請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92027完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 宋孔彬 | zh_TW |
| dc.contributor.advisor | Kung-Bin Sung | en |
| dc.contributor.author | 林國聖 | zh_TW |
| dc.contributor.author | Guo-Sheng Lin | en |
| dc.date.accessioned | 2024-02-27T16:37:58Z | - |
| dc.date.available | 2024-02-28 | - |
| dc.date.copyright | 2022-09-30 | - |
| dc.date.issued | 2022 | - |
| dc.date.submitted | 2002-01-01 | - |
| dc.identifier.citation | [1] 中華民國衛生福利部. (2021). 109年死因統計結果分析. [2] X. J. G. o. Castellsagué, "Natural history and epidemiology of HPV infection and cervical cancer," vol. 110, no. 3, pp. S4-S7, 2008. [3] R. Drezek et al., "Understanding the contributions of NADH and collagen to cervical tissue fluorescence spectra: modeling, measurements, and implications," J Biomed Opt, vol. 6, no. 4, pp. 385-96, Oct 2001. [4] G. D. Birkmayer and J. Zhang, "NADH in Cancer Prevention and Therapy," 2004, pp. 541-554. [5] J. D. Boone, B. K. Erickson, and W. K. J. J. o. g. o. Huh, "New insights into cervical cancer screening," vol. 23, no. 4, pp. 282-287, 2012. [6] 莫松恩, "兩段式曲線擬合結合雙層組織模型定量子宫頸組織的內在螢光特徵," Master, 生醫電子與資訊學研究所, 國立臺灣大學, 2019. [7] 黃贊學, "利用移動式漫反射光譜系統定量子宮頸癌前病變之組織光學參數," 生醫電子與資訊學研究所, 國立臺灣大學, 2017. [8] I. Georgakoudi et al., "NAD (P) H and collagen as in vivo quantitative fluorescent biomarkers of epithelial precancerous changes," vol. 62, no. 3, pp. 682-687, 2002. [9] R. S. Bradley and M. S. J. J. o. t. r. s. I. Thorniley, "A review of attenuation correction techniques for tissue fluorescence," vol. 3, no. 6, pp. 1-13, 2006. [10] N. N. Zhadin and R. R. J. J. o. b. o. Alfano, "Correction of the internal absorption effect in fluorescence emission and excitation spectra from absorbing and highly scattering media: theory and experiment," vol. 3, no. 2, pp. 171-186, 1998. [11] Q. Zhang, M. G. Müller, J. Wu, and M. S. J. O. l. Feld, "Turbidity-free fluorescence spectroscopy of biological tissue," vol. 25, no. 19, pp. 1451-1453, 2000. [12] M. G. Müller, I. Georgakoudi, Q. Zhang, J. Wu, and M. S. J. A. O. Feld, "Intrinsic fluorescence spectroscopy in turbid media: disentangling effects of scattering and absorption," vol. 40, no. 25, pp. 4633-4646, 2001. [13] S. H. Tabrizi, S. M. R. Aghamiri, F. Farzaneh, and H. J. J. L. i. m. s. Sterenborg, "The use of optical spectroscopy for in vivo detection of cervical pre-cancer," vol. 29, no. 2, pp. 831-845, 2014. [14] J. Q. Brown, K. Vishwanath, G. M. Palmer, and N. J. C. o. i. b. Ramanujam, "Advances in quantitative UV–visible spectroscopy for clinical and pre-clinical application in cancer," vol. 20, no. 1, pp. 119-131, 2009. [15] I. Pavlova et al., "Microanatomical and Biochemical Origins of Normal and Precancerous Cervical Autofluorescence Using Laser‐scanning Fluorescence Confocal Microscopy," vol. 77, no. 5, pp. 550-555, 2003. [16] M. Muller and B. H. J. J. o. b. o. Hendriks, "Recovering intrinsic fluorescence by Monte Carlo modeling," vol. 18, no. 2, p. 027009, 2013. [17] S. K. Chang, D. Arifler, R. A. Drezek, M. Follen, and R. R. J. J. o. b. o. Richards-Kortum, "Analytical model to describe fluorescence spectra of normal and preneoplastic epithelial tissue: comparison