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11th Symposium on Neural Network Applications in Electrical Engineering最新文献

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Application of neural networks in emotional speech recognition 神经网络在情绪语音识别中的应用
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6420016
M. Bojanic, V. Crnojevic, V. Delić
{"title":"Application of neural networks in emotional speech recognition","authors":"M. Bojanic, V. Crnojevic, V. Delić","doi":"10.1109/NEUREL.2012.6420016","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6420016","url":null,"abstract":"Emotional speech recognition (ESR) from the aspect of human-machine interaction (HCI) is a prerequisite for the framework of interacting partners within the HCI. This paper addresses the application of neural network (NN) in ESR. The performance of NN is tested using three different feature sets which are basis for ESR: prosodic features, spectral features and a set of their combination. The results of these feature sets are compared using several network topologies and two training algorithms. It has been shown that using joint prosodic-spectral feature set as input to three layer feed-forward NN trained with back-propagation algorithm has the best performance in 5-class emotional speech recognition task.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124803666","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: 16
Autonomic telemedical application for Android based mobile devices 基于Android移动设备的自主远程医疗应用程序
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6420018
S. Jokic, S. Krco, D. Sakac, I. Jokic, V. Delić
{"title":"Autonomic telemedical application for Android based mobile devices","authors":"S. Jokic, S. Krco, D. Sakac, I. Jokic, V. Delić","doi":"10.1109/NEUREL.2012.6420018","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6420018","url":null,"abstract":"In this paper, a mobile telemedicine application implemented for Android based devices is presented. The main application's functionality of ECG transmission is extended by real time ECG analysis, as well as real time analyze of acceleration data captured by embedded acceleration sensor. In this paper are presented efficient algorithms for ECG and acceleration data analysis. The ECG analysis is focused on arrhythmic heartbeats detection and pathological ST-T segment detection. Arrhythmic heartbeats detection is performed on the estimated ECG model features using Artificial Neural Networks (ANN). In the mobile application alarms could be defined, which triggering can send e-mail messages with attached ECG images and excel formatted data reports. Data from the acceleration sensor are analyzed regarding to monitor user walking activity. Mobile application is integrated in the existing telemedical system using predefined interfaces, but she also provides high autonomy to the end users with or without medical knowledge.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133765984","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
Comparison of generalized profile function models based on linear regression and neural networks 基于线性回归和神经网络的广义剖面函数模型的比较
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419959
P. Radonja
{"title":"Comparison of generalized profile function models based on linear regression and neural networks","authors":"P. Radonja","doi":"10.1109/NEUREL.2012.6419959","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419959","url":null,"abstract":"In this paper, the generalized profile function models, GPFMs, based on linear regression and neural networks, are compared. GPFM provides an approximation of individual models (models of individual stem profile) facility using only two basic measurements. GPFM based on neural network is obtained as the average of all available normalized individual models. It is shown that the application of neural networks provides a generalized model with good performance.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"709 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116123292","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
Training artificial neural networks with memristive synapses: HSPICE-matlab co-simulation 记忆突触训练人工神经网络:HSPICE-matlab联合仿真
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419974
A. Aggarwal, B. Hamilton
{"title":"Training artificial neural networks with memristive synapses: HSPICE-matlab co-simulation","authors":"A. Aggarwal, B. Hamilton","doi":"10.1109/NEUREL.2012.6419974","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419974","url":null,"abstract":"Researchers in the field of Neuromorphic Engineering are looking at ways to reduce the chip space required to mimic the huge processing capacity of the human brain and to simplify algorithms to train it. Since the recent fabrication of a memristor by the Hewlett Packard Company, there is a possibility to achieve both of these. With their crucial hysteresis properties, memristors can store charge during the training process and respond in a desired manner, electronically mimicking synapse behaviour. This arrangement can reduce chip space and potentially simplify the learning logic. This paper presents HSPICE modeling of an artificial neural network with memristive synapses and training it for `AND' logic. An alternative modification of the memristor model was tried to simplify the learning logic. Results show potential for application in neural circuits.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123424366","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
Recognition and classification of geometric shapes using neural networks 基于神经网络的几何形状识别与分类
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419966
S. Spasojevic, M. Šušić, Z. Durovic
{"title":"Recognition and classification of geometric shapes using neural networks","authors":"S. Spasojevic, M. Šušić, Z. Durovic","doi":"10.1109/NEUREL.2012.6419966","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419966","url":null,"abstract":"The research presented in this paper refers to classification of geometric shapes (cubes, pyramids and cylinders) using multilayer neural network. The input data of the algorithm are the images of shapes placed in different positions and distances from the camera. The classification is based on feature vectors that are obtained using methods of digital image processing. Feature vectors are inputs of neural network. Supervised training of neural network is performed. Reduction algorithm was used in aim of dimension reduction of feature vectors, so the classification results can be displayed graphically. Recognition and classification of geometric shapes may be of interest for realization of many robotic tasks, especially those related to catching of objects with robotic arm or movement of a robot with a set of obstacles.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129500656","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
