Auswahl der wissenschaftlichen Literatur zum Thema „Analysis of Motion Trajectories“
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Zeitschriftenartikel zum Thema "Analysis of Motion Trajectories"
Song, Huan-Sheng, Sheng-Nan Lu, Xiang Ma, Yuan Yang, Xue-Qin Liu und Peng Zhang. „Vehicle Behavior Analysis Using Target Motion Trajectories“. IEEE Transactions on Vehicular Technology 63, Nr. 8 (Oktober 2014): 3580–91. http://dx.doi.org/10.1109/tvt.2014.2307958.
Der volle Inhalt der QuelleCuriac, Daniel-Ioan, und Constantin Volosencu. „A generic method to construct new customized-shaped haotic systems using the relative motion concept“. Nonlinear Analysis: Modelling and Control 21, Nr. 3 (20.05.2016): 413–23. http://dx.doi.org/10.15388/na.2016.3.8.
Der volle Inhalt der QuelleDong, Ran, und Soichiro Ikuno. „Biomechanical Analysis of Golf Swing Motion Using Hilbert–Huang Transform“. Sensors 23, Nr. 15 (26.07.2023): 6698. http://dx.doi.org/10.3390/s23156698.
Der volle Inhalt der QuelleCarroll, Mary, Katja Weimar, Monique Flecken, Monique Lambert und Christiane von Stutterheim. „Tracing trajectories“. Language, Interaction and Acquisition 3, Nr. 2 (19.12.2012): 202–30. http://dx.doi.org/10.1075/lia.3.2.03car.
Der volle Inhalt der QuelleBENSON, NOAH C., und VALERIE DAGGETT. „WAVELET ANALYSIS OF PROTEIN MOTION“. International Journal of Wavelets, Multiresolution and Information Processing 10, Nr. 04 (Juli 2012): 1250040. http://dx.doi.org/10.1142/s0219691312500403.
Der volle Inhalt der QuelleXiang Ma, F. Bashir, A. A. Khokhar und D. Schonfeld. „Event Analysis Based on Multiple Interactive Motion Trajectories“. IEEE Transactions on Circuits and Systems for Video Technology 19, Nr. 3 (März 2009): 397–406. http://dx.doi.org/10.1109/tcsvt.2009.2013510.
Der volle Inhalt der QuelleLeem, Seung-min, Hyeon-seok Jeong und Sung-young Kim. „Remote Drawing Technology Based on Motion Trajectories Analysis“. Journal of Korea Institute of Information, Electronics, and Communication Technology 9, Nr. 2 (30.04.2016): 229–36. http://dx.doi.org/10.17661/jkiiect.2016.9.2.229.
Der volle Inhalt der QuelleMarin, Mihnea, Petre Cristian Copilusi und Ligia Rusu. „Experimental Approach Regarding the Analysis of Human Complex Motions“. Applied Mechanics and Materials 823 (Januar 2016): 119–24. http://dx.doi.org/10.4028/www.scientific.net/amm.823.119.
Der volle Inhalt der QuelleRoth, Bernard. „Finding Geometric Invariants From Time-Based Invariants for Spherical and Spatial Motions“. Journal of Mechanical Design 127, Nr. 2 (01.03.2005): 227–31. http://dx.doi.org/10.1115/1.1828462.
Der volle Inhalt der QuelleSHENGBO, LI, А. YU KORNEEV, WANG SICONG und E. V. MISHCHENKO. „THE ANALYSIS OF THE TRAJECTORIES OF MOTION RIGID ROTOR IN THE CONICAL LIQUID FRICTION BEARINGS“. Fundamental and Applied Problems of Engineering and Technology 6 (2020): 114–20. http://dx.doi.org/10.33979/2073-7408-2020-344-6-114-120.
Der volle Inhalt der QuelleDissertationen zum Thema "Analysis of Motion Trajectories"
Partsinevelos, Panayotis. „Detection and Generalization of Spatio-temporal Trajectories for Motion Imagery“. Fogler Library, University of Maine, 2002. http://www.library.umaine.edu/theses/pdf/PartsinevelosP2002.pdf.
Der volle Inhalt der QuelleChassat, Perrine. „Functional and Shape Data Analysis under the Frenet-Serret Framework : Application to Sign Language Motion Trajectories Analysis“. Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASM005.