with Monte Carlo simulations and clinical measurements," vol. 9, no. 3, pp. 511-522, 2004. [18] C. E. R. Weber et al., "Model-based analysis of reflectance and fluorescence spectra for in vivo detection of cervical dysplasia and cancer," vol. 13, no. 6, p. 064016, 2008. [19] J. Mirkovic et al., "Detecting high-grade squamous intraepithelial lesions in the cervix with quantitative spectroscopy and per-patient normalization," vol. 2, no. 10, pp. 2917-2925, 2011. [20] N. Rajaram, J. S. Reichenberg, M. R. Migden, T. H. Nguyen, and J. W. Tunnell, "Pilot clinical study for quantitative spectral diagnosis of non‐melanoma skin cancer," vol. 42, no. 10, pp. 876-887, 2010. [21] L. Shi, L. Lu, G. Harvey, T. Harvey, A. Rodríguez-Contreras, and R. R. J. S. r. Alfano, "Label-free fluorescence spectroscopy for detecting key biomolecules in brain tissue from a mouse model of Alzheimer’s disease," vol. 7, no. 1, pp. 1-7, 2017. [22] D. R. Eyre, M. A. Paz, and P. M. J. A. r. o. b. Gallop, "Cross-linking in collagen and elastin," vol. 53, no. 1, pp. 717-748, 1984. [23] T. Binzoni, T. S. Leung, A. H. Gandjbakhche, D. Ruefenacht, D. J. P. i. M. Delpy, and Biology, "The use of the Henyey–Greenstein phase function in Monte Carlo simulations in biomedical optics," vol. 51, no. 17, p. N313, 2006. [24] B. C. Wilson and G. J. M. p. Adam, "A Monte Carlo model for the absorption and flux distributions of light in tissue," vol. 10, no. 6, pp. 824-830, 1983. [25] L. Wang, S. L. Jacques, L. J. C. m. Zheng, and p. i. biomedicine, "MCML—Monte Carlo modeling of light transport in multi-layered tissues," vol. 47, no. 2, pp. 131-146, 1995. [26] E. Alerstam, T. Svensson, and S. J. J. o. b. o. Andersson-Engels, "Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration," Journal of Biomedical Optics, vol. 13, no. 6, p. 060504, 2008. [27] A. Welch et al., "Propagation of fluorescent light," Lasers in Surgery and Medicine, vol. 21, no. 2, pp. 166-178, 1997. [28] E. Pery, W. C. Blondel, C. Thomas, and F. H. J. J. o. B. O. Guillemin, "Monte Carlo modeling of multilayer phantoms with multiple fluorophores: simulation algorithm and experimental validation," Journal of Biomedical Optics, vol. 14, no. 2, p. 024048, 2009. [29] R. J. Crilly, W.-F. Cheong, B. Wilson, and J. R. J. A. o. Spears, "Forward–adjoint fluorescence model: Monte Carlo integration and experimental validation," vol. 36, no. 25, pp. 6513-6519, 1997. [30] S. Avrillier, E. Tinet, D. Ettori, J.-M. Tualle, and B. J. A. o. Gélébart, "Influence of the emission–reception geometry in laser-induced fluorescence spectra from turbid media," vol. 37, no. 13, pp. 2781-2787, 1998. [31] J. C. Finlay and T. H. J. A. o. Foster, "Recovery of hemoglobin oxygen saturation and intrinsic fluorescence with a forward-adjoint model," vol. 44, no. 10, pp. 1917-1933, 2005. [32] G. M. Palmer and N. J. J. o. b. o. Ramanujam, "Monte-Carlo-based model for the extraction of intrinsic fluorescence from turbid media," vol. 13, no. 2, p. 024017, 2008. [33] C. Liu, N. Rajaram, K. Vishwanath, T. Jiang, N. Ramanujam, and G. M. J. J. o. b. o. Palmer, "Experimental validation of an inverse fluorescence Monte Carlo model to extract concentrations of metabolically