Relevant bibliographies by topics / Automated identification

Contents

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

Academic literature on the topic 'Automated identification'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Automated identification"

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Price, C. J., N. Snooke, and J. Landry. "Automated sneak identification." Engineering Applications of Artificial Intelligence 9, no. 4 (August 1996): 423–27. http://dx.doi.org/10.1016/0952-1976(96)00030-9.

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Gaston, Kevin J., and Mark A. O'Neill. "Automated species identification: why not?" Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 359, no. 1444 (April 29, 2004): 655–67. http://dx.doi.org/10.1098/rstb.2003.1442.

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Where possible, automation has been a common response of humankind to many activities that have to be repeated numerous times. The routine identification of specimens of previously described species has many of the characteristics of other activities that have been automated, and poses a major constraint on studies in many areas of both pure and applied biology. In this paper, we consider some of the reasons why automated species identification has not become widely employed, and whether it is a realistic option, addressing the notions that it is too difficult, too threatening, too different or too costly. Although recognizing that there are some very real technical obstacles yet to be overcome, we argue that progress in the development of automated species identification is extremely encouraging that such an approach has the potential to make a valuable contribution to reducing the burden of routine identifications. Vision and enterprise are perhaps more limiting at present than practical constraints on what might possibly be achieved.
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Gulliver, Austin F., and Joachim G. Stadel. "Automated spectral line identification." Publications of the Astronomical Society of the Pacific 102 (May 1990): 587. http://dx.doi.org/10.1086/132674.

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Watson, Anna T., Mark A. O'Neill, and Ian J. Kitching. "Automated identification of live moths (Macrolepidoptera) using digital automated identification System (DAISY)." Systematics and Biodiversity 1, no. 3 (February 2004): 287–300. http://dx.doi.org/10.1017/s1477200003001208.

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Burckhardt, Irene. "Zu einer schnelleren und besseren individualisierten Diagnostik." BIOspektrum 25, no. 6 (October 2019): 620–23. http://dx.doi.org/10.1007/s12268-019-0204-1.

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Abstract Total lab automation automates inoculation, incubation and analysis of human specimens. It enables the laboratory to shorten incubation times and significantly reduce the time to report. It improves the quality of microbiologic analysis by increasing the number of grown organisms. In the future it will enable automatic reading and fully automated identification and susceptibility testing of bacteria.
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Tauran, Patricia M., Irda Handayani, and Nurhayana Sennang. "IDENTIFIKASI BAKTERI AEROB GRAM NEGATIF DAN GRAM POSITIF MENGGUNAKAN METODE KONVENSIONAL DAN OTOMATIK." INDONESIAN JOURNAL OF CLINICAL PATHOLOGY AND MEDICAL LABORATORY 19, no. 2 (March 21, 2018): 105. http://dx.doi.org/10.24293/ijcpml.v19i2.1065.

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Choosing the method of bacteria identification is crucial to obtain accurate and quick results. This study will analyze the identificationresults of Gram negative and Gram positive from aerobic bacteria by examination using conventional and automatic methods at Dr.Wahidin Sudirohusodo Hospital Laboratory. A total of 85 samples consisting of 66 Gram negative bacteria and 19 Gram positive bacteriawere identified using conventional and automated methods. In this study, there was some correspondent identification result betweenthe conventional as well as the automated methods, namely 31.5% for Gram negative bacteria and 30.8% for Gram positive bacteria.However, the non-correspondent identification result between conventional and automated methods was found greater, namely, 68.5%for Gram negative bacteria and 69.2% for Gram positive bacteria. The non-correspondent identification result was due to the developmentof bacterial taxonomy and the differences of numbers and types of the biochemical tests between conventional and automatic methods.Bacteria identification using automated method is more accurate and faster than the conventional method, so it is recommended usingthis particularly for the laboratory and educational referral center.
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Chesmore, David. "Automated bioacoustic identification of species." Anais da Academia Brasileira de Ciências 76, no. 2 (June 2004): 436–40. http://dx.doi.org/10.1590/s0001-37652004000200037.

