检索结果-南通市图书馆

Automatic recognition of sonar targets using feature selection in micro-Doppler signature

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Defence Technology（防务技术） 2023年第2期20卷 58-71页

作者： Abbas Saffari Seyed-Hamid Zahiri Mohammad khishe Department of Electrical Engineering University of BirjandBirjandIran Department of Electrical Engineering Imam Khomeini Marine Science UniversityNowshahrIran

Currently,the use of intelligent systems for the automatic recognition of targets in the fields of defence and military has increased significantly.The primary advantage of these systems is that they do not need human participation in target recognition processes.This paper uses the particle swarm optimization(PSO)algorithm to select the optimal features in the micro-Doppler signature of sonar targets.The microDoppler effect is referred to amplitude/phase modulation on the received signal by rotating parts of a target such as propellers.Since different targets'geometric and physical properties are not the same,their micro-Doppler signature is different.This Inconsistency can be considered a practical issue(especially in the frequency domain)for sonar target recognition.Despite using 128-point fast Fourier transform(FFT)for the feature extraction step,not all extracted features contain helpful information.As a result,PSO selects the most optimum and valuable features.To evaluate the micro-Doppler signature of sonar targets and the effect of feature selection on sonar target recognition,the simplest and most popular machine learning algorithm,k-nearest neighbor(k-nn),is used,which is called k-PSO in this paper because of the use of PSO for feature selection.The parameters measured are the correct recognition rate,reliability rate,and processing time.The simulation results show that k-PSO achieved a 100%correct recognition rate and reliability rate at 19.35 s when using simulated data at a 15 dB signal-tonoise ratio(SNR)angle of 40°.Also,for the experimental dataset obtained from the cavitation tunnel,the correct recognition rate is 98.26%,and the reliability rate is 99.69%at 18.46s.Therefore,the k-PSO has an encouraging performance in automatically recognizing sonar targets when using experimental datasets and for real-world use.

关键词： Micro-Doppler signature Automatic recognition Feature selection k-nn PSO

A Locality-Sensitive Hashing-Based Jamming Detection System for IoT Networks

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第12期73卷 5943-5959页

作者： P.Ganeshkumar Talal Albalawi Computer Science Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

Internet of things(IoT)comprises many heterogeneous nodes that operate together to accomplish a human friendly or a business task to ease the life.Generally,IoT nodes are connected in wireless media and thus they are prone to jamming attacks.In the present scenario jamming detection(JD)by using machine learning(ML)algorithms grasp the attention of the researchers due to its virtuous outcome.In this research,jamming detection is modelled as a classification problem which uses several features.Using one/two or minimumnumber of features produces vague results that cannot be explained.Also the relationship between the feature and the class label cannot be efficiently determined,specifically,if the chosen number of features for training is minimum(say 1 or 2).To obtain good results,machine-learning algorithms are trained by large number of data sets.However,collection of large number of datasets to solve jamming detection is not easy and most of the times generation and collection of large data sets become paradigmatic.In this paper,to solve this problem,more number of features with nominal number of data’s is considered that eases the data collection and the classification accuracy.In this research,an efficient technique based on locality sensitive hashing(LSH)for k-nearest neighbor algorithm(k-nn),which takes less time for constructing and querying the hash table that gives good accuracy is proposed and evaluated.From the results,it is clear that the obtained results are validatable and the model is more sensible.

关键词： IoT jamming detection k-nn machine learning

Hybrid GrabCut Hidden Markov Model for Segmentation

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第7期72卷 851-869页

作者： Soobia Saeed Afnizanfaizal Abdullah N.Z.Jhanjhi Mehmood Naqvi Mehedi Masud Mohammed A.AlZain Department of Software Engineering UniversitiTeknologi Malaysia-UTM81310Malaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Engineering and Technology Mohak CollegeAlbertaCanada Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the brain.The aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical images.For tumor and CSF segmentation using trained light field database(LFD)datasets of MRI images.This research proposed the new framework of the hybrid k-Nearest Neighbors(k-nn)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering algorithm.In this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin distance.This research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features transform.On the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of distant.Comparative evaluation is also performing by the comparison of existing techniques in this research.In conclusion,our proposed model gives better results than existing.This proposed model helps to common man and doctor that can identify their condition of brain easily.In future,this will model will use for other brain related diseases.