relevant fluorophores from turbid phantoms and a murine tumor model," vol. 17, no. 7, p. 077012, 2012. [34] C. Zhu, G. M. Palmer, T. M. Breslin, J. M. Harter, and N. J. J. o. b. o. Ramanujam, "Diagnosis of breast cancer using fluorescence and diffuse reflectance spectroscopy: a Monte-Carlo-model-based approach," vol. 13, no. 3, p. 034015, 2008. [35] G. M. Palmer and N. J. A. o. Ramanujam, "Monte Carlo-based inverse model for calculating tissue optical properties. Part I: Theory and validation on synthetic phantoms," vol. 45, no. 5, pp. 1062-1071, 2006. [36] D. J. T. D. A. N. Kuonen, "Data mining and Statistics: What is the connection?," vol. 30, pp. 1-6, 2004. [37] H. J. I. S. Wu, "Global stability analysis of a general class of discontinuous neural networks with linear growth activation functions," vol. 179, no. 19, pp. 3432-3441, 2009. [38] C.-Y. Wang, T.-C. Kao, Y.-F. Chen, W.-W. Su, H.-J. Shen, and K.-B. Sung, "Validation of an inverse fitting method of diffuse reflectance spectroscopy to quantify multi-layered skin optical properties," in Photonics, 2019, vol. 6, no. 2, p. 61: Multidisciplinary Digital Publishing Institute. [39] M. Mitchell, An introduction to genetic algorithms. MIT press, 1998. [40] 莊閔傑, "臨床移動式漫反射光譜系統之建構與實測," 生醫電子與資訊學研究所, 國立臺灣大學, 2015年, 2015. [41] 蕭逸嫻, "利用螢光光譜辨別黏膜癌前病變," 生醫電子與資訊學研究所, 國立臺灣大學, 2015年, 2015. [42] J. Qu, C. MacAulay, S. Lam, and B. J. A. O. Palcic, "Optical properties of normal and carcinomatous bronchial tissue," Applied Optics, vol. 33, no. 31, pp. 7397-7405, 1994. [43] S. Prahl. (1999). Optical Absorption of Hemoglobin. Available: https://omlc.org/spectra/hemoglobin/ [44] Q. Wang, D. Le, J. Ramella-Roman, and J. J. B. o. e. Pfefer, "Broadband ultraviolet-visible optical property measurement in layered turbid media," vol. 3, no. 6, pp. 1226-1240, 2012. [45] C. Kortun, Y. R. Hijazi, and D. J. J. o. b. o. Arifler, "Combined Monte Carlo and finite-difference time-domain modeling for biophotonic analysis: implications on reflectance-based diagnosis of epithelial precancer," vol. 13, no. 3, p. 034014, 2008. [46] J.-W. Su, W.-C. Hsu, J.-W. Tjiu, C.-P. Chiang, C.-W. Huang, and K.-B. J. J. o. b. o. Sung, "Investigation of influences of the paraformaldehyde fixation and paraffin embedding removal process on refractive indices and scattering properties of epithelial cells," vol. 19, no. 7, p. 075007, 2014. [47] A. M. J. Wang, V. Nammalvar, and R. A. J. J. o. b. o. Drezek, "Targeting spectral signatures of progressively dysplastic stratified epithelia using angularly variable fiber geometry in reflectance Monte Carlo simulations," vol. 12, no. 4, p. 044012, 2007. [48] S.-Y. Tsui, C.-Y. Wang, T.-H. Huang, and K.-B. Sung, "Modelling spatially-resolved diffuse reflectance spectra of a multi-layered skin model by artificial neural networks trained with Monte Carlo simulations," (in eng), Biomedical optics express, vol. 9, no. 4, pp. 1531-1544, 2018. [49] K.-B. Sung and H.