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Clustering of time activity curves for uptake pattern assessment in dynamic nuclear medicine imaging 动态核医学成像中摄取模式评价的时间活动曲线聚类
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011489
Vera Miler-Jerković, M. Janković, A. K. Markovic
{"title":"Clustering of time activity curves for uptake pattern assessment in dynamic nuclear medicine imaging","authors":"Vera Miler-Jerković, M. Janković, A. K. Markovic","doi":"10.1109/NEUREL.2014.7011489","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011489","url":null,"abstract":"Nuclear medicine instrumentation visualize the radiopharmaceutical uptake inside the body allowing the interpretation of physiological processes. In dynamic nuclear medicine imaging, time-dependent image sequences are recorded. The changes of radiopharmaceutical uptake over time (so calles time activity curves, TACs) can be analyzed in order to find abnormal patterns corresponding to either structural or functional disorders. Hierarchical Cluster Analysis (HCA) is a powerful statistical tool for classification. We applied HCA on TACs to find clusters of similar TAC patterns. Optimal number of clusters is determined by Hubert's rule. We used Principal Component Analysis (PCA) on TAC clusters to find a representative TAC that presents the uptake pattern in the region of each cluster. The application of algorithm is illustrated in the patient with the histopatologically proven parathyroid hyperplasia, but the developed tool is useful for finding the appropriate classification method of TAC patterns in all types of dynamic nuclear medicine studies.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115660944","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
Multilayer Perceptron architecture optimization for peak load estimation 峰值负荷估计的多层感知器结构优化
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011462
O. Ivanov, Mihai Gavrilac
{"title":"Multilayer Perceptron architecture optimization for peak load estimation","authors":"O. Ivanov, Mihai Gavrilac","doi":"10.1109/NEUREL.2014.7011462","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011462","url":null,"abstract":"Since the development of the Multilayer Perceptron, many types of artificial neural networks (ANNs) have emerged, each having best performances in solving particular types of problems. Current research developments focus on hybrid neural models, which combine neural and symbolic computation elements. In power engineering, ANNs are used today in a variety of applications, including optimization, approximation, forecast and classification tasks, for which an optimized ANN architecture is essential in obtaining the best results. Genetic Algorithms (GAs) can be used for identifying this architecture. While the general assumption when training a Multilayer Perceptron is that all neurons from one layer have the same activation function, this paper uses a genetic algorithm to search for the best mixed activation function configuration for the hidden layer, using as test bench a peak load estimation study.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126989118","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: 5
On projection matrices and dictionaries in ECG compressive sensing - A comparative study 心电压缩感知中投影矩阵与字典的比较研究
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011444
M. Fira, L. Goras
{"title":"On projection matrices and dictionaries in ECG compressive sensing - A comparative study","authors":"M. Fira, L. Goras","doi":"10.1109/NEUREL.2014.7011444","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011444","url":null,"abstract":"In this communication we propose and discuss comparatively several techniques for ECG signal compression inspired from the fundamentals of compressed sensing (CS) theory, focusing on acquisition techniques, projection matrices and reconstruction dictionaries and on the effects of the preprocessing involved. Essentially, we investigate and discuss two approaches. The first approach for ECG signal compression relies on the direct CS acquisition of the signal with no preprocessing of the waveforms before taking the projections, neither for the construction of the dictionaries. This “genuine” CS we will call patient specific classical compressed sensing (PSCCS) since the dictionary is built from patient initial recordings. The second approach implements a specific preprocessing stage designed to enhance sparsity and improve recoverability, based on segmenting the signal into single heart beats (also known as cardiac patterns) - denoted further as cardiac patterns compressed sensing - (CPCS) since in this case the acquired signals and the dictionary atoms are preprocessed segmented cardiac beats without or with centering of the R wave.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127254873","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: 5
Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field 一种有效定位远场随机电磁源的神经网络模型
