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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡欣穆(Hsin-Mu Tsai) | |
| dc.contributor.author | Tzu-Hsu Yu | en |
| dc.contributor.author | 游子緒 | zh_TW |
| dc.date.accessioned | 2021-05-20T00:55:58Z | - |
| dc.date.available | 2022-08-20 | |
| dc.date.available | 2021-05-20T00:55:58Z | - |
| dc.date.copyright | 2020-08-24 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-18 | |
| dc.identifier.citation | [1] E. J. Candes and M. B. Wakin. An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2):21–30, 2008. [2] P. R. Gill, A. Wang, and A. Molnar. The in-crowd algorithm for fast basis pursuit denoising. IEEE Transactions on Signal Processing, 59(10):4595–4605, 2011. [3] Roshan Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma, Abhishek Sethi, Deepak Vasisht, and Dinesh Bharadia. Deep learning based wireless localization for indoor navigation. pages 1–14, 2020. [4] Jun Qi and Guo-Ping Liu. A robust high-accuracy ultrasound indoor positioning system based on a wireless sensor network. Sensors, 17:2554, 2017. [5] Yu-Lin Wei, Chang-Jung Huang, Hsin-Mu Tsai, and Kate Ching-Ju Lin. Celli: Indoor positioning using polarized sweeping light beams. pages 136–147, 2017. [6] Zhao Tian, Yu-Lin Wei, Wei-Nin Chang, Xi Xiong, Changxi Zheng, Hsin-Mu Tsai, Kate Ching-Ju Lin, and Xia Zhou. Augmenting indoor inertial tracking with polarized light. pages 362–375, 2018. [7] Chia-Feng Chuang. Implementing indoor positioning using addressable led tube. Master’s thesis, 2018. [8] Chi Zhang and Xinyu Zhang. Litell: robust indoor localization using unmodified light fixtures. pages 230–242, 2016. [9] Wenjun Hu, Jingshu Mao, Zihui Huang, Yiqing Xue, Junfeng She, Kaigui Bian, and Guobin Shen. Strata: Layered coding for scalable visual communication. Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM, 2014. [10] P. Schultz, B. Cumby, and J. Heikenfeld. Investigation of five types of switchable retroreflector films for enhanced visible and infrared conspicuity applications. Appl. Opt., 51(17):3744–3754, 2012. [11] Lukas Janik, Marek Novak, Ales Dobesch, and Lucie Hudcova. Retroreflective optical communication. pages 1–4, 2017. [12] Sergio E. Segre and Vincenzo Zanza. Mueller calculus of polarization change in the cube-corner retroreflector. J. Opt. Soc. Am. A, 20(9):1804–1811, 2003. [13] Chai-Jhih Luo. Politag: Object detection and identification system using polarized light. Master’s thesis, 2017. [14] Jiangtao Li, Angli Liu, Guobin Shen, Liqun Li, Chao Sun, and Feng Zhao. Retrovlc: Enabling battery-free duplex visible light communication for mobile and iot applications. pages 21–26, 2015. [15] Xieyang Xu, Yang Shen, Junrui Yang, Chenren Xu, Guobin Shen, Guojun Chen, and Yunzhe Ni. Passivevlc: Enabling practical visible light backscatter communication for battery-free iot applications. pages 180–192, 2017. [16] Ya-Chi Lin. Detection and identification of passive visible light marker. Master’s thesis, 2019. [17] Dharmpal Takhar, Jason Laska, Michael Wakin, Marco Duarte, Dror Baron, Shriram Sarvotham, Kevin Kelly, and Richard Baraniuk. A new compressive imaging camera architecture using optical-domain compression - art. no. 606509. Proc. IS T/SPIE Symposium on Electronic Imaging, 2006. [18] B. Natarajan. Sparse approximate solutions to linear systems. SIAM J. Comput., 24:227–234, 1995. [19] D. Donoho and Xiaoming Huo. Uncertainty principles and ideal atomic decomposition. Information Theory, IEEE Transactions on, 47:2845 – 2862, 2001. [20] Shaobing Chen, D. Donoho, Iain Johnstone, and Michael Saunders. Basis pursuit. 1996. [21] Emmanuel Candes and Justin Romberg. l1-magic: Recovery of sparse signals via convex programming, 2005. [22] David Donoho and Michael Elad. Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. Proceedings of the National Academy of Sciences of the United States of America, 100:2197–202, 2003. [23] Albert Cohen, Wolfgang Dahmen, and Ronald DeVore. Compressed sensing and best k-term approximation. American Mathematical Society, 22:211–231, 2009. [24] E.J. Candes and T. Tao. Decoding by linear programming. IEEE Trans. Information Theory, 51:5406–5425, 2005. [25] Andreas Tillmann and Marc Pfetsch. The computational complexity of the restricted isometry property, the nullspace property, and related concepts in compressed sensing. Information Theory, IEEE Transactions on, 60:1248–1259, 2014. [26] David Donoho and Jared Tanner. Observed universality of phase transitions in highdimensional geometry, with implications for modern data analysis and signal processing. Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 367:4273–93, 2009. [27] Hatef Monajemi, Sina Jafarpour, Matan Gavish, Sivaram Ambikasaran, Sergio Bacallado, Dinesh Bharadia, Yuxin Chen, Young Choi, Mainak Chowdhury, Soham Chowdhury, Anil Damle, Will Fithian, Georges Goetz, Logan Grosenick, Sam Gross, Gage Hills, Michael Hornstein, Milinda Lakkam, Jason Lee, and David Donoho. Deterministic matrices matching the compressed sensing phase transition of gaussian random matrices. Proceedings of the National Academy of Sciences of the United States of America, 110, 2012. [28] Youness Arjoune, Naima Kaabouch, Hassan el ghazi, and Ahmed Tamtaoui. A performance comparison of measurement matrices in compressive sensing. International Journal of Communication Systems, 2017. [29] 3M Body Protection Solutions Personal Safety Division. Technical Data Sheet - 3M™ Scotchlite™ Reflective Material SOLAS Grade Products, 2016. Available at “https://multimedia.3m.com/mws/media/474370O/reflective-solas-data-sheet.pdf”. [30] Retroreflection: Definition measurement. Standard, International Commission on Illumination (CIE), 2001. [31] Thorlabs. BS013 - 50:50 Non-Polarizing Beamsplitter Cube, 400 - 700 nm, 1”, 2013. Available at “https://www.thorlabs.com/thorproduct.cfm?partnumber=BS013”. [32] Texas Instruments. DLP® DMD Technology: LIDAR ambient light reduction, 2018. Available at “https://www.ti.com/lit/an/dlpa093/dlpa093.pdf”. [33] Texas Instruments. DLP6500 0.65 1080p MVSP S600 DMD datasheet (Rev. B), 2016. Available at “https://www.ti.com/lit/gpn/dlp6500fye”. [34] DrBob on English Wikipedia. The cardinal points of a thick lens, 2006. Available at “https://commons.wikimedia.org/wiki/File:Cardinal-points-1.svg”. [35] Texas Instruments. DLP™ System Optics, 2010. Available at “https://www.ti.com/lit/an/dlpa022/dlpa022.pdf”. [36] Optics Balzers. Schematic of DLP Projection, 2016. Available at “https://www.opticsbalzers.com/en/products/prisms/lightgate.html”. [37] Texas Instruments. DMD Optical Efficiency for Visible Wavelengths, 2019. Available at “https://www.ti.com/lit/an/dlpa083a/dlpa083a.pdf”. [38] Yuanbo Deng and Daping Chu. Coherence properties of different light sources and their effect on the image sharpness and speckle of holographic displays. Scientific Reports, 7, 2017. [39] Thorlabs. PDA100A2 - Si Switchable Gain Detector, 320 - 1100 nm, 11 MHz BW, 75.4 mm2, 2017. Available at “https://www.thorlabs.com/thorproduct.cfm?partnumber=PDA100A2”. [40] D. Stokes. Principles and Practice of Variable Pressure / Environmental Scanning Electron Microscopy (VP-ESEM). RMS - Royal Microscopical Society. Wiley, 2008. [41] Ettus Research. USRP™ N200/N210 NETWORKED SERIES, 2012. Available at “https://www.ettus.com/wp-content/uploads/2019/01/07495_Ettus_N200-210_DS_Flyer_HR.pdf”. [42] Jack Volder. The cordic computing technique. 1959. [43] Larry Doolittle. Filtering and Decimation by Eight in an FPGA for SDR and Other Applications. 2006. [44] Matthew Herman. Compressive sensing with partial-complete, multiscale hadamard waveforms. 2013. [45] Cai Zhuoran, Zhao Honglin, Min Jia, Wang Gang, and Shen Jingshi. An improved hadamard measurement matrix based on walsh code for compressive sensing. pages 1–4, 2013. [46] Neal Radwell, Kevin Mitchell, Graham Gibson, Matthew Edgar, Richard Bowman, and Miles Padgett. Single-pixel infrared and visible microscope. Optica, 1:285–289, 2014. [47] David Phillips, Ming-Jie Sun, Jonathan Taylor, Matthew Edgar, Stephen Barnett, Graham Gibson, and Miles Padgett. Adaptive foveated single-pixel imaging with dynamic supersampling. Science Advances, 3:e1601782, 2017. [48] Ming-Jie Sun, Meng Tong, Matthew Edgar, Miles Padgett, and Neal Radwell. A russian dolls ordering of the hadamard basis for compressive single-pixel imaging. Scientific Reports, 7:3464, 2017. [49] Ming-Jie Sun, Matthew Edgar, David Phillips, Graham Gibson, and Miles Padgett. Improving the signal-to-noise ratio of single-pixel imaging using digital microscanning. Optics Express, 24:10476, 2016. [50] Y. Jauregui-Sánchez, P. Clemente, Pedro Carmona, E. Tajahuerce, and Jesús Lancis. Signal-to-noise ratio of single-pixel cameras based on photodiodes. Applied Optics, 57, 2018. [51] Catherine Higham, Roderick Murray-Smith, Miles Padgett, and Matthew Edgar. Deep learning for real-time single-pixel video. Scientific Reports, 8, 2018. [52] Y. Meyer. Ondelettes et opérateurs: Ondelettes. Actualités mathématiques. Hermann, 1990. [53] Emmanuel Candès and David Donoho. New tight frames of curvelets and optimal representations of objects with c2 singularities. Communications on Pure and Applied Mathematics, 57:219 – 266, 2004. [54] Emmanuel Candès, Laurent Demanet, David Donoho, and Lexing Ying. Fast discrete curvelet transforms. SIAM Journal on Multiscale Modeling and Simulation, 5, 2006. [55] Salman Asif. Primal dual pursuit: A homotopy based algorithm for the dantzig selector. 2008. [56] Zhihu Huang and J. Leng. Analysis of hu’s moment invariants on image scaling and rotation. volume 7, pages V7–476, 2010. [57] Jan Flusser. On the independence of rotation moment invariants. Pattern Recognition, 33:1405–1410, 2000. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8498 | - |
