Pub Date : 2017-04-20DOI: 10.1109/EBBT.2017.7956783
Gokalp Tulum, Özgür Dandin, T. Ergin, U. Teomete, Ferhat Cüce, O. Osman
Timely and accurate diagnosis of intraabdominal organ injuries due to trauma is critical. Computer Assisted Detection (CAD) systems are rapidly developing techniques to segment the organs or to detect the pathologies in medical applications; either automatically or semi-automatically. In this work, our aim is to propose and validate a CAD system which classifies injured kidney in Computed Tomography (CT) images. Sixteen cases containing nineteen injured and thirteen intact kidneys were considered for the validation of the method. The classification of the injured kidney was satisfactorily performed with 100% sensitivity ratio.
{"title":"Detection of injured kidney in computed tomography","authors":"Gokalp Tulum, Özgür Dandin, T. Ergin, U. Teomete, Ferhat Cüce, O. Osman","doi":"10.1109/EBBT.2017.7956783","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956783","url":null,"abstract":"Timely and accurate diagnosis of intraabdominal organ injuries due to trauma is critical. Computer Assisted Detection (CAD) systems are rapidly developing techniques to segment the organs or to detect the pathologies in medical applications; either automatically or semi-automatically. In this work, our aim is to propose and validate a CAD system which classifies injured kidney in Computed Tomography (CT) images. Sixteen cases containing nineteen injured and thirteen intact kidneys were considered for the validation of the method. The classification of the injured kidney was satisfactorily performed with 100% sensitivity ratio.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538520","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 : 2017-04-20DOI: 10.1109/EBBT.2017.7956763
Y. Kavurucu, T. Ensari
Predictive Cruise Control (PCC) is one of the most popular functionality on today vehicles. Briefly, it controls vehicle speed at the desired speed value determined by driver. In almost every vehicle sold today, cruise control could be found because it makes drivability manner easier and besides that it decreases fuel consumption with holding vehicle speed stable. Because of high popularity of cruise control, vehicle companies try to improve cruise control usage and also it is a good way to reduce fuel consumption. Therefore, new functionalities of cruise control become to emerge. One of these is predictive feature of cruise control or shortly PCC. PCC is an optimization problem for reducing fuel consumption and travel time and basically it is about finding the vehicle speed profile on a given slope and traffic profile of the road. Therefore, in this project, a PCC optimization problem is tried to solve with given road slope and traffic profile. Fuel consumption and time based cost functions are used and moreover dynamic programming structure is used for finding solution of optimization algorithm. As solution of the algorithm, vehicle speed profile is visualized with developing graphical user interface at the end of the study.
{"title":"Predictive cruise control","authors":"Y. Kavurucu, T. Ensari","doi":"10.1109/EBBT.2017.7956763","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956763","url":null,"abstract":"Predictive Cruise Control (PCC) is one of the most popular functionality on today vehicles. Briefly, it controls vehicle speed at the desired speed value determined by driver. In almost every vehicle sold today, cruise control could be found because it makes drivability manner easier and besides that it decreases fuel consumption with holding vehicle speed stable. Because of high popularity of cruise control, vehicle companies try to improve cruise control usage and also it is a good way to reduce fuel consumption. Therefore, new functionalities of cruise control become to emerge. One of these is predictive feature of cruise control or shortly PCC. PCC is an optimization problem for reducing fuel consumption and travel time and basically it is about finding the vehicle speed profile on a given slope and traffic profile of the road. Therefore, in this project, a PCC optimization problem is tried to solve with given road slope and traffic profile. Fuel consumption and time based cost functions are used and moreover dynamic programming structure is used for finding solution of optimization algorithm. As solution of the algorithm, vehicle speed profile is visualized with developing graphical user interface at the end of the study.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116199604","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 : 2017-04-20DOI: 10.1109/EBBT.2017.7956753
Ipek Karakus, E. Kaplanoglu, Mehmed Özkan, Burak Güçlü
In this study, the static characterization of a bend sensor which is commonly used for various applications was performed. Electrical properties of the sensor was measured and analyzed on a robotic hand. Thus, the feasibility of the sensor for neuroprosthetic applications was discussed. The mean coefficient of determination (R2) is 0.98 and the mean hysteresis value is 9.1 %. The joint angles on a robotic hand were estimated, but the results were different from the actual values because of the sensor placement method.