Application of neural networks in spatial signal processing (invited paper) 神经网络在空间信号处理中的应用(特邀论文)
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419950
B. Milovanovic, M. Agatonovic, Z. Stanković, N. Dončov, M. Sarevska
{"title":"Application of neural networks in spatial signal processing (invited paper)","authors":"B. Milovanovic, M. Agatonovic, Z. Stanković, N. Dončov, M. Sarevska","doi":"10.1109/NEUREL.2012.6419950","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419950","url":null,"abstract":"Neural networks (NNs) have proven to be a very powerful tool both for one-dimensional (1D) and two-dimensional (2D) direction of arrival (DOA) estimation. By avoiding complex and time-consuming mathematical calculations, NNs estimate DOAs almost instantaneously. This feature makes them very convenient for real-time applications. Further, unlike the well known MUSIC algorithm, neural network-based models provide accurate directions without additional calibration procedure of antenna array and a priori knowledge of the number of sources. In this review paper, the results achieved by the research group at the Faculty of Electronic Engineering in Nis are presented. The problem of DOA estimation of narrowband signals impinging upon different configurations of antenna arrays is addressed. Both Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are considered, and their advantages and disadvantages are discussed. To improve the resolution of DOA estimates, sectorization model is introduced. As shown in this work, neural network-based models demonstrate high-resolution localization capabilities and much better efficiency than the MUSIC.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124425031","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
Image analysis using modified multifractal measure based on sigmoid function 基于s型函数的改进多重分形测度图像分析
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6420007
M. Paskas, A. Gavrovska, M. Milivojević, B. Reljin
{"title":"Image analysis using modified multifractal measure based on sigmoid function","authors":"M. Paskas, A. Gavrovska, M. Milivojević, B. Reljin","doi":"10.1109/NEUREL.2012.6420007","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6420007","url":null,"abstract":"In this paper we propose the new multifractal measure inspired by sigmoid activation function usually used in neural networks. By using new measure the Hölder exponent and multifractal spectrum are determined in classical way. New measure is applied to image processing, especially in texture classification. It was shown that by changing the slope of the sigmoid function different details can be extracted from analyzed image. Initial results are promising and indicate to high potential of new measure in image processing.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131953618","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: 2
Confidence based learning of a two-model committee for sequence labeling 基于置信度的双模型委员会序列标注学习
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419998
D. Mancev, B. Todorovic
{"title":"Confidence based learning of a two-model committee for sequence labeling","authors":"D. Mancev, B. Todorovic","doi":"10.1109/NEUREL.2012.6419998","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419998","url":null,"abstract":"The paper presents the use of a two structural model committee, where the output of the first model together with its confidence is set as the input of the second model. The confidence for the given context of predictions in the sequence is extracted from the alternative hypotheses generated from the first model. We present experiments on the shallow parsing, comparing the performance of the proposed method to the separate models.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122926543","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
Optimization of the extended water flow algorithm for the text-line segmentation 文本行分割中扩展水流算法的优化
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419975
D. Brodic, Z. Milivojevic, D. Tanikić, D. Milivojevic
{"title":"Optimization of the extended water flow algorithm for the text-line segmentation","authors":"D. Brodic, Z. Milivojevic, D. Tanikić, D. Milivojevic","doi":"10.1109/NEUREL.2012.6419975","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419975","url":null,"abstract":"The paper proposed an approach for the optimization of the water flow algorithm for the text-line segmentation. Original method assumed the hypothetical water that flows to the document image frame from left to right and vice versa. It used the water flow angle as the only parameter. Algorithm's extended version introduced a water flow function, which is given as the power function. It exploited two parameters: water flow angle α and exponent n. To optimize these two parameters artificial neural network has been used. Results are encouraging because of the improvement of the text-line segmentation results.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133068349","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
Dynamic Inversion Control of quadrotor with complementary Fuzzy logic compensator 基于互补模糊逻辑补偿器的四旋翼动态反演控制
11th Symposium on Neural Network Applications in Electrical Engineering Pub Date : 2012-09-01 DOI: 10.1109/NEUREL.2012.6419963
A. Rodic, I. Stojković
{"title":"Dynamic Inversion Control of quadrotor with complementary Fuzzy logic compensator","authors":"A. Rodic, I. Stojković","doi":"10.1109/NEUREL.2012.6419963","DOIUrl":"https://doi.org/10.1109/NEUREL.2012.6419963","url":null,"abstract":"In this paper, an integrated quadrotor flight controller with complementary compensator of system uncertainties is presented. Proposed control law combines model-based and knowledge-based techniques into a hybrid controller that should ensure high trajectory tracking accuracy in presence of structural and parametric uncertainties of the system and external disturbances. The Computing Torque Method is used to invert nonlinear and highly coupled dynamics of the system, and turn it into linear and decoupled. Structural and parametric uncertainties of the system as well as stochastic internal and external perturbations can strongly degrade performance of Dynamic Inversion Controller (Computing Torque Method). The influence of perturbations that may act upon the system can be significantly reduced by implementation of the Fuzzy Logic Controller, that will act like a complementary compensator of uncertainties.","PeriodicalId":343718,"journal":{"name":"11th Symposium on Neural Network Applications in Electrical Engineering","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111098","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
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