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Dissertations / Theses on the topic 'Pattern recognition systems'

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Published: 4 June 2021

Last updated: 29 July 2024

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1

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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3

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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4

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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5

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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6

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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7

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

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8

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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9

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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10

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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11

Cavalin, Paulo Rodrigo. "Adaptive systems for hidden Markov model-based pattern recognition systems." Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/976/1/CAVALIN_Paulo_Rodrigo.pdf.

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Cette thèse porte sur l’étude des systèmes adaptatifs pour la reconnaissance de formes. Habituellement les systèmes de reconnaissance reposent sur une connaissance statique du problème à résoudre et cela pour la durée de vie du système. Cependant il y a des circonstances où la connaissance du problème est partielle lors de l’apprentissage initial à l’étape de la conception. Pour cette raison, les systèmes de classification adaptatifs de nouvelle génération permettent au système de base de s’adapter à la fois en apprenant sur les nouvelles données et sont également capables de s’adapter à l’environnement lors de la généralisation. Cette thèse propose une nouvelle définition d’un système de reconnaissance adaptatif où les MMCs (Modèles de Markov Cachés) sont considérés comme étude de cas. La première partie de la thèse présente une évaluation des principaux algorithmes d’apprentissage incrémental utilisés pour l’estimation des paramètres des MMCs. L’objectif de cette étude est de dégager les stratégies d’apprentissage incrémental dont la performance en généralisation se rapproche de cette obtenue avec un apprentissage hors-ligne (batch). Les résultats obtenus sur le problème de la reconnaissance de chiffres et de lettres manuscrits montrent la supériorité des approches basées sur les ensembles de modèles. De plus, nous avons montré l’importance de conserver dans une mémoire à court terme des exemples utilisés en validation, ce qui permet d’obtenir un niveau de performance qui peut même dépasser celui obtenu en mode batch. La deuxième partie de cette thèse est consacrée à la formulation d’une nouvelle approche pour la sélection dynamique des ensembles de classifieurs. Inspiré du concept de fusion appelé « organisation multi-niveau » (multistage organizations), nous avons formulé une variante de ce concept appelé DMO (dynamic multistage organization - DMO) qui permet d’adapter la fonction de fusion dynamiquement pour chaque exemple de test à classer. De plus, le concept DMO a été intégré à la méthode DSA proposée par Dos Santos et al pour la sélection dynamique d’ensembles de classifieurs. Ainsi, deux nouvelles variantes, DSAm et DSAc, ont été proposées et évaluées. Dans le premier cas (DSAm), plusieurs fonctions de sélection permettent une généralisation de la structure DMO. Pour ce qui est de la variante DSAc, nous utilisons l’information contextuelle (représentée par les profils de décisions des classifieurs de base) acquise par le système et qui est associée à la base de validation conservée dans une mémoire à court terme. L’évaluation des deux approches sur des bases de données de petite et de grande échelle ont montré que la méthode DSAc domine DSAm sur la plupart des cas étudiés. Ce résultat montre que l’utilisation d’informations contextuelles permet une meilleure performance en généralisation comparées aux méthodes non informées. Une propriété importante de l’approche DSAc est qu’elle peut également servir pour apprendre de nouvelles données dans le temps, une propriété très importante pour la conception de systèmes de reconnaissance adaptatifs dans les environnements dynamiques caractérisés par un niveau important d’incertitude sur le problème à résoudre. Finalement, un nouveau framework appelé LoGID (Local and Global Incremental Learning for Dynamic Selection) est proposé pour la conception d’un système de reconnaissance adaptatif basé sur les MMC, et capable de s’adapter dans le temps durant les phases d’apprentissage de généralisation. Le système est composé d’un pool de classifieurs de base et l’adaptation durant la phase de généralisation est effectuée par la sélection dynamique des membres du pool les plus compétents pour classer chaque exemple de test. Le mécanisme de sélection dynamique est basé sur l’algorithme des K plus proches vecteurs de décision, tandis que l’adaptation durant la phase d’apprentissage consiste à la mise à jour et à l’ajout de classifieurs de base dans le système. Durant la phase d’apprentissage, deux stratégies sont proposées pour apprendre incrémentalement sur des nouvelles données: l’apprentissage local et l’apprentissage global. L’apprentissage incrémentale local implique la mise à jour du pool de classifieurs de base en ajoutant des nouveaux membres à cet ensemble. Les nouveaux membres sont générés avec l’algorithme Learn++. L’apprentissage incrémental global consiste à la mise à jour de la base de connaissances composée des vecteurs de décisions qui seront utilisés en généralisation pour la sélection dynamique des membres les plus compétents. Le système LoGID a été validé sur plusieurs bases de données et les résultats comparés à ceux publiés dans la littérature. En général, la méthode proposée domine les autres méthodes incluant les méthodes d’apprentissage hors-ligne. Enfin, le système LoGID évalué en mode adaptatif montre qu’il est en mesure d’apprendre de nouvelles connaissances dans le temps au moment où les nouvelles données sont disponibles. Cette faculté d’adaptation est très importante également lorsque les données disponibles pour l’apprentissage sont peu nombreuses.

