Pub Date : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036595
Irtiza Hasan, A. Das, Mohammed Imamul, Hassan Bhuiyan
Premature birth, one of the root causes of maternal mortality and childhood morbidity, is growing at an increased rate worldwide. Several screening tests used in clinical settings to predict preterm births do not show satisfactory results. Electrohysterography, a noninvasive automated method of monitoring uterine contractions during labor, has shown to be effective in the classification task. In this work, we explored the potential use of several temporal nonlinear parameters for carrying out classification process. A publicly available TPEHG DB (Term Preterm Electrohysterogram Database) containing 262 term records and 38 preterm records was used to conduct the study. To execute effective classification tasks, Synthetic Minority Over-Sampling Technique (SMOTE) was applied on unbalanced dataset to generate equal number of training dataset by oversampling the minority class. New suitable nonlinear features have been addresed in this study other than the features used in the previous works to compare the performance of the classifiers. The analysis showed that extremely randomized trees or extra trees classifier shows significant improvement by using these features in the classification process of term and preterm records with cross validation accuracy 91.4%, specificity 90.2% and sensitivity 95.5%. The proposed approach could be combined with other methods to excel in the existing classification performance.
{"title":"Nonlinear Temporal Analysis of Uterine EMG for Preterm Birth Classification","authors":"Irtiza Hasan, A. Das, Mohammed Imamul, Hassan Bhuiyan","doi":"10.1109/IC4ME247184.2019.9036595","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036595","url":null,"abstract":"Premature birth, one of the root causes of maternal mortality and childhood morbidity, is growing at an increased rate worldwide. Several screening tests used in clinical settings to predict preterm births do not show satisfactory results. Electrohysterography, a noninvasive automated method of monitoring uterine contractions during labor, has shown to be effective in the classification task. In this work, we explored the potential use of several temporal nonlinear parameters for carrying out classification process. A publicly available TPEHG DB (Term Preterm Electrohysterogram Database) containing 262 term records and 38 preterm records was used to conduct the study. To execute effective classification tasks, Synthetic Minority Over-Sampling Technique (SMOTE) was applied on unbalanced dataset to generate equal number of training dataset by oversampling the minority class. New suitable nonlinear features have been addresed in this study other than the features used in the previous works to compare the performance of the classifiers. The analysis showed that extremely randomized trees or extra trees classifier shows significant improvement by using these features in the classification process of term and preterm records with cross validation accuracy 91.4%, specificity 90.2% and sensitivity 95.5%. The proposed approach could be combined with other methods to excel in the existing classification performance.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128084269","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 : 2019-07-01DOI: 10.1109/ic4me247184.2019.9036688
{"title":"IC4ME2 2019 Table of Contents","authors":"","doi":"10.1109/ic4me247184.2019.9036688","DOIUrl":"https://doi.org/10.1109/ic4me247184.2019.9036688","url":null,"abstract":"","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115825912","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 : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036489
Md. Kalim Amzad Chy, Sheikh Arif Ahmed, Ali Haider Doha, Abdul Kadar Muhammad Masum, S. I. Khan
Social media has a significant impact on our daily life, and the popularity is increasing rapidly because of the ability to be attached to people around the world and share feelings, photos, videos, etc. So, it bears a high-security concern. However, most of the social media user does not know the security level of their account, including what features of social media should consider if the account is in a risk situation. The posting, friendship, etc. sometimes brings unfortunate events like identity theft, sexual harassment, cyber-crime, etc. To overcome such kind of unexpected issues, this research proposes a classification via clustering algorithm based predictive model by which one can know his safety level in the social media. A dataset is formed through a closed-ended questionnaire. Essential features are selected via gain ration method as high dimensional data is costly to train a model. An unsupervised algorithm, hierarchical clustering, cluster the users into three groups that are labeled for further analysis. The various classification algorithm is chosen to train the predictive model. From the model evaluation result, “Logistic Regression” predicts the safety level of a social media user with high accuracy. So, this model will bring an extra dimension in social media user account safety.
