臺灣博碩士論文加值系統

由於都會區無線網路的建置漸趨完整，使用者端擁有無線上網設備的比例也漸漸增加，配合infrastructure的應用反而為現在所最缺乏的一環。本計劃將專注於博物館的應用環境，結合館內已建置的數位典藏內容及外部和館藏相關的數位內容，建構一套無線情境感知個人化數位導覽系統的雛型。系統最主要的目的在於讓參觀博物館的民眾能夠在一趟旅途中，利用個人化的導覽系統即可獲取更豐富且適性化的資訊，提升參觀者對博物館服務的满意度。參觀民眾在無線的環境下，藉由情境感知系統及可定位的手持無線裝置的支援，由館內數位導覽系統即時地取得適當或參觀者個人所想要的資訊，並透過此使用者端之無線裝置聆聽、閱覽及記錄展覽的文物資訊。

By the progressing of the computer technology, we can exploit modern, smart and cheap devices to create a context-aware and intelligent environment to mobile users. The location of mobile users, a kind of active information, is most important in the context-aware application. In order to implement the locating services, a lot of indoor positing technologies have been developed over thirty years. We use IEEE 802.11 b/g to detect the user’s position because the 802.11 b/g product is common in our living and it doesn’t require buy extra hardware.
In this paper, we propose 802.11b to locate the position of objects and people. The access points are installed in the underground and configured to use different channel number. We collect received signal strength information (RSSI) by Asus notebook, running Window XP. The notebook is equipped with a Asus Wireless LAN Card. In the off-line phase, we collect RSSI for each position at the different times of the day and input RSSI into the database. In the real-time phase, one person walked by and his/her PDA can get RSSI from each AP. The system can compare the sample value and the database or use location-determined algorithm to compute the location of the user.

Abstract I
Acknowledgements II
Content III
List of Figure V
List of Table VI
Chapter 1 Introduction 1
1.1 Motivation and Background 1
1.2 Objective 2
1.3 Methodology 2
1.4 Organization of Thesis 3
Chapter 2 Literature Review 5
2.1 IEEE 802.11b 5
2.1.1 The 802.11b Architecture 6
2.1.2 The 802.11b MAC Protocol 7
2.2 Existing Location Systems 9
2.2.1 Global Positing System 10
2.2.2 Radar 12
2.2.3 The Nearest Neighbor 13
2.2.4 Neutral Network 15
2.2.5 Propagation Model 16
2.2.6 Histogram Method 19
Chapter 3 System Analysis and Design 21
3.1 Experiment 21
3.1 Training Phase 22
3.2 Test Phase 23
3.3 Kriging 24
3.4 Algorithm 29
Chapter 4 The Experiment 30
4.1 Training Phase 30
4.2 Test Phase 37
Chapter 5 Conclusion and Future Work 40
5.1 Conclusion 40
5.2 Further Work 42
Reference 43

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