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Argialas, Demetre P. "A structural approach towards drainage pattern recognition /." The Ohio State University, 1985. http://rave.ohiolink.edu/etdc/view?acc_num=osu148726155305775.

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13

Tanawongsuwan, Rawesak. "Impact of speed variations in gait recognition." Diss., Available online, Georgia Institute of Technology, 2004:, 2003. http://etd.gatech.edu/theses/available/etd-04072004-180217/unrestricted/tanawongsuwan%5Frawesak%5F200312%5Fphd.pdf.

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14

BruÌˆckner, JoÌˆrg. "Automatic pattern recognition and learning for information systems." Thesis, University of Sussex, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262632.

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15

Schwier, Jason Montgomery. "Pattern recognition for command and control data systems." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252424695/.

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He, Yu. "Statistical mechanics of cellular automata and related dynamical systems." Connect to resource, 1986. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1173062557.

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17

Ni, Zhibo, and 倪志博. "Artificial training samples for the improvement of pattern recognitionsystems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B47849642.

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Pattern recognition is the assignment of some sort of label to a given input value or instance, according to some specific learning algorithm. The recognition performance is directly linked with the quality and size of the training data. However, in many real pattern recognition implementations, it is difficult or not so convenient to collect as many samples as possible for training up the classifier, such as face recognition or Chinese character recognition. In view of the shortage of training samples, the main object of our research is to investigate the generation and use of artificial samples for improving the recognition performance. Besides enhancing the learning, artificial samples are also used in a novel way such that a conventional Chinese character recognizer can read half or combined Chinese character segments. It greatly simplifies the segmentation procedure as well as reduces the error introduced by segmentation. Two novel generation models have been developed to evaluate the effectiveness of supplementing artificial samples in the training. One model generates artificial faces with various facial expressions or lighting conditions by morphing and warping two given sample faces. We tested our face generation model in three popular 2D face databases, which contain both gray scale and color images. Experiments show the generated faces look quite natural and they improve the recognition rates by a large margin. The other model uses stroke and radical information to build new Chinese characters. Artificial Chinese characters are produced by Bezier curves passing through some specified points. This model is more flexible in generating artificial handwritten characters than merely distorting the genuine real samples, with both stroke level and radical level variations. Another feature of this character generation model is that it does not require any real handwritten character sample at hand. In other words, we can train the conventional character classifier and perform character recognition tasks without collecting handwritten samples. Experiment results have validated its possibility and the recognition rate is still acceptable. Besides tackling the small sample size problem in face recognition and isolated character recognition, we improve the performance of bank check legal amount recognizer by proposing character segments recognition and applying Hidden Markov Model (HMM). It is hoped that this thesis can provide some insights for future researches in artificial sample generation, face morphing, Chinese character segmentation and text recognition or some other related issues.
published_or_final_version
Electrical and Electronic Engineering
Doctoral
Doctor of Philosophy

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18

Palancioglu, Haci Mustafa. "Extracting Movement Patterns Using Fuzzy and Neuro-fuzzy Approaches." Fogler Library, University of Maine, 2003. http://www.library.umaine.edu/theses/pdf/PalanciogluHM2003.pdf.

