Academic literature on the topic 'Forward projection'
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Journal articles on the topic "Forward projection"
Ni, Chengcai, and Chunmei Liu. "Evaluating behaviors of factors affecting the site index estimate on the basis of a single stand using simulation approach." Canadian Journal of Forest Research 38, no. 11 (November 2008): 2762–70. http://dx.doi.org/10.1139/x08-095.
Full textJin, Li Xin, Lian Jun Wang, and Song Lin Yang. "Comparision between UTM and GRAUSS KRUEGER Projection in NIGERIA Rail Project." Applied Mechanics and Materials 90-93 (September 2011): 2431–37. http://dx.doi.org/10.4028/www.scientific.net/amm.90-93.2431.
Full textA.G. Piloto, Paulo, and Vitor M. E. Teixeira. "Pedestrian forward projection after vehicle collision." Journal of Mechanical Engineering and Biomechanics 2, no. 5 (March 26, 2018): 75–81. http://dx.doi.org/10.24243/jmeb/2.5.161.
Full textClement, G. T., and K. Hynynen. "Forward planar projection through layered media." IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control 50, no. 12 (December 2003): 1689–98. http://dx.doi.org/10.1109/tuffc.2003.1256310.
Full textBoţ, Radu Ioan, Panayotis Mertikopoulos, Mathias Staudigl, and Phan Tu Vuong. "Minibatch Forward-Backward-Forward Methods for Solving Stochastic Variational Inequalities." Stochastic Systems 11, no. 2 (June 2021): 112–39. http://dx.doi.org/10.1287/stsy.2019.0064.
Full textvan Aarle, Wim, Wolfgang Ludwig, Andrew King, and Dayakar Penumadu. "An accurate projection model for diffraction image formation and inversion using a polychromatic cone beam." Journal of Applied Crystallography 48, no. 2 (February 14, 2015): 334–43. http://dx.doi.org/10.1107/s1600576715000928.
Full textMukhin, K. Yu. "Forward to Jackson!" Epilepsy and paroxysmal conditions 13, no. 1S (July 13, 2021): 61–64. http://dx.doi.org/10.17749/2077-8333/epi.par.con.2021.080.
Full textAckermann, K. H., F. Bieser, F. P. Brady, D. Cebra, J. E. Draper, V. Eckardt, T. Eggert, et al. "The forward time projection chamber in STAR." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 499, no. 2-3 (March 2003): 713–19. http://dx.doi.org/10.1016/s0168-9002(02)01968-x.
Full textGiselsson, Pontus. "Nonlinear Forward-Backward Splitting with Projection Correction." SIAM Journal on Optimization 31, no. 3 (January 2021): 2199–226. http://dx.doi.org/10.1137/20m1345062.
Full textMaunder, Mark N., Shelton J. Harley, and John Hampton. "Including parameter uncertainty in forward projections of computationally intensive statistical population dynamic models." ICES Journal of Marine Science 63, no. 6 (January 1, 2006): 969–79. http://dx.doi.org/10.1016/j.icesjms.2006.03.016.
Full textDissertations / Theses on the topic "Forward projection"
Pokhrel, Damodar. "Brachytherapy Seed and Applicator Localization via Iterative Forward Projection Matching Algorithm using Digital X-ray Projections." VCU Scholars Compass, 2010. http://scholarscompass.vcu.edu/etd/2283.
Full textStaub, David. "Time dependent cone-beam CT reconstruction via a motion model optimized with forward iterative projection matching." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/3092.
Full textBentata, Amel. "Projection markovienne de processus stochastiques." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2012. http://tel.archives-ouvertes.fr/tel-00766235.
Full textLiu, Chu Chuan. "Advanced Projection Ultrasound Imaging with CMOS-based Sensor Array: Development, Characterization, and Potential Medical Applications." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/40492.
Full textPh. D.
Sunnegårdh, Johan. "Combining analytical and iterative reconstruction in helical cone-beam CT." Licentiate thesis, Computer Vision, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8286.
