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A Hybrid machine Learning Approach for Improvised QoE in Video Services over 5G Wireless Networks

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Computers, Materials & Continua 2024年第3期78卷 3195-3213页

作者： K.B.Ajeyprasaath P.Vetrivelan School of electronics engineering Vellore Institute of TechnologyChennaiTamil Nadu600127India

Video streaming applications have grown considerably in recent years.As a result,this becomes one of the most significant contributors to global internet traffic.According to recent studies,the telecommunications industry loses millions of dollars due to poor video Quality of Experience(QoE)for users.Among the standard proposals for standardizing the quality of video streaming over internet service providers(ISPs)is the Mean Opinion Score(MOS).However,the accurate finding of QoE by MOS is subjective and laborious,and it varies depending on the user.A fully automated data analytics framework is required to reduce the inter-operator variability characteristic in QoE assessment.This work addresses this concern by suggesting a novel hybrid XGBStackQoE analytical model using a two-level layering technique.Level one combines multiple machine Learning(ML)models via a layer one Hybrid XGBStackQoE-model.Individual ML models at level one are trained using the entire training data set.The level two Hybrid XGBStackQoE-Model is fitted using the outputs(meta-features)of the layer one ML models.The proposed model outperformed the conventional models,with an accuracy improvement of 4 to 5 percent,which is still higher than the current traditional models.The proposed framework could significantly improve video QoE accuracy.

关键词： Hybrid XGBStackQoE-model machine learning MOS performance metrics QoE 5G video services

Programming nonlinear propagation for efficient optical learning machines

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Advanced Photonics 2024年第1期6卷 73-83页

作者： Ilker Oguz Jih-Liang Hsieh Niyazi Ulas Dinc Ugur Tegin Mustafa Yildirim Carlo Gigli Christophe Moser Demetri Psaltis École Polytechnique Fédérale de Lausanne Institute of Electrical and Micro EngineeringEcublensSwitzerland Koc University Department of Electrical and Electronics EngineeringIstanbulTurkey

The ever-increasing demand for training and inferring with larger machine-learning models requires more efficient hardware solutions due to limitations such as power dissipation and scalability.Optics is a promising contender for providing lower power computation,since light propagation through a nonabsorbing medium is a lossless operation.However,to carry out useful and efficient computations with light,generating and controlling nonlinearity optically is a necessity that is still elusive.Multimode fibers(MMFs)have been shown that they can provide nonlinear effects with microwatts of average power while maintaining parallelism and low loss.We propose an optical neural network architecture that performs nonlinear optical computation by controlling the propagation of ultrashort pulses in MMF by wavefront shaping.With a surrogate model,optimal sets of parameters are found to program this optical computer for different tasks with minimal utilization of an electronic computer.We show a remarkable decrease of 97%in the number of model parameters,which leads to an overall 99%digital operation reduction compared to an equivalently performing digital neural network.We further demonstrate that a fully optical implementation can also be performed with competitive accuracies.

关键词： neural networks nonlinear optics fiber optics surrogate optimization neuromorphic computing wavefront shaping

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Deep Learning-Based Classification of Rotten Fruits and Identification of Shelf Life

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Computers, Materials & Continua 2024年第1期78卷 781-794页

作者： S.Sofana Reka Ankita Bagelikar Prakash Venugopal V.Ravi Harimurugan Devarajan Centre for Smart Grid Technologies School of Electronics EngineeringVellore Institute of TechnologyChennai600127India School of electronics engineering Vellore Institute of TechnologyChennai600127India Department of Electrical engineering Dr.B R Ambedkar National Institute of TechnologyJalandharPunjab144008India

The freshness of fruits is considered to be one of the essential characteristics for consumers in determining their quality,flavor and nutritional value.The primary need for identifying rotten fruits is to ensure that only fresh and high-quality fruits are sold to consumers.The impact of rotten fruits can foster harmful bacteria,molds and other microorganisms that can cause food poisoning and other illnesses to the consumers.The overall purpose of the study is to classify rotten fruits,which can affect the taste,texture,and appearance of other fresh fruits,thereby reducing their shelf life.The agriculture and food industries are increasingly adopting computer vision technology to detect rotten fruits and forecast their shelf life.Hence,this research work mainly focuses on the Convolutional Neural Network’s(CNN)deep learning model,which helps in the classification of rotten fruits.The proposed methodology involves real-time analysis of a dataset of various types of fruits,including apples,bananas,oranges,papayas and guavas.Similarly,machine learningmodels such as GaussianNaïve Bayes(GNB)and random forest are used to predict the fruit’s shelf life.The results obtained from the various pre-trained models for rotten fruit detection are analysed based on an accuracy score to determine the best model.In comparison to other pre-trained models,the visual geometry group16(VGG16)obtained a higher accuracy score of 95%.Likewise,the random forest model delivers a better accuracy score of 88% when compared with GNB in forecasting the fruit’s shelf life.By developing an accurate classification model,only fresh and safe fruits reach consumers,reducing the risks associated with contaminated produce.Thereby,the proposed approach will have a significant impact on the food industry for efficient fruit distribution and also benefit customers to purchase fresh fruits.

