The Experts below are selected from a list of 62121 Experts worldwide ranked by ideXlab platform
Debasish Ghose - One of the best experts on this subject based on the ideXlab platform.
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Target localization and pursuit by sensor-equipped UAVs using distance information
2017 International Conference on Unmanned Aircraft Systems (ICUAS), 2017Co-Authors: Rolif Lima, Debasish GhoseAbstract:This paper describes a problem in which a network consisting of a sensor-equipped UAV and multiple beacons, operating in a large geographical space, uses distance measurements to estimate the position of a target and pursue it. The positions of the beacon are also not known to the UAV and needs to be estimated by the UAV, again using distance measurements. Distance measurements are assumed to have been obtained from methods which are noisy in nature, due to which Kalman filter is used to get more accurate location estimates of the beacons and the target. The trajectory of the UAV is decomposed into a Discovery Phase and a pursuit Phase. In Discovery Phase only beacon positions are estimated and in the pursuit Phase a pursuit guidance law is used to guide the UAV to approach the target. Performance of the algorithm is demonstrated through simulations.
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Pursuing a time varying and moving source signal using a sensor equipped UAV
2017 International Conference on Unmanned Aircraft Systems (ICUAS), 2017Co-Authors: Abhishek Kashyap, Debasish GhoseAbstract:A methodology to locate a target using measurements of its signal strength is discussed in this paper. One of the main challenges lie in locating the source when it is moving or has time varying signal strength. In this paper two different strategies to locate and intercept a time varying and moving source by an Unmanned Aerial Vehicle (UAV) is presented. In the first strategy the UAV traverses a complete circular path during the first Phase, also called the Discovery Phase, and uses the measurements of the signal strength to estimate the direction of the source location. This is followed by the pursuit Phase in which the UAV realigns itself towards the desired direction and then moves along it in order to intercept the source. In the second strategy the UAV moves along a semi-circular path during the Discovery Phase and then using the measurements of the signal strength realigns itself and moves towards the target in the pursuit Phase. The performance of both these algorithms is demonstrated through simulations for different kinds of target motion and variations, including measurements affected by noise.
Jae-il Jung - One of the best experts on this subject based on the ideXlab platform.
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SUTC - WAP: Wormhole Attack Prevention Algorithm in Mobile Ad Hoc Networks
2008 IEEE International Conference on Sensor Networks Ubiquitous and Trustworthy Computing (sutc 2008), 2008Co-Authors: Sun Choi, Jae-il JungAbstract:In wireless ad hoc networks, nodes compromise to forward packets for each other to communicate beyond their transmission range. Therefore, networks are vulnerable to wormhole attacks launched through compromised nodes because malicious nodes can easily participate in the networks. In wormhole attacks, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer. If source node chooses this fake route, malicious nodes have the option of delivering the packets or dropping them. It is difficult to detect wormhole attacks because malicious nodes impersonate legitimate nodes. Previous algorithms detecting a wormhole require special hardware or tight time synchronization. In this paper, we develop an effective method called wormhole attack prevention (WAP) without using specialized hardware. The WAP not only detects the fake route but also adopts preventive measures against action wormhole nodes from reappearing during the route Discovery Phase. Simulation results show that wormholes can be detected and isolated within the route Discovery Phase.
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wap wormhole attack prevention algorithm in mobile ad hoc networks
Sensor Networks Ubiquitous and Trustworthy Computing, 2008Co-Authors: Sun Choi, Jae-il JungAbstract:In wireless ad hoc networks, nodes compromise to forward packets for each other to communicate beyond their transmission range. Therefore, networks are vulnerable to wormhole attacks launched through compromised nodes because malicious nodes can easily participate in the networks. In wormhole attacks, one malicious node tunnels packets from its location to the other malicious node. Such wormhole attacks result in a false route with fewer. If source node chooses this fake route, malicious nodes have the option of delivering the packets or dropping them. It is difficult to detect wormhole attacks because malicious nodes impersonate legitimate nodes. Previous algorithms detecting a wormhole require special hardware or tight time synchronization. In this paper, we develop an effective method called wormhole attack prevention (WAP) without using specialized hardware. The WAP not only detects the fake route but also adopts preventive measures against action wormhole nodes from reappearing during the route Discovery Phase. Simulation results show that wormholes can be detected and isolated within the route Discovery Phase.
