本文主要为广大网友提供“计算机安全论文参考文献书写格式举例114篇”,希望对需要计算机安全论文参考文献书写格式举例114篇网友有所帮助,学习一下!
没有参考文献的毕业论文(paper)是不完整的毕业论文(paper),可是参考文献究竟要怎么选择?如何去写?事实上参考文献这一部分在写作过程中还是有一定的技巧的,下面小编整理了计算机安全论文(paper)类目下的114篇参考文献,希望对你有帮助。
[1] Han D, Ming Y. Facial expression recognition with LBP and SLPP combined method[A]. // International Conference on Signal Processing[C], Shenzhen: IEEE, 2015: 1418-1422.
[2] Cootes T F, Taylor C J, Cooper D H, et al. Active Shape Models-Their Training and Application[J]. Computer Vision & Image Understanding, 1995, 61(1):38-59.
[3] Ekman P. Facial Action Coding System[J]. A Technique for the Measurement of Facial Action, 1978.
[4] Hu C, Chang Y, Feris R, et al. Manifold Based Analysis of Facial Expression[A]. // Computer Vision and Pattern Recognition Workshop[C], Washington: IEEE, 2005:605-614.
[5] Ekman P, Friesen W V, Ellsworth P. Emotion in the human face: Guidelines for research and an integration of findings[M]. Pergamon Press, 1972.
[6] Burges, C J C. A Tutorial on Support Vector Machines for Pattern Recognition[M]. Data mining and Knowledge Discovery, 1998, 2 (2): 121-167.
[7] Zheng W, Tang H, Lin Z, et al. Emotion Recognition from Arbitrary View Facial Images[A]. // European Conf. on Computer Vision[C], Crete, 2010: 490-503.
[8] Zhao G Y, Pietikainen, M. Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions[J]. IEEE Trans. on Pattern Analysis Machine Intelligence, 2007, 19 (6): 915-928.
[9] Lyons M J, Budynek J, Akamatsu S. Automatic classification of single facial images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2002, 21 (12): 1357-1362.
[10] Zheng W, Tang H, Lin Z, Huang, T S. A Novel Approach to Expression Recognition from Non-frontal Face Images[A]. // Int. Conf. on Computer Vision[C], Kyoto, 2009, 30 (2): 1901-1908.
[11] Oliveira L, Mansano M, Koerich A, et al. 2D Principal Component Analysis for Face and Facial-Expression Recognition[J]. Computing in Science & Engineering, 2011, 13 (3): 9-13.
[12] Niu Z, Qiu X. Facial expression recognition based on weighted principal component analysis and support vector machines[A]. // Int. Conf. on Advanced Computer Theory and Engineering[C], Chengdu, 2010: 174-178.
[13] Maronidis A, Bolis D, Tefas A, et al. Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets[J]. Neural Networks, 2011, 24 (8): 814-823.
[14] Bolis D, Maronidis A, Tefas A, et al. Improving the robustness of subspace learning techniques for facial expression recognition[A]. // Int. Conf. on Artificial Neural Networks[C], Thessaloniki, 2010: 470-479.
[15] Chuang F R K. Spectral Graph Theory[J]. American Mathematical Society, 2015, 9 (6): 212.
[16] Chao L, Ding J, Liu Z. Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection[J]. Signal Processing, 2015, 117 (C): 1-10.
[17] Jain D, Shikkenawis G, Mitra K, et al. Face and facial expression recognition using Extended Locality Preserving Projection[A]. // Fourth National Conf. on Computer Vision, Pattern Recognition, Image Processing and Graphics[C], Jodhpur, 2013: 1-4.
[18] Nikitidis S, Tefas A, Pitas I. Maximum Margin Discriminant Projections for facial expression recognition[A]. // Signal Processing Conference[C], Marrakech, 2013: 1-5.
[19] Siddiqi M H, Ali R, Khan A M, et al. Human facial expression recognition using stepwise linear discriminant analysis and hidden conditional random fields[J]. IEEE Transactions on Image Processing, 2015, 24 (4): 1386-1398.