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Research into the automated identification of animals by bioacoustics is becoming more widespread mainly due to difficulties in carrying out manual surveys. This paper describes automated recognition of insects (Orthoptera) using time domain signal coding and artificial neural networks. Results of field recordings made in the UK in 2002 are presented which show that it is possible to accurately recognize 4 British Orthoptera species in natural conditions under high levels of interference. Work is under way to increase the number of species recognized.
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Balodis, Jānis, Ansis Zariņš, Diāna Haritonova, and Inese Janpaule. "PARAMETERS FOR AUTOMATED STAR IDENTIFICATION." Geodesy and cartography 40, no. 4 (December 16, 2014): 163–70. http://dx.doi.org/10.3846/20296991.2014.987457.

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The determination of parameters for identifying stars sensed by charge-coupled device (CCD) is discussed. Numerical experiments are summarized which support the parameter space bound estimation practicality of the proposed star pattern recognition and identification by matching with coordinate list in star catalogue. The parameter analysis performed to apply them for proper identification algorithm which is developed and used at the Institute of Geodesy and Geoinformatics. This algorithm is applied for identification of large volume star sets.
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Shu, Wei. "Automated personal identification by palmprint." Optical Engineering 37, no. 8 (August 1, 1998): 2359. http://dx.doi.org/10.1117/1.601756.

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Shen, W., and T. Tan. "Automated biometrics-based personal identification." Proceedings of the National Academy of Sciences 96, no. 20 (September 28, 1999): 11065–66. http://dx.doi.org/10.1073/pnas.96.20.11065.

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Dissertations / Theses on the topic "Automated identification"

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Chen, Chun-Cheng Richard 1977. "Automated cardiovascular system identification." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/81537.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 64-65).
by Chun-Cheng Chen.
S.B.and M.Eng.
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Wong, Poh Lee. "Automated fish detection and identification." Thesis, La Rochelle, 2015. http://www.theses.fr/2015LAROS009.

