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BLE 비콘을 이용한 스파스 신호 처리를 실내 근접 검출
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 홍송남 | - |
| dc.contributor.author | ZHU LI | - |
| dc.date.accessioned | 2018-11-08T08:28:07Z | - |
| dc.date.available | 2018-11-08T08:28:07Z | - |
| dc.date.issued | 2018-08 | - |
| dc.identifier.other | 28128 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/14048 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2018. 8 | - |
| dc.description.tableofcontents | Contents List of Figures 6 List of Tables 7 Chapter 1 Introduction 8 1.1 Background and Motivation 8 1.2 Contribution 9 1.3 Overview 9 Chapter 2 Related Works 10 2.1 Overview of Bluetooth Low Energy (BLE) Beacons 10 2.2 Location-Based Service (LBS) 10 2.3 Proximity-based Service (PBS) 10 Chapter 3 Sparse Beacon Network 12 3.1 Beacon-based PBS 12 3.2 Compressive Sensing based Algorithm 13 Chapter 4 Proposed Algorithm 17 4.1 Similarity Filter based CoSaMP algorithm 17 4.2 Sparse Beacon Network using Deep Learning 21 Chapter 5 Performance Evalution 26 5.1 Beacon Testbed Setup 26 5.2 Simulation Results using Similarity Filter based CoSaMP algorithm 28 5.3 Simulation Results using Deep Learning 29 5.4 Comparison of each simulation Results 31 Chapter 6 Conclusion 33 Reference 34 Abstract 36 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | BLE 비콘을 이용한 스파스 신호 처리를 실내 근접 검출 | - |
| dc.title.alternative | ZHU LI | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | ZHU LI | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2018. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 887559 | - |
| dc.identifier.uci | I804:41038-000000028128 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/common/orgView/000000028128 | - |
| dc.subject.keyword | Indoor wireless localization | - |
| dc.subject.keyword | Sparse beacon deployment | - |
| dc.subject.keyword | Bluetooth Low Energy beacon | - |
| dc.subject.keyword | Proximity Service(PBS) | - |
| dc.subject.keyword | Compressive sensing | - |
| dc.subject.keyword | Deep learning. | - |
| dc.description.alternativeAbstract | Indoor wireless localization has attracted considerable attention with great improvements achieved in wireless technology in past decades year. In this paper, we address the problem of sparse beacon deployment due to an incomplete signals acquisition in the real-world scenarios. Considering the sparsity nature, it motivates us to exploit Compressive sensing (CS) algorithms for proximity service(PBS) using Bluetooth Low Energy beacons by referring to their success in indoor positioning system. For this purpose, a compressive sampling matching pursuit extended with generalized similarity filter is proposed and also concern about the effect of different similarity measures. In addition, another approach using a two-phase neural network including Deep Neural Network(DNN) and Stacked Denoising Autoencoder(SDA) to cope with predict the location of a mobile device since it has an excellent performance on extracting and reconstructing data among multitudinous deep learning algorithms. Simulation results show that the accuracy of the two-phase neural network can reach 0.875, and the accuracy of the generalized similarity filter for chord distance measurement can achieve 0.9, which can be considered as a better performance. | - |
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- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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