[20] Wang Z, Ruan Q, An G. Facial expression recognition based on tensor local linear discriminant analysis[A]. // IEEE Int. Conf. on Signal Processing[C], Beijing: IEEE, 2012: 1226-1229.
[21] Wang Z, Ruan Q, An G. Facial expression recognition using sparse local Fisher discriminant analysis[J]. Neurocomputing, 2016, 174 (174): 756-766.
[22] Imran M A, Miah M S U, Rahman H. Face recognition using eigenfaces[J]. International Journal of Computer Applications, 2015, 118 (5): 12-16.
[23] Fukunaga K. Introduction to Statistical Pattern Recognition[M]. Academic Press, 1990.
[24] Tao D, Tang X, Li X, et al. Asymmetric bagging and random subspace for support vector machines based relevance feedback in image retrieval[J]. IEEE Trans. Pattern Anal., 2006, 28 (7): 1088-1099.
[25] Yan S, Xu D, Zhang B, et al. Graph embedding: a general framework for dimensionality reduction[J]. IEEE Tran. on Pattern Analysis and Machine Intelligence, 2007, 29 (1): 40-51.
[26] Chen L, Liao H, Ko M, et al. A new LDA-based face recognition system which can solve the small-sample-size problem[J]. Pattern Recognition, 2000, 33 (10): 1713-1726.
[27] Howland P, Park H. Generalizing discriminant analysis using the generalized singular value decomposition[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26 (8): 995-1006.
[28] Ye J, Li Q. A two-stage linear discriminant analysis via QR decom-position[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27 (6): 929-941.
[29] Qiao Z, Zhou L, Huang J Z. Sparse linear discriminant analysis with applications to high dimensional low sample size data[J]. IAENG Int. J. Appl., 2009, 9 (1): 48–60.
[30] Zou H, Hastie T, Tibshirani R. Sparse principal component analysis[J]. J. Comput. Graph. Stat., 2006, 15 (2): 265-286.
[31] Cai J, Osher S, Shen Z. Linearized Bregman Iterations for Compressed Sensing[J]. Mathematics of Computation, 2009, 78 (267): 1515-1536.
[32] Lyons M, Akamatsu S, Kamachi M, et al. Coding facial expressions with Gabor wavelets[A]. // Proceedings of the Third IEEE Conference on Face and Gesture Recognition, Nara, 1998: 200-205.
[33] Lee K, Ho J, Kriegman D. Acquiring linear subspaces for face recognition under variable lighting[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27 (5): 684-698.
[34] Yin W, Osher S, Goldfarb D, et al. Bregman iterative algorithms for L1-minimization with applications to compressed sensing[J]. Siam Journal on Imaging Sciences, 2008, 1 (1): 143-168.
[35] Darbon J, Osher S. Fast discrete optimization for sparse approximations and deconvolutions[J]. Mathematics of Computation, 2009, 78 (267): 1515-1536.
[36] Osher S, Mao Y, Dong B, et al. Fast Linearized Bregman Iteration for Compressed Sensing and Sparse Denoising[J]. Mathematics of Computation, 2010, 8 (1): 93-111.
[37] Goldstein T, Osher S. The Split Bregman Algorithm for L1 Regularized Problems[J]. Siam Journal on Imaging Sciences, 2009, 2 (2): 323-343.
[38] Cetin E. Reconstruction of signals from Fourier transform samples[J]. Signal Process., 1989, 16 (2): 129-148.
[39] Chu D, Liao L Z, Ng M K, et al. Sparse canonical correlation analysis: new formulation and algorithm[J]. IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35 (12): 3050-3065.
[40] Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance[J]. Journal of the American Statistical Association, 1937, 32 (200): 675-701.
[41] Friesen W, Ekman P. Emfacs-7: emotional facial action coding system. Unpublished manuscript, University of California at San Francisco, 1983.
[42] Matias R, Cohn J, Ross S. A comparison of two systems to code infants’ affective expression[J]. Dev. Psychol., 1989, 25: 483–489.
[43] Walecki R, Rudovic O, Pavlovic V, et al. Variable-state Latent Conditional Random Field models for facial expression analysis[J]. Image & Vision Computing, 2016, 58(C):25-37.