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L’utilisation de techniques informatiques pour la reconnaissance et l'identification des poissons est devenue assez populaire parmi les chercheurs. Ces nouvelles approches sont importantes, puisque les informations extraites sur les poissons telles que leurs trajectoires, leurs positions ou leurs couleurs, permettent de déterminer si les poissons sont en bonne santé ou en état de stress. Les méthodes existantes ne sont pas assez précises notamment lorsque des éléments tels que les bulles ou des zones éclairées peuvent être identifiées comme étant des poissons. De plus, les taux de reconnaissance et d'identification des systèmes existants peuvent encore être améliorés afin d’obtenir des résultats à la fois meilleurs et plus précis. Afin d’obtenir de meilleurs taux de reconnaissance et d'identification, un système amélioré a été construit en combinant plusieurs méthodes de détection et d’analyse. Tout d'abord, la première étape a consisté à proposer une méthode de suivi d'objets dans le but de localiser en temps réel la position des poissons à partir de vidéos. Celle-ci inclut le suivi automatisé multi-cibles de poissons dans un aquarium. Les performances en termes de détection et d’identification risquaient d’être faibles notamment en raison du processus de suivi dans un environnement temps réel. Une méthode de suivi des poissons plus précise est donc proposée ainsi qu'une méthode complète pour identifier et détecter les modèles de nage des poissons. Dans ces travaux, nous proposons, pour le suivi des poissons, une amélioration de l’algorithme du filtre particulaire en l’associant à un algorithme de détection de mouvement. Un système doté de deux caméras est également proposé afin d'obtenir un meilleur taux de détection. La seconde étape comprend la conception et le développement d'une méthode améliorée pour le recadrage et la segmentation dynamique des images dans un environnement temps réel. Ce procédé est proposé pour extraire de la vidéo les images représentant les poissons en éliminant les éléments provenant de l’arrière-plan. La troisième étape consiste à caractériser les objets (les poissons). La méthode proposée est basée sur des descripteurs utilisant la couleur pour caractériser les poissons. Ces descripteurs sont ensuite utilisés dans la suite des traitements. Dans nos travaux, les descripteurs couleurs généralisés de Fourier (GCFD : Generalized Color Fourier Descriptor) sont utilisés et une adaptation basée sur la détection de l’environnement est proposée afin d’obtenir une identification plus précise des poissons. Une méthode de mise en correspondance basée sur un calcul de distance est utilisée pour comparer les vecteurs de caractéristiques des images segmentées afin de classifier les poissons présents dans la vidéo. Un prototype dont le but est de modéliser les profils de nage des poissons a été développé. Celui-ci intègre toutes les méthodes proposées et a permis d’évaluer la validité de notre approche. Les résultats montrent que les méthodes proposées améliorent la reconnaissance et l’identification en temps réel des poissons. La méthode de suivi proposée montre une amélioration par rapport au procédé basé sur le filtre particulaire classique. Le recadrage dynamique et la méthode de segmentation temps-réel présentent en termes de précision un pourcentage moyen de 84,71%. La méthode de caractérisation des objets développée pour reconnaitre et identifier en temps réel les poissons montre également une amélioration par rapport aux descripteurs couleurs classiques. Le travail réalisé peut trouver une application directe auprès des aquaculteurs afin de suivre en temps réel et de manière automatique le comportement des poissons et éviter ainsi un suivi « visuel » tel qu’il est réalisé actuellement
Recognition and identification of fish using computational methods have increasingly become a popular research endeavour among researchers. The methods are important as the information displayed by the fish such as trajectory patterns, location and colour could determine whether the fish are healthy or under stress. Current methods are not accurate especially when there exist thresholds such as bubbles and some lighted areas which might be identified as fish. Besides, the recognition and identification rate of the existing systems can still be improved to obtain better and more accurate results. In order to achieve a better recognition and identification rate, an improved scheme consisting of a combination of several methods is constructed. First of all, the first approach is to propose an object tracking method for the purpose of locating the position of fish for real-time videos. This includes the consideration of tracking multiple fish in a single tank in an automated way. The detection and identification rate may be slow due to the on-going tracking process especially in a real-time environment. A more accurate fish tracking method is proposed as well as a systematic method to identify and detect fish swimming patterns. In this research, the particle filter algorithm is enhanced and further combined with the motion detection algorithm for fish tracking. A dual camera system is also proposed to obtain better detection rate. The second approach includes the design and development of an enhanced method for dynamically cropping and segmenting images in real-time environment. This method is proposed to extract each image of the fish from every successive video frame to reduce the tendency of detecting the background as an object. The third approach includes an adapted object characterisation method which utilises colour feature descriptors to represent the fish in a computational form for further processing. In this study, an object characterisation method, GCFD (Generalized Colour Fourier Descriptor) is adapted to suit the environment for more accurate identification of the fish. A feature matching method based on distance matching is used to match the feature vectors of the segmented images for classifying the specific fish in the recorded video. In addition, a real-time prototype system which models the fish swimming pattern incorporating all the proposed methods is developed to evaluate the methods proposed in this study. Based on the results, the proposed methods show improvements which result in a better real-time fish recognition and identification system. The proposed object tracking method shows improvement over the original particle filter method. Based on the average percentage in terms of the accuracy for the dynamic cropping and segmentation method in real time, an acceptable value of 84.71% was recorded. The object characterisation method which is adapted for fish recognition and identification in real time shows an improvement over existing colour feature descriptors. As a whole, the main output of this research could be used by aquaculturist to track and monitor fish in the water computationally in real-time instead of manually
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Waly, Hashem. "Automated Fault Identification - Kernel Trace Analysis." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28246/28246.pdf.

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Silva, Bruno Miguel Santos Antunes. "Automated acoustic identification of bat species." Master's thesis, Universidade de Évora, 2013. http://hdl.handle.net/10174/9101.