Full textContemporary algorithms employed for reconstruction of 3D volumes from helical cone beam projections are so called non-exact algorithms. This means that the reconstructed volumes contain artifacts irrespective of the detector resolution and number of projection angles employed in the process. In this thesis, three iterative schemes for suppression of these so called cone artifacts are investigated.
The first scheme, iterative weighted filtered backprojection (IWFBP), is based on iterative application of a non-exact algorithm. For this method, artifact reduction, as well as spatial resolution and noise properties are measured. During the first five iterations, cone artifacts are clearly reduced. As a side effect, spatial resolution and noise are increased. To avoid this side effect and improve the convergence properties, a regularization procedure is proposed and evaluated.
In order to reduce the cost of the IWBP scheme, a second scheme is created by combining IWFBP with the so called ordered subsets technique, which we call OSIWFBP. This method divides the projection data set into subsets, and operates sequentially on each of these in a certain order, hence the name “ordered subsets”. We investigate two different ordering schemes and number of subsets, as well as the possibility to accelerate cone artifact suppression. The main conclusion is that the ordered subsets technique indeed reduces the number of iterations needed, but that it suffers from the drawback of noise amplification.
The third scheme starts by dividing input data into high- and low-frequency data, followed by non-iterative reconstruction of the high-frequency part and IWFBP reconstruction of the low-frequency part. This could open for acceleration by reduction of data in the iterative part. The results show that a suppression of artifacts similar to that of the IWFBP method can be obtained, even if a significant part of high-frequency data is non-iteratively reconstructed.
Sunnegårdh, Johan. "Iterative Enhancement of Non-Exact Reconstruction in Cone Beam CT." Thesis, Computer Vision, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2577.
Full textContemporary algorithms employed for reconstruction of 3D volumes from helical cone beam projections are so called non-exact algorithms. This means that the reconstructed volumes will contain artifacts irrespective of the detector resolution and number of projections angles employed in the process.
It has been proposed that these artifacts can be suppressed using an iterative scheme which comprises computation of projections from the already reconstructed volume as well as the non-exact reconstruction itself.
The purpose of the present work is to examine if the iterative scheme can be applied to the non-exact reconstruction method PI-original in order to improve the reconstruction result. An important part in this implementation is a careful design of the projection operator, as a poorly designed projection operator may result in aliasing and/or other artifacts in the reconstruction result. Since the projection data is truncated, special care must be taken along the boundaries of the detector. Three different ways of handling this interpolation problem is proposed and examined.
The results show that artifacts caused by the PI-original method can indeed be reduced by the iterative scheme. However, each iteration requires at least three times more processing time than the initial reconstruction, which may call for certain compromises, smartness and/or parallelization in the innermost loops. Furthermore, at higher cone angles certain types of artifacts seem to grow by each iteration instead of being suppressed.
Breutel, Stephan Werner. "Analysing the behaviour of neural networks." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15943/.
Full textNassif, Roula. "Estimation distribuée adaptative sur les réseaux multitâches." Thesis, Université Côte d'Azur (ComUE), 2016. http://www.theses.fr/2016AZUR4118/document.
Full textDistributed adaptive learning allows a collection of interconnected agents to perform parameterestimation tasks from streaming data by relying solely on local computations and interactions with immediate neighbors. Most prior literature on distributed inference is concerned with single-task problems, where agents with separable objective functions need to agree on a common parameter vector. However, many network applications require more complex models and flexible algorithms than single-task implementations since their agents involve the need to estimate and track multiple objectives simultaneously. Networks of this kind, where agents need to infer multiple parameter vectors, are referred to as multitask networks. Although agents may generally have distinct though related tasks to perform, they may still be able to capitalize on inductive transfer between them to improve their estimation accuracy. This thesis is intended to bring forth advances on distributed inference over multitask networks. First, we present the well-known diffusion LMS strategies to solve single-task estimation problems and we assess their performance when they are run in multitask environments in the presence of noisy communication links. An improved strategy allowing the agents to adapt their cooperation to neighbors sharing the same objective is presented in order to attain improved learningand estimation over networks. Next, we consider the multitask diffusion LMS strategy which has been proposed to solve multitask estimation problems where the network is decomposed into clusters of agents seeking different
Millán, Reinier Díaz. "Vários algoritmos para os problemas de desigualdade variacional e inclusão." Universidade Federal de Goiás, 2015. http://repositorio.bc.ufg.br/tede/handle/tede/4562.