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19

Evans, Fiona H. "Syntactic models with applications in image analysis /." [Perth, W.A.] : [University of W.A.], 2006. http://theses.library.uwa.edu.au/adt-WU2007.0001.

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Atukorale, Don Ajantha Sanjeewa. "A hybrid learning system with a hierarchical architecture for pattern classification /." [St. Lucia, Qld.], 2002. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe16886.pdf.

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21

Wang, Min. "Pattern recognition methodology for network-based diagnostics of power quality problems /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/6099.

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22

Chen, Dapeng. "Pattern recognition in software engineering trend adapting." Morgantown, W. Va. : [West Virginia University Libraries], 2001. http://etd.wvu.edu/templates/showETD.cfm?recnum=1892.

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Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains iii, 51 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 50-51).

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Pershits, Edward. "Recognition of Face Images." Thesis, University of North Texas, 1994. https://digital.library.unt.edu/ark:/67531/metadc277785/.

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The focus of this dissertation is a methodology that enables computer systems to classify different up-front images of human faces as belonging to one of the individuals to which the system has been exposed previously. The images can present variance in size, location of the face, orientation, facial expressions, and overall illumination. The approach to the problem taken in this dissertation can be classified as analytic as the shapes of individual features of human faces are examined separately, as opposed to holistic approaches to face recognition. The outline of the features is used to construct signature functions. These functions are then magnitude-, period-, and phase-normalized to form a translation-, size-, and rotation-invariant representation of the features. Vectors of a limited number of the Fourier decomposition coefficients of these functions are taken to form the feature vectors representing the features in the corresponding vector space. With this approach no computation is necessary to enforce the translational, size, and rotational invariance at the stage of recognition thus reducing the problem of recognition to the k-dimensional clustering problem. A recognizer is specified that can reliably classify the vectors of the feature space into object classes. The recognizer made use of the following principle: a trial vector is classified into a class with the greatest number of closest vectors (in the sense of the Euclidean distance) among all vectors representing the same feature in the database of known individuals. A system based on this methodology is implemented and tried on a set of 50 pictures of 10 individuals (5 pictures per individual). The recognition rate is comparable to that of most recent results in the area of face recognition. The methodology presented in this dissertation is also applicable to any problem of pattern recognition where patterns can be represented as a collection of black shapes on the white background.

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Wong, Pak-kwong. "Multifont printed Chinese character recognition system /." [Hong Kong : University of Hong Kong], 1991. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13068556.

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Hayes, William S. "Pattern recognition and signal detection in gene finding." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/25420.

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Marsden, Christopher J. "Nonlinear dynamics of pattern recognition and optimization." Thesis, Loughborough University, 2012. https://dspace.lboro.ac.uk/2134/10694.

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We associate learning in living systems with the shaping of the velocity vector field of a dynamical system in response to external, generally random, stimuli. We consider various approaches to implement a system that is able to adapt the whole vector field, rather than just parts of it - a drawback of the most common current learning systems: artificial neural networks. This leads us to propose the mathematical concept of self-shaping dynamical systems. To begin, there is an empty phase space with no attractors, and thus a zero velocity vector field. Upon receiving the random stimulus, the vector field deforms and eventually becomes smooth and deterministic, despite the random nature of the applied force, while the phase space develops various geometrical objects. We consider the simplest of these - gradient self-shaping systems, whose vector field is the gradient of some energy function, which under certain conditions develops into the multi-dimensional probability density distribution of the input. We explain how self-shaping systems are relevant to artificial neural networks. Firstly, we show that they can potentially perform pattern recognition tasks typically implemented by Hopfield neural networks, but without any supervision and on-line, and without developing spurious minima in the phase space. Secondly, they can reconstruct the probability density distribution of input signals, like probabilistic neural networks, but without the need for new training patterns to have to enter the network as new hardware units. We therefore regard self-shaping systems as a generalisation of the neural network concept, achieved by abandoning the "rigid units - flexible couplings'' paradigm and making the vector field fully flexible and amenable to external force. It is not clear how such systems could be implemented in hardware, and so this new concept presents an engineering challenge. It could also become an alternative paradigm for the modelling of both living and learning systems. Mathematically it is interesting to find how a self shaping system could develop non-trivial objects in the phase space such as periodic orbits or chaotic attractors. We investigate how a delayed vector field could form such objects. We show that this method produces chaos in a class systems which have very simple dynamics in the non-delayed case. We also demonstrate the coexistence of bounded and unbounded solutions dependent on the initial conditions and the value of the delay. Finally, we speculate about how such a method could be used in global optimization.