{"title":"Characterization of a bend sensor for neuroprosthetic applications","authors":"Ipek Karakus, E. Kaplanoglu, Mehmed Özkan, Burak Güçlü","doi":"10.1109/EBBT.2017.7956753","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956753","url":null,"abstract":"In this study, the static characterization of a bend sensor which is commonly used for various applications was performed. Electrical properties of the sensor was measured and analyzed on a robotic hand. Thus, the feasibility of the sensor for neuroprosthetic applications was discussed. The mean coefficient of determination (R2) is 0.98 and the mean hysteresis value is 9.1 %. The joint angles on a robotic hand were estimated, but the results were different from the actual values because of the sensor placement method.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127336617","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 : 2017-04-20DOI: 10.1109/EBBT.2017.7956785
Z. Mortezaei, A. Kalayci, T. Balcıoğlu, Adil Deniz Duru, H. B. Çotuk
The brain is one of the most complex and integrated organ in the human body which directs our muscle movements, our breathing and internal temperature, furthermore every imaginative sight, perception, and diagram are derived by the brain. The brain's neurons are effected by internal and external stimulations. Those stimulations might have positive and negative influence on brain activity and structure. There are several factors which can increase the activation of the brain, as well as increase gray matter (GM) and white matter(WM) volume, in some regions of the brain, such as learning new language, playing musical instrument as well as performing physical exercise. Studies suggested that aerobic exercice can enhance brain plasticity and may decrease risk for developing brain diseases in older adults. In this research, the effect of three factors which are age, gender and aerobic fitness level on brain structure were investigated by means of tissue volumes. Higher aerobic capacity did not indicate a change in structural volumetric brain volume. Moreover age — related gray matter (GM) atrophy was significantly observed. Finally, greater hippocampal volume in female volunteers was found when compared to male volunteers.
{"title":"Effects of aerobic capacity, age and gender on brain neural matter","authors":"Z. Mortezaei, A. Kalayci, T. Balcıoğlu, Adil Deniz Duru, H. B. Çotuk","doi":"10.1109/EBBT.2017.7956785","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956785","url":null,"abstract":"The brain is one of the most complex and integrated organ in the human body which directs our muscle movements, our breathing and internal temperature, furthermore every imaginative sight, perception, and diagram are derived by the brain. The brain's neurons are effected by internal and external stimulations. Those stimulations might have positive and negative influence on brain activity and structure. There are several factors which can increase the activation of the brain, as well as increase gray matter (GM) and white matter(WM) volume, in some regions of the brain, such as learning new language, playing musical instrument as well as performing physical exercise. Studies suggested that aerobic exercice can enhance brain plasticity and may decrease risk for developing brain diseases in older adults. In this research, the effect of three factors which are age, gender and aerobic fitness level on brain structure were investigated by means of tissue volumes. Higher aerobic capacity did not indicate a change in structural volumetric brain volume. Moreover age — related gray matter (GM) atrophy was significantly observed. Finally, greater hippocampal volume in female volunteers was found when compared to male volunteers.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123414529","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 : 2017-04-20DOI: 10.1109/EBBT.2017.7956756
Amirmasoud Ahmadi, V. Shalchyan, M. Daliri
Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be better identified when localized in time-frequency basis projections. In this work, a novel method for epileptic seizure classification based on wavelet packets (WPs) is presented in which both mother wavelet function and WP bases are adapted a posteriori to improve the seizure classification. A support vector machine (SVM) as classifier is used for seizure versus non-seizure EEG segment classification. In order to evaluate the proposed algorithm, a publicly available dataset containing different groups' patient with epilepsy and healthy individuals are used. The obtained results indicate that the proposed method outperforms some previously proposed algorithms in epileptic seizure classification.
