Pub Date : 2017-11-01DOI: 10.1109/MICC.2017.8311734
Mangasi Napitupulu, Achmad Munirf
This paper deals with the development of compact dual band printed planar inverted-F antenna (PIFA) for wireless communications. The antenna is intended to be implemented for mobile devices, hence a cheap and compact antenna is absolutely required. The structure of inverted-F shape is chosen for the basic design of antenna as it has some advantages such as compactness, large bandwidth and easy manufacturability. Prior hardware realization, the proposed antenna is designed and analyzed through 3D simulation software. The antenna is realized on a 1.6mm thick FR4 epoxy dielectric substrate with the total dimension of 32mm × 26mm. The characterization result shows that the realized antenna has the lower band resonant frequency of 2.34GHz with the −10dB working bandwidth of 416MHz, and the higher band resonant frequency of 3.3GHz with the −10dB working bandwidth of 802MHz in which it satisfies with the desired wireless communications.
{"title":"Compact dual band printed planar inverted-F antenna for wireless communications","authors":"Mangasi Napitupulu, Achmad Munirf","doi":"10.1109/MICC.2017.8311734","DOIUrl":"https://doi.org/10.1109/MICC.2017.8311734","url":null,"abstract":"This paper deals with the development of compact dual band printed planar inverted-F antenna (PIFA) for wireless communications. The antenna is intended to be implemented for mobile devices, hence a cheap and compact antenna is absolutely required. The structure of inverted-F shape is chosen for the basic design of antenna as it has some advantages such as compactness, large bandwidth and easy manufacturability. Prior hardware realization, the proposed antenna is designed and analyzed through 3D simulation software. The antenna is realized on a 1.6mm thick FR4 epoxy dielectric substrate with the total dimension of 32mm × 26mm. The characterization result shows that the realized antenna has the lower band resonant frequency of 2.34GHz with the −10dB working bandwidth of 416MHz, and the higher band resonant frequency of 3.3GHz with the −10dB working bandwidth of 802MHz in which it satisfies with the desired wireless communications.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"30 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115929291","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319370
Rehab Ibrahem, Meryem Erbilek
In this paper, we analyse the differences and similarities of features in the context of blind and visual signing data collection protocols with respect to the signature biometrics identification performance. As a result of this performed experimental analysis, powerful features which maximises system accuracy while minimising the performance differential across different signature data collection protocols (visual and blind signing) is extensively tested and documented.
{"title":"Feature analysis of blind and visual signature data collection protocols based on the identification performance","authors":"Rehab Ibrahem, Meryem Erbilek","doi":"10.1109/CICN.2017.8319370","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319370","url":null,"abstract":"In this paper, we analyse the differences and similarities of features in the context of blind and visual signing data collection protocols with respect to the signature biometrics identification performance. As a result of this performed experimental analysis, powerful features which maximises system accuracy while minimising the performance differential across different signature data collection protocols (visual and blind signing) is extensively tested and documented.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125331582","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319380
Anwesh Marwade, Nakul Kumar, Shubham Mundada, J. Aghav
Customer specific personalization has become imperative for e-commerce websites, helping them to convert browsers (visitors) into buyers. The e-commerce industry predominantly uses various machine learning models for product recommendations and analyzing a customer's behavioral patterns, which play a crucial role in exposing customers to new products based on their online behavior. Psychology studies show that if customers are shown products suited to their personality type or complementing their lifestyle, the chances of them buying the said product grow considerably. By incorporating the personality of a customer in a recommendation system, can we achieve increased level of customer-personalization? The answer to this question forms the crux of this paper. With a view to ascertain a customer's personality, we obtain relevant markers from text samples along the five psychological dimensions. We then experiment with various classification models and analyze the effects of different sets of markers on the accuracy. Results demonstrate certain markers contribute more significantly to a personality trait and hence give better classification accuracies. Considering the existence of an ecommerce based conversational bot, we utilize the personality insights to develop a unique recommendation system based on order history and conversational data that the bot-application would gather over time from users.