[44] Abbasnejad M, Masnadishirazi M A. Facial Expression Recognition Using Sparse Gaussian Conditional Random Field[J]. Computer Science, 2015, 121(4):S95.
[45] Zhang S, Sim T. When Fisher Meets Fukunaga-Koontz: A New Look at Linear Discreminants[A]. // IEEE Conf. Computer Vision and Pattern Recognition[C], 2006: 323-329.
[46]李志强,李永斌.车牌识别技术的发展及研究现状[J].科技信息,2012(05):110+125.
[47]李彦冬,郝宗波,雷航.卷积神经网络研究综述[J].计算机应用,2016,36(09):2508-2515+2565.
[48]董欣. 车牌精确定位算法探究[D].浙江大学,2017.
[49]郭捷,施鹏飞.基于颜色和纹理分析的车牌定位方法[J].中国图象图形学报,2002(05):58-62.
[50]赵振兴. 基于深度学习的车牌识别技术研究[D].青岛科技大学,2017.
[51]曾泉. 复杂背景下基于OpenCV的车牌识别系统研究[D].广东工业大学,2016.
[52]陈玮,曹志广,李剑平.改进的模板匹配方法在车牌识别中的应用[J].计算机工程与设计,2013,34(05):1808-1811.
[53]彭清,季桂树,谢林江,张少波.卷积神经网络在车辆识别中的应用[J].计算机科学与探索,2018,12(02):282-291.
[54]陈格.人工神经网络技术发展综述[J].中国科技信息,2009(17):88-89.
[55]中华人民共利国公安部.GA36—2007.中华人民共和国机动车号牌[S].
[56]曾江源.图像边缘检测常用算子研究[J].现代商贸工业,2009,21(19):282-283.
[57]石伟,宋跃. 基于FPGA/SOPC的灰度图像腐蚀膨胀运算的设计与实现[A]. 教育部中南地区高等学校电子电气基础课教学研究会.教育部中南地区高等学校电子电气基础课教学研究会第二十届学术年会会议论文(paper)集(下册)[C].教育部中南地区高等学校电子电气基础课教学研究会:2010:4.
[58]李晓飞,马大玮,粘永健,孙晶菁.图像腐蚀和膨胀的算法研究[J].影像技术,2005(01):37-39.
[59]梁一江.图像平滑处理方法初探及简单的算法介绍[J].才智,2009(04):134-135.
[60]魏有法.基于Matlab的图像平滑算法浅析[J].机电技术,2013,36(03):50-52.
[61]常亮,邓小明,周明全,武仲科,袁野,杨硕,王宏安.图像理解中的卷积神经网络[J].自动化学报,2016,42(09):1300-1312.
[62]L.Juhas,A.Vujani,N.Adamovi,L.Nagy,B.Borovac.A platform for micropositioning based on piezo legs[J].Mechatronics,2001,11(7):869.
[63]Rakotondrabe M, Haddab Y, Lutz P. High-Stroke Motion Modelling and Voltage/Frequency Proportional Control of a Stick-Slip Microsystem[C]. IEEE International Conference on Robotics & Automation, 2007:4490-4496.
[64]Nomura Y, Aoyama H. Development of inertia driven micro robot with nano tilting stage for SEM operation[J]. Microsystem Technologies, 2007, 13(8-10):1347-1352.
[65]Meyer, Christine, Sqalli, Omar, Lorenz, Heribert. et,al. Slip-stick stepscanner for scanning probe microscopy[C]. Review of Scientific Instruments,2005,76(6): 063706-063706-5.
[66]https://www.pi-china.cn/zh_cn/products/parallel-kinematic-hexapods/hexapods-with-piezomotor/q-821-q-motionspacefab-103209/
[67]https://www.pi-china.cn/zh_cn/products/linear-stages-and-actuators/piezo-stages/p-853-p-854-704100/
[68]邵明坤.粘滑式惯性压电精密驱动器设计分析与试验研究[D].吉林大学.2015.
[69]华顺明,张宏壮,程光明等.压电薄膜型精密运动平台研究[J].光学精密工程, 2006,14(4):635-640.
