Pub Date : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234493
A. Chatterjee, Hugo Flores, S. Sen, Khondker S. Hasan, Ashish Mani
Detecting precise location of aircraft during the entire flight duration is a challenge in the domain of commercial aviation. Using radar and other available technology, flights operating entirely over land can be tracked easily. However, with long haul intercontinental flights, where majority of the flight path is over water bodies and out of range of radar, detecting the location of aircraft at all times is a challenge. In recent times, there have been disasters in commercial aviation, where an aircraft has gone missing. This has a huge social and financial impact on the specific airline and commercial aviation in general. Therefore, in this paper we study the problem of location detection for commercial aircraft and methods to improve location detection over any terrain the flight path traverses. We propose techniques based on the Internet-of-things (IoT) model for aircraft, where the aircraft can communicate with each other within a certain range. We introduce distributed algorithms to detect location using such methods that work effectively when the aircraft is outside the range of radar and on an oceanic route. Our results show that using the proposed methods, the precise location of all aircraft, including those intercontinental flights, can be tracked to a higher degree. Techniques to minimize the communication overhead introduced due to the proposed methods are also provided.
{"title":"Distributed location detection algorithms using IoT for commercial aviation","authors":"A. Chatterjee, Hugo Flores, S. Sen, Khondker S. Hasan, Ashish Mani","doi":"10.1109/ICRCICN.2017.8234493","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234493","url":null,"abstract":"Detecting precise location of aircraft during the entire flight duration is a challenge in the domain of commercial aviation. Using radar and other available technology, flights operating entirely over land can be tracked easily. However, with long haul intercontinental flights, where majority of the flight path is over water bodies and out of range of radar, detecting the location of aircraft at all times is a challenge. In recent times, there have been disasters in commercial aviation, where an aircraft has gone missing. This has a huge social and financial impact on the specific airline and commercial aviation in general. Therefore, in this paper we study the problem of location detection for commercial aircraft and methods to improve location detection over any terrain the flight path traverses. We propose techniques based on the Internet-of-things (IoT) model for aircraft, where the aircraft can communicate with each other within a certain range. We introduce distributed algorithms to detect location using such methods that work effectively when the aircraft is outside the range of radar and on an oceanic route. Our results show that using the proposed methods, the precise location of all aircraft, including those intercontinental flights, can be tracked to a higher degree. Techniques to minimize the communication overhead introduced due to the proposed methods are also provided.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"44 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":"129703395","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234527
Sinjini Banerjee, Priyabrata Sahoo, Mahamuda Sultana, Ayan Chaudhuri, D. Sengupta, A. Chaudhuri
The growing research in reversible computation has been duly complimented by several proposals of reversible circuit synthesis algorithms. A certain amount of proposals have also been presented for optimizing reversible circuit designs. This communication proposes a fresh synthesis algorithm for four bit reversible functions based on a pre-defined library of Control Line Sets. The library contains a set of Toffoli Netlists for a certain transformation. An optimal Netlist selection choice based on Hamming Distance synthesizes an optimized reversible circuit for a given four variable reversible function which eliminates need of post synthesis optimization. The study has been compared with a peer synthesis algorithm and found to generate better reversible circuits.
{"title":"Toffoli netlist based synthesis of four variable reversible functions","authors":"Sinjini Banerjee, Priyabrata Sahoo, Mahamuda Sultana, Ayan Chaudhuri, D. Sengupta, A. Chaudhuri","doi":"10.1109/ICRCICN.2017.8234527","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234527","url":null,"abstract":"The growing research in reversible computation has been duly complimented by several proposals of reversible circuit synthesis algorithms. A certain amount of proposals have also been presented for optimizing reversible circuit designs. This communication proposes a fresh synthesis algorithm for four bit reversible functions based on a pre-defined library of Control Line Sets. The library contains a set of Toffoli Netlists for a certain transformation. An optimal Netlist selection choice based on Hamming Distance synthesizes an optimized reversible circuit for a given four variable reversible function which eliminates need of post synthesis optimization. The study has been compared with a peer synthesis algorithm and found to generate better reversible circuits.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"11 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":"131140112","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234533
Subhradeep Biswas, Sudipa Biswas
This paper proposes a password security system that allows the host not to store the passwords of its users at its end. Instead it creates and stores a derivative of the password with the help of a bitmap image uploaded by the user during the user creation process. During the login attempts of users, the user is required to enter the password and upload the same image. the proposed system verifies if the image uploaded during login matches with the original image that was provided during user creation by comparing their pixel information. Then, the system derives the password from the image with the help of the stored derivative. Then, the derived password is matched with the password entered by the user.
{"title":"Password security system with 2-way authentication","authors":"Subhradeep Biswas, Sudipa Biswas","doi":"10.1109/ICRCICN.2017.8234533","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234533","url":null,"abstract":"This paper proposes a password security system that allows the host not to store the passwords of its users at its end. Instead it creates and stores a derivative of the password with the help of a bitmap image uploaded by the user during the user creation process. During the login attempts of users, the user is required to enter the password and upload the same image. the proposed system verifies if the image uploaded during login matches with the original image that was provided during user creation by comparing their pixel information. Then, the system derives the password from the image with the help of the stored derivative. Then, the derived password is matched with the password entered by the user.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"5 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":"126490915","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234474
P. Das, S. Naskar, S. N. Patra
In this paper, we propose design of even order low pass FIR filter and odd order bandpass FIR filter using coefficients optimized by an adaptive Global Best steered Cuckoo Search Algorithm (gbest CSA). For optimization, we use a mean square error based cost function as the fitness function. We evaluated the efficacy of the proposed technique by comparing the filter responses with responses of the filters designed using standard Cuckoo Search Algorithm and traditional technique of filter design with Parks McClellan algorithm. Efficacy of the proposed algorithm compared to the conventional CSA is proved using seven standard benchmark functions.
