Relevant bibliographies by topics / Pattern recognition systems

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Pattern recognition systems'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 July 2024

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Journal articles on the topic "Pattern recognition systems"

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Rizki, M. M., M. A. Zmuda, and L. A. Tamburino. "Evolving pattern recognition systems." IEEE Transactions on Evolutionary Computation 6, no. 6 (December 2002): 594–609. http://dx.doi.org/10.1109/tevc.2002.806167.

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ZAGORUIKO, N. G. "EXPERT SYSTEMS AND PATTERN RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (March 1989): 1–7. http://dx.doi.org/10.1142/s0218001489000024.

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A definition of expert systems is given, its pragmatic demands are cited and its structure is described. The methods and ways of pattern recognition are used in the subsystems DIALOGUE, ANALYTIC and HOMEOSTAT. The recognition algorithms which work on the information to be retained in the data base and knowledge base are described. The problems of recognition appearing under the construction of expert systems are noted.
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Lapko, A. V., and V. A. Lapko. "Hybrid systems of pattern recognition." Pattern Recognition and Image Analysis 18, no. 1 (January 2008): 7–13. http://dx.doi.org/10.1134/s1054661808010021.

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Fred, Ana, and Anil K. Jain. "Pattern recognition in information systems." Pattern Recognition 35, no. 12 (December 2002): 2671–72. http://dx.doi.org/10.1016/s0031-3203(02)00094-8.

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POTAPOV, ALEXEI B., and M. K. ALI. "PATTERN RECOGNITION WITH HAMILTONIAN DYNAMICS." International Journal of Modern Physics C 12, no. 05 (June 2001): 751–58. http://dx.doi.org/10.1142/s0129183101001948.

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We consider pattern recognition schemes that are based upon Hamiltonian dynamical system. Different oscillatory modes are used for storing and encoding patterns, and the effect of resonance is used for determining the most excited mode. We also propose a new technique for pattern orthogonalization resorting to hidden dimensions. Numerical experiments confirm high storage capacity and absence of false memories for the proposed system. Hamiltonian systems may be important as classical analogs of quantum computing systems or quantum neural networks.
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Pham, D. T., and E. Oztemel. "Control Chart Pattern Recognition Using Combinations of Multi-Layer Perceptrons and Learning-Vector-Quantization Neural Networks." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 207, no. 2 (May 1993): 113–18. http://dx.doi.org/10.1243/pime_proc_1993_207_325_02.

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Pattern recognition systems made up of independent multi-layer perceptrons and learning-vector-quantization neural network modules have been developed for classifying control chart patterns. These composite pattern recognition systems have better classification capabilities than their individual modules. The paper describes the structures of these pattern recognition systems and the results obtained on using them.
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Gray, J. O., and M. L. Sanderson. "Parallel Processing for Pattern Recognition Systems." IFAC Proceedings Volumes 19, no. 9 (June 1986): 155–58. http://dx.doi.org/10.1016/s1474-6670(17)57523-0.

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Ciaccio, E. J., S. M. Dunn, and M. Akay. "Biosignal pattern recognition and interpretation systems." IEEE Engineering in Medicine and Biology Magazine 12, no. 3 (September 1993): 89–95. http://dx.doi.org/10.1109/51.232348.

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Hassan, Maguid H. M. "Smart pattern recognition of structural systems." Smart Structures and Systems 6, no. 1 (January 25, 2010): 39–56. http://dx.doi.org/10.12989/sss.2010.6.1.039.

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Giakoumakis, E., G. Papaconstantinou, and E. Skordalakis. "Rule-based systems and pattern recognition." Pattern Recognition Letters 5, no. 4 (April 1987): 267–72. http://dx.doi.org/10.1016/0167-8655(87)90056-0.

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Dissertations / Theses on the topic "Pattern recognition systems"

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An, Kyung Hee. "Concurrent Pattern Recognition and Optical Character Recognition." Thesis, University of North Texas, 1991. https://digital.library.unt.edu/ark:/67531/metadc332598/.