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Nesta tese apresentamos v arios algoritmos para resolver os problemas de Desigualdade Variacional e Inclus~ao. Para o problema de desigualdade variacional propomos, no Cap tulo 2 uma generaliza c~ao do algoritmo cl assico extragradiente, utilizando vetores normais n~ao nulos do conjunto vi avel. Em particular, dois algoritmos conceituais s~ao propostos e cada um deles cont^em tr^es variantes diferentes de proje c~ao que est~ao relacionadas com algoritmos extragradientes modi cados. Duas buscas diferentes s~ao propostas, uma sobre a borda do conjunto vi avel e a outra ao longo das dire c~oes vi aveis. Cada algoritmo conceitual tem uma estrat egia diferente de busca e tr^es formas de proje c~ao especiais, gerando tr^es sequ^encias com diferente e interessantes propriedades. E feito a an alise da converg^encia de ambos os algoritmos conceituais, pressupondo a exist^encia de solu c~oes, continuidade do operador e uma condi c~ao mais fraca do que pseudomonotonia. No Cap tulo 4, n os introduzimos um algoritmo direto de divis~ao para o problema variacional em espa cos de Hilbert. J a no Cap tulo 5, propomos um algoritmo de proje c~ao relaxada em Espa cos de Hilbert para a soma de m operadores mon otonos maximais ponto-conjunto, onde o conjunto vi avel do problema de desigualdade variacional e dado por uma fun c~ao n~ao suave e convexa. Neste caso, as proje c~oes ortogonais ao conjunto vi avel s~ao substitu das por proje c~oes em hiperplanos que separam a solu c~ao da itera c~ao atual. Cada itera c~ao do m etodo proposto consiste em proje c~oes simples de tipo subgradientes, que n~ao exige a solu c~ao de subproblemas n~ao triviais, utilizando apenas os operadores individuais, explorando assim a estrutura do problema. Para o problema de Inclus~ao, propomos variantes do m etodo de divis~ao de forward-backward para achar um zero da soma de dois operadores, a qual e a modi ca c~ao cl assica do forwardbackward proposta por Tseng. Um algoritmo conceitual e proposto para melhorar o apresentado por Tseng em alguns pontos. Nossa abordagem cont em, primeramente, uma busca linear tipo Armijo expl cita no esp rito dos m etodos tipo extragradientes para desigualdades variacionais. Durante o processo iterativo, a busca linear realiza apenas um c alculo do operador forward-backward em cada tentativa de achar o tamanho do passo. Isto proporciona uma consider avel vantagem computacional pois o operador forward-backward e computacionalmente caro. A segunda parte do esquema consiste em diferentes tipos de proje c~oes, gerando sequ^encias com caracter sticas diferentes.
In this thesis we present various algorithms to solve the Variational Inequality and Inclusion Problems. For the variational inequality problem we propose, in Chapter 2, a generalization of the classical extragradient algorithm by utilizing non-null normal vectors of the feasible set. In particular, two conceptual algorithms are proposed and each of them has three di erent projection variants which are related to modi ed extragradient algorithms. Two di erent linesearches, one on the boundary of the feasible set and the other one along the feasible direction, are proposed. Each conceptual algorithm has a di erent linesearch strategy and three special projection steps, generating sequences with di erent and interesting features. Convergence analysis of both conceptual algorithms are established, assuming existence of solutions, continuity and a weaker condition than pseudomonotonicity on the operator. In Chapter 4 we introduce a direct splitting method for solving the variational inequality problem for the sum of two maximal monotone operators in Hilbert space. In Chapter 5, for the same problem, a relaxed-projection splitting algorithm in Hilbert spaces for the sum of m nonsmooth maximal monotone operators is proposed, where the feasible set of the variational inequality problem is de ned by a nonlinear and nonsmooth continuous convex function inequality. In this case, the orthogonal projections onto the feasible set are replaced by projections onto separating hyperplanes. Furthermore, each iteration of the proposed method consists of simple subgradient-like steps, which does not demand the solution of a nontrivial subproblem, using only individual operators, which explores the structure of the problem. For the Inclusion Problem, in Chapter 3, we propose variants of forward-backward splitting method for nding a zero of the sum of two operators, which is a modi cation of the classical forward-backward method proposed by Tseng. The conceptual algorithm proposed here improves Tseng's method in many instances. Our approach contains rstly an explicit Armijo-type line search in the spirit of the extragradient-like methods for variational inequalities. During the iterative process, the line search performs only one calculation of the forward-backward operator in each tentative for nding the step size. This achieves a considerable computational saving when the forward-backward operator is computationally expensive. The second part of the scheme consists of special projection steps bringing several variants.