Der volle Inhalt der QuelleThis thesis, conducted in collaboration with MocapLab, a company specializing in motion capture, aims to determine the optimal mathematical framework and relevant descriptors for analyzing sign language motion trajectories. Drawing on principles of motor control, we identified the framework defined by the Frenet-Serret formulas, including curvature, torsion, and velocity parameters, as particularly suitable for this task. By introducing new curve analysis approaches based on the Frenet framework, this thesis contributes to developing novel methods in functional data analysis and shape analysis. The first part of this thesis addresses the challenge of smoothly estimating Frenet curvature parameters, treating the problem as parameter estimation of differential equation in SO(d), (d ≥ 1). We introduce a functional Expectation-Maximization algorithm that defines a unified variable estimation method in the SE(3) group, providing smoother estimators that are more reliable and robust than existing methods. In the second part, two new curve representations are introduced: unparametrized Frenet curvatures and the Square Root Curvatures (SRC) transform, establishing new Riemannian geometric frameworks for smooth curves in ℝᵈ, (d ≥ 1). Leveraging higher-order geometric information and parametrization dependence, the Square Root Curvatures transform outperforms the state-of-the-art Square-Root Velocity Function (SRVF) representation on synthetic results. Given a collection of curves, this type of geometry allows us to define efficient statistical criteria for estimating Karcher mean shapes on the associated Riemannian shape spaces, proving particularly effective on noisy data. Finally, this developed framework opens the door to more practical applications in sign language processing, including the study of power laws on our data and the development of a generative model for a point motion in sign language
Jetchev, Nikolay N. [Verfasser]. „Learning representations from motion trajectories : analysis and applications to robot planning and control / Nikolay Nikolaev Jetchev“. Berlin : Freie Universität Berlin, 2012. http://d-nb.info/1027151604/34.
Der volle Inhalt der QuelleBeaudry, Cyrille. „Analyse et reconnaissance de séquences vidéos d'activités humaines dans l'espace sémantique“. Thesis, La Rochelle, 2015. http://www.theses.fr/2015LAROS042/document.
Der volle Inhalt der QuelleThis thesis focuses on the characterization and recognition of human activities in videos. This research domain is motivated by a large set of applications such as automatic video indexing, video monitoring or elderly assistance. In the first part of our work, we develop an approach based on the optical flow estimation in video to recognize human elementary actions. From the obtained vector field, we extract critical points and trajectories estimated at different spatio-temporal scales. The late fusion of local characteristics such as motion orientation and shape around critical points, combined with the frequency description of trajectories allow us to obtain one of the best recognition rate among state of art methods. In a second part, we develop a method for recognizing complex human activities by considering them as temporal sequences of elementary actions. In a first step, elementary action probabilities over time is calculated in a video sequence with our first approach. Vectors of action probabilities lie in a statistical manifold called semantic simplex. Activities are then represented as trajectories on this manifold. Finally, a new descriptor is introduced to discriminate between activities from the shape of their associated trajectories. This descriptor takes into account the induced geometry of the simplex manifold
Almuhisen, Feda. „Leveraging formal concept analysis and pattern mining for moving object trajectory analysis“. Thesis, Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738/document.
Der volle Inhalt der QuelleThis dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
Almuhisen, Feda. „Leveraging formal concept analysis and pattern mining for moving object trajectory analysis“. Electronic Thesis or Diss., Aix-Marseille, 2018. http://www.theses.fr/2018AIXM0738.
Der volle Inhalt der QuelleThis dissertation presents a trajectory analysis framework, which includes both a preprocessing phase and trajectory mining process. Furthermore, the framework offers visual functions that reflect trajectory patterns evolution behavior. The originality of the mining process is to leverage frequent emergent pattern mining and formal concept analysis for moving objects trajectories. These methods detect and characterize pattern evolution behaviors bound to time in trajectory data. Three contributions are proposed: (1) a method for analyzing trajectories based on frequent formal concepts is used to detect different trajectory patterns evolution over time. These behaviors are "latent", "emerging", "decreasing", "lost" and "jumping". They characterize the dynamics of mobility related to urban spaces and time. The detected behaviors are automatically visualized on generated maps with different spatio-temporal levels to refine the analysis of mobility in a given area of the city, (2) a second trajectory analysis framework that is based on sequential concept lattice extraction is also proposed to exploit the movement direction in the evolution detection process, and (3) prediction method based on Markov chain is presented to predict the evolution behavior in the future period for a region. These three methods are evaluated on two real-world datasets. The obtained experimental results from these data show the relevance of the proposal and the utility of the generated maps
Khalid, Shehzad. „Motion classification using spatiotemporal approximation of object trajectories“. Thesis, University of Manchester, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492915.
Der volle Inhalt der QuelleSand, Peter (Peter M. ). 1977. „Long-range video motion estimation using point trajectories“. Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/38319.
