D. Lacrama, Vasile Gherheș, F. Alexa, T. M. Karnyanszky
{"title":"Automatic survey processing using a MLP neural net","authors":"D. Lacrama, Vasile Gherheș, F. Alexa, T. M. Karnyanszky","doi":"10.1109/NEUREL.2010.5644092","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644092","url":null,"abstract":"This paper presents a method to automatically process the education quality assessment quiz test. The propose technique use Pattern Recognition methodology and the final decision is taken using a MLP neural network. The Xj subject's answers are numerically encoded in a descriptor vector VXj. This vector is fed to the net and it decides the Xj's degree of satisfaction or dissatisfaction over the most important characteristics of the educational process in a faculty or a university.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127327086","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":"Classification of muscle twitch response using ANN: Application in multi-pad electrode optimization","authors":"N. Malešević, L. Popovic, G. Bijelic, G. Kvascev","doi":"10.1109/NEUREL.2010.5644042","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644042","url":null,"abstract":"In this paper we present a method for optimization of spatial selectivity of multi-pad electrode during transcutaneous Functional Electrical Stimulation (FES). The presented method is based on measurent of individual muscle twitches using Micro-Electro-Mechanical Systems (MEMS) accelerometers positioned on hand, while stimulating with low frequency electrical stimulation via pads within multi-pad electrode. When elicited, wrist or fingers flexion/extension produce different, characteristic wave shapes of acceleration, by using trained Artificial Neural Network (ANN) we can detect these characteristic signals and detect correlation of each pad and activated muscle beneath. Results presented in this paper show high degree of accurate classification of the elicited movement in inter-subject testing.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126880835","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":"Combining acoustic and visual modalities in vowel recognition system for laryngectomees","authors":"Rafal Pietruch, A. Grzanka","doi":"10.1109/NEUREL.2010.5644075","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644075","url":null,"abstract":"This paper addresses the problem of vowels recognition in patients after total laryngectomy using combined visual and acoustic features. The linear prediction coefficients were estimated from speech signal using weighted recursive least squares algorithm. Ten cross-sectional areas of vocal tract model were calculated. Face expression parameters related to the spoken vowel were extracted from video recordings. Lips width, lips height and jaw opening were measured from grabbed video frames. The principal component analysis was applied to show correlations of auditory and visual features. The vowel recognition procedures were based on single hidden layer neural networks. The recognition performances of visual, acoustic and fused modalities were compared. It was presented that recognition performance of sustained vowels using 10 cross-sectional areas estimates is very low. Facial expression analysis is needed when there is problem with estimation of standard acoustic parameters of pathological speech.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134492711","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":"Metrological verification of FPGA based device for measuring EEG signal","authors":"P. Sovilj, V. Vujicic, N. Pjevalica","doi":"10.1109/NEUREL.2010.5644106","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644106","url":null,"abstract":"The paper presents a field-programmable gate array (FPGA) based model for measurement of electroencephalography (EEG) signal. The novelty of this system is implementation of digital stochastic block based on stochastic analog-to-digital (A/D) conversion and accumulation, with a novel hardware structure tailored for harmonic measurements. Metrologically verified stochastic measurement EEG system measured DC component of the signal and 15 harmonics with the base measurement interval of 20ms, and results showed consistency with the developed theory.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132631955","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":"Farsi and Latin script identification using curvature scale space features","authors":"M. Khoddami, A. Behrad","doi":"10.1109/NEUREL.2010.5644061","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644061","url":null,"abstract":"Script recognition is a necessary process before OCR algorithm in multilingual systems. In this paper, a novel method is proposed for identifying Farsi and Latin scripts in bilingual document using curvature scale space features. The proposed features are rotation and scale invariant and can be used to identify scripts with different fonts. We assumed that the bilingual scripts may have Farsi and English words and characters together; therefore the algorithm is designed to be able to recognize scripts in the connected components level. The output of the recognition is then generalized to word, line and page levels. Experimental results show that the proposed method has good accuracy especially in word and connected component levels.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966996","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":"Coherence resonance in chains of noisy excitable neurons coupled by interactions with delay","authors":"K. Todorović, N. Buric, A. Samčović, N. Vasovic","doi":"10.1109/NEUREL.2010.5644082","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644082","url":null,"abstract":"Influence of the interaction time-delay on the noise induced system size resonance in a system of all-to-all electrically coupled FitzHugh-Nagumo excitable neurons is studied. It is observed that small time-lags decrease and that large time-lags increase the coherence of spiking. Bifurcations of the system's stationary state are used to explain the observed non-monotonic dependence of coherence on the time-lag.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114078588","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}
