{"title":"ST Segment Change Detection by Means of Wavelets","authors":"N. Milosavljevic, A. Petrovic","doi":"10.1109/NEUREL.2006.341196","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341196","url":null,"abstract":"This research aims to contribute to the automatic interpretation of long sequences of electrocardiograms (ECG) typical for Holter monitoring. We developed a method that uses wavelets for extracting ECG patterns that are characteristic for myocardial ischemia. It was our intention to detect the beats in the simplest possible manner and generate a quantitative estimate of myocardial ischemia likelihood which would suit needs of cardiologists. Biorthogonal wavelets were applied in order to define ST segment properties at different scales. The new method was tested on data from the European ST-T change database. Results show that this method it effective for distinguishing normal from ischemic ECGs. The element that makes the distinction is the correlation of number of ST deviations with the time of consecutive appearances","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127260906","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}
{"title":"Reinforcement Learning in Humanoid Robotics Dusko Katic","authors":"D. Katic","doi":"10.1109/NEUREL.2006.341182","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341182","url":null,"abstract":"Summary form only given. Dynamic bipedal walking is difficult to learn because combinatorial explosion in order to optimize performance in every possible configuration of the robot, uncertainties of the robot dynamics that must be only experimentally validated, and because coping with dynamic discontinuities caused by collisions with the ground and with the problem of delayed reward-torques applied at one time may have an effect on the performance many steps into the future. The detailed and precise training data for learning is often hard to obtain or may not be available in the process of biped control synthesis. Since no exact teaching information is available, this is a typical reinforcement learning problem and the failure signal serves as the reinforcement signal. Reinforcement learning (RL) offers one of the most general framework to humanoid robotics towards true autonomy and versatility. Various straightforward and hybrid intelligent control algorithms based RL for active and passive biped locomotion is presented. The proposed reinforcement learning algorithms is based on two different learning structures: actor-critic architecture and Q-learning structures. Also, RL algorithms can use numerical and fuzzy evaluative feedback information for external reinforcement. The proposed RL algorithms use the learning elements that consist of various types of neural networks, fuzzy logic nets or fuzzy-neuro networks with focus on fast convergence properties and small number of learning trials","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122565927","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}
{"title":"Data Mining a Prostate Cancer Dataset Using Neural Networks","authors":"K. Revett","doi":"10.1109/NEUREL.2006.341201","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341201","url":null,"abstract":"Prostate cancer remains one of the leading causes of cancer death worldwide, with a reported incidence rate of 650,000 cases per annum worldwide. The causal factors of prostate cancer still remain to be determined. In this paper, we investigate a medical dataset containing clinical information on 502 prostate cancer patients using the machine learning technique of rough sets and radial basis function neural network. Our preliminary results yield a classification accuracy of 90%, with high sensitivity and specificity (both at approximately 91%). Our results yield a predictive positive value (PPN) of 81% and a predictive negative value (PNV) of 95%","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123904755","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}
{"title":"Percolation approach to formation of synfire chains in two dimensional neural networks","authors":"I. Franović, V. Miljkovic","doi":"10.1109/NEUREL.2006.341178","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341178","url":null,"abstract":"We consider the propagation of spike packets in two dimensional networks consisting of locally coupled neural pools. The dynamic attractors of this model, synfire chains, appear for some values of network parameters. The synfire chain formation exhibits behavior, which may be discribed with the percolation phase transition. Using finite-size scaling method, we obtained the critical probabilities and the critical parameter ratio beta/v for different sets of refractoriness and synaptic weights, connecting neighbouring neural pools","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128713247","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}
{"title":"Application of Neural Network for Automatic Classification of Leukocytes","authors":"Stanislav Mircic, Nikola Jorgovanovic","doi":"10.1109/NEUREL.2006.341197","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341197","url":null,"abstract":"Differential blood count is one of the most frequently used diagnostic methods in medicine. An algorithm for leukocytes classification represents the crucial part of any device for the automatic compilation of a differential blood count. This paper demonstrates a new algorithm for the automatic classification of leukocytes based on neural networks and digital image processing. The results of the algorithm testing show a high sensitivity of the algorithm in leukocyte detection, as well as classification accuracy of 86%","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129275872","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}