Hartsell, Bradley. "Projecting Culture Through Literary Exportation: How Imitation in Scandinavian Crime Fiction Reveals Regional Mores." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3323.
Full textBooks on the topic "Forward projection"
Canada, Bank of. The Bank of Canada's new quarterly projection model. Part 2 . A Robust Method for stimulating forward-looking models. [Ottawa: Bank of Canada, 1994.
Find full textAbad, José Vicente, ed. Research on Language Teaching and Learning: Advances and Projection. Fondo Editorial Universidad Católica Luis Amigó, 2021. http://dx.doi.org/10.21501/9789588943701.
Full textAuthority, Personal Investment, ed. Projections: The way forward. London: Personal Investment Authority, 1998.
Find full textSteele, Vaughn R., Vani Pariyadath, Rita Z. Goldstein, and Elliot A. Stein. Reward Circuitry and Drug Addiction. Edited by Dennis S. Charney, Eric J. Nestler, Pamela Sklar, and Joseph D. Buxbaum. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190681425.003.0044.
Full textAraújo, Kathleen. Low Carbon Energy Transitions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199362554.001.0001.
Full textBook chapters on the topic "Forward projection"
Al-Yahyai, Sultan, Yassine Charabi, Said Al-Sarmi, and Juma Al-Maskari. "Scenarios Based Climate Projection for Oman Water Resources." In Water Resources in Arid Areas: The Way Forward, 43–58. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51856-5_3.
Full textAgrawal, Amit, Yuichi Taguchi, and Srikumar Ramalingam. "Analytical Forward Projection for Axial Non-central Dioptric and Catadioptric Cameras." In Computer Vision – ECCV 2010, 129–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15558-1_10.
Full textZhang, Zhenya, Hongmei Cheng, and Xufa Wang. "Research on Stereographic Projection and It’s Application on Feed Forward Neural Network." In Lecture Notes in Computer Science, 89–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881070_14.
Full textPokhrel, D., M. J. Murphy, D. A. Todor, D. Lazos, E. Weiss, Y. Motai, and J. F. Williamson. "Brachytherapy seed localization via iterative forward projection matching (IFPM) algorithm using intraoperative cone-beam-CT sinogram projections." In IFMBE Proceedings, 307–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03474-9_87.
Full textSagitarian Warganegara, Doni, Agus Zahron, Edwin Mirfazli, and Oleh Pasko. "The effect of spot exchange rate and forward exchange rate on projection of futures spot of Rupiah on Dollar currencies." In The Future Opportunities and Challenges of Business in Digital Era 4.0, 58–61. Leiden, The Netherlands : CRC Press/Balkema, [2020]: Routledge, 2020. http://dx.doi.org/10.1201/9780367853778-15.
Full textBarthe, Gilles, Raphaëlle Crubillé, Ugo Dal Lago, and Francesco Gavazzo. "On the Versatility of Open Logical Relations." In Programming Languages and Systems, 56–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44914-8_3.
Full text"Basing and Forward Force Projection." In The Palgrave Encyclopedia of Imperialism and Anti-Imperialism, 188. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-29901-9_300094.
Full textAnderson, Lisa. "Middle East Studies for the New Millennium: Infrastructures of Knowledge." In Middle East Studies for the New Milleniu. NYU Press, 2016. http://dx.doi.org/10.18574/nyu/9781479827787.003.0013.