Der volle Inhalt der QuelleIncludes bibliographical references (leaves 97-104).
This thesis describes a new approach to video motion estimation, in which motion is represented using a set of particles. Each particle is an image point sample with a long-duration trajectory and other properties. To optimize these particles, we measure point-based matching along the particle trajectories and distortion between the particles. The resulting motion representation is useful for a variety of applications and differs from optical flow, feature tracking, and parametric or layer-based models. We demonstrate the algorithm on challenging real-world videos that include complex scene geometry, multiple types of occlusion, regions with low texture, and non-rigid deformation.
by Peter Sand.
Ph.D.
Oliveira, Fábio Luiz Marinho de. „Video motion description based on histograms of sparse trajectories“. Universidade Federal de Juiz de Fora (UFJF), 2016. https://repositorio.ufjf.br/jspui/handle/ufjf/4838.
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Descrição de movimento tem sido um tema desafiador e popular há muitos anos em visão computacional e processamento de sinais, mas também intimamente relacionado a aprendizado de máquina e reconhecimento de padrões. Frequentemente, para realizar essa tarefa, informação de movimento é extraída e codificada em um descritor. Este trabalho apresenta um método simples e de rápida computação para extrair essa informação e codificá-la em descritores baseados em histogramas de deslocamentos relativos. Nossos descritores são compactos, globais, que agregam informação de quadros inteiros, e o que chamamos de auto-descritor, que não depende de informações de sequências senão aquela que pretendemos descrever. Para validar estes descritores e compará-los com outros tra balhos, os utilizamos no contexto de Reconhecimento de Ações Humanas, no qual cenas são classificadas de acordo com as ações nelas exibidas. Nessa validação, obtemos resul tados comparáveis aos do estado-da-arte para a base de dados KTH. Também avaliamos nosso método utilizando as bases UCF11 e Hollywood2, com menores taxas de reconhe cimento, considerando suas maiores complexidades. Nossa abordagem é promissora, pelas razoáveis taxas de reconhecimento obtidas com um método muito menos complexo que os do estado-da-arte, em termos de velocidade de computação e compacidade dos descritores obtidos. Adicionalmente, experimentamos com o uso de Aprendizado de Métrica para a classificação de nossos descritores, com o intuito de melhorar a separabilidade e a com pacidade dos descritores. Os resultados com Aprendizado de Métrica apresentam taxas de reconhecimento inferiores, mas grande melhoria na compacidade dos descritores.
Motion description has been a challenging and popular theme over many years within computer vision and signal processing, but also very closely related to machine learn ing and pattern recognition. Very frequently, to address this task, one extracts motion information from image sequences and encodes this information into a descriptor. This work presents a simple and fast computing method to extract this information and en code it into descriptors based on histograms of relative displacements. Our descriptors are compact, global, meaning it aggregates information from whole frames, and what we call self-descriptors, meaning they do not depend on information from sequences other than the one we want to describe. To validate these descriptors and compare them to other works, we use them in the context of Human Action Recognition, where scenes are classified according to the action portrayed. In this validation, we achieve results that are comparable to those in the state-of-the-art for the KTH dataset. We also evaluate our method on the UCF11 and Hollywood2 datasets, with lower recognition rates, considering their higher complexity. Our approach is a promising one, due to the fairly good recogni tion rates we obtain with a much less complex method than those of the state-of-the-art, in terms of speed of computation and final descriptor compactness. Additionally, we ex periment with the use of Metric Learning in the classification of our descriptors, aiming to improve the separability and compactness of the descriptors. Our results for Metric Learning show inferior recognition rates, but great improvement for the compactness of the descriptors.
Chen, Ni. „Contouring control in high performance motion systems /“. View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?ELEC%202005%20CHENN.
Der volle Inhalt der QuelleBücher zum Thema "Analysis of Motion Trajectories"
Center, Langley Research, und Georgia Institute of Technology. School of Aerospace Engineering., Hrsg. Singular perturbation analysis of AOTV-related trajectory optimization problems. Atlanta, GA: Georgia Institute of Technology, School of Aerospace Engineering, 1990.
Den vollen Inhalt der Quelle findenTserpes, Konstantinos, Chiara Renso und Stan Matwin, Hrsg. Multiple-Aspect Analysis of Semantic Trajectories. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38081-6.
Der volle Inhalt der QuelleMettrick, Christopher J. Analysis of the trajectories of miniature sonobuoys. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1991.
Den vollen Inhalt der Quelle findenWorkshop, on Visual Motion (1989 Irvine Calif ). Proceedings: Analysis, motion. Washington, D.C: IEEE Computer Society Press, 1989.