{"title":"Augmenting e-commerce product recommendations by analyzing customer personality","authors":"Anwesh Marwade, Nakul Kumar, Shubham Mundada, J. Aghav","doi":"10.1109/CICN.2017.8319380","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319380","url":null,"abstract":"Customer specific personalization has become imperative for e-commerce websites, helping them to convert browsers (visitors) into buyers. The e-commerce industry predominantly uses various machine learning models for product recommendations and analyzing a customer's behavioral patterns, which play a crucial role in exposing customers to new products based on their online behavior. Psychology studies show that if customers are shown products suited to their personality type or complementing their lifestyle, the chances of them buying the said product grow considerably. By incorporating the personality of a customer in a recommendation system, can we achieve increased level of customer-personalization? The answer to this question forms the crux of this paper. With a view to ascertain a customer's personality, we obtain relevant markers from text samples along the five psychological dimensions. We then experiment with various classification models and analyze the effects of different sets of markers on the accuracy. Results demonstrate certain markers contribute more significantly to a personality trait and hence give better classification accuracies. Considering the existence of an ecommerce based conversational bot, we utilize the personality insights to develop a unique recommendation system based on order history and conversational data that the bot-application would gather over time from users.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125682978","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319351
Bubakari Joda, Z. Dereboylu
Several studies confirmed the severity of breast cancer as most mortal in women, worldwide. Premature discovery and diagnosis of cancer of breast is of significance importance in the treatment option and increased patients' possible survival opportunity. Image enhancement is one of the frequently applied techniques to curtail lethal rate by providing enhanced image, which would aid early detection and diagnosis of cancer tumor. Image enhancement is applied on the mammogram images to reduce the speckle noise and increase the contrast of the image. In this research work, it is proposed to use a novel Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm which estimates the Clip Limit adaptively by using Otsu's Method in order to enhance mammogram images. Two different threshold calculations are proposed and the proposed methods are compared with a Fuzzy Logic based adaptive clip limit CLAHE method. The experimental images were obtained from mini-MIAS mammogram database. Experiments were carried out for three different breast types; namely fatty, fatty glandular and dense glandular. The subjective test results indicate that to detect breast cancer at its earliest stage, there is need during analysis and diagnosis of the breast cancer to use both of the images obtained with the two proposed methods.
{"title":"Digital mammogram enhancement based on automatic histogram clipping","authors":"Bubakari Joda, Z. Dereboylu","doi":"10.1109/CICN.2017.8319351","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319351","url":null,"abstract":"Several studies confirmed the severity of breast cancer as most mortal in women, worldwide. Premature discovery and diagnosis of cancer of breast is of significance importance in the treatment option and increased patients' possible survival opportunity. Image enhancement is one of the frequently applied techniques to curtail lethal rate by providing enhanced image, which would aid early detection and diagnosis of cancer tumor. Image enhancement is applied on the mammogram images to reduce the speckle noise and increase the contrast of the image. In this research work, it is proposed to use a novel Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm which estimates the Clip Limit adaptively by using Otsu's Method in order to enhance mammogram images. Two different threshold calculations are proposed and the proposed methods are compared with a Fuzzy Logic based adaptive clip limit CLAHE method. The experimental images were obtained from mini-MIAS mammogram database. Experiments were carried out for three different breast types; namely fatty, fatty glandular and dense glandular. The subjective test results indicate that to detect breast cancer at its earliest stage, there is need during analysis and diagnosis of the breast cancer to use both of the images obtained with the two proposed methods.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116206411","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319387
M. Akay, M. C. Yüksel, F. Abut, F. M. Taş, J. George
This study was carried out with the intention to create new models to predict the maximum endurance time for the left-side bridge exercise using machine learning methods and hybrid data. Particularly, four different methods including Multilayer Feed-Forward Artificial Neural Network (MFANN), Generalized Regression Neural Network (GRNN), Radial Basis Function Neural Network (RBFNN) and Single Decision Tree (SDT) have been used for model development. The dataset used to create the prediction models includes physiological, exercise and questionnaire data related to individuals who performed the left-side bridge exercise and completed the Perceived Activity Rating (PAR) and Perceived Functional Ability (PFA) questionnaires. To evaluate the performance of the models, two well-known metrics, namely Root Mean Square Error (RMSE) and Multiple Correlation Coefficient (R) have been used, whereas the generalization errors have been assessed using 10-fold cross validation. The best prediction performance among the models has been obtained by using MFANN along with the predictor variables gender, age, body mass index (BMI), the times to reach a rate of perceived exertion values of 7 and 8 (RPE-7 and RPE-8, respectively) and PAR, producing the lowest RMSE and the highest R with 10.61 seconds (s) and 0.92, respectively.