Mohamad Saad - One of the best experts on this subject based on the ideXlab platform.
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imputation of sequence variants for identification of genetic risks for parkinson s disease a meta analysis of genome wide association studies
The Lancet, 2011Co-Authors: Michael A. Nalls, Vincent Plagnol, Dena G. Hernandez, Manu Sharma, Una-marie Sheerin, Mohamad Saad, Javier SimonsanchezAbstract:Methods We did a meta-analysis of datasets from fi ve Parkinson ’s disease GWAS from the USA and Europe to identify loci associated with Parkinson’s disease (Discovery Phase). We then did replication analyses of signifi cantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the Discovery and replication Phases combined, and risk-profi le estimates for loci identifi ed in the Discovery Phase were calculated. Findings The Discovery Phase consisted of 5333 case and 12 019 control samples, with genotyped and imputed data at 7 689 524 SNPs. The replication Phase consisted of 7053 case and 9007 control samples. W e identifi ed 11 loci that surpassed the threshold for genome-wide signifi cance (p<5×10 – ⁸). Six were previously identifi ed loci (MAPT, SNCA, HLA-DRB5, BST1, GAK and LRRK2) and fi ve were newly identifi ed loci (ACMSD, STK39, MCCC1/LAMP3, SYT11, and CCDC62/HIP1R). The combined population-attributable risk was 60·3% (95% CI 43·7–69·3). In the risk-profi le analysis, the odds ratio in the highest quintile of disease risk was 2·51 (95% CI 2·23–2·83) compared with 1·00 in the lowest quintile of disease risk.
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Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies.
The Lancet, 2011Co-Authors: Michael A. Nalls, Vincent Plagnol, Dena G. Hernandez, Manu Sharma, Una-marie Sheerin, Mohamad Saad, Javier Simón-sánchez, Claudia Schulte, Suzanne Lesage, Sigurlaug SveinbjörnsdóttirAbstract:Methods We did a meta-analysis of datasets from fi ve Parkinson ’s disease GWAS from the USA and Europe to identify loci associated with Parkinson’s disease (Discovery Phase). We then did replication analyses of signifi cantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the Discovery and replication Phases combined, and risk-profi le estimates for loci identifi ed in the Discovery Phase were calculated. Findings The Discovery Phase consisted of 5333 case and 12 019 control samples, with genotyped and imputed data at 7 689 524 SNPs. The replication Phase consisted of 7053 case and 9007 control samples. W e identifi ed 11 loci that surpassed the threshold for genome-wide signifi cance (p
Manu Sharma - One of the best experts on this subject based on the ideXlab platform.
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imputation of sequence variants for identification of genetic risks for parkinson s disease a meta analysis of genome wide association studies
The Lancet, 2011Co-Authors: Michael A. Nalls, Vincent Plagnol, Dena G. Hernandez, Manu Sharma, Una-marie Sheerin, Mohamad Saad, Javier SimonsanchezAbstract:Methods We did a meta-analysis of datasets from fi ve Parkinson ’s disease GWAS from the USA and Europe to identify loci associated with Parkinson’s disease (Discovery Phase). We then did replication analyses of signifi cantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the Discovery and replication Phases combined, and risk-profi le estimates for loci identifi ed in the Discovery Phase were calculated. Findings The Discovery Phase consisted of 5333 case and 12 019 control samples, with genotyped and imputed data at 7 689 524 SNPs. The replication Phase consisted of 7053 case and 9007 control samples. W e identifi ed 11 loci that surpassed the threshold for genome-wide signifi cance (p<5×10 – ⁸). Six were previously identifi ed loci (MAPT, SNCA, HLA-DRB5, BST1, GAK and LRRK2) and fi ve were newly identifi ed loci (ACMSD, STK39, MCCC1/LAMP3, SYT11, and CCDC62/HIP1R). The combined population-attributable risk was 60·3% (95% CI 43·7–69·3). In the risk-profi le analysis, the odds ratio in the highest quintile of disease risk was 2·51 (95% CI 2·23–2·83) compared with 1·00 in the lowest quintile of disease risk.