S. Micera, J. DiGiovanna, A. Berthoz, A. Demosthenous, J. Guyot, K. Hoffmann, D. Merfeld, M. Morari
{"title":"A closed-loop neural prosthesis for vestibular disorders","authors":"S. Micera, J. DiGiovanna, A. Berthoz, A. Demosthenous, J. Guyot, K. Hoffmann, D. Merfeld, M. Morari","doi":"10.1109/NEUREL.2010.5644048","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644048","url":null,"abstract":"Vestibular disorders can cause severe problems including nausea, inability to concentrate, and visual deficits. The CLONS project is developing a closed-loop sensory neural prosthesis to alleviate these symptoms. Conceptually, the prosthesis restores vestibular information by stimulating the semicircular canals according to measurements from inertial sensors rigidly affixed to the user. Here we present a project overview and brief update of our progress in animal models and selected human volunteers.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116480102","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":"An efficient finite precision RBF-M neural network architecture using support vectors","authors":"R. Dogaru, I. Dogaru","doi":"10.1109/NEUREL.2010.5644089","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644089","url":null,"abstract":"This paper investigates the effects of using limited precision for efficient implementations of the RBF-M neural network. This architecture employs only simple arithmetic operators and is characterized by simple LMS training in an expanded feature space generated by RBF functions centered around support vectors selected via a simple algorithm. The classification performances of our low complexity, finite precision architecture are similar and even better to those obtained using the more complex SVM.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126450236","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}
S. Vukmirovic, A. Erdeljan, Lendak Imre, D. Capko, Nemanja Nedic
{"title":"Adaptive neural network workflow management for Utility Management Systems","authors":"S. Vukmirovic, A. Erdeljan, Lendak Imre, D. Capko, Nemanja Nedic","doi":"10.1109/NEUREL.2010.5644102","DOIUrl":"https://doi.org/10.1109/NEUREL.2010.5644102","url":null,"abstract":"This paper focuses on grid performance optimization in large scale workflow applications with an intelligent workflow scheduling mechanism. Utility Management Systems (UMS) are managing very large numbers of workflows with very high resource requirements. This paper proposes a UMS scheduling architecture which dynamically executes a scheduling algorithm using near real-time feedback about the current status of grid nodes. Workflow scheduling was performed with an artificial neural network (ANN). The network was trained in a system with three workflows. The case study presented in this paper shows results achieved in a three workflow system, as well as results achieved in a five workflow system where an adaptive ANN was used. The results testify that significant improvement of overall execution time can be achieved by adapting weights in the neural network.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133443369","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":"Hierarchical approach to diagnosis of electronic circuits using ANNs","authors":"M. A. Stosovic, V. Litovski","doi":"10.2298/JAC1001045A","DOIUrl":"https://doi.org/10.2298/JAC1001045A","url":null,"abstract":"Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of mixed-mode electronic circuit. In order to tackle the circuit complexity and to reduce the number of test points hierarchical approach to the diagnosis generation was implemented with two levels of decision: the system level and the circuit level. For every level, using the simulation-before-test (SBT) approach, fault dictionary was created first, containing data relating the fault code and the circuit response for a given input signal. ANNs were used to model the fault dictionaries. At the topmost level, the fault dictionary was split into parts simplifying the implementation of the concept. During the learning phase, the ANNs were considered as an approximation algorithm to capture the mapping enclosed within the fault dictionary. Later on, in the diagnostic phase, the ANNs were used as an algorithm for searching the fault dictionary. A voting system was created at the topmost level in order to distinguish which ANN's output is to be accepted as the final diagnostic statement. The approach was tested on an example of an analog-to-digital converter, and only one test point was used i.e. the digital output. Full diversity of faults was considered in both digital (stuck-at and delay faults) and analog (parametric and catastrophic faults) part of the diagnosed system. Special attention was paid to the faults related to the A/D and D/A interfaces within the circuit.","PeriodicalId":227890,"journal":{"name":"10th Symposium on Neural Network Applications in Electrical Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131269008","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}