{"title":"A new method for epileptic seizure classification in EEG using adapted wavelet packets","authors":"Amirmasoud Ahmadi, V. Shalchyan, M. Daliri","doi":"10.1109/EBBT.2017.7956756","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956756","url":null,"abstract":"Electroencephalography (EEG), as the most common tool for epileptic seizure classification, contains useful information about different physiological states of the brain. Seizure related features in EEG signals can be better identified when localized in time-frequency basis projections. In this work, a novel method for epileptic seizure classification based on wavelet packets (WPs) is presented in which both mother wavelet function and WP bases are adapted a posteriori to improve the seizure classification. A support vector machine (SVM) as classifier is used for seizure versus non-seizure EEG segment classification. In order to evaluate the proposed algorithm, a publicly available dataset containing different groups' patient with epilepsy and healthy individuals are used. The obtained results indicate that the proposed method outperforms some previously proposed algorithms in epileptic seizure classification.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131059014","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 : 2017-04-20DOI: 10.1109/EBBT.2017.7956773
Mehmet Iscan, K. Kadipasaoglu
Left ventricular assist devices (LVADs) have become one of the most effective treatment modalities for end-stage congestive heart failure, particularly where heart treatment becomes a limited option due to donor shortages. The development of local (national) technologies, therefore, emerges as a medical, technical, scientific, humanitarian and economic necessity. The mathematical models used for concept design and simulation of SVDP fluid dynamics contain highly non-linear, implicit partial differential equations which preclude an analytical solution. When these equations are solved using conventional computational tools, the time and resources consumed turn the concept design and simulation phase into the most costly step of SVDP R&D. In this study, we developed an algorithm and tested its potential as a quicker alternative to classical computational methods for determining the optimal pump geometry (design parameters: axial-flow turbine blade inlet angles and radii) based on given design specifications (performance parameters: Pump pressure head and back-flow). The algorithm operates on the principle of Multiple Gradient Descent. From a given set of Design Parameters, a Prediction Polynomial is created first which, in turn, generates (predicts) a set of Performance Parameters. Data from our previous geometric optimization studies (run with conventional numeric methods) were used as the source of one-to-one matching sets of Design-Performance Parameters. Matching sets were divided into two groups, one to be used for the purposes of training the algorithm (i.e. creating the Prediction Polynomial) and the other for estimating the predictive power of the polynomial. Training and predictive power estimation of the algorithm was realized using 8 and 34 matching data sets, respectively. The polynomial predicted pressure head and back-flow values of given geometries with 5.21% and 11.24% error, respectively; and the rate of change of these parameters with respect to unit change in design parameters was estimated with 3.22% and 7.51% error, respectively. We conclude that the algorithm can be trained to generate a polynomial, which can accurately predict performance parameters from any given set of design parameters. The prediction is realized with acceptable error compared to classical numeric methods and virtually at no cost (time and resources).
{"title":"Optimization of the heart pump geometry based on multiple gradient descent algorithm","authors":"Mehmet Iscan, K. Kadipasaoglu","doi":"10.1109/EBBT.2017.7956773","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956773","url":null,"abstract":"Left ventricular assist devices (LVADs) have become one of the most effective treatment modalities for end-stage congestive heart failure, particularly where heart treatment becomes a limited option due to donor shortages. The development of local (national) technologies, therefore, emerges as a medical, technical, scientific, humanitarian and economic necessity. The mathematical models used for concept design and simulation of SVDP fluid dynamics contain highly non-linear, implicit partial differential equations which preclude an analytical solution. When these equations are solved using conventional computational tools, the time and resources consumed turn the concept design and simulation phase into the most costly step of SVDP R&D. In this study, we developed an algorithm and tested its potential as a quicker alternative to classical computational methods for determining the optimal pump geometry (design parameters: axial-flow turbine blade inlet angles and radii) based on given design specifications (performance parameters: Pump pressure head and back-flow). The algorithm operates on the principle of Multiple Gradient Descent. From a given set of Design Parameters, a Prediction Polynomial is created first which, in turn, generates (predicts) a set of Performance Parameters. Data from our previous geometric optimization studies (run with conventional numeric methods) were used as the source of one-to-one matching sets of Design-Performance Parameters. Matching sets were divided into two groups, one to be used for the purposes of training the algorithm (i.e. creating the Prediction Polynomial) and the other for estimating the predictive power of the polynomial. Training and predictive power estimation of the algorithm was realized using 8 and 34 matching data sets, respectively. The polynomial predicted pressure head and back-flow values of given geometries with 5.21% and 11.24% error, respectively; and the rate of change of these parameters with respect to unit change in design parameters was estimated with 3.22% and 7.51% error, respectively. We conclude that the algorithm can be trained to generate a polynomial, which can accurately predict performance parameters from any given set of design parameters. The prediction is realized with acceptable error compared to classical numeric methods and virtually at no cost (time and resources).","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134434976","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 : 2017-04-20DOI: 10.1109/EBBT.2017.7956772
O. Cerezci, S. Yener, Feyza Cerezci
Electromagnetic waves caused from base stations, which are rapidly increasing due to the opportunities provided by modern technology, create an undesired effective electromagnetic pollution in the environment. Mobile phones, as an important electromagnetic radiation source in the human body, are deliberately preferred devices for their use. However, those who live near base stations are exposed to electromagnetic radiation outside of personal preference and often are unaware of this effect. Especially, in districts with high populations such as Kadıköy, possible negative effects should be systematically examined by independent measurement institutions. In this study, the results of the 3-year project entitled “Determination of Electromagnetic Radiation from Base Stations at Istanbul Kadıköy and Determination of Exposure Levels” are discussed in comparison with national and international limits.
{"title":"Electromagnetic radiation interaction and pollution measurements","authors":"O. Cerezci, S. Yener, Feyza Cerezci","doi":"10.1109/EBBT.2017.7956772","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956772","url":null,"abstract":"Electromagnetic waves caused from base stations, which are rapidly increasing due to the opportunities provided by modern technology, create an undesired effective electromagnetic pollution in the environment. Mobile phones, as an important electromagnetic radiation source in the human body, are deliberately preferred devices for their use. However, those who live near base stations are exposed to electromagnetic radiation outside of personal preference and often are unaware of this effect. Especially, in districts with high populations such as Kadıköy, possible negative effects should be systematically examined by independent measurement institutions. In this study, the results of the 3-year project entitled “Determination of Electromagnetic Radiation from Base Stations at Istanbul Kadıköy and Determination of Exposure Levels” are discussed in comparison with national and international limits.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126258663","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.2017.7956786
Hilal Altun, Önder Aydemir
The human brain, nerve center of command system, receives stimulus from the sense organs and sends these signals out to the muscles. There are many kinds of techniques about watching answer the brain for inputs coming from the sense organs. Functional magnetic resonance imaging, electrocorticography, magnetoencephalography and electroencephalography (EEG) techniques are frequently used to measure these signals, but EEG is the most widely used all of these techniques. Advantages such as easy acquisition, painless and low cost make EEG preferable. In this work, EEG signals recorded during smelling of rosewater and lotus flower odors were analyzed and classified. The features calculated and classified are skewness, kurtosis and second order derivation of variance of EEG signals. The EEG signals recorded in Swiss Federal Institute of Technology are from 5 healthy subjects in two different conditions; eyes open and eyes closed. The data are classified by k-nearest neighbor algorithm and the mean of classification accuracy rate is obtained as 97.31 % for the subject eyes open condition and 97.34% for the subject eyes closed. The results achieved with this work prove that the proposed method have great potential for classification the EEG signals.