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011455
Z. Stanković, N. Dončov, I. Milovanovic, B. Milovanovic
{"title":"Neural network model for efficient localization of a number of mutually arbitrary positioned stochastic EM sources in far-field","authors":"Z. Stanković, N. Dončov, I. Milovanovic, B. Milovanovic","doi":"10.1109/NEUREL.2014.7011455","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011455","url":null,"abstract":"An efficient direction of arrival (DOA) estimation of multiple electromagnetic sources by using artificial neural network (ANN) approach is presented in the paper. Electromagnetic sources considered here are of stochastic radiation nature, mutually uncorrelated and at arbitrary angular distance. The approach is based on training of the ANN in which the calculation of correlation matrix in the far-field scan area is done by using the Green function and the correlation of antenna elements feed currents used to describe stochastic sources radiation and then mapping this matrix to the space of DOA in angular coordinate. Once successfully trained, the neural network model is capable to perform an accurate DOA estimation within the training boundaries. Presented example verifies the accuracy of the proposed neural network model.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115092389","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: 9
Adaptive fractal filtering of echocardiograms 超声心动图的自适应分形滤波
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011449
M. Paskas, A. Gavrovska, D. Dujković, B. Reljin
{"title":"Adaptive fractal filtering of echocardiograms","authors":"M. Paskas, A. Gavrovska, D. Dujković, B. Reljin","doi":"10.1109/NEUREL.2014.7011449","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011449","url":null,"abstract":"Echocardiograms are inherently corrupted by the speckle noise. Elimination of the noise is usually treated with low-pass filters which can degrade edges in the image. Adaptive approaches employ masks for edges and restrict low-pass filtering mainly to homogeneous regions. Masks are based on statistical parameters or gradients. In this paper are applied local dimension matrices from fractal model as masks. Experimental tests are conducted for two simple low-pass filters (i) average filter and Gaussian filter (ii) and using three multifractal measures known from the literature - MIN, MAX and OSC measure. Obtained results for adaptive approaches show improvements over non-adaptive approaches in all analyzed scenarios.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129391981","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
Application of inverse filtering in enhancement of whisper recognition 逆滤波在增强耳语识别中的应用
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011492
Dorde T. Grozdic, S. Jovicic, J. Galic, B. Markovic
{"title":"Application of inverse filtering in enhancement of whisper recognition","authors":"Dorde T. Grozdic, S. Jovicic, J. Galic, B. Markovic","doi":"10.1109/NEUREL.2014.7011492","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011492","url":null,"abstract":"The differences between normal speech and whisper, particularly in terms of their acoustic characteristics, are serious problem of ASR (Automatic Speech Recognition) systems. This paper presents the preliminary results of the new way of speech signal pre-processing, which is based on inverse filtering. This method of signal pre-processing improves whisper recognition with ANNs (Artificial Neural Networks). The ANNs showed high capabilities in speech and whisper recognition in matched train/test scenarios, with the average recognition accuracy of 99.8%. However, the recognition scores in mismatched train/test scenarios were highly degraded. Because of their practical significance, the mismatched train/test scenarios were analyzed in detail in this research. Particularly, the speech/whisper scenario is important. This scenario corresponds to real life situation when speaker is in front of ASR system and from speech switches to whisper. The use of inverse filter enhanced whisper recognition by 9.48%, which in this scenario amounts 70.25%.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"13 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132732821","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: 7
Computer vision with Microsoft Kinect for control of functional electrical stimulation: ANN classification of the grasping intentions 微软Kinect的计算机视觉控制功能电刺激:抓取意图的人工神经网络分类
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011491
Matija Štrbac, D. Popović
