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2018 14th Symposium on Neural Networks and Applications (NEUREL)最新文献

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Integrating Mobile Vehicle Sensor Diagnostic Procedures into the Intelligent Transportation Network 将移动车辆传感器诊断程序集成到智能交通网络中
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586998
Nikola Slavković, G. Zajic, A. Gavrovska, I. Reljin, B. Reljin, Milan Bjelica
{"title":"Integrating Mobile Vehicle Sensor Diagnostic Procedures into the Intelligent Transportation Network","authors":"Nikola Slavković, G. Zajic, A. Gavrovska, I. Reljin, B. Reljin, Milan Bjelica","doi":"10.1109/NEUREL.2018.8586998","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586998","url":null,"abstract":"This paper analyzes possibilities to integrate scanning devices and methods of diagnostics road conditions, into the more global network such is the Internet of Vehicles, connecting vehicles and/or stationary access points. Moreover, initial estimation of real time processing effectivity, based on the interaction of diagnostic equipment with the appropriate road surface material, is considered.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116836487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Biometric Clustering of ECG using Wave Peaks 基于波峰的心电生物特征聚类
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587016
M. Milivojević, A. Gavrovska, I. Reljin
{"title":"Biometric Clustering of ECG using Wave Peaks","authors":"M. Milivojević, A. Gavrovska, I. Reljin","doi":"10.1109/NEUREL.2018.8587016","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587016","url":null,"abstract":"The use of ECG signals for biometric recognition is in the focus of scientific research. For each person electrocardiogram which contain specific biometric characteristics can be recorded making it suitable for biometric application. A comparison of features in terms of potential person identification, i.e. clustering needs, is made, where amplitude characteristics are extracted in time domain. This paper analyzes data from the Physionet ECG-ID database, and show promising results for future ECG based considerations.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122856345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning in Video Stabilization Homography Estimation 深度学习在稳像单应性估计中的应用
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587021
Nataša Vlahović, Nemanja Ilić, M. Stanković
{"title":"Deep Learning in Video Stabilization Homography Estimation","authors":"Nataša Vlahović, Nemanja Ilić, M. Stanković","doi":"10.1109/NEUREL.2018.8587021","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587021","url":null,"abstract":"The main goal of digital video stabilization algorithms is to remove unwanted motion from a video sequence. The undesired motion is typically present in videos recorded by hand-held cameras, by cameras mounted on some moving platform (vehicle, boat, Unmanned Aerial Vehicle), or by stationary cameras under severe wind conditions. In this paper, the motion estimation step in video stabilization is performed in a novel way using deep learning homography matrix estimation. Convolutional Neural Network (CNN) takes two grayscale images as inputs, and produces a six degree of freedom affine transformation matrix that maps the pixels from the first image to the second one. After obtaining the homography transformation using a trained CNN, Kalman filter is used to separate the intentional from unintentional motion and calculate the final motion compensation transformation, stabilizing the video sequence.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127868944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Lake Level Estimation using Machine Learning and Physically-based Approaches in Lake Tana, Ethiopia 在埃塞俄比亚塔纳湖使用机器学习和基于物理的方法进行湖泊水位估计
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587035
Z. Dokou, N. Reljin, M. Kheirabadi, A. Bagtzoglou, E. Anagnostou
{"title":"Lake Level Estimation using Machine Learning and Physically-based Approaches in Lake Tana, Ethiopia","authors":"Z. Dokou, N. Reljin, M. Kheirabadi, A. Bagtzoglou, E. Anagnostou","doi":"10.1109/NEUREL.2018.8587035","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587035","url":null,"abstract":"This study aims to estimate the monthly water levels of Lake Tana, Ethiopia, which is the source of the Blue Nile and as such is of great importance not only for the local communities but for all the countries depending on its waters. Two different approaches are used for this purpose: a physically-based model and a data-driven algorithm which uses support vector regression. A comparative analysis of their errors, applicability, ease of use and computational speed is performed. Although the physically-based model outperformed the data-driven model in all but the bias metric, the latter has multiple competitive advantages such as reduced computational effort, shorter training/calibration time, and the fact that it requires the selection of fewer model parameters.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121019572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Unsupervised Classification of Premature Ventricular Contractions Based on RR Interval and Heartbeat Morphology 基于RR间期和心跳形态的室性早搏无监督分类
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586997
V. Atanasoski, M. Ivanovic, M. Marinković, G. Gligoric, B. Bojovic, A. Shvilkin, J. Petrovic
{"title":"Unsupervised Classification of Premature Ventricular Contractions Based on RR Interval and Heartbeat Morphology","authors":"V. Atanasoski, M. Ivanovic, M. Marinković, G. Gligoric, B. Bojovic, A. Shvilkin, J. Petrovic","doi":"10.1109/NEUREL.2018.8586997","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586997","url":null,"abstract":"Accurate automated detection of premature ventricular contractions from electrocardiogram requires a training set or expert intervention. We propose a fully automated unsupervised detection method. The algorithm first clusters morphologically similar heartbeats and then performs classification based on RR intervals and morphology. Tests on clinically recorded datasets show sensitivity of 94.7%, specificity of 99.6% and accuracy of 99.5%.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116123955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Power Definitions for Fractional Order Elements and Non-Active Power Compensation 分数阶元的功率定义与无功功率补偿