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Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies.
The Lancet, 2011Co-Authors: Michael A. Nalls, Vincent Plagnol, Dena G. Hernandez, Manu Sharma, Una-marie Sheerin, Mohamad Saad, Javier Simón-sánchez, Claudia Schulte, Suzanne Lesage, Sigurlaug SveinbjörnsdóttirAbstract:Methods We did a meta-analysis of datasets from fi ve Parkinson ’s disease GWAS from the USA and Europe to identify loci associated with Parkinson’s disease (Discovery Phase). We then did replication analyses of signifi cantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the Discovery and replication Phases combined, and risk-profi le estimates for loci identifi ed in the Discovery Phase were calculated. Findings The Discovery Phase consisted of 5333 case and 12 019 control samples, with genotyped and imputed data at 7 689 524 SNPs. The replication Phase consisted of 7053 case and 9007 control samples. W e identifi ed 11 loci that surpassed the threshold for genome-wide signifi cance (p
Dena G. Hernandez - One of the best experts on this subject based on the ideXlab platform.
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imputation of sequence variants for identification of genetic risks for parkinson s disease a meta analysis of genome wide association studies
The Lancet, 2011Co-Authors: Michael A. Nalls, Vincent Plagnol, Dena G. Hernandez, Manu Sharma, Una-marie Sheerin, Mohamad Saad, Javier SimonsanchezAbstract:Methods We did a meta-analysis of datasets from fi ve Parkinson ’s disease GWAS from the USA and Europe to identify loci associated with Parkinson’s disease (Discovery Phase). We then did replication analyses of signifi cantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the Discovery and replication Phases combined, and risk-profi le estimates for loci identifi ed in the Discovery Phase were calculated. Findings The Discovery Phase consisted of 5333 case and 12 019 control samples, with genotyped and imputed data at 7 689 524 SNPs. The replication Phase consisted of 7053 case and 9007 control samples. W e identifi ed 11 loci that surpassed the threshold for genome-wide signifi cance (p<5×10 – ⁸). Six were previously identifi ed loci (MAPT, SNCA, HLA-DRB5, BST1, GAK and LRRK2) and fi ve were newly identifi ed loci (ACMSD, STK39, MCCC1/LAMP3, SYT11, and CCDC62/HIP1R). The combined population-attributable risk was 60·3% (95% CI 43·7–69·3). In the risk-profi le analysis, the odds ratio in the highest quintile of disease risk was 2·51 (95% CI 2·23–2·83) compared with 1·00 in the lowest quintile of disease risk.
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Imputation of sequence variants for identification of genetic risks for Parkinson's disease: a meta-analysis of genome-wide association studies.
The Lancet, 2011Co-Authors: Michael A. Nalls, Vincent Plagnol, Dena G. Hernandez, Manu Sharma, Una-marie Sheerin, Mohamad Saad, Javier Simón-sánchez, Claudia Schulte, Suzanne Lesage, Sigurlaug SveinbjörnsdóttirAbstract:Methods We did a meta-analysis of datasets from fi ve Parkinson ’s disease GWAS from the USA and Europe to identify loci associated with Parkinson’s disease (Discovery Phase). We then did replication analyses of signifi cantly associated loci in an independent sample series. Estimates of population-attributable risk were calculated from estimates from the Discovery and replication Phases combined, and risk-profi le estimates for loci identifi ed in the Discovery Phase were calculated. Findings The Discovery Phase consisted of 5333 case and 12 019 control samples, with genotyped and imputed data at 7 689 524 SNPs. The replication Phase consisted of 7053 case and 9007 control samples. W e identifi ed 11 loci that surpassed the threshold for genome-wide signifi cance (p