{"title":"Computer vision with Microsoft Kinect for control of functional electrical stimulation: ANN classification of the grasping intentions","authors":"Matija Štrbac, D. Popović","doi":"10.1109/NEUREL.2014.7011491","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011491","url":null,"abstract":"We present a method for recognizing intended grasp type based on data from the Microsoft Kinect. A computer vision algorithm estimates the vertical and the transversal distance of the hand from the center of the object and the hand orientation from the Kinect depth images. Based on this set of features in the reaching phase of grasp artificial neural network recognizes the intended grasp type. This is demonstrated with an example of a coffee cup on a working desk. Trained neural network classified the grasp with accuracy above 85%. By adding this feature to the existing computer vision system for control of the functional electrical stimulation assisted grasping we facilitate the compliance between the applied electrical stimulation and the user intentions.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126920628","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
On variance based methods in computer-aided phonocardiography 基于方差的计算机辅助心音造影方法研究
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011445
A. Gavrovska, M. Paskas, I. Reljin, B. Reljin
{"title":"On variance based methods in computer-aided phonocardiography","authors":"A. Gavrovska, M. Paskas, I. Reljin, B. Reljin","doi":"10.1109/NEUREL.2014.7011445","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011445","url":null,"abstract":"New trends in cardiosignal processing show that local fluctuations of the signals may reveal important information for computer-aided analysis. Even though the calculation of variance as a global measure is valuable, variation described locally and in one or more scales can represent a convenient way to describe details found in a signal. In this paper, several experimental results obtained using variance based methods, such as local variance estimation and multifractal detrended fluctuation analysis (MF-DFA), are discussed in interpreting phonocardiograms and their content. The experiments show promising results for computer-aided phonocardiography that can be implemented at low cost.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130368967","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
Implementation of a feed-forward Artificial Neural Network in VHDL on FPGA 基于VHDL的前馈神经网络在FPGA上的实现
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011454
P. Dondon, Julien Carvalho, Remi Gardere, Paul Lahalle, G. Tsenov, V. Mladenov
{"title":"Implementation of a feed-forward Artificial Neural Network in VHDL on FPGA","authors":"P. Dondon, Julien Carvalho, Remi Gardere, Paul Lahalle, G. Tsenov, V. Mladenov","doi":"10.1109/NEUREL.2014.7011454","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011454","url":null,"abstract":"Describing an Artificial Neural Network (ANN) using VHDL allows a further implementation of such a system on FPGA. Indeed, the principal point of using FPGA for ANNs is flexibility that gives it an advantage toward other systems like ASICS which are entirely dedicated to one unique architecture and allowance to parallel programming, which is inherent to ANN calculation system and one of their advantages. Usually FPGAs do not have unlimited logical resources integrated in a single package and this limitation forcesrequirement for optimizations for the design in order to have the best efficiency in terms of speed and resource consumption. This paper deals with the VHDL designing problems which can be encountered when trying to describe and implement such ANNs on FPGAs.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128610465","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: 22
Neural network-based modeling of a thermal power plant feedwater pump 基于神经网络的火电厂给水泵建模
12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL) Pub Date : 2014-11-01 DOI: 10.1109/NEUREL.2014.7011467
I. Nikolic, Vesna N. Petkovski, G. Kvascev
{"title":"Neural network-based modeling of a thermal power plant feedwater pump","authors":"I. Nikolic, Vesna N. Petkovski, G. Kvascev","doi":"10.1109/NEUREL.2014.7011467","DOIUrl":"https://doi.org/10.1109/NEUREL.2014.7011467","url":null,"abstract":"Obtaining an accurate model of a real-world system using linear systems theory can prove to be a complex task due to the nonlinear characteristics that systems exhibit. Neural networks have the ability to reproduce the complex nonlinear relations which makes them a useful tool in system identification and modeling. The purpose of this paper is to obtain the model of a thermal power plant feedwater pump in order to test various control approaches. The neural network used in this paper is a multi-layer feed-forward network. The comparison of the results obtained by using this approach with the results obtained from a mathematical model confirms that the neural network-based model is a better approximation of the observed system.","PeriodicalId":402208,"journal":{"name":"12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124743491","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
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