关键词： Rotten fruit detection shelf life deep learning convolutional neural network machine learning gaussian naïve bayes random forest visual geometry group16

Prediction of Bandwidth of Metamaterial Antenna Using Pearson Kernel-Based Techniques

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Computers, Materials & Continua 2024年第3期78卷 3449-3467页

作者： Sherly Alphonse S.Abinaya Sourabh Paul School of Computer Science and engineering Vellore Institute of TechnologyChennai600127India School of electronics engineering Vellore Institute of TechnologyChennai600127India

The use of metamaterial enhances the performance of a specific class of antennas known as metamaterial antennas.The radiation cost and quality factor of the antenna are influenced by the size of the antenna.Metamaterial antennas allow for the circumvention of the bandwidth restriction for small antennas.Antenna parameters have recently been predicted using machine learning algorithms in existing literature.machine learning can take the place of the manual process of experimenting to find the ideal simulated antenna parameters.The accuracy of the prediction will be primarily dependent on the model that is used.In this paper,a novel method for forecasting the bandwidth of the metamaterial antenna is proposed,based on using the Pearson Kernel as a standard kernel.Along with these new approaches,this paper suggests a unique hypersphere-based normalization to normalize the values of the dataset attributes and a dimensionality reduction method based on the Pearson kernel to reduce the dimension.A novel algorithm for optimizing the parameters of Convolutional Neural Network(CNN)based on improved Bat Algorithm-based Optimization with Pearson Mutation(BAO-PM)is also presented in this work.The prediction results of the proposed work are better when compared to the existing models in the literature.

关键词： Antenna pearson optimization bandwidth metamaterial

machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical engineering Seoul National University IEN Center for Human-Centric Interfaces and engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer engineering Georgia Institute of Technology Department of Chemical engineering Stanford University Department of Mechanical engineering Ajou University George W.Woodruff School of Mechanical engineering Georgia Institute of Technology Institute of Advanced machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

Properties of radiation defects and threshold energy of displacement in zirconium hydride obtained by new deep-learning potential

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Chinese Physics B 2024年第7期33卷 456-465页

作者：王玺唐孟蒋明璇陈阳春刘智骁邓辉球 School of Physics and electronics Hunan UniversityChangsha 410082China College of Materials Science and engineering Hunan UniversityChangsha 410082China

Zirconium hydride(ZrH_2) is an ideal neutron moderator material. However, radiation effect significantly changes its properties, which affect its behavior and the lifespan of the reactor. The threshold energy of displacement is an important quantity of the number of radiation defects produced, which helps us to predict the evolution of radiation defects in ZrH_2.Molecular dynamics(MD) and ab initio molecular dynamics(AIMD) are two main methods of calculating the threshold energy of displacement. The MD simulations with empirical potentials often cannot accurately depict the transitional states that lattice atoms must surpass to reach an interstitial state. Additionally, the AIMD method is unable to perform largescale calculation, which poses a computational challenge beyond the simulation range of density functional theory. machine learning potentials are renowned for their high accuracy and efficiency, making them an increasingly preferred choice for molecular dynamics simulations. In this work, we develop an accurate potential energy model for the ZrH_2 system by using the deep-potential(DP) method. The DP model has a high degree of agreement with first-principles calculations for the typical defect energy and mechanical properties of the ZrH_2 system, including the basic bulk properties, formation energy of point defects, as well as diffusion behavior of hydrogen and zirconium. By integrating the DP model with Ziegler–Biersack–Littmark(ZBL) potential, we can predict the threshold energy of displacement of zirconium and hydrogen in ε-ZrH_2.

