Proximity problems is a class of important problems which involve estimation of distances between geometric objects. The nearest neighbor search which is a subset of proximity problems, arises in numerous fields of applications, including Pattern Recognition, Statistical classification, Computer vision and etc. In this study, a nearest neighbor search is presented to move points in the plane, while query point is static. The proposed method works in the black-box KDS model, in which the points location received at regular time steps while at the same time, an upper bound dmax is known on the maximum displacement of any point at each time step. In this paper, a new algorithm is presented for kinetic nearest neighbor search problem in the black-box model, under assumptions on the distribution of the moving point set P. It has been shown how the kinetic nearest neighbor will be updated at each time step in O(k∆k logn) amortized time, where ∆k is the k-spread of a point set P. Key words: Computational Geometry, Black Box Model, Kinetic, Nearest Neighbor.
{"title":"Kinetic Nearest Neighbor Search in Black-Box Model","authors":"B. Sadeghi Bigham, Maryam Nezami, M. Eskandari","doi":"10.2139/ssrn.3726671","DOIUrl":"https://doi.org/10.2139/ssrn.3726671","url":null,"abstract":"Proximity problems is a class of important problems which involve estimation of distances between geometric objects. The nearest neighbor search which is a subset of proximity problems, arises in numerous fields of applications, including Pattern Recognition, Statistical classification, Computer vision and etc. In this study, a nearest neighbor search is presented to move points in the plane, while query point is static. The proposed method works in the black-box KDS model, in which the points location received at regular time steps while at the same time, an upper bound dmax is known on the maximum displacement of any point at each time step. In this paper, a new algorithm is presented for kinetic nearest neighbor search problem in the black-box model, under assumptions on the distribution of the moving point set P. It has been shown how the kinetic nearest neighbor will be updated at each time step in O(k∆k logn) amortized time, where ∆k is the k-spread of a point set P. Key words: Computational Geometry, Black Box Model, Kinetic, Nearest Neighbor.","PeriodicalId":406666,"journal":{"name":"Applied Computing eJournal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128345114","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 : 1900-01-01DOI: 10.34218/ijeet.11.4.2020.011
V. Naveen, K. v
This paper investigates on the mobile speed estimation of broadband wireless communications with severe inter symbol interference duet to large number of fading channel taps. This research is based on the received signals which consists of unknown transmitted data, selective fading channel, unknown frequency coefficients including line-of-sight (LOS) components, and random receiver noise. In-order to estimate the speed of mobiles with the received signal power Modified normalized auto-covariance method is used. The developed algorithm shows a better results for Rician , Rayleigh and Nakagami channels. The algorithm provides accurate speed estimation even if the signal-to-noise ratio (SNR) is low. Simulation results shows that the new algorithm is suitable for estimating mobile speed relative to a maximum Doppler of 500 H.
{"title":"Estimation of Mobile Speed Using Doppler Over Fading Channels","authors":"V. Naveen, K. v","doi":"10.34218/ijeet.11.4.2020.011","DOIUrl":"https://doi.org/10.34218/ijeet.11.4.2020.011","url":null,"abstract":"This paper investigates on the mobile speed estimation of broadband wireless communications with severe inter symbol interference duet to large number of fading channel taps. This research is based on the received signals which consists of unknown transmitted data, selective fading channel, unknown frequency coefficients including line-of-sight (LOS) components, and random receiver noise. In-order to estimate the speed of mobiles with the received signal power Modified normalized auto-covariance method is used. The developed algorithm shows a better results for Rician , Rayleigh and Nakagami channels. The algorithm provides accurate speed estimation even if the signal-to-noise ratio (SNR) is low. Simulation results shows that the new algorithm is suitable for estimating mobile speed relative to a maximum Doppler of 500 H.","PeriodicalId":406666,"journal":{"name":"Applied Computing eJournal","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129954094","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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