Den vollen Inhalt der Quelle findenZ, Bober Miroslaw. Robust motion analysis. Baldock, Hertfordshire, England: Research Studies Press, 1999.
Den vollen Inhalt der Quelle findenAksu, Ibrahim. Performance analysis of image motion analysis algorithms. Monterey, Calif: Naval Postgraduate School, 1991.
Den vollen Inhalt der Quelle findenCanada. Defence Research Establishment Atlantic. Ship Motion Analysis Program. S.l: s.n, 1986.
Den vollen Inhalt der Quelle findenSun, Yan. High-Orders Motion Analysis. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9191-4.
Der volle Inhalt der QuelleGrossman, Robert. The analysis of control trajectories using symbolic and database computing. [Washington, DC?: National Aeronautics and Space Administration, 1991.
Den vollen Inhalt der Quelle findenPenn, Roger. Employment trajectories of Asian migrants in Rochdale: An integrated analysis. [London]: Economic and Social Research Council, 1990.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Analysis of Motion Trajectories"
Min, Junghye, Jin Hyeong Park und Rangachar Kasturi. „Extraction of Multiple Motion Trajectories in Human Motion“. In Image Analysis, 1050–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45103-x_138.
Der volle Inhalt der QuelleJabłoński, Bartosz, und Marek Kulbacki. „Nonlinear Multiscale Analysis of Motion Trajectories“. In Computer Vision and Graphics, 122–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15910-7_14.
Der volle Inhalt der QuelleElaoud, Amani, Walid Barhoumi, Hassen Drira und Ezzeddine Zagrouba. „Modeling Trajectories for 3D Motion Analysis“. In Communications in Computer and Information Science, 409–29. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41590-7_17.
Der volle Inhalt der QuelleWeiss, Dieter G., Günther Galfe, Josef Gulden, Dieter Seitz-Tutter, George M. Langford, Albrecht Struppler und Adolf Weindl. „Motion Analysis of Intracellular Objects: Trajectories with and without Visible Tracks“. In Biological Motion, 95–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-51664-1_7.
Der volle Inhalt der QuelleBuus, Ole Thomsen, Johannes Ravn Jørgensen und Jens Michael Carstensen. „Analysis of Seed Sorting Process by Estimation of Seed Motion Trajectories“. In Image Analysis, 273–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21227-7_26.
Der volle Inhalt der QuelleDel Bue, Alessio, Xavier Lladó und Lourdes Agapito. „Segmentation of Rigid Motion from Non-rigid 2D Trajectories“. In Pattern Recognition and Image Analysis, 491–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72847-4_63.
Der volle Inhalt der QuelleDemircan, Emel, Luis Sentis, Vincent De Sapio und Oussama Khatib. „Human Motion Reconstruction by Direct Control of Marker Trajectories“. In Advances in Robot Kinematics: Analysis and Design, 263–72. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8600-7_28.
Der volle Inhalt der QuelleMajerník, Jaroslav. „Reconstruction of Human Motion Trajectories to Support Human Gait Analysis in Free Moving Subjects“. In Computational Intelligence, Medicine and Biology, 57–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16844-9_4.
Der volle Inhalt der QuelleMarteau, Pierre-François, und Sylvie Gibet. „Adaptive Sampling of Motion Trajectories for Discrete Task-Based Analysis and Synthesis of Gesture“. In Lecture Notes in Computer Science, 224–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11678816_25.
Der volle Inhalt der QuelleLoseva, Elizaveta, Jaap van Krugten, Aniruddha Mitra und Erwin J. G. Peterman. „Single-Molecule Fluorescence Microscopy in Sensory Cilia of Living Caenorhabditis elegans“. In Single Molecule Analysis, 133–50. New York, NY: Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3377-9_7.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Analysis of Motion Trajectories"
Devanne, Maxime, Hazem Wannous, Mohamed Daoudi, Stefano Berretti, Alberto Del Bimbo und Pietro Pala. „Learning shape variations of motion trajectories for gait analysis“. In 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016. http://dx.doi.org/10.1109/icpr.2016.7899749.
Der volle Inhalt der QuelleKihwan Kim, Dongryeol Lee und Irfan Essa. „Gaussian process regression flow for analysis of motion trajectories“. In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126365.
Der volle Inhalt der QuelleGesel, Paul, Momotaz Begum und Dain La Roche. „Learning Motion Trajectories from Phase Space Analysis of the Demonstration“. In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794381.
Der volle Inhalt der QuelleGoto, Akihiko, Naoki Sugiyama und Tomoko Ota. „Motion analysis of drone pilot operations and drone flight trajectories“. In 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1003749.