{"title":"Predicting the maximum endurance time for left-side bridge exercise using machine learning methods and hybrid data","authors":"M. Akay, M. C. Yüksel, F. Abut, F. M. Taş, J. George","doi":"10.1109/CICN.2017.8319387","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319387","url":null,"abstract":"This study was carried out with the intention to create new models to predict the maximum endurance time for the left-side bridge exercise using machine learning methods and hybrid data. Particularly, four different methods including Multilayer Feed-Forward Artificial Neural Network (MFANN), Generalized Regression Neural Network (GRNN), Radial Basis Function Neural Network (RBFNN) and Single Decision Tree (SDT) have been used for model development. The dataset used to create the prediction models includes physiological, exercise and questionnaire data related to individuals who performed the left-side bridge exercise and completed the Perceived Activity Rating (PAR) and Perceived Functional Ability (PFA) questionnaires. To evaluate the performance of the models, two well-known metrics, namely Root Mean Square Error (RMSE) and Multiple Correlation Coefficient (R) have been used, whereas the generalization errors have been assessed using 10-fold cross validation. The best prediction performance among the models has been obtained by using MFANN along with the predictor variables gender, age, body mass index (BMI), the times to reach a rate of perceived exertion values of 7 and 8 (RPE-7 and RPE-8, respectively) and PAR, producing the lowest RMSE and the highest R with 10.61 seconds (s) and 0.92, respectively.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125431059","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319386
Bora Uğurlu, Lütfullah Kaynak
When an emergency happens in a rural area, accessing the patient's basic medical record, such as age, blood type, and drug allergy if any, via mobile network may sometimes not possible. It is up most important to access quickly the medical records of the patient would be vital when the passing time is considered. Most of the time, this saves lives. Another issue is the notification of survivor's close relatives. They are needed to be informed as soon as the emergency happens. Sharing some information with them such as accident location, hospital name would make them less worried. In this study, we have developed an instant information support and notification software system. Our goal is to provide the survivor's basic medical record to first aid team as well as to notify his/her close relatives as quickly as possible.
{"title":"Instant information support and notification system for emergency","authors":"Bora Uğurlu, Lütfullah Kaynak","doi":"10.1109/CICN.2017.8319386","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319386","url":null,"abstract":"When an emergency happens in a rural area, accessing the patient's basic medical record, such as age, blood type, and drug allergy if any, via mobile network may sometimes not possible. It is up most important to access quickly the medical records of the patient would be vital when the passing time is considered. Most of the time, this saves lives. Another issue is the notification of survivor's close relatives. They are needed to be informed as soon as the emergency happens. Sharing some information with them such as accident location, hospital name would make them less worried. In this study, we have developed an instant information support and notification software system. Our goal is to provide the survivor's basic medical record to first aid team as well as to notify his/her close relatives as quickly as possible.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123179909","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319375
R. Tan, Şebnem Bora
Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.