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The problem of interest as indicated is to develop a general purpose technique that is a combination of the structural approach, and an extension of the Finite Inductive Sequence (FI) technique. FI technology is pre-algebra, and deals with patterns for which an alphabet can be formulated.
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Ihnatenko, N. V. "Systems for automatic pattern recognition." Thesis, Сумський державний університет, 2014. http://essuir.sumdu.edu.ua/handle/123456789/34837.

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Pattern recognition aims to make the process of learning and detection of patterns explicit, such that it can partially or entirely be implemented on computers. Automatic (machine) recognition, description, classification (grouping of patterns into pattern classes) have become important problems in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, artificial intelligence, and remote sensing. In almost any area of science in which observations are studied but the underlying mathematical or statistical models are not available, pattern recognition can be used to support human concept acquisition or decision making. Given a group of objects, there are two ways to build a classification or recognition system, supervised, i.e., with a teacher, or unsupervised, without the help of a teacher. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/34837
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Suh, Bongwon. "Image management using pattern recognition systems." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/2455.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Computer Science. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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YOUSSIF, ROSHDY S. "HYBRID INTELLIGENT SYSTEMS FOR PATTERN RECOGNITION AND SIGNAL PROCESSING." University of Cincinnati / OhioLINK, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1085714219.

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Ruta, Dymitr. "Classifier diversity in combined pattern recognition systems." Thesis, University of the West of Scotland, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398320.

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Lee, Yuchun. "Classifiers : adaptive modules in pattern recognition systems." Thesis, Massachusetts Institute of Technology, 1989. http://hdl.handle.net/1721.1/14496.

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Yao, Xiaoqiang. "Pattern-recognition scheduling." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1177698616.

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Mankoff, Jennifer C. "An architecture and interaction techniques for handling ambiguity in recognition-based input." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/8214.

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Xiao, Xiangye. "Efficient co-location pattern discovery /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20XIAOX.

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Scott, Emily A. "Recognition of aerospace acoustic sources using advanced pattern recognition techniques." Thesis, This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-03022010-020131/.

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Books on the topic "Pattern recognition systems"

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Theodoridis, S. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.

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James, Mike. Pattern recognition. New York: Wiley, 1988.

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Morton, Nadler. Pattern recognition engineering. New York: Wiley, 1993.

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1967-, Koutroumbas Konstantinos, and ScienceDirect (Online service), eds. Pattern recognition. 4th ed. Amsterdam: Elsevier/Academic Press, 2009.

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Bennour, Akram, Tolga Ensari, Yousri Kessentini, and Sean Eom, eds. Intelligent Systems and Pattern Recognition. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08277-1.

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Bennour, Akram, Ahmed Bouridane, and Lotfi Chaari, eds. Intelligent Systems and Pattern Recognition. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46335-8.

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Bennour, Akram, Ahmed Bouridane, and Lotfi Chaari, eds. Intelligent Systems and Pattern Recognition. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-46338-9.

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1967-, Koutroumbas Konstantinos, ed. Pattern recognition. 2nd ed. Amsterdam: Academic Press, 2003.

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1967-, Koutroumbas Konstantinos, ed. Pattern recognition. San Diego: Academic Press, 1999.

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Fournier, Monica D. Perspectives on pattern recognition. Hauppauge, N.Y: Nova Science Publisher's, 2010.

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Book chapters on the topic "Pattern recognition systems"

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Bubnicki, Zdzislaw. "Pattern Recognition." In Analysis and Decision Making in Uncertain Systems, 339–60. London: Springer London, 2004. http://dx.doi.org/10.1007/978-1-4471-3760-3_14.

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Singh, Harpreet. "Pattern Recognition Receptors." In Encyclopedia of Systems Biology, 1667–70. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_103.