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Zhu, Jia Jun. "A language for financial chart patterns and template-based pattern classification." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3950603.

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Mansjur, Dwi Sianto. "Statistical pattern recognition approaches for retrieval-based machine translation systems." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42821.

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This dissertation addresses the problem of Machine Translation (MT), which is defined as an automated translation of a document written in one language (the source language) to another (the target language) by a computer. The MT task requires various types of knowledge of both the source and target language, e.g., linguistic rules and linguistic exceptions. Traditional MT systems rely on an extensive parsing strategy to decode the linguistic rules and use a knowledge base to encode those linguistic exceptions. However, the construction of the knowledge base becomes an issue as the translation system grows. To overcome this difficulty, real translation examples are used instead of a manually-crafted knowledge base. This design strategy is known as the Example-Based Machine Translation (EBMT) principle. Traditional EBMT systems utilize a database of word or phrase translation pairs. The main challenge of this approach is the difficulty of combining the word or phrase translation units into a meaningful and fluent target text. A novel Retrieval-Based Machine Translation (RBMT) system, which uses a sentence-level translation unit, is proposed in this study. An advantage of using the sentence-level translation unit is that the boundary of a sentence is explicitly defined and the semantic, or meaning, is precise in both the source and target language. The main challenge of using a sentential translation unit is the limited coverage, i.e., the difficulty of finding an exact match between a user query and sentences in the source database. Using an electronic dictionary and a topic modeling procedure, we develop a procedure to obtain clusters of sensible variations for each example in the source database. The coverage of our MT system improves because an input query text is matched against a cluster of sensible variations of translation examples instead of being matched against an original source example. In addition, pattern recognition techniques are used to improve the matching procedure, i.e., the design of optimal pattern classifiers and the incorporation of subjective judgments. A high performance statistical pattern classifier is used to identify the target sentences from an input query sentence in our MT system. The proposed classifier is different from the conventional classifier in terms of the way it addresses the generalization capability. A conventional classifier addresses the generalization issue using the parsimony principle and may encounter the possibility of choosing an oversimplified statistical model. The proposed classifier directly addresses the generalization issue in terms of training (empirical) data. Our classifier is expected to generalize better than the conventional classifiers because our classifier is less likely to use over-simplified statistical models based on the available training data. We further improve the matching procedure by the incorporation of subjective judgments. We formulate a novel cost function that combines subjective judgments and the degree of matching between translation examples and an input query. In addition, we provide an optimization strategy for the novel cost function so that the statistical model can be optimized according to the subjective judgments.

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McCallum, Stephen John. "A multimodality magnetic resonance system for studying free radicals in biological systems." Thesis, University of Aberdeen, 1997. http://digitool.abdn.ac.uk/R?func=search-advanced-go&find_code1=WSN&request1=AAIU090119.