{"title":"Adaptive global best steered Cuckoo search algorithm for FIR filter design","authors":"P. Das, S. Naskar, S. N. Patra","doi":"10.1109/ICRCICN.2017.8234474","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234474","url":null,"abstract":"In this paper, we propose design of even order low pass FIR filter and odd order bandpass FIR filter using coefficients optimized by an adaptive Global Best steered Cuckoo Search Algorithm (gbest CSA). For optimization, we use a mean square error based cost function as the fitness function. We evaluated the efficacy of the proposed technique by comparing the filter responses with responses of the filters designed using standard Cuckoo Search Algorithm and traditional technique of filter design with Parks McClellan algorithm. Efficacy of the proposed algorithm compared to the conventional CSA is proved using seven standard benchmark functions.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"5 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":"125192666","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234481
Raj Vipani, Sambit Hore, Souryadeep Basak, S. Dutta
In this paper, we have employed a machine learning approach for automatic classification of healthy and pathological gait signals and subsequent identification of the neurological disorder in the pathological gait signals. The machine learning algorithm we have proposed is the Logit model of the Logical Regression Classifier. As the process of walking is automatically controlled by the nervous system it is important to develop a non-invasive method so that patients with serious neurological disorders like Huntington's disease and Parkinson's disease receive early medical attention and they get proper care before they are more affected. Swing, Stance and double support intervals (expressed as percentages of stride) of 63 subjects were analyzed. In this paper, a relevant gait signal feature extractor is developed which is combined with Logistic Regression Classifier to classify healthy subjects and pathological subjects. Analysis of real-time gait signals is simplified using the Hilbert Transform which converts the real signals into an analytic signal. The proposed algorithm was developed using the MATLAB platform and the average accuracy of multiclass classification is found to be 86.05% while the accuracy of detecting healthy subjects from pathological subjects is 87.79% and the accuracy of classifying subjects having the Huntington's disease and Parkinson's disease is found to be 85.22%.
{"title":"Gait signal classification tool utilizing Hilbert transform based feature extraction and logistic regression based classification","authors":"Raj Vipani, Sambit Hore, Souryadeep Basak, S. Dutta","doi":"10.1109/ICRCICN.2017.8234481","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234481","url":null,"abstract":"In this paper, we have employed a machine learning approach for automatic classification of healthy and pathological gait signals and subsequent identification of the neurological disorder in the pathological gait signals. The machine learning algorithm we have proposed is the Logit model of the Logical Regression Classifier. As the process of walking is automatically controlled by the nervous system it is important to develop a non-invasive method so that patients with serious neurological disorders like Huntington's disease and Parkinson's disease receive early medical attention and they get proper care before they are more affected. Swing, Stance and double support intervals (expressed as percentages of stride) of 63 subjects were analyzed. In this paper, a relevant gait signal feature extractor is developed which is combined with Logistic Regression Classifier to classify healthy subjects and pathological subjects. Analysis of real-time gait signals is simplified using the Hilbert Transform which converts the real signals into an analytic signal. The proposed algorithm was developed using the MATLAB platform and the average accuracy of multiclass classification is found to be 86.05% while the accuracy of detecting healthy subjects from pathological subjects is 87.79% and the accuracy of classifying subjects having the Huntington's disease and Parkinson's disease is found to be 85.22%.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"17 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":"133869678","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 : 2017-11-01DOI: 10.1109/ICRCICN.2017.8234519
Shimei Jin, Wei Chen, J. Han
Machine learning is widely used in various applications such as data mining, computer vision, and bioinformatics owing to the explosion of available data. However, in practice, many data have some missing attributes. The graphic theory serves as a powerful tool for modeling and analyzing many such practical problems, such as networks of communication and data organization. This paper focuses on semi-supervised learning algorithms based on the graph theory, aiming at establishing robust models in the input space with a very limited number of training samples. The use of such algorithm in multiple data mining applications is also discussed.
{"title":"Graph-based machine learning algorithm with application in data mining","authors":"Shimei Jin, Wei Chen, J. Han","doi":"10.1109/ICRCICN.2017.8234519","DOIUrl":"https://doi.org/10.1109/ICRCICN.2017.8234519","url":null,"abstract":"Machine learning is widely used in various applications such as data mining, computer vision, and bioinformatics owing to the explosion of available data. However, in practice, many data have some missing attributes. The graphic theory serves as a powerful tool for modeling and analyzing many such practical problems, such as networks of communication and data organization. This paper focuses on semi-supervised learning algorithms based on the graph theory, aiming at establishing robust models in the input space with a very limited number of training samples. The use of such algorithm in multiple data mining applications is also discussed.","PeriodicalId":166298,"journal":{"name":"2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)","volume":"35 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":"132876382","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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