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Automated acoustic identification of bat species Recent improvements in bat survey methods in Portugal, especially automatic recording stations, have led to an analysis problem due to the amount of data obtained. In this thesis we propose to develop an automated analysis and classification method for bat echolocation calls by developing a computer program based on statistical models and using a reference database of bat calls recorded in Portugal to quickly analyze and classify large amounts of recordings. We recorded 2968 calls from 748 bats of 20 (of the 25) bat species known in mainland Portugal and coded a program in R that automatically detects bat calls in a recording, isolates the calls from the background noise and measures 19 parameters from each call. A two stage hierarchical classification bat call scheme was implemented based on logistic regression models and ensembles of artificial neural networks. In the first stage calls were classified in six major groups with individual correct classification rates that varied between 93% and 100%. In the second stage calls were classified in species or groups of species with classification rates that varied between 50% and 100%; ### Identificação acústica automatizada de espécies de morcegos Desenvolvimentos recentes nas metodologias de monitorização de morcegos utilizadas em Portugal, especialmente estações de gravação automáticas, conduziram a um problema de análise devido à quantidade de dados obtida. Nesta tese propomos desenvolver um método automatizado de análise e classificação de pulsos de ecolocalização de morcegos através do desenvolvimento de um programa de computador baseado em modelos estatísticos e utilizando uma base de dados de pulsos de morcegos gravados em Portugal continental para rapidamente analisar e classificar grandes quantidades de gravações. Gravámos 2968 pulsos de 748 morcegos de 20 (das 25) espécies de morcegos conhecidas em Portugal continental e codificámos em R um programa para automaticamente detectar pulsos de morcego numa gravação, isolar os pulsos do ruído de fundo e medir 19 parâmetros de cada pulso. Foi implementado um esquema hierárquico de classificação de pulsos em duas etapas baseado em modelos de regressão logística e conjuntos de redes neuronais artificiais. Numa primeira etapa os pulsos foram classificados em seis grupos com taxas individuais de classificações correctas que variaram entre 93% e 100%. Numa segunda fase os pulsos foram classificados em espécies ou grupos de espécies com taxas de classificação correctas que variaram entre 50% e 100%.
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Moody, Sarah Jean. "Automated Data Type Identification And Localization Using Statistical Analysis Data Identification." DigitalCommons@USU, 2008. https://digitalcommons.usu.edu/etd/9.

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This research presents a new and unique technique called SÁDI, statistical analysis data identification, for identifying the type of data on a digital device and its storage format based on data type, specifically the values of the bytes representing the data being examined. This research incorporates the automation required for specialized data identification tools to be useful and applicable in real-world applications. The SÁDI technique utilizes the byte values of the data stored on a digital storage device in such a way that the accuracy of the technique does not rely solely on the potentially misleading metadata information but rather on the values of the data itself. SÁDI provides the capability to identify what digitally stored data actually represents. The identification of the relevancy of data is often dependent upon the identification of the type of data being examined. Typical file type identification is based upon file extensions or magic keys. These typical techniques fail in many typical forensic analysis scenarios, such as needing to deal with embedded data, as in the case of Microsoft Word files or file fragments. These typical techniques for file identification can also be easily circumvented, and individuals with nefarious purposes often do so.
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Siricharoen, Punnarai. "Plant disease identification using automated image analysis." Thesis, Ulster University, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725343.

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Hetherington, Jorden Hicklin. "Automated lumbar vertebral level identification using ultrasound." Thesis, University of British Columbia, 2017. http://hdl.handle.net/2429/62945.

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Estrada, Vargas Ana Paula. "Black-Box identification of automated discrete event systems." Thesis, Cachan, Ecole normale supérieure, 2013. http://www.theses.fr/2013DENS0006/document.

