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系統識別號 U0002-1907202117074600
中文論文名稱 發展移動裝置聯網之有效整合系統
英文論文名稱 Development of an Effective Integrated System for Internet of Mobile Devices
校院名稱 淡江大學
系所名稱(中) 機械與機電工程學系碩士班
系所名稱(英) Department of Mechanical and Electro-Mechanical Engineering
學年度 109
學期 2
出版年 110
研究生中文姓名 周佩妙
研究生英文姓名 Nattanee Charoenlarpkul
學號 608375043
學位類別 碩士
語文別 英文
口試日期 2021-06-23
論文頁數 40頁
口試委員 指導教授-王銀添
委員-羅智榮
委員-李宜勳
中文關鍵字 R-CNN  藍牙連接  行動裝置 
英文關鍵字 R-CNN  Bluetooth connection  Mobile Devices 
學科別分類 學科別應用科學機械工程
中文摘要 該研究提出了一種低成本有效的人工智能(AI)算法集成系統,用於商務辦公中經常使用的移動設備的通信。移動設備包括安卓、基於 ROS 的設備和機器人。用於通信的工具包括藍牙和串列通訊。在研究中,使用名為基於區域的卷積神經網絡 (R-CNN) 的 AI 算法來偵測對象。該研究的目的是將所有應用程序集成在一起,以提高數據通信和監控的效率。
英文摘要 The study proposed a low-cost and effective artificial intelligence (AI) algorithm integrated system for communication of mobile devices that are frequently used in a business office. The mobile devices include android, ROS based devices, and robots. The tools used for communication include Bluetooth and serial port. In the study, an AI algorithm named region-based Convolutional Neural Networks (R-CNN) was used to detect object. The objective of the study is to integrate all applications together in order to improve the efficiency of data communication and monitoring.
論文目次 Table of contents III
Chapter 1 Introduction 1
1.1 Background 1
1.2 Objectives 1
1.3 Scopes 1
Chapter 2 Literature Reviews 2
Chapter 3 Related theories 6
3.1 Tensor flow 6
3.2 Tensor RT 7
3.3 Convolutional Neural Networks (CNN) 7
3.4 Regional-based Convolutional Neural Networks (R-CNN) 7
3.5 You Only Look Once (YOLO) 8
3.6 Fovea Box 8
3.7 Bluetooth 9
3.7.1 L2CAP 10
3.7.2 RFCOMM port 10
3.8 Overview of the thesis 11
Chapter 4 Robot Model and Algorithm Design 12
4.1 Hardware Architecture 12
4.1.1 4Robot Model 12
4.1.2 Jetson and board 21
4.2 Algorithm 24
4.2.1 AI algorithms 24
4.2.2 Distance calculation 25
4.3 Mobile Application 25
Chapter 5 Experiment 27
5.1 Mobile application 27
5.2 Bluetooth server 31
5.3 Object detection 32
5.4 Video Transfer 33
Chapter 6 Conclusion and Discussion 35
6.1 Conclusion 35
6.2 Discussion 35
Appendix A Internship in Aetina company 36
References 38

List of Figures
Figure 1 System Overview Architecture 1
Figure 2 R-CNN pipeline with ROI Pool and ROI Align [22] 7
Figure 3 YOLO Network [9] 8
Figure 4 Bluetooth Architecture [16] 9
Figure 5 RFCOMM Structure 10
Figure 6 Overview Structure Architecture 11
Figure 7 Turtlebot3 Burger 13
Figure 8 OpenCR Board 15
Figure 9 OpenCR structure 16
Figure 10 Jetson Xavier NX 21
Figure 11 System Flow 26
Figure 12 Application’s page 27
Figure 13 Bluetooth Device 28
Figure 14 Report file 29
Figure 15 Jetson Data on Mobile Application 29
Figure 16 Internet connection 30
Figure 17 Control view on Mobile Application 31
參考文獻 [1] T. Kong, F. Sun, H. Liu, Y. Jiang, L. Li, and J. Shi, "Foveabox: Beyound anchor-based object detection," IEEE Transactions on Image Processing, vol. 29, pp. 7389-7398, 2020.
[2] H. Zhang, K. Watanabe, K. Motegi, and Y. Shiraishi, "ROS Based Framework for Autonomous Driving of AGVs," Proceedings of ICMEMIS, pp. 4-6, 2019.
[3] H. Hu, J. Gu, Z. Zhang, J. Dai, and Y. Wei, "Relation networks for object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 3588-3597.
[4] S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, vol. 28, pp. 91-99, 2015.
[5] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: towards real-time object detection with region proposal networks," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 6, pp. 1137-1149, 2016.
[6] R. Girshick, "Fast r-cnn," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440-1448.
[7] J. Dai, Y. Li, K. He, and J. Sun, "R-fcn: Object detection via region-based fully convolutional networks," in Advances in neural information processing systems, 2016, pp. 379-387.
[8] Z. Li, C. Peng, G. Yu, X. Zhang, Y. Deng, and J. Sun, "Detnet: A backbone network for object detection," arXiv preprint arXiv:1804.06215, 2018.
[9] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779-788.
[10] R. Huang, J. Pedoeem, and C. Chen, "YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers," in 2018 IEEE International Conference on Big Data (Big Data), 2018: IEEE, pp. 2503-2510.
[11] C. Szegedy, A. Toshev, and D. Erhan, "Deep neural networks for object detection," 2013.
[12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, pp. 1097-1105, 2012.
[13] S. Banerjee, D. Mondal, S. Das, and R. B. Guin, "Real-time video streaming over Bluetooth network between two mobile nodes," International Journal of Computer Science (IJCSI), vol. 7, no. 3, pp. 37-39, 2010.
[14] D. Catania and S. Zammit, "Video streaming over Bluetooth," 2008.
[15] X. Wang, "Video Streaming over Bluetooth," Institute for Infocoram Research (12R), School of Computing, National University of Singapore, 2004.
[16] S. Gupta, S. K. Singh, and R. Jain, "Analysis and optimisation of various transmission issues in video streaming over Bluetooth," International Journal of Computer Applications, vol. 11, no. 7, pp. 44-48, 2010.
[17] M. Abadi et al., "Tensorflow: A system for large-scale machine learning," in 12th {USENIX} symposium on operating systems design and implementation ({OSDI} 16), 2016, pp. 265-283.
[18] M. Abadi et al., "Tensorflow: Large-scale machine learning on heterogeneous distributed systems," arXiv preprint arXiv:1603.04467, 2016.
[19] Object_detection. Website github.com: https://github.com/tensorflow/models/tree/master/res earch/object_detection. sited in May 2021.
[20] Cudnn. nvidia.com: https://developer.nvidia.com/cudnn. sited in May 2021.
[21] H. Vanholder, "Efficient inference with tensorrt," ed: ed, 2016.
[22] L. Yang, Q. Song, Z. Wang, and M. Jiang, "Parsing r-cnn for instance-level human analysis," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 364-373.
[23] RFCOMM, Website: https://www.programmersought.com/article/9546874968/, Sited in May2021.
[24] TurtleBot feature. Website: https://emanual.robotis.com/docs/en/platform/turtlebot3/feature s/. sited in May 2021.
[25] COCO test-dev standard, Website: https://paperswithcode.com/sota/object-detection-on-coco, Sited in May 2021.
[26] A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson, "CNN features off-the-shelf: an astounding baseline for recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2014, pp. 806-813.
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