{"title":"Social Media User’s Safety Level Detection through Classification via Clustering Approach","authors":"Md. Kalim Amzad Chy, Sheikh Arif Ahmed, Ali Haider Doha, Abdul Kadar Muhammad Masum, S. I. Khan","doi":"10.1109/IC4ME247184.2019.9036489","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036489","url":null,"abstract":"Social media has a significant impact on our daily life, and the popularity is increasing rapidly because of the ability to be attached to people around the world and share feelings, photos, videos, etc. So, it bears a high-security concern. However, most of the social media user does not know the security level of their account, including what features of social media should consider if the account is in a risk situation. The posting, friendship, etc. sometimes brings unfortunate events like identity theft, sexual harassment, cyber-crime, etc. To overcome such kind of unexpected issues, this research proposes a classification via clustering algorithm based predictive model by which one can know his safety level in the social media. A dataset is formed through a closed-ended questionnaire. Essential features are selected via gain ration method as high dimensional data is costly to train a model. An unsupervised algorithm, hierarchical clustering, cluster the users into three groups that are labeled for further analysis. The various classification algorithm is chosen to train the predictive model. From the model evaluation result, “Logistic Regression” predicts the safety level of a social media user with high accuracy. So, this model will bring an extra dimension in social media user account safety.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132146987","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 : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036683
Amrit Regmi, B. R. Bhattarai, S. K. Gautam
The ZnS semiconductor nanoparticles were synthesized by wet chemical synthesis routes from zinc acetate [Zn(CH3 COO$)_{2}]$ as source of zinc and sodium sulfide (Na2S) as source of sulfur, where ascorbic acid were used as capping agents. The structural, morphological, and optical properties of synthesized nanoparticles had been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM) and UV-visible spectra (UV-Vis). XRD analysis shows that as synthesized samples were cubic structure and the average sizes of crystal were estimated 2.3 nm and 2.1 nm at $20^{circ}C$ and $45^{circ}C$, respectively using Debye Scherer’s equation. The band gap energy of ZnS nanoparticles determined from the UV-Vis spectra are 3.9 eV and 4.19 eV synthesized at $20^{circ}C$ and $45^{circ}C$, respectively. The size estimated from XRD pattern was further verified from TEM image and UV-Vis spectra.
{"title":"Synthesis and Microscopic Study of Zinc Sulfide Nanoparticles","authors":"Amrit Regmi, B. R. Bhattarai, S. K. Gautam","doi":"10.1109/IC4ME247184.2019.9036683","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036683","url":null,"abstract":"The ZnS semiconductor nanoparticles were synthesized by wet chemical synthesis routes from zinc acetate [Zn(CH3 COO$)_{2}]$ as source of zinc and sodium sulfide (Na2S) as source of sulfur, where ascorbic acid were used as capping agents. The structural, morphological, and optical properties of synthesized nanoparticles had been characterized by X-ray diffraction (XRD), transmission electron microscope (TEM) and UV-visible spectra (UV-Vis). XRD analysis shows that as synthesized samples were cubic structure and the average sizes of crystal were estimated 2.3 nm and 2.1 nm at $20^{circ}C$ and $45^{circ}C$, respectively using Debye Scherer’s equation. The band gap energy of ZnS nanoparticles determined from the UV-Vis spectra are 3.9 eV and 4.19 eV synthesized at $20^{circ}C$ and $45^{circ}C$, respectively. The size estimated from XRD pattern was further verified from TEM image and UV-Vis spectra.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117310994","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 : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036637
A. Islam, S. M. M. Ahsan, J. Tan
Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method.
{"title":"Saliency Detection using the Combination of Boundary Aware Color-map and Seam-map","authors":"A. Islam, S. M. M. Ahsan, J. Tan","doi":"10.1109/IC4ME247184.2019.9036637","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036637","url":null,"abstract":"Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121609775","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}
As addiction is said to be a mental health condition which can be derived from Electroencephalogram, this study focuses on changes in EEG pattern due to smoking. A methodology is proposed here to identify smoker and nonsmoker by EEG domain analysis. In this research, three ANNs were built for domain analysis to differentiate smokers from non-smokers. It was found that the neural network built with the attributes of both time and frequency domain provided best quality with MSE of $6.238 times 10^{-08}$ than the neural networks using either time or frequency domain features. On the other hand, frequency domain based ANN solely gives better performance than time domain properties based neural network. It is concluded in this paper that values of PSD and FFT is much higher in EEG of smokers than the non-smokers.
{"title":"Effect of Smoking in EEG Pattern and Time-Frequency Domain Analysis for Smoker and Non-Smoker","authors":"Md Mahmudul Hasan, Nafiul Hasan, Azizur Rahman, Md. Mustafizur Rahman","doi":"10.1109/IC4ME247184.2019.9036492","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036492","url":null,"abstract":"As addiction is said to be a mental health condition which can be derived from Electroencephalogram, this study focuses on changes in EEG pattern due to smoking. A methodology is proposed here to identify smoker and nonsmoker by EEG domain analysis. In this research, three ANNs were built for domain analysis to differentiate smokers from non-smokers. It was found that the neural network built with the attributes of both time and frequency domain provided best quality with MSE of $6.238 times 10^{-08}$ than the neural networks using either time or frequency domain features. On the other hand, frequency domain based ANN solely gives better performance than time domain properties based neural network. It is concluded in this paper that values of PSD and FFT is much higher in EEG of smokers than the non-smokers.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958747","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}
In today’s world the number of consumers of cloud computing is increasing day by day. So, security is a big concern for cloud computing environment to keep user’s data safe and secure. Among different types of attacks in cloud one of the harmful and frequently occurred attack is Distributed Denial of Service (DDoS) attack. DDoS is one type of flooding attack which is initiated by sending a large number of invalid packets to limit the services of the victim server. As a result, server can not serve the legitimate requests. DDoS attack can be done by a lot of strategies like malformed packets, IP spoofing, smurf attack, teardrop attack, syn flood attack, local area network denial (LAND) attack etc. This paper focuses on IP spoofing and LAND based DDoS attack. The objective of this paper is to propose an algorithm to detect and prevent IP spoofing and LAND attack. To achieve this objective a new approach is proposed combining two existing solutions of DDoS attack caused by IP spoofing and ill-formed packets. The proposed approach will provide a transparent solution, filter out the spoofed packets and minimize memory exhaustion through minimizing the number of insertions and updates required in the datatable. Finally, the approach is implemented and simulated using CloudSim 3.0 toolkit (a virtual cloud environment) followed by result analysis and comparison with existing algorithms.