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Free radicals are defined as molecules with one or more unpaired electrons in their outer orbits. They have been implicated in a large number of disease states and consequently there is increasing interest in detecting them in vivo. Having an un-cancelled electron spin, free radicals are amenable to magnetic resonance experiments. For reasons of sensitivity commercially available electron paramagnetic resonance (EPR) spectrometers operate in the X-band (9 GHz). Such frequencies are unsuitable for large biological samples because of excessive electromagnetic losses. This thesis describes the development of a radio frequency continuous wave (RFCW) EPR spectrometer operating around 280 MHz suitable for in vivo studies. The instrument is based around an existing low field NMR imager. The spectrometer includes both automatic frequency control and automatic coupling systems to combat the problems of animal motion. The instrument has been able to detect free radicals in living animals. PEDRI is a technique that can provide high resolution images showing free radical distribution in living systems. The method is based on conventional pulsed NMR imaging combined with dynamic nuclear polarisation The disadvantage of PEDRI is that it is difficult to obtain spectral information such as EPR line-width and g-factor. These parameters are easy to obtain by CW-EPR, and can give useful information. A further development was the combining of the RF CW-EPR instrument with a PEDRI imager to produce a multimodality instrument capable of sequential PEDRI and CW-EPR on the same sample. Switch-over between the two modes of operation takes less than 5 seconds. This instrument combines the advantages of the two types of free radical detection in a single instrument providing an extremely useful and flexible tool.

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Confer, William James. "The activity metric for low resource, on-line character recognition." Auburn, Ala., 2005. http://repo.lib.auburn.edu/2005%20Fall/Dissertation/CONFER_WILLIAM_33.pdf.

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Alphonso, Issac John. "Network training for continuous speech recognition." Master's thesis, Mississippi State : Mississippi State University, 2003. http://library.msstate.edu/etd/show.asp?etd=etd-10252003-105104.

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Wu, Yongfeng. "New Statistical Methods to Get the Fractal Dimension of Bright Galaxies Distribution from the Sloan Digital Sky Survey Data." Fogler Library, University of Maine, 2007. http://www.library.umaine.edu/theses/pdf/WuY2007.pdf.

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Hanson, Adam. "Character recognition of optically blurred textual images using moment invariants /." Online version of thesis, 1993. http://hdl.handle.net/1850/11748.

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黃業新 and Yip-san Wong. "A two-level model-based object recognition technique." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31213807.

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Wong, Yip-san. "A two-level model-based object recognition technique /." Hong Kong : University of Hong Kong, 1995. http://sunzi.lib.hku.hk/hkuto/record.jsp?B14705552.

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Bairaktaris, Dimitrios. "Adaptive pattern recognition in a real-world environment." Thesis, University of St Andrews, 1991. http://hdl.handle.net/10023/9261.

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This thesis introduces and explores the notion of a real-world environment with respect to adaptive pattern recognition and neural network systems. It then examines the individual properties of a real-world environment and proposes Continuous Adaptation, Persistence of information and Context-sensitive recognition to be the major design criteria a neural network system in a real-world environment should satisfy. Based on these criteria, it then assesses the performance of Hopfield networks and Associative Memory systems and identifies their operational limitations. This leads to the introduction of Randomized Internal Representations, a novel class of neural network systems which stores information in a fully distributed way yet is capable of encoding and utilizing context. It then assesses the performance of Competitive Learning and Adaptive Resonance Theory systems and again having identified their operational weakness, it describes the Dynamic Adaptation Scheme which satisfies all three design criteria for a real-world environment.

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DeMaris, David Lee. "Synchronization opponent networks : dynamics, computation, and coding for similarity and object recognition /." Full text (PDF) from UMI/Dissertation Abstracts International, 2001. http://wwwlib.umi.com/cr/utexas/fullcit?p3008312.

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Taylor, Jack Rodney. "Tactile sensing : a case study of the Lord Corporation LTS-300T." Thesis, Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/19013.

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Kani, Bijan. "Enhanced logical adaptive systems for image processing and pattern recognition." Thesis, Brunel University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.358406.

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Gonzalez, Adrian. "Spatial pattern recognition for crop-livestock systems using multispectral data." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/3790.