关键词： SVM tumor CSF k-nn MRI grabcut segmentation HMM

A Portable Fall Detection and Alerting System Based on k-nn Algorithm and Remote Medicine

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

China Communications 2015年第4期12卷 23-31页

作者： HE Jian HU Chen School of Software Engineering Beijing University of Technology

This paper presents information on a portable fall detection and alerting system mainly consisting of a custom vest and a mobile smart phone. A wearable motion detection sensor integrated with tri-axial accelerometer, gyroscope and Bluetooth is built into a custom vest worn by elderly. The vest can capture the reluctant acceleration and angular velocity about the activities of daily living(ADLs) of elderly in real time. The data via Bluetooth is then sent to a mobile smart phone running a fall detection program based on k-nn algorithm. When a fall occurs the phone can alert a family member or health care center through a call or emergent text message using a built in Global Positioning System. The experimental results show that the system discriminates falls from ADLs with a sensitivity of 95%, and a specificity of 96.67%. This system can provide remote monitoring and timely help for the elderly.

关键词：预警系统运动检测 k-nn 远程医学算法便携式秋季全球定位系统

Super-Resolution for Face Image with an Improved k-nn Search Strategy

在线全文

学校读者我要写书评

暂无评论

China Communications 2016年第4期13卷 151-161页

作者： QU Shenming HU Ruimin CHEN Shihong JIANG Junjun WANG Zhongyuan ZHANG Maosheng National Engineering Research Center for Multimedia Software Computer School of Wuhan University China Collaborative Innovation Center of Geospatial Technology China School of Software Henan University China

Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified k-nearest neighbor(RIknn) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches k-nn of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate k-nn and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.

关键词： face hallucination k-nn re-identify super-resolution manifold learning

Devanagari Handwriting Grading System Based on Curvature Features

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2017年第2期113卷 195-202页

作者： Munish kumar Simpel Rani Jindal Department of Computer Applications GZS Campus College of Engineering&Technology(Maharaja Ranjit Singh Punjab Technical University)BathindaPunjabIndia Computer Science and Engineering Section Yadavindera College of EngineeringTalwandi SaboBathindaPunjabIndia.

Grading of writers in perspective of their handwriting is a challenging task owing to various writing styles of different individuals.This paper presents a framework for grading of Devanagari writers in perspective of their handwriting.This framework of grading can be useful in conducting the handwriting competitions and then deciding the winners on the basis of an automated process.Selecting the set of features is a challenging task for implementing a handwriting grading system of particular language.In this paper,curvature features,namely,parabola curve fitting and power curve fitting have been considered for extracting the vital information of writers,which can be helpful in grading the writers.For obtaining the classification score,k-nn classifier has been considered in the present work.Four printed Devanagari font styles,namely,Devlys,krishna,krutidev,and Utsaah have been considered for train the proposed model of handwriting evaluation.For evaluating the effectiveness of the proposed framework,we have conducted a mock test of 75 Devanagari writers(Left handed and Right handed)and noticed that the proposed framework performing well suitable for conducting the handwriting competition of Devanagari text writers.This work is also newly in the ground of Devanagari text recognition.

关键词： Handwritten character recognition curvature features parabola curve fitting power curve fitting k-nn

Comparison of wrist motion classification methods using surface electromyogram

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Central South University 2013年第4期20卷 960-968页

作者： JEONG Eui-chul kIM Seo-jun SONG Young-rok LEE Sang-min Department of Electronic Engineering Inha University

The Gaussian mixture model (GMM), k-nearest neighbor (k-nn), quadratic discriminant analysis (QDA), and linear discriminant analysis (LDA) were compared to classify wrist motions using surface electromyogram (EMG). Effect of feature selection in EMG signal processing was also verified by comparing classification accuracy of each feature, and the enhancement of classification accuracy by normalization was confirmed. EMG signals were acquired from two electrodes placed on the forearm of twenty eight healthy subjects and used for recognition of wrist motion. Features were extracted from the obtained EMG signals in the time domain and were applied to classification methods. The difference absolute mean value (DAMV), difference absolute standard deviation value (DASDV), mean absolute value (MAV), root mean square (RMS) were used for composing 16 double features which were combined of two channels. In the classification methods, the highest accuracy of classification showed in the GMM. The most effective combination of classification method and double feature was (MAV, DAMV) of GMM and its classification accuracy was 96.85%. The results of normalization were better than those of non-normalization in GMM, k-nn, and LDA.