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Rao, L. Koteswara, Md Zia Ur Rahman, and P. Rohini. "Features Used for Image Retrieval Systems." In Image Pattern Recognition, 9–23. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003123514-2.

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Pham, Duc Truong, and Ercan Oztemel. "Control Chart Pattern Recognition." In Intelligent Quality Systems, 80–109. London: Springer London, 1996. http://dx.doi.org/10.1007/978-1-4471-1498-7_4.

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Gesu', Vito. "Trends in pattern recognition." In Intelligent Perceptual Systems, 41–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57379-8_3.

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Bartlow, Nick, and Bojan Cukic. "Keystroke Dynamics-Based Credential Hardening Systems." In Advances in Pattern Recognition, 329–47. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-385-3_14.

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Wu, Yingquan, K. Ianakiev, and V. Govindaraju. "Confidence Combination Methods in Multi-expert Systems." In Advances in Pattern Recognition, 641–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44522-6_66.

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Volna, Eva, Michal Janosek, Martin Kotyrba, and Vaclav Kocian. "Pattern Recognition Algorithm Optimization." In Advances in Intelligent Systems and Computing, 251–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33227-2_26.

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Homenda, Wladyslaw, Agnieszka Jastrzebska, Piotr Waszkiewicz, and Anna Zawadzka. "Pattern Recognition with Rejection." In Computer Information Systems and Industrial Management, 589–602. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45378-1_52.

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Phillips, Ihsin T., and Atul K. Chhabra. "A benchmark for Raster to vector conversion systems." In Advances in Pattern Recognition, 242–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033242.

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Conference papers on the topic "Pattern recognition systems"

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Ginzburg, Vera M. "Anthropomorphic pattern recognition systems." In AeroSense '97, edited by David P. Casasent and Tien-Hsin Chao. SPIE, 1997. http://dx.doi.org/10.1117/12.270378.

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Haken, H. "Pattern Formation, Pattern Recognition, and Associative Memory." In Nonlinear Dynamics in Optical Systems. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/nldos.1992.wc2.

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Spatial and temporal patterns can be spontaneously formed in a variety of systems treated in physics, chemistry, biology and other disciplines. Such patterns may be coherent oscillations in the laser and their interactions with each other, spatio-temporal patterns in fluids, chemical reactions and a great variety of morphogenetic processes in biology.
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Oksyuta, O., Le Xu, and R. Lopatin. "DEEP LEARNING TECHNOLOGY IN PATTERN RECOGNITION." In Modern aspects of modeling systems and processes. FSBE Institution of Higher Education Voronezh State University of Forestry and Technologies named after G.F. Morozov, 2021. http://dx.doi.org/10.34220/mamsp_89-94.

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The article discusses the methods of face recognition based on convolutional neural net-works, the problems of face recognition in the presence of interference or face masking, the main stages of training neural networks and the process of actual recognition.
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Şchiopu, Paul, Apetrechioaie Bogdan, and Luminiţa Mateescu. "Pattern recognition in embedded systems." In Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies 2012, edited by Paul Schiopu and Razvan Tamas. SPIE, 2012. http://dx.doi.org/10.1117/12.962135.

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Kozlova, Liudmila P., and Olga A. Kozlova. "Intelligent Dynamic Pattern Recognition Systems." In 2022 Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus). IEEE, 2022. http://dx.doi.org/10.1109/elconrus54750.2022.9755789.

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Hsu, Ken Y., and Matthias U. Gruber. "Optical preprocessing for pattern-recognition systems." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1993. http://dx.doi.org/10.1364/oam.1993.ff.4.

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Ginzburg, Vera M. "Anthropomorphic pattern formation and recognition systems." In AeroSense 2000, edited by David P. Casasent and Tien-Hsin Chao. SPIE, 2000. http://dx.doi.org/10.1117/12.381601.

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Prasad, Nadipuram R., Jay B. Jordan, and Mahmoud Zayan. "Pattern recognition application to power systems." In Aerospace Sensing, edited by Vibeke Libby and Ivan Kadar. SPIE, 1992. http://dx.doi.org/10.1117/12.138251.