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Within the field of pattern recognition (PR) a very active area is the clustering and classification of multispectral data, which basically aims to allocate the right class of ground category to a reflectance or radiance signal. Generally, the problem complexity is related to the incorporation of spatial characteristics that are complementary to the nonlinearities of land surface process heterogeneity, remote sensing effects and multispectral features. The present research describes the application of learning machine methods to accomplish the above task by inducting a relationship between the spectral response of farms’ land cover, and their farming system typology from a representative set of instances. Such methodologies are not traditionally used in crop-livestock studies. Nevertheless, this study shows that its application leads to simple and theoretically robust classification models. The study has covered the following phases: a)geovisualization of crop-livestock systems; b)feature extraction of both multispectral and attributive data and; c)supervised farm classification. The first is a complementary methodology to represent the spatial feature intensity of farming systems in the geographical space. The second belongs to the unsupervised learning field, which mainly involves the appropriate description of input data in a lower dimensional space. The last is a method based on statistical learning theory, which has been successfully applied to supervised classification problems and to generate models described by implicit functions. In this research the performance of various kernel methods applied to the representation and classification of crop-livestock systems described by multispectral response is studied and compared. The data from those systems include linear and nonlinearly separable groups that were labelled using multidimensional attributive data. Geovisualization findings show the existence of two well-defined farm populations within the whole study area; and three subgroups in relation to the Guarico section. The existence of these groups was confirmed by both hierarchical and kernel clustering methods, and crop-livestock systems instances were segmented and labeled into farm typologies based on: a)milk and meat production; b)reproductive management; c)stocking rate; and d)crop-forage-forest land use. The minimum set of labeled examples to properly train the kernel machine was 20 instances. Models inducted by training data sets using kernel machines were in general terms better than those from hierarchical clustering methodologies. However, the size of the training data set represents one of the main difficulties to be overcome in permitting the more general application of this technique in farming system studies. These results attain important implications for large scale monitoring of crop-livestock system; particularly to the establishment of balanced policy decision, intervention plans formulation, and a proper description of target typologies to enable investment efforts to be more focused at local issues.

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SECCO, JACOPO. "Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2680573.

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Abstract:

In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death.

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42

Wanderley, Juliana Fernandes Camapum. "Colour-based recognition for remote sensing in environmental systems." Thesis, Coventry University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.266844.

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43

Styne, Bruce Alan. "Management systems for computer graphics." Thesis, University of Cambridge, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.303247.

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44

Dobie, Mark Ralph. "Motion analysis in multimedia systems." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.359240.

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45

Li, Duwang. "Invariant pattern recognition algorithm using the Hough Transform." PDXScholar, 1989. https://pdxscholar.library.pdx.edu/open_access_etds/3899.

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A new algorithm is proposed which uses the Hough Transform to recognize two dimensional objects independent of their orientations, sizes and locations. The binary image of an object is represented by a set of straight lines. Features of the straight lines, namely the lengths and the angles of their normals, their lengths and the end point positions are extracted using the Hough Transform. A data structure for the extracted lines is constructed so that it is efficient to match the features of the lines of one object to those of another object, and determine if one object is a rotated and/or scaled version of the other. Finally a generalized Hough Transform is used to match the end points of the two sets of lines. The simulation experiments show good results for objects with significant linear features .

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46

黃伯光 and Pak-kwong Wong. "Multifont printed Chinese character recognition system." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1991. http://hub.hku.hk/bib/B31210600.

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Leung, Cheung-hoi. "Computer recognition of handprinted Chinese characters /." [Hong Kong : University of Hong Kong], 1986. http://sunzi.lib.hku.hk/hkuto/record.jsp?B12322131.

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48

Wang, Yuanxun. "Radar signature prediction and feature extraction using advanced signal processing techniques /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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49

Zhu, Tao. "Extended cluster weighted modeling methods for transient recognition control." Diss., Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/zhu/ZhuT0806.pdf.

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50

Heck, Larry Paul. "A subspace approach to the auomatic design of pattern recognition systems for mechanical system monitoring." Diss., Georgia Institute of Technology, 1991. http://hdl.handle.net/1853/15016.

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