| dc.description.abstract | 基於相機的可見光定位系統有容易受環境光干擾的缺點。本研究提出一基於單像素成像系統的定位系統。此系統可利用頻率選擇性從干擾中分離出調變的光訊號，而一般相機因為其幀率太低無法利用頻率選擇性。另外，此系統亦可利用空間選擇性。基於 retroreflector 的反射式標記可集中反射光至入射光的來向，使得環境中其他物體的反射光相對黯淡。此定位系統中的單像素成像系統由一數位微鏡裝置及一光電探測器構成，並利用壓縮感知重建拍攝的場景。在壓縮感知重建的過程中加入非負的限制可以大幅增強重建影像的品質，但一般的重建演算法需要非常長的重建時間。本研究提出一改進版本的 in-crowd 演算法以加速重建過程，實現實時的定位。 | zh_TW |
| dc.description.abstract | Visible light positioning systems based on cameras suffer from interference from ambient light. A positioning system based on a single-pixel camera is implemented in this work. The system can utilize selectiveness in frequency to extract modulated light signals from interference, which is hard for cameras due to their low frame rate. Furthermore, selectiveness in space can also be utilized. Reflective markers are made of retroreflector, which concentrates reflection from markers, making reflection from the surrounding environment relatively dimmed. The single-pixel camera consists of a Digital Micromirror Device (DMD) and a photodetector, and uses Compressed Sensing (CS) to reconstruct the captured scene. Adding nonnegative constraints in CS improves reconstruction quality significantly, but also slows down the common algorithms. A modified version of the in-crowd algorithm is used to accelerate the nonnegative-constrained CS problem, enabling realtime positioning of markers. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T00:55:58Z (GMT). No. of bitstreams: 1 U0001-1806202008342400.pdf: 10920706 bytes, checksum: 485569d57bd0ec94054cdabefcf0570a (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 摘要 ...................................................................... iii Abstract ................................................................... iv 1 Introduction .............................................................. 1 2 Related Works ............................................................. 3 2.1 Self-positioning VLP systems ............................................ 3 2.2 Surveillance VLP systems ................................................ 4 3 Preliminary ............................................................... 6 3.1 Digital Micromirror Device (DMD) ........................................ 6 3.2 L^p norm ................................................................ 7 3.3 Compressed sensing ...................................................... 7 4 System Design ............................................................ 11 4.1 Signal flow ............................................................ 12 4.2 Placement of the light source and the camera ........................... 13 4.3 Phases of operation .................................................... 13 5 Optical Design ........................................................... 14 5.1 Placing the light source off the optical axis .......................... 14 5.2 Beamsplitter-based solution ............................................ 15 5.3 Tilting the DMD ........................................................ 16 5.3.1 Trade-off between FoV and effective aperture ......................... 16 5.3.2 Placing Lens 1 in the incident cone .................................. 18 5.3.3 Difference in depth .................................................. 20 5.4 TIR and RTIR prisms .................................................... 21 5.5 Pond of mirrors (POM) .................................................. 23 5.6 Diffraction on the DMD ................................................. 23 5.7 Summary ................................................................ 25 6 Signal Processing ....................................................... 26 6.1 Signal flow ............................................................ 26 6.2 Sources of noise ....................................................... 27 6.3 Quantization noise and oversampling .................................... 27 6.4 Observed noise in the system ........................................... 