[70]李宗伟.基于惯性粘滑驱动的跨尺度精密运动平台研究[D].苏州大学.2016.
[71]张世忠.用于SEM微纳操作的粘滑驱动精密运动定位台的研究[D].哈尔滨工业大学.2014.
[72] N. Horchidan, C.E.Ciomaga, R.C.Frunza, C.Capiani, C.Galassi, L.Mitoseriu et,al.A comparative study of hard/soft PZT-based ceramic composites[C]. Ceramics International.,2016:9125-9132.
[73] N. Wongdamnern *, N. Triamnak, A. Ngamjarurojana,Y. Laosiritaworn, S. Ananta, R. Yimnirun. Comparative studies of dynamic hysteresis responses in hard and soft PZT ceramics[C]. Ceramics International.,2008:731–734.
[74] Yanfang Liu, Jinjun Shan, Naiming Qi. Creep modeling and identification for piezoelectric actuators based on fractional-order system[C]. Mechatronics, 2013:840–847.
[75] Y. Zhang, W. J. Zhang, J. Hesselbach, H. Kerle. Development of a two-degree-of-freedom piezoelectric rotary-linear actuator with high driving force and unlimited linear movement[C]. Review of scientific instruments, 2006,77, 035112.
[76] Che Liu,Yanling Guo. Modeling and Positioning of a PZT Precision Drive System[C]. Sensors 2017, 17, 2577:10.3390/s17112577.
[77]杨飞雨,潘鹏,徐伟,汝长海. 基于压电陶瓷的摩擦可调粘滑定位平台[J].压电与声光,2017,39(6):1004-2474.
[78] Kim B,Lee M,Lee Y, et al. An earthworm-like micro robot using shape memory alloy actuator[J]. Sensors & Actuators A Physical, 2006, 125(2):429-437.
[79] Xie H, Onal C, Régnier S, et al. Automated Control of AFM Based Nanomanipulation[M]. In: Atomic Force Microscopy Based Nanorobotics. Berlin Heidelberg: Springer, 2012:237-311.
[80] Hemsel T, Mracek M, Vasiljev P, et al. A novel approach for high power ultrasonics motors[C]. In: Proceedings of IEEE Ultrasonics, Ferroelectrics, and Frequency Control Conference, Montreal: IEEE, 2004:1161-1164.
[81] Leang K K, Zou Q, Devasia S. Feedforward Control of Piezoactuators in Atomic Force Microscope Systems: Inversion-Based Compensation for Dynamics and Hysteresis[J]. IEEE Transactions on Control Systems Technology, 2009, 19(1): 70– 82.
[82] 程仁志, 杨培霞, 程冬梅. 压电陶瓷的应用进展与发展趋势[J]. 河南教育学院学报:自然科学版, 2009, 18(3):17-19.
[83] 梁帅. 基于粘滑驱动的精密定位台建模与控制[D]. 哈尔滨工业大学, 2014.
[84] Changde, Hu, Yongqiang, Li, Meirong, Zhao, et,al. Rotatory stepping piezoelectric motor with micro-angle. 9th International Conference on Electronic Measurement & Instruments (ICEMI). 2009(4):545-549
[85]张涛,孙立宁,蔡鹤皋.压电陶瓷基本特性研究 [J]. 光学精密工程,1998, 6(5):26-32.
[86] 朱珠.压电陶瓷驱动器特性研究及二维微纳定位平台结构设计[D]. 浙江大学硕士学位论文(paper),2011.
[87] Edeler C, Meyer I, Fatikow S. Modeling of stick-slip micro-drives[J]. Journal of Micro-Nano Mechatronics, 2011, 6(3-4): 65-87.
[88] Cahyadi A I, Yamamoto Y. Modelling a Micro Manipulation System with Flexure Hinge[C]. Robotics, 2006 IEEE Conference on Automation and Mechatronics, 2006:11/1-11/5.
[89] Nicolae Lobontiu, Ephrahim Garcia. Analytical model of displacement amplification and stiffness optimization for a class of flexure-based compliant mechanisms. Coputers & Structures, 2003, 81: 2797~2810.
