibration of Mechanical Structure. The topics include: Vibrations of a Planar System of Cantilever Beams Cylindrically Jointed. Part ii: Example;oscillations of a Vehicle Equipped with Hydraulic Shock Absorbers;determination of Annual Environmental Noise Indicators Using a Series of Short-Term Measurements;repeatability of Impulse Response Measurements Using Raspberry Pi;protecting Residential Areas Against Noise Generated by Car Wash Units. A Case Study;workers’ Occupational Exposure to Noise in Steel Industry. A Case Study;measurement and Reduction of Underwater Noise from Anthropic Activities;a Study on the Exposure to Vibration of the Whole Body and the Hand-Arm Segment of Motorcyclists;three Segments Model for Computing Angular Momentum of Lower Limb in Gait;self-driving Car: Challenges in Vibration;influence of the Stent Geometry on Flow Parameters After Arterial Stent Placement;numerical analysis of a Total Hip Prosthesis Under Static Loading Conditions;Designing and FEA Analyzing an Intramedullary Nail for Tibial Fractures;smart Posture Corrector;vibratory analysis of a Robotic Hand;experimental Active Vibration control of a Highly Flexible Composite Manipulator with Acceleration Feedback;artificial Neural Network Approach to Extension of the Frequency Range for Experimental Determination of Dispersion Relationship Using Sparse Spatial data;parametric Assessment of the Elastomeric Anti-vibration Devices According to the Dynamic Test Regime;the Dynamic Compaction Regime of the Discretely Variable Viscoelastic Soil Parameters During the Work process;dynamic Aspect for Self-Propelled Carriages;nonlinear Vibrations control of Rotating Thin-Walled Piezo-Composite Beams.

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Integrative modeling and analysis of the Interplay Between Epidemic and News Propagation processes

Integrative Modeling and Analysis of the Interplay Between E...

引用

American control conference (ACC)

作者： Madhu Dhiman Chen Peng Veeraruna Kavitha Quanyan Zhu Indian Institute of Technology Bombay Department of Electrical and Computer Engineering New York University

The COVID-19 pandemic has witnessed the role of online social networks (OSNs) in the spread of infectious diseases. The rise in severity of the epidemic augments the need for proper guidelines, but also promotes the propagation of fake news-items. The popularity of a news-item can reshape the public health behaviors and affect the epidemic processes. There is a clear inter-dependency between the epidemic process and the spreading of news-items. This work creates an integrative framework to understand the interplay. We first develop a population-dependent ‘saturated branching process’ to continually track the propagation of trending news-items on OSNs. A two-time scale dynamical system is obtained by integrating the news-propagation model with SIRS epidemic model, to analyze the holistic system. It is observed that a pattern of periodic infections emerges under a linear behavioral influence, which explains the waves of infection and reinfection that we have experienced in the pandemic. We use numerical experiments to corroborate the results and use Twitter and COVID-19 data-sets to recreate the historical infection curve using the integrative model.

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A Multi-objective Optimization Algorithm for Air-path System of Diesel Engines 6th

A Multi-objective Optimization Algorithm for Air-path System...

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6th IFAC conference on Engine Powertrain control, Simulation and modeling (E-COSM)

作者： Yu, Jingjiang Chen, Ran Li, Yuzhe Kang, Mingxin Yu, Shengping Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China

Modern engine is a typical multi-objective control system. This paper proposed a multi-objective Bayesian optimization strategy to deal with the performance optimization for diesel engines. Since the objective functions of diesel engines are complicated and computationally expensive, Gaussian processes(GPs) are constructed by using the data collected from the diesel engine to approximate the real objective functions. Non-dominated sorting genetic algorithm ii(NSGAii) leverages the Gaussian process to generate the Pareto-optimal solutions. The Gaussian process will be updated iteratively by Bayesian posterior information, which increases the reliability of the models. The acquisition function Expected HyperVolume Improvement(EHVI), which can balance the trade-off between exploration and exploitation throughout the optimization process, is used to select the solutions for real computationally expensive multi-objective evaluation. The proposed algorithm is applied on a diesel engine, which shows its reliability and high efficiency. The metrics hypervolume(HV) and the control results demonstrate that the proposed algorithm has outstanding effects for performance optimization of diesel engine airpath. Copyright (C) 2021 The Authors.

关键词： data-driven optimization Gaussian process multi-objective Bayesian optimization diesel engine performance optimization

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