-H. J. J. o. b. o. Chen, "Enhancing the sensitivity to scattering coefficient of the epithelium in a two-layered tissue model by oblique optical fibers: Monte Carlo study," vol. 17, no. 10, p. 107003, 2012. [50] M. S. Patterson and B. W. J. A. O. Pogue, "Mathematical model for time-resolved and frequency-domain fluorescence spectroscopy in biological tissues," vol. 33, no. 10, pp. 1963-1974, 1994. [51] 許芳瑋, "以GPU加速蒙地卡羅演算法並分析漫反射和螢光光譜," Master, 生醫電子與資訊學研究所, 國立臺灣大學, 2014. [52] Y. Pu, W. Wang, G. Tang, and R. R. J. J. o. b. o. Alfano, "Changes of collagen and nicotinamide adenine dinucleotide in human cancerous and normal prostate tissues studied using native fluorescence spectroscopy with selective excitation wavelength," vol. 15, no. 4, p. 047008, 2010. [53] S. K. Chang, N. Marín, M. Follen, and R. R. J. J. o. b. o. Richards-Kortum, "Model-based analysis of clinical fluorescence spectroscopy for in vivo detection of cervical intraepithelial dysplasia," vol. 11, no. 2, p. 024008, 2006. [54] C. Zhu, Q. Liu, and N. J. J. o. B. O. Ramanujam, "Effect of fiber optic probe geometry on depth-resolved fluorescence measurements from epithelial tissues: a Monte Carlo simulation," vol. 8, no. 2, pp. 237-247, 2003. [55] P. Thueler et al., "In vivo endoscopic tissue diagnostics based on spectroscopic absorption, scattering, and phase function properties," vol. 8, no. 3, pp. 495-503, 2003. [56] I. Fredriksson, M. Larsson, and T. J. J. o. b. o. Strömberg, "Inverse Monte Carlo method in a multilayered tissue model for diffuse reflectance spectroscopy," vol. 17, no. 4, p. 047004, 2012. [57] R. Hennessy, M. K. Markey, and J. W. J. J. o. b. o. Tunnell, "Impact of one-layer assumption on diffuse reflectance spectroscopy of skin," vol. 20, no. 2, p. 027001, 2015. [58] B. L. Meena, A. Agarwal, C. Pantola, K. Pandey, and A. J. J. o. b. o. Pradhan, "Concentration of FAD as a marker for cervical precancer detection," vol. 24, no. 3, p. 035008, 2019. [59] I. Georgakoudi et al., "Trimodal spectroscopy for the detection and characterization of cervical precancers in vivo," vol. 186, no. 3, pp. 374-382, 2002. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/92027 | - |
| dc.description.abstract | 本論文研究的最終目的是測量活體子宮頸黏膜光譜，以數值模型推算組織內螢光物質含量，研究其是否有偵測癌前病變之能力。依據先前研究基於兩段式曲線擬合流程，建立單層均質組織模型並與雙層模型比較確立組織層數對於定量子宮頸黏膜組織內在螢光特徵之影響，以找出降低擬合活體光譜誤差的方法。其中內在螢光代表不受組織散射吸收影響的內在螢光光譜，可由螢光效率及螢光波型組成，可以確實反映出組織內部螢光物質的情況。而整套流程是基於蒙地卡羅法(Monte Carlo method, MC)作為描述分析光譜之模型。輸入組織光學參數通過數值模型計算出不同波長下的反射率會形成光譜為正向模型，透過調整輸入參數使輸出的光譜與目標活體光譜有最小誤差為光譜擬合為逆向模型，此時的輸入參數一定程度象徵光譜的組成。在漫反射光譜的分析上使用人工神經網路(artificial neuron network, ANN)取代MC作為產生光譜的正向模型，因為在逆向模型中需要多次使用正向模型輸出光譜，透過ANN可以大幅降低正向模型時間增加擬合效率。最終利用三個實驗完成整個研究，首先以100組隨機參數組合對應的模擬光譜驗證單層模型的可用性，並證明單層與雙層模型皆可以順利使用。接著使用活體光譜擬合結果的37組參數組合以雙層模型MC模擬出目標光譜後分別以單層模型和雙層模型擬合。定量內在螢光效率的結果，使用單層模型進行擬合，誤差至少是使用雙層模型擬合的七倍。而內在螢光波形在兩種組織模型假設下結果差異不大。此外調整了光源偵測器距離為0.22 mm光纖的光譜中螢光物強度貢獻比。菸鹼醯胺腺嘌呤二核苷酸磷酸(nicotinamide adenine dinucleotide, NADH)和膠原蛋白的比值分別為0.25和1代表正常組織和癌化組織，在雙層模型下結合多通道系統相較單層組織模型可以更準確定量此兩種螢光物質效率比例。最後招募懷疑有子宮頸癌前病變並接受陰道鏡檢查的受試者，進行活體實驗，測量並分析每位受試者組織切片部位光譜和正常部位做為對照組至少兩個部位。在37組活體光譜的螢光光譜分析中單層模型和雙層模型各自平均光譜誤差分別使用學生t檢定和F檢定皆有顯著差異，代表使用更貼近真實組織的雙層組織模型萃取活體光譜內在螢光可以有效降低擬合光譜誤差。進一步分析具有完整分析條件的12位受試者， 計算NADH與膠原蛋白的螢光效率比值，並使用對照組比值正規化切片部位比值，以解決個體差異的問題，結果顯示癌前病變部位約會是正常部位2倍以上。 | zh_TW |