{"title":"Parameter tuning in modeling and simulations by using swarm intelligence optimization algorithms","authors":"R. Tan, Şebnem Bora","doi":"10.1109/CICN.2017.8319375","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319375","url":null,"abstract":"Modeling and simulation of real-world environments has in recent times being widely used. The modeling of environments whose examination in particular is difficult and the examination via the model becomes easier. The parameters of the modeled systems and the values they can obtain are quite large, and manual tuning is tedious and requires a lot of effort while it often it is almost impossible to get the desired results. For this reason, there is a need for the parameter space to be set. The studies conducted in recent years were reviewed, it has been observed that there are few studies for parameter tuning problem in modeling and simulations. In this study, work has been done for a solution to be found to the problem of parameter tuning with swarm intelligence optimization algorithms Particle swarm optimization and Firefly algorithms. The performance of these algorithms in the parameter tuning process has been tested on 2 different agent based model studies. The performance of the algorithms has been observed by manually entering the parameters found for the model. According to the obtained results, it has been seen that the Firefly algorithm where the Particle swarm optimization algorithm works faster has better parameter values. With this study, the parameter tuning problem of the models in the different fields were solved.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128822943","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319361
P. Maheshwari, Alankar Singhal, M. Qadeer
Ours is a data centric world. Organizations around the globe are looking for ways to exploit the propulsive growth of data to find ways of exploring previously hidden insights so as to publish new revenue streams, gaining operational efficiencies and understanding customer needs better. Analytics comes to the rescue and with the aid of Cloud Computing it aids to explore this paradigm to a previously unimaginable extent. In this paper we discuss presently available and practiced methods to perform Cloud Analytics.
{"title":"Data analytics using cloud computing","authors":"P. Maheshwari, Alankar Singhal, M. Qadeer","doi":"10.1109/CICN.2017.8319361","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319361","url":null,"abstract":"Ours is a data centric world. Organizations around the globe are looking for ways to exploit the propulsive growth of data to find ways of exploring previously hidden insights so as to publish new revenue streams, gaining operational efficiencies and understanding customer needs better. Analytics comes to the rescue and with the aid of Cloud Computing it aids to explore this paradigm to a previously unimaginable extent. In this paper we discuss presently available and practiced methods to perform Cloud Analytics.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114540588","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319382
M. Akay, E. Çetin, İmdat Yarım, Özge Bozkurt, M. Özçiloglu
Maximal oxygen uptake (VO2max) is the maximum rate of oxygen consumption as measured during maximal exercise. The purpose of this study is to produce new prediction models for Turkish college students by using machine learning methods including Support Vector Machines (SVM), Generalized Regression Neural Networks (GRNN), Radial Basis Function Network (RBFN) and Decision Tree Forest (DTF). The dataset comprises data of 98 subjects and the predictor variables are gender, age, height, weight, maximum heart rate (HRmax), grade, speed and exercise time. Fifteen different VO2max prediction models have been created with the variables listed above. The performance of the prediction models has been calculated by using common metrics such as standard error of estimate (SEE) and multiple correlation coefficient (R). The results show that GRNN based models usually produced much lower SEE's and higher R's than the ones given by SVM, DTF and RBFN based models. On the other hand, the RBFN based models yielded the worst performance with unacceptable error rates. Also, this study shows that the predictor variables grade, speed and time play a significant role in VO2max prediction.