{"title":"Classification of electroencephalography signals recorded during smelling of rosewater and lotus flower odors","authors":"Hilal Altun, Önder Aydemir","doi":"10.1109/EBBT.2017.7956786","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956786","url":null,"abstract":"The human brain, nerve center of command system, receives stimulus from the sense organs and sends these signals out to the muscles. There are many kinds of techniques about watching answer the brain for inputs coming from the sense organs. Functional magnetic resonance imaging, electrocorticography, magnetoencephalography and electroencephalography (EEG) techniques are frequently used to measure these signals, but EEG is the most widely used all of these techniques. Advantages such as easy acquisition, painless and low cost make EEG preferable. In this work, EEG signals recorded during smelling of rosewater and lotus flower odors were analyzed and classified. The features calculated and classified are skewness, kurtosis and second order derivation of variance of EEG signals. The EEG signals recorded in Swiss Federal Institute of Technology are from 5 healthy subjects in two different conditions; eyes open and eyes closed. The data are classified by k-nearest neighbor algorithm and the mean of classification accuracy rate is obtained as 97.31 % for the subject eyes open condition and 97.34% for the subject eyes closed. The results achieved with this work prove that the proposed method have great potential for classification the EEG signals.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"20 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":"125156911","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.2017.7956766
Z. Sadreddini, Tugrul Çavdar
With the development of new applications, the number of users in telecommunication networks is increasing considerably. Cognitive radio (CR) is a promising solution to manage radio resources by opening up the underutilized parts of the licensed spectrum for secondary reuse. A proper radio resource management (RSM) policy should be implemented to improve overall network performance with paying regard to both network operator (NO) and user satisfaction. Instant Overbooking Framework for Cognitive Radio Network (IOFCR) is provided in order to pave the way for better utilization of radio resources in the network. In this study, performance of dynamic switch IOFCR is evaluated in peak/off-peak hours. Simulation results show the overbooking limit of each IOFCR overbooking policies in both peak/off-peak hours. Based on the dynamic switch system, NO tradeoffs cost-performance of IOFCR via different overbooking thresholds. Simulation results show that the selecting appropriate overbooking policy leads to decrease the number of eliminated active secondary users (ASUs) then increase network revenue as NO pays reasonable compensation cost (CC) for eliminated ASUs.
{"title":"Performance analysis of dynamic spectrum management in cognitive radio networks","authors":"Z. Sadreddini, Tugrul Çavdar","doi":"10.1109/EBBT.2017.7956766","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956766","url":null,"abstract":"With the development of new applications, the number of users in telecommunication networks is increasing considerably. Cognitive radio (CR) is a promising solution to manage radio resources by opening up the underutilized parts of the licensed spectrum for secondary reuse. A proper radio resource management (RSM) policy should be implemented to improve overall network performance with paying regard to both network operator (NO) and user satisfaction. Instant Overbooking Framework for Cognitive Radio Network (IOFCR) is provided in order to pave the way for better utilization of radio resources in the network. In this study, performance of dynamic switch IOFCR is evaluated in peak/off-peak hours. Simulation results show the overbooking limit of each IOFCR overbooking policies in both peak/off-peak hours. Based on the dynamic switch system, NO tradeoffs cost-performance of IOFCR via different overbooking thresholds. Simulation results show that the selecting appropriate overbooking policy leads to decrease the number of eliminated active secondary users (ASUs) then increase network revenue as NO pays reasonable compensation cost (CC) for eliminated ASUs.","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":"131255570","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.2017.7956755
Muhammed Bilgin, T. Ensari
This report describes the Monte Carlo approach to the localization of a robot or autonomous system. Localization in robot or autonomous systems is the problem of position determination using sensor data. The Monte Carlo method is estimated by making statistical inferences. However, The noisy data from the sensors can change the instantaneous state of the robot or an autonomous system. To overcome this problem, the Monte Carlo algorithm family uses the state tree of the Particle Filter. Monte Carlo algorithm predicts the posterior proximity of a robot using a set of weighted sampling methods. Experimental results show the effectiveness of the proposed algorithm.
{"title":"Robot localization with Monte Carlo method","authors":"Muhammed Bilgin, T. Ensari","doi":"10.1109/EBBT.2017.7956755","DOIUrl":"https://doi.org/10.1109/EBBT.2017.7956755","url":null,"abstract":"This report describes the Monte Carlo approach to the localization of a robot or autonomous system. Localization in robot or autonomous systems is the problem of position determination using sensor data. The Monte Carlo method is estimated by making statistical inferences. However, The noisy data from the sensors can change the instantaneous state of the robot or an autonomous system. To overcome this problem, the Monte Carlo algorithm family uses the state tree of the Particle Filter. Monte Carlo algorithm predicts the posterior proximity of a robot using a set of weighted sampling methods. Experimental results show the effectiveness of the proposed algorithm.","PeriodicalId":293165,"journal":{"name":"2017 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)","volume":"3 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":"127792091","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}
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