关键词： zirconium hydride deep learning potential radiation defects molecular dynamics threshold energy of displacement

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The Investigation of Data Voting Algorithm for Train Air-Braking System Based on Multi-Classification SVM and ANFIS

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Chinese Journal of electronics 2024年第1期33卷 274-281页

作者： Juhan WANG Ying GAO Yuan CAO Tao TANG School of electronics and Information engineering Beijing Jiaotong University Graduate Department China Academy of Railway Science National engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University

The pressure data of the train air braking system is of great significance to accurately evaluate its operation state. In order to overcome the influence of sensor fault on the pressure data of train air braking system, it is necessary to design a set of sensor fault-tolerant voting mechanism to ensure that in the case of a pressure sensor fault, the system can accurately identify and locate the position of the faulty sensor, and estimate the fault data according to other normal data. A fault-tolerant mechanism based on multi-classification support vector machine(SVM) and adaptive network-based fuzzy inference system(ANFIS) is introduced. Multi-classification SVM is used to identify and locate the system fault state, and ANFIS is used to estimate the real data of the fault sensor. After estimation, the system will compare the real data of the fault sensor with the ANFIS estimated data. If it is similar,the system will recognize that there is a false alarm and record it. Then the paper tests the whole mechanism based on the real data. The test shows that the system can identify the fault samples and reduce the occurrence of false alarms.

关键词： Multi-classification support vector machine Adaptive network-based fuzzy inference system Train air braking system Fault-tolerant voting Multi-sensors

A comprehensive analysis of DAC-SDC FPGA low power object detection challenge

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Science China(Information Sciences) 2024年第8期67卷 300-320页

作者： Jingwei ZHANG Guoqing LI Meng ZHANG Xinye CAO Yu ZHANG Xiang LI Ziyang CHEN Jun YANG School of electronics Science and engineering Southeast University

The lower power object detection challenge(LPODC) at the IEEE/ACM Design Automation Conference is a premier contest in low-power object detection and algorithm(software)-hardware co-design for edge artificial intelligence, which has been a success in the past five years. LPODC focused on designing and implementing novel algorithms on the edge platform for object detection in images taken from unmanned aerial vehicles(UAVs), which attracted hundreds of teams from dozens of countries to participate. Our team SEUer has been participating in this competition for three consecutive years from 2020 to 2022 and obtained sixth place respectively in 2020 and 2021. Recently, we achieved the championship in 2022. In this paper, we presented the LPODC for UAV object detection from 2018 to 2022, including the dataset, hardware platform,and evaluation method. In addition, we also introduced and discussed the details of methods proposed by each year's top three teams from 2018 to 2022 in terms of network, accuracy, quantization method, hardware performance, and total score. Additionally, we conducted an in-depth analysis of the selected entries and results, along with summarizing representative methodologies. This analysis serves as a valuable practical resource for researchers and engineers in deploying the UAV application on edge platforms and enhancing its feasibility and reliability. According to the analysis and discussion, it becomes evident that the adoption of a hardware-algorithm co-design approach is paramount in the context of tiny machine learning(TinyML).This approach surpasses the mere optimization of software and hardware as separate entities, proving to be essential for achieving optimal performance and efficiency in TinyML applications.

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Manipulator tracking technology based on FSRUKF

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Journal of Systems engineering and electronics 2024年第2期35卷 473-484页

作者： SHI Guoqing ZHANG Boyan ZHANG Jiandong YANG Qiming HUANG Xiaofeng QUE Jianyao PU Junwei GENG Xiutang School of electronics and Information Northwestern Polytechnical UniversityXi’an 710072China Northwest Institute of Mechanical&Electrical engineering Xianyang 712000China

Aiming at the shortcoming that the traditional industrial manipulator using off-line programming cannot change along with the change of external environment,the key technologies such as machine vision and manipulator control are studied,and a complete manipulator vision tracking system is designed.Firstly,Denavit-Hartenberg(D-H)parameters method is used to construct the model of the manipulator and analyze the forward and inverse kinematics equations of the manipulator.At the same time,a binocular camera is used to obtain the threedimensional position of the target.Secondly,in order to make the manipulator track the target more accurately,the fuzzy adaptive square root unscented Kalman filter(FSRUKF)is proposed to estimate the target state.Finally,the manipulator tracking system is built by using the position-based visual servo.The simulation experiments show that FSRUKF converges faster and with less error than the square root unscented Kalman filter(SRUKF),which meets the application requirements of the manipulator tracking system,and basically meets the application requirements of the manipulator tracking system in the practical experiments.

关键词： square root unscented Kalman filter(SRUKF) fuzzy inference manipulator visual servo

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