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Cette thèse traite de l'identification des systèmes à événements discrets (SED) automatisés dans un contexte industriel. En particulier, le travail aborde les systèmes formés par un processus et un automate programmable (AP) fonctionnant en boucle fermée - l'identification a pour but d’obtenir un modèle approximatif exprimé en réseaux de Petri interprétés (RPI) à partir du comportement externe observé sous la forme d'une seule séquence de vecteurs d’entrée-sortie de l’AP. Tout d'abord, une analyse des méthodes d'identification est présentée, ainsi qu’une étude comparative des méthodes récentes pour l'identification des SED. Puis le problème abordé est décrit - des importantes caractéristiques technologiques dans les systèmes automatisés par l’AP sont détaillées. Ces caractéristiques doivent être prises en compte dans la résolution du problème, mais elles ne peuvent pas être traitées par les méthodes existantes d’identification. La contribution principale de cette thèse est la création de deux méthodes d’identification complémentaires. La première méthode permet de construire systématiquement un modèle RPI à partir d'une seule séquence entrée-sortie représentant le comportement observable du SED. Les modèles RPI décrivent en détail l’évolution des entrées et sorties pendant le fonctionnement du système. La seconde méthode considère des SED grands et complexes - elle est basée sur une approche statistique qui permettre la construction des modèles en RPI compactes et expressives. Elle est composée de deux étapes - la première calcule à partir de la séquence entrée-sortie, la partie réactive du modèle, constituée de places observables et de transitions. La deuxième étape fait la construction de la partie non-observable, en rajoutant des places pour permettre la reproduction de la séquence entrée-sortie. Les méthodes proposées, basées sur des algorithmes de complexité polynomiale, ont été implémentées en outils logiciels, lesquels ont été testés avec des séquences d’entrée-sortie obtenues à partir des systèmes réels en fonctionnement. Les outils sont décrits et leur application est illustrée à travers deux cas d’étude
This thesis deals with the identification of automated discrete event systems (DES) operating in an industrial context. In particular the work focuses on the systems composed by a plant and a programmable logic controller (PLC) operating in a closed loop- the identification consists in obtaining an approximate model expressed in interpreted Petri nets (IPN) from the observed behaviour given under the form of a single sequence of input-output vectors of the PLC. First, an overview of previous works on identification of DES is presented as well as a comparative study of the main recent approaches on the matter. Then the addressed problem is stated- important technological characteristics of automated systems and PLC are detailed. Such characteristics must be considered in solving the identification problem, but they cannot be handled by previous identification techniques. The main contribution in this thesis is the creation of two complementary identification methods. The first method allows constructing systematically an IPN model from a single input-output sequence representing the observable behaviour of the DES. The obtained IPN models describe in detail the evolution of inputs and outputs during the system operation. The second method has been conceived for addressing large and complex industrial DES- it is based on a statistical approach yielding compact and expressive IPN models. It consists of two stages- the first one obtains, from the input-output sequence, the reactive part of the model composed by observable places and transitions. The second stage builds the non observable part of the model including places that ensure the reproduction of the observed input-output sequence. The proposed methods, based on polynomial-time algorithms, have been implemented in software tools, which have been tested with input-output sequences obtained from real systems in operation. The tools are described and their application is illustrated through two case studies
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Estrada, Vargas Ana Paula, and Vargas Ana Paula Estrada. "Black-Box identification of automated discrete event systems." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00846194.

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This thesis deals with the identification of automated discrete event systems (DES) operating in an industrial context. In particular the work focuses on the systems composed by a plant and a programmable logic controller (PLC) operating in a closed loop- the identification consists in obtaining an approximate model expressed in interpreted Petri nets (IPN) from the observed behaviour given under the form of a single sequence of input-output vectors of the PLC. First, an overview of previous works on identification of DES is presented as well as a comparative study of the main recent approaches on the matter. Then the addressed problem is stated- important technological characteristics of automated systems and PLC are detailed. Such characteristics must be considered in solving the identification problem, but they cannot be handled by previous identification techniques. The main contribution in this thesis is the creation of two complementary identification methods. The first method allows constructing systematically an IPN model from a single input-output sequence representing the observable behaviour of the DES. The obtained IPN models describe in detail the evolution of inputs and outputs during the system operation. The second method has been conceived for addressing large and complex industrial DES- it is based on a statistical approach yielding compact and expressive IPN models. It consists of two stages- the first one obtains, from the input-output sequence, the reactive part of the model composed by observable places and transitions. The second stage builds the non observable part of the model including places that ensure the reproduction of the observed input-output sequence. The proposed methods, based on polynomial-time algorithms, have been implemented in software tools, which have been tested with input-output sequences obtained from real systems in operation. The tools are described and their application is illustrated through two case studies.
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Duncan-Drake, Natasha. "Exploiting human expert techniques in automated writer identification." Thesis, University of Kent, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365222.

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Books on the topic "Automated identification"

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Komarinski, Peter. Automated Fingerprint Identification Systems (AFIS). Amsterdam: Academic Press, 2004.

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Komarinski, Peter. Automated fingerprint identification systems (AFIS). Amsterdam: Elsevier Academic, 2005.

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Zhang, David. Automated biometrics: Technologies and systems. Boston: Kluwer Academic Publishers, 2000.

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Wilson, Thomas F. Automated fingerprint identification systems: Technology and policy issues. Washington, D.C: U.S. Dept. of Justice, Bureau of Justice Statistics, 1987.

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Zimmermann, Wendy. SAVE and automated verification of immigration status. Washington, D.C. (2100 M St., N.W., Washington 20037): Urban Institute, 1990.

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Sweeney, Mark Shaw. ANIS - an automated newt identification system: An application of image processing and pattern recognition techniques. Leicester: De Montfort University, 1995.

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(Emilio), Mordini E., and Green Manfred, eds. Identity, security and democracy: The wider social and ethical implications of automated systems for human identification. Amsterdam: Ios Press, 2009.