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587032
J. Mikulović, T. Šekara
{"title":"Power Definitions for Fractional Order Elements and Non-Active Power Compensation","authors":"J. Mikulović, T. Šekara","doi":"10.1109/NEUREL.2018.8587032","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587032","url":null,"abstract":"This paper considers power definitions for electrical circuits with fractional order resistive-capacitive and resistive-inductive elements. The definitions of apparent, active, reactive and scattered powers are proposed. Also, a procedure for optimal non-active power compensation of the load is presented. A simulation example is given to illustrate the methods of calculating power quantities and improving the power factor of the load.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127380932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Adaptation of Local Binary Patterns toward Robustness to Gaussian Noise 局部二值模式对高斯噪声鲁棒性的自适应
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587002
M. Paskas, Marijeta S. Slavkovic-Ilic, I. Reljin, B. Reljin
{"title":"Adaptation of Local Binary Patterns toward Robustness to Gaussian Noise","authors":"M. Paskas, Marijeta S. Slavkovic-Ilic, I. Reljin, B. Reljin","doi":"10.1109/NEUREL.2018.8587002","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587002","url":null,"abstract":"Local binary patterns represent very powerful feature for image classification. It evolved from the original model to many modifications and adaptations. However, almost all of derived models are based on the basic idea to code each pixel’s 3×3 neighborhood binary. In this paper we propose modification of this idea in order to increase its robustness to Gaussian noise. Instead of calculating differences on 3×3 neighborhood regarding central pixel, we apply calculation of differences regarding average pixel intensity on that neighborhood. This kind of averaging improves classification performances with respect to noise. Proposed method is further tested for classification on two publicly available texture datasets and obtained results, with and without noise addition, prove our assumptions.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122379773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
NEUREL 2018 Index
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/neurel.2018.8587014
{"title":"NEUREL 2018 Index","authors":"","doi":"10.1109/neurel.2018.8587014","DOIUrl":"https://doi.org/10.1109/neurel.2018.8587014","url":null,"abstract":"","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126942178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Artificial neural networks and statistical classification applied to Electrical Impedance Spectroscopy data for Melanoma diagnosis in Dermatology (DermaSense) 应用人工神经网络和统计分类的阻抗谱数据诊断皮肤黑色素瘤(DermaSense)
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8586995
S. Gilou, C. Dimitrousis, A. Zogkas, C. Kemanetzi, C. Korfitis, E. Lazaridou, P. Bamidis, A. Astaras
{"title":"Artificial neural networks and statistical classification applied to Electrical Impedance Spectroscopy data for Melanoma diagnosis in Dermatology (DermaSense)","authors":"S. Gilou, C. Dimitrousis, A. Zogkas, C. Kemanetzi, C. Korfitis, E. Lazaridou, P. Bamidis, A. Astaras","doi":"10.1109/NEUREL.2018.8586995","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8586995","url":null,"abstract":"The gold standard for diagnosis of cutaneous melanoma is excisional biopsy and histological examination, however dermoscopy remains the most common examination during a patient’s first visit to the dermatologist. Dermoscopy is non-invasive but relies on a clinician’s subjective pattern recognition skills and experience. We designed and built a novel, non-invasive dermatological scanner based on electrical impedance spectroscopy, which can complement dermoscopy by providing objective measurement data within seconds, at the point-of-examination. In this paper we present the DermaSense scanner as well as associated statistical and neural network classification algorithms aimed at correlating acquired with verified diagnostic data. In addition, we present an initial pilot study on measurements of melanoma against nevi as well as against clear patches of skin.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131735244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Robust Machine Learning Based Acoustic Classification of a Material Transport Process 基于鲁棒机器学习的物料输送过程声学分类
2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI: 10.1109/NEUREL.2018.8587031
Adnan Husaković, E. Pfann, M. Huemer
{"title":"Robust Machine Learning Based Acoustic Classification of a Material Transport Process","authors":"Adnan Husaković, E. Pfann, M. Huemer","doi":"10.1109/NEUREL.2018.8587031","DOIUrl":"https://doi.org/10.1109/NEUREL.2018.8587031","url":null,"abstract":"This paper discusses the performance of machine learning classification algorithms based on psychoacoustic features for the monitoring of a material transport process. Reliable and robust classification strongly depends on the proper choice of the feature vector. The method of Principal Component Analysis (PCA) is applied in combination with a classification performance analysis of the individual psycho-acoustic feature types in order to select the best performing features and achieve a feature reduction. The resulting feature subsets are applied to a data set of a material transport process.","PeriodicalId":371831,"journal":{"name":"2018 14th Symposium on Neural Networks and Applications (NEUREL)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121224625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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