2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN)最新文献_第7页

Distributed location detection algorithms using IoT for commercial aviation 商用航空使用物联网的分布式位置检测算法

2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2017-11-01 DOI: 10.1109/ICRCICN.2017.8234493

A. Chatterjee, Hugo Flores, S. Sen, Khondker S. Hasan, Ashish Mani

{"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}

引用次数: 8

Toffoli netlist based synthesis of four variable reversible functions 基于Toffoli网表的四变量可逆函数综合

2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2017-11-01 DOI: 10.1109/ICRCICN.2017.8234527

Sinjini Banerjee, Priyabrata Sahoo, Mahamuda Sultana, Ayan Chaudhuri, D. Sengupta, A. Chaudhuri

引用次数: 1

Password security system with 2-way authentication 密码安全系统，双向认证

2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2017-11-01 DOI: 10.1109/ICRCICN.2017.8234533

Subhradeep Biswas, Sudipa Biswas

引用次数: 8

Adaptive global best steered Cuckoo search algorithm for FIR filter design 自适应全局最佳导向杜鹃搜索算法在FIR滤波器设计中的应用

2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2017-11-01 DOI: 10.1109/ICRCICN.2017.8234474

P. Das, S. Naskar, S. N. Patra

引用次数: 2

Gait signal classification tool utilizing Hilbert transform based feature extraction and logistic regression based classification 步态信号分类工具利用希尔伯特变换为基础的特征提取和逻辑回归为基础的分类

2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2017-11-01 DOI: 10.1109/ICRCICN.2017.8234481

Raj Vipani, Sambit Hore, Souryadeep Basak, S. Dutta

{"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}

引用次数: 4

Graph-based machine learning algorithm with application in data mining 基于图的机器学习算法及其在数据挖掘中的应用

2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) Pub Date : 2017-11-01 DOI: 10.1109/ICRCICN.2017.8234519

Shimei Jin, Wei Chen, J. Han

引用次数: 1