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Lee, L. S. Identification of gust front cases in Hong Kong using data from automated weather station of the Royal Observatory. Hong Kong: Royal Observatory, 1996.

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Meyers, Richard B. Automatic identification: Questions & answers. Cleveland, OH: Automatic I.D. News, 1992.

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Office, Illinois Attorney General's. Ser informado es poder: El poder ofrece tranquilidad a las victimas. Springfield, Ill.]: Illinois Attorney General, Lisa Madigan, 2003.

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Book chapters on the topic "Automated identification"

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Wakaumi, Hiroo. "Automated Identification." In Mechatronics, 133–67. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118614549.ch5.

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Everingham, Mark, and Andrew Zisserman. "Automated Person Identification in Video." In Lecture Notes in Computer Science, 289–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27814-6_36.

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Mamidipally, Chandrasekhar, Santosh B. Noronha, and Sumantra Dutta Roy. "Automated Identification of Protein Structural Features." In Lecture Notes in Computer Science, 171–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-11164-8_28.

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Scoccia, Gian Luca. "Automated Feature Identification for Android Apps." In Software Engineering and Formal Methods, 77–84. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57506-9_7.

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Stolle, Reinhard, and Elizabeth Bradley. "Communicable Knowledge in Automated System Identification." In Lecture Notes in Computer Science, 17–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73920-3_2.

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Marciuska, Sarunas, Cigdem Gencel, and Pekka Abrahamsson. "Automated Feature Identification in Web Applications." In Software Quality. Model-Based Approaches for Advanced Software and Systems Engineering, 100–114. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03602-1_7.

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Zubavicius, Paulius, Antanas Cenys, and Lukas Radvilavicius. "Industrial Automated Fingerprint-Based Identification System." In Advances in Information Systems Development, 259–66. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-70761-7_22.

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Volkov, Sergey S., Dmitry A. Devyatkin, Ilia V. Sochenkov, Ilya A. Tikhomirov, and Natalia V. Toganova. "Towards Automated Identification of Technological Trajectories." In Communications in Computer and Information Science, 143–53. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30763-9_12.

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Ananda Kanagaraj, S., N. Kamalakannan, M. Devosh, S. Uma Maheswari, A. Shahina, and A. Nayeemulla Khan. "Automated Health Monitoring Through Emotion Identification." In Proceedings of the International Conference on Soft Computing Systems, 199–207. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2671-0_19.

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Lee, Younjeong, Joohun Lee, and Ki Yong Lee. "PCA Fuzzy Mixture Model for Speaker Identification." In Intelligent Data Engineering and Automated Learning, 992–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_140.

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Conference papers on the topic "Automated identification"

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Sponsler, Jeffrey L., and Charles Parker. "Automated Electromyography Analysis: Update." In Modelling, Simulation and Identification. Calgary,AB,Canada: ACTAPRESS, 2018. http://dx.doi.org/10.2316/p.2018.858-001.

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Dufourq, Emmanuel, and Bruce A. Bassett. "Automated problem identification." In the South African Institute of Computer Scientists and Information Technologists. New York, New York, USA: ACM Press, 2017. http://dx.doi.org/10.1145/3129416.3129429.

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Fischer, Robert, and Claus Vielhauer. "Automated Firearm Identification." In IH&MMSec '15: ACM Information Hiding and Multimedia Security Workshop. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2756601.2756619.

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Thomopoulos, Stelios C., and James G. Reisman. "Fusion-based, high-volume automated fingerprint identification system (AFIS)." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. A. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib. SPIE, 1994. http://dx.doi.org/10.1117/12.172536.

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Pack, Kenneth. "Automated False Track Identification." In 2007 Integrated Communications, Navigation and Surveillance Conference. IEEE, 2007. http://dx.doi.org/10.1109/icnsurv.2007.384169.

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Seve, Emmanuel, Camille Delezoide, Jelena Pesic, Sebastien Bigo, and Yvan Pointurier. "Automated Fiber Type Identification." In 2018 European Conference on Optical Communication (ECOC). IEEE, 2018. http://dx.doi.org/10.1109/ecoc.2018.8535361.

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Hamers, Juan, and Lieven Eeckhout. "Automated hardware-independent scenario identification." In the 45th annual conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1391469.1391710.

