Pub Date : 2017-04-01DOI: 10.1109/EBBT.2017.7956771
Othmane El Badlaoui, A. Hammouch
In this work, a new method for discrimination between normal and heart murmurs sound is presented. Statistical parameters, such as standard deviation (SD), are extracted from two datasets of heartbeats. Several classification technics, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), discriminative analysis, and classification tree, are used. Simulation results obtained from yielding methods are compared and discussed. The developed method (scheme) return good results from deferent dataset. Results obtained by using different classification methods versus two dataset are, significantly, accurate compared to the existing methods.
{"title":"Discrimination between normal and heart murmurs sound, based on statistical parameters extraction and classification","authors":"Othmane El Badlaoui, A. Hammouch","doi":"10.1109/EBBT.2017.7956771","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956771","url":null,"abstract":"In this work, a new method for discrimination between normal and heart murmurs sound is presented. Statistical parameters, such as standard deviation (SD), are extracted from two datasets of heartbeats. Several classification technics, such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), discriminative analysis, and classification tree, are used. Simulation results obtained from yielding methods are compared and discussed. The developed method (scheme) return good results from deferent dataset. Results obtained by using different classification methods versus two dataset are, significantly, accurate compared to the existing methods.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124884042","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}
Pub Date : 2017-04-01DOI: 10.1109/EBBT.2017.7956774
S. Yener, R. Mutlu
To extend the concept of the memristive systems to capacitive systems, memcapacitive systems have been described in 2009. Memcapacitors which are a subset of memcapacitive systems are flux-dependent nonlinear circuit elements with memory. Materials with memcapacitive properties has already been reported in literature. The elusive memcapacitor show promise for new type of applications because of their unusual characteristics which cannot be mimicked with linear circuit elements. Since these elements are not commercially available yet, their analytical solutions and simulation studies are very important. Then these solutions may provide valuable insight for their usage, behavior and predicting of their new application areas. In this study, a memcapacitor-inductor oscillation circuit is examined using simulations and also its small signal equivalent circuit is obtained using perturbation theory since such a circuit does not have an exact solution.
{"title":"Small signal model of memcapacitor-inductor oscillation circuit","authors":"S. Yener, R. Mutlu","doi":"10.1109/EBBT.2017.7956774","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956774","url":null,"abstract":"To extend the concept of the memristive systems to capacitive systems, memcapacitive systems have been described in 2009. Memcapacitors which are a subset of memcapacitive systems are flux-dependent nonlinear circuit elements with memory. Materials with memcapacitive properties has already been reported in literature. The elusive memcapacitor show promise for new type of applications because of their unusual characteristics which cannot be mimicked with linear circuit elements. Since these elements are not commercially available yet, their analytical solutions and simulation studies are very important. Then these solutions may provide valuable insight for their usage, behavior and predicting of their new application areas. In this study, a memcapacitor-inductor oscillation circuit is examined using simulations and also its small signal equivalent circuit is obtained using perturbation theory since such a circuit does not have an exact solution.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132109114","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}
Pub Date : 2017-04-01DOI: 10.1109/EBBT.2017.7956779
R. M. Demirer, Oya Demirer
A mathematical model of cell-autonomous mammalian circadian clock is integrated into an adaptive control scheme. We analyze circadian clock rhythms with time delayed biophysically meaning parameter sets to generate self-oscillation behavior of a SCN neuron (Suprachiasmatic Nucleus Neuron). We demonstrate how an adaptive control scheme is utilized to stabilize of time varying mRNA and protein expression levels. Biological system optimizes the control parameters of coupled ODE with respect to minimizing energy metabolism or the time to satisfy the control goal. We will also investigate the robustness of inherent control mechanism to perturbations of coupled ODE system.
{"title":"Inherent biological adaptive control of feedback gain of circadian rhythms in a mathematical model: Stabilization of the sustained oscillations in a mRNA-protein model","authors":"R. M. Demirer, Oya Demirer","doi":"10.1109/EBBT.2017.7956779","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956779","url":null,"abstract":"A mathematical model of cell-autonomous mammalian circadian clock is integrated into an adaptive control scheme. We analyze circadian clock rhythms with time delayed biophysically meaning parameter sets to generate self-oscillation behavior of a SCN neuron (Suprachiasmatic Nucleus Neuron). We demonstrate how an adaptive control scheme is utilized to stabilize of time varying mRNA and protein expression levels. Biological system optimizes the control parameters of coupled ODE with respect to minimizing energy metabolism or the time to satisfy the control goal. We will also investigate the robustness of inherent control mechanism to perturbations of coupled ODE system.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"614 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123950005","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}
Pub Date : 2017-04-01DOI: 10.1109/EBBT.2017.7956784
Selin Isoglu, E. Koca, D. G. Duru
In this study, the unsupervised clustering method namely K-means algorithm is applied for identifying the multiple sclerosis (MS) lesions in magnetic resonance (MR) images automatically. MS lesion detection is essential for diagnosing the disease and monitoring its progression. The automated method aims to eliminate user-dependent classification errors and to improve computational capacity in detecting more reliable MS segmentation results. K-means algorithm that relies on k cluster number on data is addressed to determine lesions in pathological brain MR images. Comparative segmentation is aimed by generating an in-house developed binary image segmentation routine in MATLAB. Segmented regions are compared to the results of K-means algorithm with respect to the predefined ROIs of lesions. The proposed K-means lesion detection routine is applied on real brain MR images and the results are qualitatively compared, and the method manages to locate the lesions successfully.
{"title":"Comparative multiple sclerosis lesion segmentation in magnetic resonance images","authors":"Selin Isoglu, E. Koca, D. G. Duru","doi":"10.1109/EBBT.2017.7956784","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956784","url":null,"abstract":"In this study, the unsupervised clustering method namely K-means algorithm is applied for identifying the multiple sclerosis (MS) lesions in magnetic resonance (MR) images automatically. MS lesion detection is essential for diagnosing the disease and monitoring its progression. The automated method aims to eliminate user-dependent classification errors and to improve computational capacity in detecting more reliable MS segmentation results. K-means algorithm that relies on k cluster number on data is addressed to determine lesions in pathological brain MR images. Comparative segmentation is aimed by generating an in-house developed binary image segmentation routine in MATLAB. Segmented regions are compared to the results of K-means algorithm with respect to the predefined ROIs of lesions. The proposed K-means lesion detection routine is applied on real brain MR images and the results are qualitatively compared, and the method manages to locate the lesions successfully.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128442317","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：