Full textGoldfinger, Eliot. "Animals with Limb Variations Skeleton & Superficial Muscles (Side View)." In Animal Anatomy for Artists. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780195142143.003.0013.
Full textMoulton, Keir. "Remarks on propositional nominalization." In Nominalization, 255–76. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198865544.003.0011.
Full textConference papers on the topic "Forward projection"
Jeng, Elvis Ko-Yung, and Zhigang Xiang. "Forward area light map projection." In the 2nd international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/602330.602346.
Full textHong, I. K., S. T. Chung, H. K. Kim, Y. B. Kim, Y. D. Son, and Z. H. Cho. "Fast forward projection and backward projection algorithm using SIMD." In 2006 IEEE Nuclear Science Symposium Conference Record. IEEE, 2006. http://dx.doi.org/10.1109/nssmic.2006.353723.
Full textJin, Kyung-Chan. "Forward projection model for noisy computerized tomography projection enhancement." In 2014 International Symposium on Consumer Electronics (ICSE). IEEE, 2014. http://dx.doi.org/10.1109/isce.2014.6884391.
Full textClement, Gregory T. "Nonlinear planar forward and backward projection." In 2008 IEEE Ultrasonics Symposium (IUS). IEEE, 2008. http://dx.doi.org/10.1109/ultsym.2008.0442.
Full textFugger, Thomas F., Bryan C. Randles, Jesse L. Wobrock, and Jerry J. Eubanks. "Pedestrian Throw Kinematics in Forward Projection Collisions." In SAE 2002 World Congress & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2002. http://dx.doi.org/10.4271/2002-01-0019.
Full textDuda, Alexander, and Christopher Gaudig. "Refractive forward projection for underwater flat port cameras." In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2016. http://dx.doi.org/10.1109/iros.2016.7759318.
Full textWalton, Simon, and Mark Jones. "Interacting with Volume Data: Deformations using Forward Projection." In International Conference on Medical Information Visualisation - BioMedical Visualisation (MediVis 2007). IEEE, 2007. http://dx.doi.org/10.1109/medivis.2007.12.
Full textYang, Kaixin, Jin Chen, Haitao Lin, and Zhao Shen. "A Subspace Projection Method for Forward-looking Imaging." In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE). IEEE, 2021. http://dx.doi.org/10.1109/iccece51280.2021.9342417.
Full textZeng, DaZhi, Tao Zeng, Cheng Hu, and Teng Long. "Back-Projection Algorithm Characteristic Analysis in Forward-Looking Bistatic SAR." In 2006 CIE International Conference on Radar. IEEE, 2006. http://dx.doi.org/10.1109/icr.2006.343181.
Full textXie, Xiaobin, Madison G. McGaffin, Yong Long, Jeffrey A. Fessler, Minhua Wen, and James Lin. "Accelerating separable footprint (SF) forward and back projection on GPU." In SPIE Medical Imaging, edited by Thomas G. Flohr, Joseph Y. Lo, and Taly Gilat Schmidt. SPIE, 2017. http://dx.doi.org/10.1117/12.2252010.
Full textReports on the topic "Forward projection"
Alexander, Charles R., and Jr. Strategic Creep: From Power Projection Back to Forward Presence. Fort Belvoir, VA: Defense Technical Information Center, March 1999. http://dx.doi.org/10.21236/ada362989.
Full textBauerle, Matthew. Developing a Massively Parallel Forward Projection Radiography Model for Large-Scale Industrial Applications. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1171557.
Full textCarlucci, D., J. Cordes, S. Morris, and R. Gast. Muzzle Exit (Set Forward) Effects on Projectile Dynamics. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada455215.
Full textCoastal Lidar And Radar Imaging System (CLARIS) mobile terrestrial lidar survey along the Outer Banks, North Carolina in Currituck and Dare counties. Coastal and Hydraulics Laboratory (U.S.), January 2020. http://dx.doi.org/10.21079/11681/39419.
Full textCoastal Lidar And Radar Imaging System (CLARIS) mobile terrestrial lidar survey along the Outer Banks, North Carolina in Currituck and Dare counties. Coastal and Hydraulics Laboratory (U.S.), January 2020. http://dx.doi.org/10.21079/11681/39419.
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