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Ammar, Hany, Robert Howell, Mohamed Abdel-Mottaleb, and Anil Jain. "Automated dental identification system (ADIS)." In the 2006 national conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1146598.1146706.

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Lee, S. L. A., A. Z. Kouzani, and E. J. Hu. "Automated identification of lung nodules." In 2008 IEEE 10th Workshop on Multimedia Signal Processing (MMSP). IEEE, 2008. http://dx.doi.org/10.1109/mmsp.2008.4665129.

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Seve, Emmanuel, Camille Delezoide, Jelena Pesic, Fabien Boitier, Andrea Sgambelluri, Nicola Sambo, Alessio Giorgetti, Sebastien Bigo, and Yvan Pointurier. "Interactive Automated Fiber Type Identification." In 2018 European Conference on Optical Communication (ECOC). IEEE, 2018. http://dx.doi.org/10.1109/ecoc.2018.8535384.

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Reports on the topic "Automated identification"

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Little, L. M. Automated Identification of Human Incursion. Office of Scientific and Technical Information (OSTI), July 2019. http://dx.doi.org/10.2172/1558329.

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Fuccillo, Nancy E. Prototype Automated Identification System for Parachutes. Fort Belvoir, VA: Defense Technical Information Center, November 1991. http://dx.doi.org/10.21236/ada242945.

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Tappen, D. V., E. Hayder, and L. W. Mooney. BESTSRCH: An Automated System for Identification of GC/MS Peaks. Fort Belvoir, VA: Defense Technical Information Center, October 1988. http://dx.doi.org/10.21236/ada418603.

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Wilson, D. M. III. Automated approach for the identification of functionally-relevant small molecule inhibitors. Office of Scientific and Technical Information (OSTI), February 2000. http://dx.doi.org/10.2172/15001996.

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Ballou, Susan, Anthony Clay, Joi Dickerson, Mike Garris, Peter T. Higgins, Elizabeth Fong, Janet Hoin, et al. Writing Guidelines for Requests for Proposals for Automated Fingerprint Identification Systems. National Institute of Standards and Technology, April 2013. http://dx.doi.org/10.6028/nist.sp.1155.

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Bartsch, Michael S., Sara Bird, Steven Branda, Harrison Edwards, Harikrishnan Jayamohan, Raga Krishnakumar, Kamlesh Patel, Joseph S. Schoeniger, and Anupama Sinha. Real-Time Automated Pathogen Identification by Enhanced Ribotyping (RAPIER) LDRD Final Report. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1481615.

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Ballou, Susan, Anthony Clay, Joi Dickerson, Mike Garris, Peter T. Higgins, Janet Hoin, Lisa Jackson, et al. Writing Guidelines to Develop a Memorandum of Understanding for Interoperable Automated Fingerprint Identification Systems. National Institute of Standards and Technology, May 2013. http://dx.doi.org/10.6028/nist.sp.1156.

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St. Germain, Shawn, Ronald Boring, Thomas Ulrich, Brandon Rice, and Ahmad Y. Al Rashdan. Automated Work Packages: Radio Frequency Identification, Bluetooth Beacons, and Video Applications in the Nuclear Power Industry. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1472110.

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Trenkle, Allen H. Evaluation of Rumen Boluses as an Electronic Identification System for Cattle in an Automated Data Collection System. Ames (Iowa): Iowa State University, January 2006. http://dx.doi.org/10.31274/ans_air-180814-591.

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Downard, Alicia, Stephen Semmens, and Bryant Robbins. Automated characterization of ridge-swale patterns along the Mississippi River. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40439.

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Abstract:
The orientation of constructed levee embankments relative to alluvial swales is a useful measure for identifying regions susceptible to backward erosion piping (BEP). This research was conducted to create an automated, efficient process to classify patterns and orientations of swales within the Lower Mississippi Valley (LMV) to support levee risk assessments. Two machine learning algorithms are used to train the classification models: a convolutional neural network and a U-net. The resulting workflow can identify linear topographic features but is unable to reliably differentiate swales from other features, such as the levee structure and riverbanks. Further tuning of training data or manual identification of regions of interest could yield significantly better results. The workflow also provides an orientation to each linear feature to support subsequent analyses of position relative to levee alignments. While the individual models fall short of immediate applicability, the procedure provides a feasible, automated scheme to assist in swale classification and characterization within mature alluvial valley systems similar to LMV.
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