[90] Jape, S S, Ganapathysubramanian, B and Wickert, J A. Exploring the Effect of Stick-Slip Friction Transition Across Tape-Roller Interface on the Transmission of Lateral Vibration[C]. IEEE Transactions on Magnetics, 2012,48(3): 1189-1199.
[91] Mehmood,A, Laghrouche,S and El Bagdouri,M. Sensitivity analysis of LuGre friction model for pneumatic actuator control[C]. IEEE Vehicle Power and Propulsion Conference, 2010:1-6.
[92]孙艳军,董连和,陈宇,冷雁冰.频率选择表面的分析方法和仿真技术研究[J].红外,2010,31(03):24-29.
[93]曹先觉,王汝敏,齐暑华.电磁双复微波吸收轻质隐身材料的研究进展[J].粘接,2016,37(11):68-71+52.
[94]赵灵智,胡社军,李伟善,何琴玉,陈俊芳,汝强.吸波材料的吸波原理及其研究进展[J].现代防御技术,2007(01):27-31+48.
[95]李伟文. PAN/MWCNTs微纳米纤维膜的制备及其层合板复合材料的吸波性能研究[D].东华大学,2015.
[96]李利伟. GF/ACF电路屏复合材料的吸波性能研究[D].东华大学,2012.
[97]肖钢. 多层吸波材料计算设计及优化研究[D].哈尔滨工程大学,2003.
[98]郑书峰. 频率选择表面的小型化设计与优化技术研究[D].西安电子科技大学,2012.
[99]邬洁. 带阻型频率选择表面的仿真分析[D].山东大学,2012.
[100]张晓红,乔英杰.纤维增强隐身复合材料的研究进展[J].材料工程,2011(08):87-92.
[101]王向楠.隔离器用高性能微波吸收材料的研制[D].西南科技大学,2010.
[102]夏峰. 钛酸钡和钛酸锶钡纳米颗粒的制备、晶体结构及微波吸收性能的研究[D].复旦大学,2012.
[103]张健,张文彦,奚正平.隐身吸波材料的研究进展[J].稀有金属材料与工程,2008,37(S4):504-508.
[104]来侃,陈美玉,孙润军,尹方方.吸波材料在雷达隐身领域的应用[J].西安工程大学学报,2015,29(06):655-665.
[105]吴瑜,周胜,徐增波.碳纤维集合体材料吸波性能研究进展[J].扬州职业大学学报,2010,14(04):37-41.
[106]闫陇刚. 金属—氧化物复合材料的微波吸收特性[D].兰州大学,2010.
[107]张月芳,郝万军,段玉平,刘顺华.水泥基电磁波吸收材料研究进展[J].硅酸盐通报,2014,33(11):2908-2912.
[108]王宁,朱俊,冯俊明,张强.隐身材料研究开发现状与矿物材料在其中的应用[J].矿物学报,2001(03):345-350.
[109]Salisbury W W. Absorbent body for electromagnetic waves:, US 2599944 A[P]. 1952.
[110]Costa F, Monorchio A, Manara G. Analysis and Design of Ultra Thin Electromagnetic Absorbers Comprising Resistively Loaded High Impedance Surfaces[J]. IEEE Transactions on Antennas & Propagation, 2010, 58(5):1551-1558.
[111]Costa F, Genovesi S, Monorchio A. On the bandwidth of printed frequency selective surfaces for designing high impedance surfaces[C]// IEEE Antennas and Propagation Society International Symposium. IEEE, 2009:1-4.
[112]Costa F, Genovesi S, Monorchio A. On the Bandwidth of High-Impedance Frequency Selective Surfaces[J]. IEEE Antennas & Wireless Propagation Letters, 2009, 8(4):1341-1344.
[113]Costa F, Monorchio A, Manara G. Ultra-thin absorbers by using high impedance surfaces with resistive frequency selective surfaces[C]// Antennas and Propagation Society International Symposium. IEEE, 2007:861-864.
[114]Costa F, Monorchio A, Manara G. An equivalent circuit model of Frequency Selective Surfaces embedded within dielectric layers[C]// Antennas and Propagation Society International Symposium, 2009. APSURSI '09. IEEE. IEEE, 2009:1-4.