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Ferariu, L., and D. Panescu. "Pattern recognition for holonic manufacturing systems." In 2009 European Control Conference (ECC). IEEE, 2009. http://dx.doi.org/10.23919/ecc.2009.7074578.

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Martin, T., W. G. Kobel, S. K. Rogers, M. Kabrisky, and J. P. Mills. "A Distortion-Invariant Pattern Recognition Algorithm." In Cambridge Symposium_Intelligent Robotics Systems, edited by David P. Casasent. SPIE, 1987. http://dx.doi.org/10.1117/12.937713.

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Reports on the topic "Pattern recognition systems"

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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, December 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these traits, and their applications, vein pattern recognition is reviewed.
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Kroll, Joshua A. ACM TechBrief: Facial Recognition Technology. ACM, February 2022. http://dx.doi.org/10.1145/3520137.

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Facial recognition is not a monolithic technology or a particular technique. Rather, facial recognition refers to any technology that automatically processes and purports to identify faces in images or videos. While humans interpret faces easily, computers must extract patterns from data or humans must code patterns into the system. Applying these patterns yields the facial descriptors (often referred to as faceprints) on which facial recognition systems rely to achieve their function.
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Rohatgi, Upendra, and Michael Furey. Development of Application of Pattern Recognition System. Office of Scientific and Technical Information (OSTI), March 2010. http://dx.doi.org/10.2172/1012395.

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Searles, D. B. Foundations for a syntatic pattern recognition system for genomic DNA sequences. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/6707698.

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Kokurina, O. Yu. VIABILITY AND RESILIENCE OF THE MODERN STATE: PATTERNS OF PUBLIC-LEGAL ADMINISTRATION AND REGULATION. Kokurina O.Yu., February 2022. http://dx.doi.org/10.12731/kokurina-21-011-31155.

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The modern understanding of the state as a complex social system allows us to assert that its resilience is based on ensuring systemic homeostasis as a stabilizing dynamic mechanism for resolving contradictions arising in society associated with the threat of losing control over the processes of public administration and legal regulation. Public administration is a kind of social management that ensures the organization of social relations and processes, giving the social system the proper coordination of actions, the necessary orderliness, sustainability and stability. The problem of state resilience is directly related to the resilience of state (public) administration requires a «breakthrough in traditional approaches» and recognition of «the state administration system as an organic system, the constituent parts and elements of which are diverse and capable of continuous self-development». Within the framework of the «organizational point of view» on the control methodology, there are important patterns and features that determine the viability and resilience of public administration and regulation processes in the state and society. These include: W. Ashby's cybernetic law of required diversity: for effective control, the degree of diversity of the governing body must be no less than the degree of diversity of the controlled object; E. Sedov’s law of hierarchical compensations: in complex, hierarchically organized and networked systems, the growth of diversity at the top level in the structure of the system is ensured by a certain limitation of diversity at its lower levels; St. Beer’s principle of invariance of the structure of viable social systems. The study was supported by the RFBR and EISI within the framework of the scientific project No. 21-011-31155.
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Searles, D. B. Foundations for a syntatic pattern recognition system for genomic DNA sequences. [Annual] report, 1 December 1991--31 March 1993. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/10132898.

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Datta, Bithin, Jean Beegle, M. Kavvas, and Gerald Orlob. Development of an expert-system embedding pattern-recognition techniques for pollution-source identification. Report for 30 September 1987-29 November 1989. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/6855981.

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Sessa, Guido, and Gregory Martin. role of FLS3 and BSK830 in pattern-triggered immunity in tomato. United States Department of Agriculture, January 2016. http://dx.doi.org/10.32747/2016.7604270.bard.