{"title":"Detecting and Preventing IP Spoofing and Local Area Network Denial (LAND) Attack for Cloud Computing with the Modification of Hop Count Filtering (HCF) Mechanism","authors":"Subrina Sultana, Sumaiya Nasrin, Farhana Kabir Lipi, Md. Afzal Hossain, Zinia Sultana, Fatima Jannat","doi":"10.1109/IC4ME247184.2019.9036507","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036507","url":null,"abstract":"In today’s world the number of consumers of cloud computing is increasing day by day. So, security is a big concern for cloud computing environment to keep user’s data safe and secure. Among different types of attacks in cloud one of the harmful and frequently occurred attack is Distributed Denial of Service (DDoS) attack. DDoS is one type of flooding attack which is initiated by sending a large number of invalid packets to limit the services of the victim server. As a result, server can not serve the legitimate requests. DDoS attack can be done by a lot of strategies like malformed packets, IP spoofing, smurf attack, teardrop attack, syn flood attack, local area network denial (LAND) attack etc. This paper focuses on IP spoofing and LAND based DDoS attack. The objective of this paper is to propose an algorithm to detect and prevent IP spoofing and LAND attack. To achieve this objective a new approach is proposed combining two existing solutions of DDoS attack caused by IP spoofing and ill-formed packets. The proposed approach will provide a transparent solution, filter out the spoofed packets and minimize memory exhaustion through minimizing the number of insertions and updates required in the datatable. Finally, the approach is implemented and simulated using CloudSim 3.0 toolkit (a virtual cloud environment) followed by result analysis and comparison with existing algorithms.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130256039","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 : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036594
Mst. Rehena Khatun, Md. Ekramul Hamid, Md. Iqbal Aziz Khan
This paper presents classification of gas bubble in a Doppler ultrasound signal using Synchrosqueezing Transform (SST). The SST decomposes the signal into a number of scales. In this research work, initially two statistical parameters, the peak value and variance are estimated to Figure out the scales that contains gas bubbles. Then the signal is reconstructed from the coefficient values within the selected scale. Some parameters are defined and calculated from the reconstructed signal. These parameters are used to classify gas bubble signal using naïve Bayes classifier. However, two classes “bubble” and “not bubble” are identified by training data sets. Therefore, on the basis of posterior probability, the class of the signal is defined. Finally, performance of gas bubble detection is evaluated in terms of sensitivity and positive predictivity tests. Our proposed method is applied on grade 0, I, II, and III signals. It is observed that, good classification result is achieved in grade I and grade II. In grade 0, no gas bubble is found. In the experiment, 92% gas bubble is classified in grade I, 84% gas bubble is classified in grade II and 80% gas bubble is classified in grade III. Experimental result shows that the proposed method achieves better accuracy than the conventional method in the literature.