Der volle Inhalt der QuelleGhaffari, Maryam, Yu-Fen Chang, Boris Balakin und Alex C. Hoffmann. „CFD modeling of PEPT results of particle motion trajectories in a pipe over an obstacle“. In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756095.
Der volle Inhalt der QuelleNarayan, Sanath, und Kalpathi R. Ramakrishnan. „A Cause and Effect Analysis of Motion Trajectories for Modeling Actions“. In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.337.
Der volle Inhalt der QuelleAyachi, Nimish, Piyush Kejriwal, Lalit Kane und Pritee Khanna. „Analysis of the Hand Motion Trajectories for Recognition of Air-Drawn Symbols“. In 2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2015. http://dx.doi.org/10.1109/csnt.2015.95.
Der volle Inhalt der QuelleSeemann, Wolfgang, Gu¨nther Stelzner und Christian Simonidis. „Correction of Motion Capture Data With Respect to Kinematic Data Consistency for Inverse Dynamic Analysis“. In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84964.
Der volle Inhalt der QuelleCotton, R. James, Allison DeLillo, Anthony Cimorelli, Kunal Shah, J. D. Peiffer, Shawana Anarwala, Kayan Abdou und Tasos Karakostas. „Optimizing Trajectories and Inverse Kinematics for Biomechanical Analysis of Markerless Motion Capture Data“. In 2023 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2023. http://dx.doi.org/10.1109/icorr58425.2023.10304683.
Der volle Inhalt der QuelleEne, Nicoleta M., Florin Dimofte und David A. Clark. „An Analysis of a Journal Bearing Sleeve Motion With a Transient Approach“. In STLE/ASME 2010 International Joint Tribology Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ijtc2010-41183.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Analysis of Motion Trajectories"
Sharbaugh, R. C. Follow-up investigations of GPHS motion during heat pulse intervals of reentries from gravity-assist trajectories. Office of Scientific and Technical Information (OSTI), März 1992. http://dx.doi.org/10.2172/6365933.
Der volle Inhalt der QuelleNevatia, Ram. Motion Analysis and its Applications. Fort Belvoir, VA: Defense Technical Information Center, Dezember 1990. http://dx.doi.org/10.21236/ada232945.
Der volle Inhalt der QuelleLucero, E. F., und R. C. Sharbaugh. GPHS motion studies for heat pulse intervals of reentries from gravity-assist trajectories. Aerospace Nuclear Safety Program. Office of Scientific and Technical Information (OSTI), März 1990. http://dx.doi.org/10.2172/10149710.
Der volle Inhalt der QuelleZhou, H. Numerical analysis of slender vortex motion. Office of Scientific and Technical Information (OSTI), Februar 1996. http://dx.doi.org/10.2172/245550.
Der volle Inhalt der QuelleLucero, E. F., und R. C. Sharbaugh. GPHS motion studies for heat pulse intervals of reentries from gravity-assist trajectories. [General Purpose Heat Source Module (GPHS)]. Office of Scientific and Technical Information (OSTI), März 1990. http://dx.doi.org/10.2172/6128798.
Der volle Inhalt der QuelleRooks, Drew, und Trelanah McCalla. Human Dipping and Inserting Manipulation Motion Analysis. RPAL, Dezember 2018. http://dx.doi.org/10.32555/2018.ir.001.
Der volle Inhalt der QuelleFoster, Michelle. MMWG Predictive Technologies - Case Study using Vibration Analysis, Phase Analysis, and Motion Amplification and other Motion Amplification Examples. Office of Scientific and Technical Information (OSTI), Februar 2022. http://dx.doi.org/10.2172/1846901.
Der volle Inhalt der QuelleSharbaugh, R. C. Follow-up investigations of GPHS motion during heat pulse intervals of reentries from gravity-assist trajectories. Aerospace Nuclear Safety Program. Office of Scientific and Technical Information (OSTI), März 1992. http://dx.doi.org/10.2172/10149701.
Der volle Inhalt der QuelleWhite, Jonathan R., und Damon J. Burnett. Analysis of Debris Trajectories at the Scaled Wind Farm Technology (SWiFT) Facility. Office of Scientific and Technical Information (OSTI), Januar 2016. http://dx.doi.org/10.2172/1235649.
Der volle Inhalt der QuelleCosteira, Joao, und Takeo Kanade. A Multi-Body Factorization Method for Motion Analysis,. Fort Belvoir, VA: Defense Technical Information Center, September 1994. http://dx.doi.org/10.21236/ada295489.
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