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Pattern-recognition receptors (PRRs) located on the plant cell surface initiate immune responses by perceiving conserved pathogen molecules known as pathogen-associated molecular patterns (PAMPs). PRRs typically function in multiprotein complexes that include transmembrane and cytoplasmickinases and contribute to the initiation and signaling of pattern-triggered immunity (PTI). An important challenge is to identify molecular components of PRR complexes and downstream signaling pathways, and to understand the molecular mechanisms that mediate their function. In research activities supported by BARD-4931, we studied the role of the FLAGELLIN SENSING 3 (FLS3) PRR in the response of tomato leaves to flagellin-derivedPAMPs and PTI. In addition, we investigated molecular properties of the tomato brassinosteroid signaling kinase 830 (BSK830) that physically interacts with FLS3 and is a candidate for acting in the FLS3 signaling pathway. Our investigation refers to the proposal original objectives that were to: 1) Investigate the role of FLS3 and its interacting proteins in PTI; 2) Investigate the role of BSK830 in PTI; 3) Examine molecular and phosphorylation dynamics of the FLS3-BSK830 interaction; 4) Examine the possible interaction of FLS3 and BSK830 with Pstand Xcveffectors. We used CRISPR/Cas9 techniques to develop plants carrying single or combined mutations in the FLS3 gene and in the paralogsFLS2.1 and FLS2.2 genes, which encode the receptor FLAGELLIN SENSING2 (FLS2), and analyzed their function in PTI. Domain swapping analysis of the FLS2 and FLS3 receptors revealed domains of the proteins responsible for PAMP detection and for the different ROS response initiated by flgII-28/FLS3 as compared to flg22/FLS2. In addition, in vitro kinase assays and point mutations analysis identified FLS2 and FLS3 domains required for kinase activity and ATP binding. In research activities on tomato BSK830, we found that it interacts with PRRs and with the co-receptor SERK3A and PAMP treatment affects part of these interactions. CRISPR/Cas9 bsk830 mutant plants displayed enhanced pathogen susceptibility and reduced ROS production upon PAMP treatment. In addition, BSK830 interacted with 8 Xanthomonastype III secreted effectors. Follow up analysis revealed that among these effectors XopAE is part of an operon, is translocated into plant cells, and displays E3 ubiquitinligase activity. Our investigation was also extended to other Arabidopsis and tomato BSK family members. Arabidopsis BSK5 localized to the plant cell periphery, interacted with receptor-like kinases, and it was phosphorylatedin vitro by the PEPR1 and EFRPRRs. bsk5 mutant plants displayed enhanced susceptibility to pathogens and were impaired in several, but not all, PAMP-induced responses. Conversely, BSK5 overexpression conferred enhanced disease resistance and caused stronger PTI responses. Genetic complementation suggested that proper localization, kinase activity, and phosphorylation by PRRs are critical for BSK5 function. BSK7 and BSK8 specifically interacted with the FLS2 PRR, their respective mutant plants were more susceptible to B. cinereaand displayed reduced flg22-induced responses. The tomato BSK Mai1 was found to interact with the M3KMAPKKK, which is involved in activation of cell death associated with effector-triggered immunity. Silencing of Mai1 in N. benthamianaplants compromised cell death induced by a specific class of immune receptors. In addition, co-expression of Mai1 and M3Kin leaves enhanced MAPKphosphorylation and cell death, suggesting that Mai1 acts as a molecular link between pathogen recognition and MAPK signaling. Finally, We identified the PP2C phosphatase Pic1 that acts as a negative regulator of PTI by interacting with and dephosphorylating the receptor-like cytoplasmickinase Pti1, which is a positive regulator of plant immunity. The results of this investigation shed new light on the molecular characteristics and interactions of components of the immune system of crop plants providing new knowledge and tools for development of novel strategies for disease control.
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Psuty, Norbert, Tanya Silveira, Andrea Habeck, Dennis Skidds, Sara Stevens, Katy Ames, and Glenn Liu. Northeast Coastal and Barrier Network geomorphological monitoring protocol: Part II ? coastal topography, version 2. National Park Service, 2024. http://dx.doi.org/10.36967/2301966.