{"title":"Dynamic-Order-Extended Time-Delay Dynamic Neural Units","authors":"I. Bukovský, G. Simeunovic","doi":"10.1109/NEUREL.2006.341189","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341189","url":null,"abstract":"The paper introduces a linear dynamic-order-extended time-delay dynamic neural unit, which is one possible modification of novel class of artificial neurons called time-delay dynamic neural units (TmD-DNU). In standalone implementations, these artificial dynamic neural architectures can be understood as an analogy to continuous time-delay differential equations. TmD-DNU is capable of identification of all parameters of continuous time differential equation including unknown time delays both in the unit's inputs as well as in its state variable. A modification of dynamic backpropagation learning algorithm is shown. Results on system identification of an unknown system with dynamics of higher-order including unknown time delays are shown in comparison to achievements by common identification methods applied to the same system. Robust identification capabilities and network implementations of TmD-DNU are briefly discussed","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127929953","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}
{"title":"GA-Based Feature Extraction for Clapping Sound Detection","authors":"J. Olajec, R. Jarina, M. Kuba","doi":"10.1109/NEUREL.2006.341166","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341166","url":null,"abstract":"Automatically extracting semantic content from audio streams can be helpful in many multimedia applications. In this paper, we introduce a framework for automatic feature subspace selection from a common feature vector. The selected features build a new representation which is better suitable for a given learning task and recognition. In order to solve this problem, we propose the GA-based (genetic algorithm) method to improve the representativeness and robustness of the features generic audio recognition task","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126717094","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}
{"title":"Synchronization of Chaotic Cellular Neural Networks based on Rössler Cells","authors":"D. Rijlaarsdam, V. Mladenov","doi":"10.1109/NEUREL.2006.341171","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341171","url":null,"abstract":"Using and extending the approach in previous studies we demonstrate synchronization of two hyper chaotic cellular neural networks consisting of 25 cells governed by chaotic Rossler dynamics. We guarantee global asymptotic stability of the synchronization manifold by designing a nonlinear observer in such a way that the resulting error system is linear and time invariant. This linear error system is evaluated and a state feedback is designed to accomplish full state synchronization. Analytical as well as numerical simulation results are presented","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121972479","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}
{"title":"Bias-Dependent Model of Microwave FET S-parameters Based on Prior Knowledge ANNs","authors":"Z. Marinković, O. Pronic, V. Markovic","doi":"10.1109/NEUREL.2006.341208","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341208","url":null,"abstract":"The applications of artificial neural networks (ANNs) in bias-dependent modeling of S-parameters of microwave FETs have been proposed earlier. Here, a model based on an ANN with additional prior knowledge at its inputs (PKI ANN) is introduced. S-parameters of the device that belongs to the same class as the modeled device are used as the prior knowledge. The PKI concept allows ANN model to be developed with less training data, which is very advantageous when training data is expensive or time consuming to obtain. The proposed modeling concept is illustrated by an appropriate modeling example","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131451711","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}
{"title":"Face Recognition by Using Unitary Vector Spaces","authors":"G. Kekovic, D. Raković","doi":"10.1109/NEUREL.2006.341173","DOIUrl":"https://doi.org/10.1109/NEUREL.2006.341173","url":null,"abstract":"Dynamic developments of science and technology have demanded necessity of interdisciplinary approach and appearance of novel scientific disciplines. In this respect, face recognition using quantum mechanical methods of unitary vector spaces, represents very interesting field due to possible applications in the field of quantum informatics. Thus traditional quantum mechanical methods widely applied to microsystems during the past century are now successfully extrapolated in macroscopic information framework as well","PeriodicalId":231606,"journal":{"name":"2006 8th Seminar on Neural Network Applications in Electrical Engineering","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131057703","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}