最大摄氧量(VO2max)是在最大运动期间测量的最大耗氧量。本研究的目的是利用支持向量机(SVM)、广义回归神经网络(GRNN)、径向基函数网络(RBFN)和决策树森林(DTF)等机器学习方法，为土耳其大学生建立新的预测模型。该数据集包括98名受试者的数据，预测变量为性别、年龄、身高、体重、最大心率(HRmax)、年级、速度和运动时间。使用上面列出的变量创建了15种不同的VO2max预测模型。采用标准估计误差(standard error of estimation, SEE)和多重相关系数(multiple correlation coefficient, R)等常用指标对预测模型的性能进行了计算。结果表明，与SVM、DTF和RBFN模型相比，基于GRNN的模型通常产生更低的SEE和更高的R。另一方面，基于RBFN的模型产生的性能最差，错误率不可接受。预测变量等级、速度和时间对最大摄氧量的预测有显著影响。
{"title":"Development of novel maximal oxygen uptake prediction models for Turkish college students using machine learning and exercise data","authors":"M. Akay, E. Çetin, İmdat Yarım, Özge Bozkurt, M. Özçiloglu","doi":"10.1109/CICN.2017.8319382","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319382","url":null,"abstract":"Maximal oxygen uptake (VO2max) is the maximum rate of oxygen consumption as measured during maximal exercise. The purpose of this study is to produce new prediction models for Turkish college students by using machine learning methods including Support Vector Machines (SVM), Generalized Regression Neural Networks (GRNN), Radial Basis Function Network (RBFN) and Decision Tree Forest (DTF). The dataset comprises data of 98 subjects and the predictor variables are gender, age, height, weight, maximum heart rate (HRmax), grade, speed and exercise time. Fifteen different VO2max prediction models have been created with the variables listed above. The performance of the prediction models has been calculated by using common metrics such as standard error of estimate (SEE) and multiple correlation coefficient (R). The results show that GRNN based models usually produced much lower SEE's and higher R's than the ones given by SVM, DTF and RBFN based models. On the other hand, the RBFN based models yielded the worst performance with unacceptable error rates. Also, this study shows that the predictor variables grade, speed and time play a significant role in VO2max prediction.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669928","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 : 2017-09-01DOI: 10.1109/CICN.2017.8319346
Achmad Munirf, Bunga Dwi Wulandari, W. Aditomo, Yogi Prasetio
In this paper, the development of defected ground structure (DGS) based ultra-wideband (UWB) microstrip bandpass filter (BPF) and its equivalent circuit are proposed. The use of DGS is aimed to enhance the characteristic of filter and other microwave devices as well. The proposed BPF which is designed on a 0.8mm thick FR4 epoxy dielectric substrate with the dimension of 32mm χ 11mm is constructed of microstrip coupled lines and open stubs with DGS underneath. Meanwhile, the used DGS is built from 3 circular dumbbells. The equivalent circuit of BPF comprises of lumped elements of inductor and capacitor and is performed using EM & Circuit simulator. From the characterization, the measured result is agreed qualitatively with the simulated result of equivalent circuit. The characteristic of realized BPF which is comparable with the simulation result has −3dB working bandwidth of 5.36GHz in the frequency range of 1.84GHz to 7.2GHz.
{"title":"DGS-based UWB microstrip BPF and its equivalent circuit","authors":"Achmad Munirf, Bunga Dwi Wulandari, W. Aditomo, Yogi Prasetio","doi":"10.1109/CICN.2017.8319346","DOIUrl":"https://doi.org/10.1109/CICN.2017.8319346","url":null,"abstract":"In this paper, the development of defected ground structure (DGS) based ultra-wideband (UWB) microstrip bandpass filter (BPF) and its equivalent circuit are proposed. The use of DGS is aimed to enhance the characteristic of filter and other microwave devices as well. The proposed BPF which is designed on a 0.8mm thick FR4 epoxy dielectric substrate with the dimension of 32mm χ 11mm is constructed of microstrip coupled lines and open stubs with DGS underneath. Meanwhile, the used DGS is built from 3 circular dumbbells. The equivalent circuit of BPF comprises of lumped elements of inductor and capacitor and is performed using EM & Circuit simulator. From the characterization, the measured result is agreed qualitatively with the simulated result of equivalent circuit. The characteristic of realized BPF which is comparable with the simulation result has −3dB working bandwidth of 5.36GHz in the frequency range of 1.84GHz to 7.2GHz.","PeriodicalId":339750,"journal":{"name":"2017 9th International Conference on Computational Intelligence and Communication Networks (CICN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133559363","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：