关键词：表面肌电图分类方法运动手腕线性判别分析高斯混合模型 k-nn 信号处理

Automated recognition and classification of adulteration levels from bulk paddy grain samples

在线全文

学校读者我要写书评

暂无评论

Information Processing in Agriculture 2019年第1期6卷 47-60页

作者： Basavaraj S.Anami Naveen N.Malvade Surendra Palaiah Department of Computer Science and Engineering K L E Institute of TechnologyHubballi 580030India Department of Information Science and Engineering K L E Institute of TechnologyHubballi 580030India Department of Genetics and Plant Breeding University of Agricultural SciencesDharwar580002India

Fraudulent labeling and adulteration are the major concerns in the global rice industry.Almost all the paddy varieties being sold in the market are prone to adulteration.It is very difficult to differentiate paddy grains of various varieties in the mixed bulk sample based on visual observation.Currently,there is no sophisticated appearance-based commercial scale technology to reliably detect and quantify adulteration in bulk paddy grain samples.The paper presents a cost-effective image processing technique for the recognition of adulteration and classification of adulteration levels(%)from the images of adulterated bulk paddy samples using state-of-the-art color and texture features.In this work,seven adulterated bulk paddy samples are considered and each of the samples is prepared by mixing a premium paddy variety with the identical looking and commercially inferior paddy variety at five different adulteration levels(weight ratios)of 10%,15%,20%,25%and 30%.The study compares the performances of three different classification models,namely,Multilayer Back Propagation Neural Network(BPnn),Support Vector Machine(SVM)and k-Nearest Neighbor(k-nn).The Principal Component Analysis(PCA)and Sequential Forward Floating Selection(SFFS)methods have been employed separately for the automatic selection of optimal feature subsets from the combined color and texture features.The maximum average adulteration level classification accuracy of 93.31%is obtained using the BPnn classification model trained with PCA-based reduced features.The proposed technique can be used as an economic,rapid,non-destructive and quantitative technique for testing adulteration,authenticity,and quality of bulk paddy grain samples.

关键词： Bulk paddy grains Adulteration quantification Feature selection GLCM LBP BPnn PCA SVM k-nn

Condition Monitoring of Roller Bearing by k-star Classifier andk-nearest Neighborhood Classifier Using Sound Signal

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Structural Durability & Health Monitoring 2017年第1期11卷 1-17页

作者： Rahul kumar Sharma V.Sugumaran Hemantha kumar M.Amarnath V.I.T.university ChennaiIndia N.I.T.karnataka SrinivasanagarSurathkalMangaloreKarnatakaIndia I.I.I.T.D.&M.Jabalpur KhamariaJabalpurIndia Department of Mechanical Engineering School of Mechanical and Building SciencesV.I.T University

Most of the machineries in small or large-scale industry have rotating elementsupported by bearings for rigid support and accurate movement. For proper functioning ofmachinery, condition monitoring of the bearing is very important. In present study soundsignal is used to continuously monitor bearing health as sound signals of rotatingmachineries carry dynamic information of components. There are numerous studies inliterature that are reporting superiority of vibration signal of bearing fault diagnosis.However, there are very few studies done using sound signal. The cost associated withcondition monitoring using sound signal (Microphone) is less than the cost of transducerused to acquire vibration signal (Accelerometer). This paper employs sound signal forcondition monitoring of roller bearing by k-star classifier and k-nearest neighborhoodclassifier. The statistical feature extraction is performed from acquired sound signals. Thentwo-layer feature selection is done using J48 decision tree algorithm and random treealgorithm. These selected features were classified using k-star classifier and k-nearestneighborhood classifier and parametric optimization is performed to achieve the maximumclassification accuracy. The classification results for both k-star classifier and k-nearestneighborhood classifier for condition monitoring of roller bearing using sound signals werecompared.

关键词： k-star k-nearest neighborhood k-nn machine learning approach conditionmonitoring fault diagnosis roller bearing decision tree algorithm J-48 random treealgorithm decision making two-layer feature selection sound signal statistical features