| dc.description.abstract | The final purpose of this study is to measure the spectrum of the cervical mucosa in vivo, to estimate the fluorescent substance content in the tissue with a numerical model, and to study whether it can detect precancerous lesions. According to previous studies, based on a two-step curve fitting process, a single-layer homogeneous tissue model is established and compared with the two-layer model to determine the effect of the number of tissue layers on quantifying the intrinsic fluorescence characteristics of cervical mucosal tissue, to find out and reduce the fitting error of the in vivo spectrum methods. The intrinsic fluorescence that is not affected by tissue scattering and absorption, which can be composed of intensity or intrinsic fluorescence efficiency, and intrinsic fluorescence waveform, which can truly reflect the situation of fluorescent substances in the tissue. The whole process is based on the Monte Carlo method (MC) as a model to describe the analytical spectrum. The input optical parameters are calculated by the numerical model and the reflectance at different wavelengths will form a spectrum as a forward model. The input parameters are continuously adjusted so that the output has the smallest error with the target spectrum as a fitting inverse model, and the input parameters will represent the composition of the spectrum. In the single-layer model, an artificial neural network (ANN) is used to replace the MC as the forward model for generating the diffuse reflectance spectrum, because the forward model needs to be used multiple times to output the spectrum in the reverse model, and the forward model can be greatly reduced through ANN. To increase the fitting efficiency to the model time, the forward model of the two-layer model is also replaced by an ANN. Finally, three experiments were used to complete the whole research. First, the availability of the single-layer model was verified by the simulation spectra corresponding to 100 sets of random parameter sets, and it was proved that both the single-layer and two-layer models could be used. Then, using the 37 sets of parameter sets of the in vivo spectrum fitting results, the target spectrum was simulated with the two-layer model MC, and then the single-layer model and the two-layer model were respectively fitted. It was found that the quantitative intrinsic fluorescence efficiency, fitted using a single-layer model, was at least seven times more error than fitting using a two-layer model. The shape of the emission fluorescence spectrum was not significantly different under the assumptions of the two tissue models. In addition, the intensity contribution rate of the fluorescent substance in the optical fiber spectrum with a photodetector distance of 0.22 mm was adjusted. The ratio of nicotinamide adenine dinucleotide (NADH) to collagen was 0.25 and 1, respectively, for normal and cancerous tissues. In a two-layer model combined with a multi-channel system, the efficiency ratio of fluorescent species can be quantified more accurately than in single-layer tissue. Finally, subjects with suspected precancerous cervical lesions who underwent vaginal examination were recruited for in vivo experiments to measure and analyze the spectrum of each subject's tissue sections, both normal and biopsy sites. In the fluorescence spectrum analysis of 37 sets of in