28 6.5 Fixed-point arithmetic in the USRP ..................................... 30 6.5.1 DC offset removal .................................................... 31 6.5.2 COordinate Rotation DIgital Computer (CORDIC) ........................ 31 6.5.3 Cascaded-integrator-comb (CIC) and half-band filters ................. 33 6.5.4 Rounding error ....................................................... 36 6.6 Window function and spectral leakage ................................... 38 6.7 Properties of additive white Gaussian noise (AWGN) ..................... 40 6.8 Aggregating samples .................................................... 42 6.9 DC block ............................................................... 45 6.10 Possible ways to improve SNR .......................................... 45 6.11 Summary ............................................................... 46 7 Compressed Sensing ....................................................... 47 7.1 Measurement matrix ..................................................... 47 7.1.1 Single-pattern observations and complementary observations ........... 47 7.1.2 Hadamard matrix and Walsh matrix ..................................... 49 7.1.3 Random matrix with Bernoulli distribution ............................ 50 7.1.4 Identity matrix ...................................................... 50 7.2 Theoretical SNR analysis ............................................... 51 7.2.1 Magnification of the measurement matrix .............................. 51 7.2.2 Mathematical model for SNR estimation ................................ 54 7.3 Sparsifying basis ...................................................... 56 7.3.1 Meyer wavelet ........................................................ 57 7.3.2 Fast discrete curvelet transform (FDCT) .............................. 58 7.3.3 Meyer-based basis and Gaussian-based basis ........................... 60 7.4 Nonnegative constraints in the image domain ............................ 61 7.5 Simulation of the aligning phase ....................................... 62 7.6 In-crowd algorithm ..................................................... 63 7.6.1 Modified in-crowd algorithm .......................................... 66 7.7 Summary ................................................................ 69 8 Schemes to Identify Rotated Markers ...................................... 70 8.1 Marker design .......................................................... 70 8.1.1 Dictionary ........................................................... 71 8.2 Image moments .......................................................... 71 8.3 Compressed sensing with Hough transform ................................ 73 8.4 Aligning and scanning .................................................. 74 9 Evaluation ............................................................... 75 9.1 Exposure time in the aligning phase .................................... 77 9.2 Exposure time in the scanning phase .................................... 79 9.3 Rotation ............................................................... 80 9.4 Marker ................................................................. 81 9.5 Position in the FoV .................................................... 82 9.6 Distance ............................................................... 83 9.7 Marker size ............................................................ 84 9.8 Summary ................................................................ 84 10 Conclusion and Future Work .............................................. 85 Bibliography ............................................................... 87 | |
| dc.language.iso | en | |
| dc.title | 以單像素成像系統定位及辨識反射式標記 | zh_TW |
| dc.title | Localization and Identification of Reflective Markers with Single-Pixel Camera | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林靖茹(Ching-Ju Lin),陳冠文(Kuan-Wen Chen),陳鴻文(Hung-Wen Chen) | |
| dc.subject.keyword | 單像素成像系統,壓縮感知,數位微鏡裝置,數位訊號處理,可見光定位, | zh_TW |
| dc.subject.keyword | Single-Pixel Camera,Compressed Sensing,Digital Micromirror Device,Digital Signal Processing,Visible Light Positioning, | en |
| dc.relation.page | 92 | |
| dc.identifier.doi | 10.6342/NTU202000915 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2020-08-19 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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|---|---|---|---|
| U0001-1806202008342400.pdf | 10.66 MB | Adobe PDF | 檢視/開啟 |
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