Pub Date : 2014-05-15DOI: 10.1109/ICADIWT.2014.6814675
Mamatha Vemula, P. Prasad
In this paper a novel receiver window function, for the reduction of Inter carrier Interference(ICI) in Orthogonal Frequency Division Multiplexing (OFDM) system, is proposed. The performance of the proposed window has been compared with raised-cosine (RC) window, better than raised-cosine (BTRC) window, rectangular window, and the modified bartlet hanning(MBH) window with reference to ICI and SIR. It has been observed that ICI power of the proposed window is 17.74 dB better than that of BTRC and 7.3 dB better than that of MBH window function at a normalized frequency offset of 0.05.
{"title":"A novel receiver window function for ICI reduction in OFDM system","authors":"Mamatha Vemula, P. Prasad","doi":"10.1109/ICADIWT.2014.6814675","DOIUrl":"https://doi.org/10.1109/ICADIWT.2014.6814675","url":null,"abstract":"In this paper a novel receiver window function, for the reduction of Inter carrier Interference(ICI) in Orthogonal Frequency Division Multiplexing (OFDM) system, is proposed. The performance of the proposed window has been compared with raised-cosine (RC) window, better than raised-cosine (BTRC) window, rectangular window, and the modified bartlet hanning(MBH) window with reference to ICI and SIR. It has been observed that ICI power of the proposed window is 17.74 dB better than that of BTRC and 7.3 dB better than that of MBH window function at a normalized frequency offset of 0.05.","PeriodicalId":339627,"journal":{"name":"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124867340","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 : 2014-04-22DOI: 10.1109/ICADIWT.2014.6814694
Tanmay Sinha
Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.
{"title":"Together we stand, Together we fall, Together we win: Dynamic team formation in massive open online courses","authors":"Tanmay Sinha","doi":"10.1109/ICADIWT.2014.6814694","DOIUrl":"https://doi.org/10.1109/ICADIWT.2014.6814694","url":null,"abstract":"Massive Open Online Courses (MOOCs) offer a new scalable paradigm for e-learning by providing students with global exposure and opportunities for connecting and interacting with millions of people all around the world. Very often, students work as teams to effectively accomplish course related tasks. However, due to lack of face to face interaction, it becomes difficult for MOOC students to collaborate. Additionally, the instructor also faces challenges in manually organizing students into teams because students flock to these MOOCs in huge numbers. Thus, the proposed research is aimed at developing a robust methodology for dynamic team formation in MOOCs, the theoretical framework for which is grounded at the confluence of organizational team theory, social network analysis and machine learning. A prerequisite for such an undertaking is that we understand the fact that, each and every informal tie established among students offers the opportunities to influence and be influenced. Therefore, we aim to extract value from the inherent connectedness of students in the MOOC. These connections carry with them radical implications for the way students understand each other in the networked learning community. Our approach will enable course instructors to automatically group students in teams that have fairly balanced social connections with their peers, well defined in terms of appropriately selected qualitative and quantitative network metrics.","PeriodicalId":339627,"journal":{"name":"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132323383","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 : 2014-02-01DOI: 10.1109/ICADIWT.2014.6814703
G. Valantina, S. Jayashri
Vehicular Ad hoc Network (Vanet) is one of the emerging technology to support safety, traffic monitoring and comfort related services. Vanet is a subclass of Manet but its topology changes rapidly and network gets disconnected frequently. The prevailing routing protocol of Manet is very much applicable to Vanet. Because of its frequently disconnecting routes it is difficult to design an efficient routing protocol. Proper design of routing protocol for Vanet makes the network a successful one. In this paper we bring a concept of introducing Mesh routers in the network thereby optimal route is selected which leads to a decrease in routing overhead, packet end-to-end delay and an increase in packet delivery ratio.
{"title":"A novel approach to efficient and reliable routing in Vanets","authors":"G. Valantina, S. Jayashri","doi":"10.1109/ICADIWT.2014.6814703","DOIUrl":"https://doi.org/10.1109/ICADIWT.2014.6814703","url":null,"abstract":"Vehicular Ad hoc Network (Vanet) is one of the emerging technology to support safety, traffic monitoring and comfort related services. Vanet is a subclass of Manet but its topology changes rapidly and network gets disconnected frequently. The prevailing routing protocol of Manet is very much applicable to Vanet. Because of its frequently disconnecting routes it is difficult to design an efficient routing protocol. Proper design of routing protocol for Vanet makes the network a successful one. In this paper we bring a concept of introducing Mesh routers in the network thereby optimal route is selected which leads to a decrease in routing overhead, packet end-to-end delay and an increase in packet delivery ratio.","PeriodicalId":339627,"journal":{"name":"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127994135","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 : 2014-02-01DOI: 10.1109/ICADIWT.2014.6814699
K. S. Lakshmi, G. Santhosh Kumar
Medical databases serve as rich knowledge sources for effective medical diagnosis. Recent advances in medical technology and extensive usage of electronic medical record systems, helps in massive production of medical text data in hospitals and other health institutions. Most of this text data that contain valuable information are just filed and not utilized to the full extent. Proper usage of medical information can bring about tremendous changes in medical field. This paper present a new method of uncovering valid association rules from medical transcripts. The extracted rules describes association of disease with other diseases, symptoms of a particular disease, medications used for treating diseases, the most prominent age group of patients for developing a particular disease. NLP (Natural Language Processing) tools were combined with data mining algorithms (Apriori algorithm and FP-Growth algorithm) for the extraction of rules. Interesting rules were selected using the correlation measure, lift.
{"title":"Association rule extraction from medical transcripts of diabetic patients","authors":"K. S. Lakshmi, G. Santhosh Kumar","doi":"10.1109/ICADIWT.2014.6814699","DOIUrl":"https://doi.org/10.1109/ICADIWT.2014.6814699","url":null,"abstract":"Medical databases serve as rich knowledge sources for effective medical diagnosis. Recent advances in medical technology and extensive usage of electronic medical record systems, helps in massive production of medical text data in hospitals and other health institutions. Most of this text data that contain valuable information are just filed and not utilized to the full extent. Proper usage of medical information can bring about tremendous changes in medical field. This paper present a new method of uncovering valid association rules from medical transcripts. The extracted rules describes association of disease with other diseases, symptoms of a particular disease, medications used for treating diseases, the most prominent age group of patients for developing a particular disease. NLP (Natural Language Processing) tools were combined with data mining algorithms (Apriori algorithm and FP-Growth algorithm) for the extraction of rules. Interesting rules were selected using the correlation measure, lift.","PeriodicalId":339627,"journal":{"name":"The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535442","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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