vivo spectra, the average spectral errors of the single-layer model and the two-layer model were significantly different using the Student's t-test and the F-test, respectively, which means that the two-layer tissue model that is closer to the real tissue is used to extract the intrinsic properties of the in vivo spectrum. the 12 subjects with full conditions were further analyzed. The ratio of fluorescence efficiency of NADH to collagen was calculated, and the ratio of the normal site was used to normalize the ratio of the biopsied site to solve the problem the ratio of fluorescence efficiency of NADH to collagen individual differences. The ratio can be used as a reliable indicator for detecting precancerous lesions and it are found to be more than double the ratio of normal site. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-27T16:37:58Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-02-27T16:37:58Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 # 誌謝 i 中文摘要 ii ABSTRACT iv 目錄 vii 圖目錄 x 表目錄 xii 第 1 章 緒論 1 1.1 前言 1 1.2 研究動機 1 1.3 文獻回顧 2 1.4 研究目標 3 第 2 章 原理 5 2.1 本實驗光譜原理 5 2.1.1 漫反射光譜 5 2.1.2 螢光光譜 6 2.2 蒙地卡羅法 7 2.2.1 概述 7 2.2.2 多層漫反射蒙地卡羅 7 2.2.3 螢光蒙地卡羅 12 2.3 人工神經網路 15 2.3.1 概述 15 2.3.2 神經網路 16 2.4 曲線擬合和基因演算法 17 2.4.1 概述 17 2.4.2 迭代曲線擬合(iterative curve fitting) 17 2.4.3 基因演算法 18 第 3 章 研究方法 20 3.1 校正與臨床實驗 20 3.1.1 硬體系統 20 3.1.2 校正仿體 21 3.1.3 臨床實驗 23 3.2 組織模型 24 3.2.1 組織模型及漫反射光學參數 24 3.2.2 漫反射光譜正向和逆向模型 25 3.2.3 螢光光譜模型計算 29 3.2.4 內在螢光光譜萃取 31 3.2.5 兩段式曲線擬合 33 3.3 單雙層模型驗證 35 3.3.1 測試光譜建立及模型驗證 35 3.3.2 雙層測試光譜單雙層模型測試 35 第 4 章 研究結果 37 4.1 正向模型建立 37 4.2 單雙層模型驗證 39 4.2.1 測試光譜 39 4.2.2 雙層測試光譜單雙層模型測試 41 4.3 臨床螢光光譜分析結果 45 第 5 章 討論與未來展望 50 5.1 討論 50 5.2 未來展望 52 參考文獻 53 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 內在螢光 | zh_TW |
| dc.subject | 蒙地卡羅法 | zh_TW |
| dc.subject | 漫反射光譜 | zh_TW |
| dc.subject | 螢光光譜 | zh_TW |
| dc.subject | 子宮頸上皮內瘤樣病變 | zh_TW |
| dc.subject | 人工神經網路 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | Monte Carlo method | en |
| dc.subject | Genetic algorithm | en |
| dc.subject | Artificial neuron network | en |
| dc.subject | cervical intraepithelial neoplasia | en |
| dc.subject | fluorescence spectroscopy | en |
| dc.subject | diffuse reflectance spectroscopy | en |
| dc.subject | intrinsic fluorescence | en |
| dc.title | 雙層組織模型對定量子宮頸黏膜組織內在螢光特徵之影響 | zh_TW |
| dc.title | Effects of Tissue Model Layers on the Quantification of Intrinsic Fluorescence Characteristics of Cervical Mucosa | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 林致廷;江惠華 | zh_TW |
| dc.contributor.oralexamcommittee | Chih-Ting Lin;Huihua-Kenny Chiang | en |
| dc.subject.keyword | 內在螢光,蒙地卡羅法,漫反射光譜,螢光光譜,子宮頸上皮內瘤樣病變,人工神經網路,基因演算法, | zh_TW |
| dc.subject.keyword | intrinsic fluorescence,Monte Carlo method,diffuse reflectance spectroscopy,fluorescence spectroscopy,cervical intraepithelial neoplasia,Artificial neuron network,Genetic algorithm, | en |
| dc.relation.page | 57 | - |
| dc.identifier.doi | 10.6342/NTU202201673 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2022-09-06 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-111-1.pdf 授權僅限NTU校內IP使用（校園外請利用VPN校外連線服務） | 2.66 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。