{"title":"Classification of Gas Bubble in A Doppler Ultrasound Signal Using Synchrosqueezing Transform","authors":"Mst. Rehena Khatun, Md. Ekramul Hamid, Md. Iqbal Aziz Khan","doi":"10.1109/IC4ME247184.2019.9036594","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036594","url":null,"abstract":"This paper presents classification of gas bubble in a Doppler ultrasound signal using Synchrosqueezing Transform (SST). The SST decomposes the signal into a number of scales. In this research work, initially two statistical parameters, the peak value and variance are estimated to Figure out the scales that contains gas bubbles. Then the signal is reconstructed from the coefficient values within the selected scale. Some parameters are defined and calculated from the reconstructed signal. These parameters are used to classify gas bubble signal using naïve Bayes classifier. However, two classes “bubble” and “not bubble” are identified by training data sets. Therefore, on the basis of posterior probability, the class of the signal is defined. Finally, performance of gas bubble detection is evaluated in terms of sensitivity and positive predictivity tests. Our proposed method is applied on grade 0, I, II, and III signals. It is observed that, good classification result is achieved in grade I and grade II. In grade 0, no gas bubble is found. In the experiment, 92% gas bubble is classified in grade I, 84% gas bubble is classified in grade II and 80% gas bubble is classified in grade III. Experimental result shows that the proposed method achieves better accuracy than the conventional method in the literature.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130825827","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 : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036647
K. M. Zubair Hasan, Md. Zahid Hasan, N. Zahan
Phishing is one of the ruinous issues encountered by the World Wide Web (WWW) and steers to the financial catastrophes for individuals and businesses. It has been perpetually a perplexing issue to identify phishing attacks with high exactness. The tremendous outcomes in the area of classification have been succeeded by the state-of-the-art invention of the deep convolutional neural networks (DCNNs). This paper is concerned with an accurate identifying approach for web phishing attacks based on deep convolutional neural networks. Our developed model has the ability to classify the attacked phishing websites from legitimate sites. However, due to the limitation of samples in the dataset, other machine learning algorithms (SVM, AdaBoost, Decision Tree, KNN) cannot perform proficiently for analyzing the data. In this respect, our proposed Deep Convolution Neural Network (DCNN) model has an automated approach to predict the phishing sites within the earlier stage. The empirical results show that the overall accuracy of 99% is achieved by the recommended methodology.
{"title":"Automated Prediction of Phishing Websites Using Deep Convolutional Neural Network","authors":"K. M. Zubair Hasan, Md. Zahid Hasan, N. Zahan","doi":"10.1109/IC4ME247184.2019.9036647","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036647","url":null,"abstract":"Phishing is one of the ruinous issues encountered by the World Wide Web (WWW) and steers to the financial catastrophes for individuals and businesses. It has been perpetually a perplexing issue to identify phishing attacks with high exactness. The tremendous outcomes in the area of classification have been succeeded by the state-of-the-art invention of the deep convolutional neural networks (DCNNs). This paper is concerned with an accurate identifying approach for web phishing attacks based on deep convolutional neural networks. Our developed model has the ability to classify the attacked phishing websites from legitimate sites. However, due to the limitation of samples in the dataset, other machine learning algorithms (SVM, AdaBoost, Decision Tree, KNN) cannot perform proficiently for analyzing the data. In this respect, our proposed Deep Convolution Neural Network (DCNN) model has an automated approach to predict the phishing sites within the earlier stage. The empirical results show that the overall accuracy of 99% is achieved by the recommended methodology.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126139746","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 : 2019-07-01DOI: 10.1109/IC4ME247184.2019.9036623
Ahsan Yaqub Rabbi, M. R. Kaysir, Md Jahirul Islam
Fiber Bragg Gratings (FBGs) result in an optical fiber due to the variation of the refractive index in the core material. Sensors based on FBG accumulate the advantages of optical fiber such as immunity to electromagnetic interference, compact size and lightweight, suitability for remote monitoring, flexibility, and multiplexing capabilities. Recently, FBG based strain sensors attain intense research interest due to their high sensitivity and comparatively lower cost. These sensors work with simple principle; Bragg wavelength shifts when strain is induced on the fiber. In this paper, we aim to design a FBG based strain sensor and then numerically modeled it in COMSOL environment to investigate the sensor performance. To design the system, we incorporate a solid mechanical model to the FBG model in Radio Frequency module to apply external force to produce stain in the FBG. Finally the results from the numerical model is compared with the existing analytical model, which shows good agreement.
{"title":"Numerical Modeling to evaluate the performance of FBG-based Strain Sensors","authors":"Ahsan Yaqub Rabbi, M. R. Kaysir, Md Jahirul Islam","doi":"10.1109/IC4ME247184.2019.9036623","DOIUrl":"https://doi.org/10.1109/IC4ME247184.2019.9036623","url":null,"abstract":"Fiber Bragg Gratings (FBGs) result in an optical fiber due to the variation of the refractive index in the core material. Sensors based on FBG accumulate the advantages of optical fiber such as immunity to electromagnetic interference, compact size and lightweight, suitability for remote monitoring, flexibility, and multiplexing capabilities. Recently, FBG based strain sensors attain intense research interest due to their high sensitivity and comparatively lower cost. These sensors work with simple principle; Bragg wavelength shifts when strain is induced on the fiber. In this paper, we aim to design a FBG based strain sensor and then numerically modeled it in COMSOL environment to investigate the sensor performance. To design the system, we incorporate a solid mechanical model to the FBG model in Radio Frequency module to apply external force to produce stain in the FBG. Finally the results from the numerical model is compared with the existing analytical model, which shows good agreement.","PeriodicalId":368690,"journal":{"name":"2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122579952","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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