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You searched for +publisher:"Universidade Federal do Amazonas" +contributor:("http://lattes.cnpq.br/9314744999783676"). Showing records 1 – 2 of 2 total matches.

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1. Lobo, Felipe Leite. Cooperative localization improvement in vehicular ad hoc networks.

Degree: 2020, Universidade Federal do Amazonas

In Vehicular Ad Hoc Networks (VANets), a precise localization system is a crucial factor for several critical safety applications. Even though the Global Positioning System (GPS) can be used to provide the position estimation of vehicles, it still has an undesired error that can increase even more in some areas, such as tunnels and indoor parking lots, making it unreliable and unfeasible for most critical safety applications. In this work, we present a new position estimation technique by two algorithms, the CoVaLID (Cooperative Vehicle Localization Improvement using Distance Information), which improves GPS positions of nearby vehicles and minimize their errors using Extended Kalman Filter (EKF) to perform Data Fusion of both GPS and distance information, and the COLIDAP that uses Particle Filter (PF). Our solution also uses distance information to assess the position accuracy related to three different aspects: the number of vehicles, vehicle trajectory, and distance information error. For that purpose, we use a weighted average method to put more confidence in distance information given by neighbors closer to the target.We implement and evaluate the performance of CoVaLID using realworld data, as well as discuss the impact of different distance sensors in our proposed solution. Our results clearly show that our algorithms are capable of reducing the GPS error by 63%, and 53% when compared to the state-of-the-art VANet LOCation Improve (VLOCI) algorithm.

Em redes veiculares ad hoc (VANets), um sistema de localiza??o preciso ? um fator crucial para v?rias aplica??es cr?ticas de seguran?a. Embora o Sistema de Posicionamento Global (GPS) possa ser usado para fornecer a estimativa de posi??o de ve?culos, ele ainda possui erros indesejados que pode aumentar ainda mais em algumas ?reas, como t?neis e pr?dios de estacionamento, tornando-o n?o confi?vel e invi?vel para a maioria aplica??es cr?ticas de seguran?a. Neste trabalho, apresentamos uma nova t?cnica de estimativa de posi??o atrav?s de dois algoritmos, o CoVaLID (melhoria de localiza??o de ve?culo cooperativa usando informa??es de dist?ncia), que melhora as posi??es de GPS de ve?culos pr?ximos e minimiza seus erros usando o Extended Kalman Filter (EKF) para executar a fus?o de dados de informa??es de GPS e dist?ncia, e o COLIDAP que utiliza filtro de part?culas (PF). Nossa solu??o tamb?m usa informa??es de dist?ncia para avaliar a precis?o da posi??o relacionada a tr?s aspectos diferentes: n?mero de ve?culos, trajet?ria do ve?culo e erro de informa??es de dist?ncia. Para esse fim, usamos um m?todo de m?dia ponderada para aumentar a confian?a nas informa??es de dist?ncia fornecidas pelos vizinhos mais pr?ximos do alvo. Implementamos e avaliamos o desempenho dos nossos algoritmos usando cen?rios do mundo real simulados, al?m de discutir o impacto de diferentes sensores de dist?ncia em nossa solu??o proposta. Nossos resultados mostram claramente que nossos algoritmos s?o capazes de reduzir o erro de GPS em 63% e 53% quando comparado ao algoritmo estado da arte…

Advisors/Committee Members: Oliveira, Hor?cio Antonio Braga Fernandes de, http://lattes.cnpq.br/9314744999783676, Souto, Eduardo James Pereira, http://lattes.cnpq.br/3875301617975895, Barreto, Raimundo da Silva, http://lattes.cnpq.br/1132672107627968, Balico, Leandro Nelinho, http://lattes.cnpq.br/7704628402527376.

Subjects/Keywords: Data Fusion; Localization Systems; Vehicular Ad-hoc Networks; Distance Information; Precise Localization System; CI?NCIAS EXATAS E DA TERRA: CI?NCIA DA COMPUTA??O; Vehicular Ad-hoc Networks; Localization Systems; Data Fusion; Distance Information

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lobo, F. L. (2020). Cooperative localization improvement in vehicular ad hoc networks. (Doctoral Dissertation). Universidade Federal do Amazonas. Retrieved from https://tede.ufam.edu.br/handle/tede/7714

Chicago Manual of Style (16th Edition):

Lobo, Felipe Leite. “Cooperative localization improvement in vehicular ad hoc networks.” 2020. Doctoral Dissertation, Universidade Federal do Amazonas. Accessed March 03, 2021. https://tede.ufam.edu.br/handle/tede/7714.

MLA Handbook (7th Edition):

Lobo, Felipe Leite. “Cooperative localization improvement in vehicular ad hoc networks.” 2020. Web. 03 Mar 2021.

Vancouver:

Lobo FL. Cooperative localization improvement in vehicular ad hoc networks. [Internet] [Doctoral dissertation]. Universidade Federal do Amazonas; 2020. [cited 2021 Mar 03]. Available from: https://tede.ufam.edu.br/handle/tede/7714.

Council of Science Editors:

Lobo FL. Cooperative localization improvement in vehicular ad hoc networks. [Doctoral Dissertation]. Universidade Federal do Amazonas; 2020. Available from: https://tede.ufam.edu.br/handle/tede/7714

2. Lima, Max Willian Soares. Efficient indoor localization using graphs.

Degree: 2019, Universidade Federal do Amazonas

The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample we need to classify must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, high scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 96% when compared to the classic kNN and at least 77% when compared to the tree-based approaches.

The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample we need to classify must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, high scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 96% when compared to the classic kNN and at least 77% when compared to the tree-based approaches.

CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Advisors/Committee Members: Moura, Edleno Silva de, 407487582-91, http://lattes.cnpq.br/4737852130924504, Oliveira, Horácio Antonio Braga Fernandes de, http://lattes.cnpq.br/9314744999783676, Balico, Leandro Nelinho, http://lattes.cnpq.br/7704628402527376, [email protected].

Subjects/Keywords: Sistemas de posicionamento indoor (localização sem fio); CIÊNCIAS EXATAS E DA TERRA: CIÊNCIA DA COMPUTAÇÃO; Small world graphs; Indoor positioning systems; Nearest neighborsc

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lima, M. W. S. (2019). Efficient indoor localization using graphs. (Masters Thesis). Universidade Federal do Amazonas. Retrieved from https://tede.ufam.edu.br/handle/tede/7308

Chicago Manual of Style (16th Edition):

Lima, Max Willian Soares. “Efficient indoor localization using graphs.” 2019. Masters Thesis, Universidade Federal do Amazonas. Accessed March 03, 2021. https://tede.ufam.edu.br/handle/tede/7308.

MLA Handbook (7th Edition):

Lima, Max Willian Soares. “Efficient indoor localization using graphs.” 2019. Web. 03 Mar 2021.

Vancouver:

Lima MWS. Efficient indoor localization using graphs. [Internet] [Masters thesis]. Universidade Federal do Amazonas; 2019. [cited 2021 Mar 03]. Available from: https://tede.ufam.edu.br/handle/tede/7308.

Council of Science Editors:

Lima MWS. Efficient indoor localization using graphs. [Masters Thesis]. Universidade Federal do Amazonas; 2019. Available from: https://tede.ufam.edu.br/handle/tede/7308

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