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Coastal topography was ranked as one of the most important variables for monitoring following a review of potential vital signs in the coastal parks of the Northeast Coastal and Barrier Network (NCBN). Changes in coastal topography, whether caused by erosion or accretion, vary both spatially and temporally. Understanding these variations is key to early recognition of potential problems affecting natural and cultural resources in coastal parks. For managers, understanding spatial and temporal patterns of geomorphologic change is basic to optimal management of any coastal park because the interface of marine and land systems 1) is highly dynamic and driven by multiple forcing mechanisms, 2) results in alterations to resource patterns and dynamics at habitat and ecosystem levels, and 3) can eventually result in the loss of static resources. The establishment of local, long-term monitoring programs help us to understand the processes that are driving coastal change of beaches, dunes, and bluffs within the parks. This Coastal Topography Monitoring Protocol has been developed for use in the Northeast Coastal and Barrier Network parks. Monitoring is accomplished with survey-grade Global Positioning System (GPS)/Global Navigation Satellite Systems (GNSS) equipment that collects topographic data along pre-established transects spaced at regular intervals, augmented by more intense data-collection in areas of special concern to the parks. A network of high-quality survey control monuments (often referred to as benchmarks), used as accuracy assessment reference is located within each of the NCBN parks, providing a robust basis for long-term monitoring. Spring and/or fall surveys conducted in accordance with standard operating procedures generate coastal topography datasets that are organized and assembled by the NCBN data manager into a database for analysis and archival purposes. Dimensional parameters are measured to describe the beach-dune-bluff system, and attributes are compared and analyzed in a cross-shore and alongshore perspective, providing information about the temporal and spatial changes on beach-dune-bluff morphologies in the parks. The overall goal is to create a replicable means of data gathering that is efficient, adheres to scientific principles, and meets the management needs of the coastal parks.
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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, December 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This system has the improved chick holding cups and motion mechanisms that enable chicks to open wings through the view section. The mechanical system has achieved the speed of 4 chicks per second which exceeds the design specs of 3 chicks per second. In the center of the conveyor, a high-speed camera with UV sensitive optical system, shown in Fig.2, was installed that captures chick images at multiple frames (45 images and system selectable) when the chick passing through the view area. Through intensive discussions and efforts, the PIs of Maryland and ARO have created the protocol of joint hardware and software that uses sequential images of chick in its fall motion to capture opening wings and extract the optimal opening positions. This approached enables the reliable feather feature extraction in dynamic motion and pattern recognition. Improving of Chick Wing Deployment The mechanical system for chick conveying and especially the section that cause chicks to deploy their wings wide open under the fast video camera and the UV light was investigated along the third study year. As a natural behavior, chicks tend to deploy their wings as a mean of balancing their body when a sudden change in the vertical movement was applied. In the latest two years, this was achieved by causing the chicks to move in a free fall, in the earth gravity (g) along short vertical distance. The chicks have always tended to deploy their wing but not always in wide horizontal open situation. Such position is requested in order to get successful image under the video camera. Besides, the cells with checks bumped suddenly at the end of the free falling path. That caused the chicks legs to collapse inside the cells and the image of wing become bluer. For improving the movement and preventing the chick legs from collapsing, a slowing down mechanism was design and tested. This was done by installing of plastic block, that was printed in a predesign variable slope (Fig. 3) at the end of the path of falling cells (Fig.4). The cells are moving down in variable velocity according the block slope and achieve zero velocity at the end of the path. The slop was design in a way that the deacceleration become 0.8g instead the free fall gravity (g) without presence of the block. The tests showed better deployment and wider chick's wing opening as well as better balance along the movement. Design of additional sizes of block slops is under investigation. Slops that create accelerations of 0.7g, 0.9g, and variable accelerations are designed for improving movement path and images.
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