Relevant bibliographies by topics / Automatic Identification System

Contents

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

Academic literature on the topic 'Automatic Identification System'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Automatic Identification System"

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Johannsen, K. G. "Automatic transmitter identification system." IEEE Transactions on Broadcasting 38, no. 2 (June 1992): 127–32. http://dx.doi.org/10.1109/11.142664.

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Li, Kung-Pu. "Automatic language identification/verification system." Journal of the Acoustical Society of America 104, no. 1 (July 1998): 31. http://dx.doi.org/10.1121/1.424049.

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Komarov, V. V., and S. A. Garagan. "Functional Requirements for the Automatic System of Vehicle Accident Identification." Izvestiya MGTU MAMI 5, no. 2 (January 20, 2011): 15–23. http://dx.doi.org/10.17816/2074-0530-69824.

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There is a necessity to develop the functional requirements to the automatic system for vehicle accident identification in the automated systems of emergency response to the accident. The list of accident types, which are subject to automatic identification is substantiated. Proposals on the design and use of some sensors, in particular, vehicle element deformation sensors are presented.
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Pech-Pacheco, José Luis. "Automatic system for phytoplanktonic algae identification." Limnetica 20, no. 1 (June 15, 2001): 143–58. http://dx.doi.org/10.23818/limn.20.14.

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Poo, Danny C. C., Shaojiang Cai, and James T. L. Mah. "UASIS: Universal Automatic SNP Identification System." BMC Genomics 12, Suppl 3 (2011): S9. http://dx.doi.org/10.1186/1471-2164-12-s3-s9.

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Steinholtz, B., and K. Walden. "Automatic Identification of Software System Differences." IEEE Transactions on Software Engineering SE-13, no. 4 (April 1987): 493–97. http://dx.doi.org/10.1109/tse.1987.233186.

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Schumann-Bischoff, Jan, Stefan Luther, and Ulrich Parlitz. "Nonlinear system identification employing automatic differentiation." Communications in Nonlinear Science and Numerical Simulation 18, no. 10 (October 2013): 2733–42. http://dx.doi.org/10.1016/j.cnsns.2013.02.017.

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Dahana, U., and R. O. S. Gurning. "Maritime Aerial Surveillance: Integration Manual Identification System to Automatic Identification System." IOP Conference Series: Earth and Environmental Science 557 (September 15, 2020): 012014. http://dx.doi.org/10.1088/1755-1315/557/1/012014.

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Jin, Zuo Tao, and Yun Wang. "Research of a Novel Automatic Management System for Material Storages." Advanced Materials Research 977 (June 2014): 476–79. http://dx.doi.org/10.4028/www.scientific.net/amr.977.476.

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According to the management efficiency of material storages, a design scheme of automatic management system is designed based on radio frequency identification devices. The hardware platform of the system is realized based on nRF2401 and embedded computer technology. The software design of each subsystem is achieved by using the middleware technology of radio frequency identification devices and embedded software development and database programming technology. The field trial experiments and the laboratory testing results indicate that the system can realize effectively the automatic identification and management of material storages. It can improve the operation efficiency and greatly enhance the automation level of material storages management and save the cost of management.
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Hussein, Enas. "Fingerprint Identification using Multiwavelet Transform." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 26, 2021): 131–38. http://dx.doi.org/10.55562/jrucs.v23i2.485.

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identification systems have been developed to achieve automatic identification of a person based on his physiological or behavioral characteristics. Biometric systems are critical in a wide range of applications such as banking system, E-commerce, smart cards, and access control to secure system. Automatic fingerprint identification is one of the most reliable biometric system, which is used for identifying persons. This study, aim is to design a fingerprint identification system, which is capable of identifying a fingerprint with high level of accuracy. Therefore, this system can be applied to a wide range of forensic applications. The proposed algorithm based upon multiwavelet transform (one level) as feature extraction and minimum distance classifier ( Euclidean distance) to make automatic fingerprint identification . The identification accuracy of this algorithm has been found to be 72%.
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Dissertations / Theses on the topic "Automatic Identification System"

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Nassar, Diaa Eldin M. "A prototype automatic dental identification system (ADIS)." Morgantown, W. Va. : [West Virginia University Libraries], 2001. http://etd.wvu.edu/templates/showETD.cfm?recnum=1977.

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Thesis (M.S.)--West Virginia University, 2001.
Title from document title page. Document formatted into pages; contains v, 72 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 70-72).
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Burrell, Tiffany. "System Identification in Automatic Database Memory Tuning." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1583.

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Databases are very complex systems that require database system administrators to perform system tuning in order to achieve optimal performance. Memory tuning is vital to the performance of a database system because when the database workload exceeds its memory capacity, the results of the queries running on a system are delayed and can cause substantial user dissatisfaction. In order to solve this problem, this thesis presents a platform modeled after a closed control feedback loop to control the level of multi-query processing. Utilizing this platform provides two key assets. First, the system identification is acquired, which is one of two crucial steps involved in developing a closed feedback loop. Second, the platform provides a means to experimentally study database tuning problem and verify the effectiveness of research ideas related to database performance.
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Mårtensson, Jonas. "Geometric analysis of stochastic model errors in system identification." Doctoral thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4506.

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Models of dynamical systems are important in many disciplines of science, ranging from physics and traditional mechanical and electrical engineering to life sciences, computer science and economics. Engineers, for example, use models for development, analysis and control of complex technical systems. Dynamical models can be derived from physical insights, for example some known laws of nature, (which are models themselves), or, as considered here, by fitting unknown model parameters to measurements from an experiment. The latter approach is what we call system identification. A model is always (at best) an approximation of the true system, and for a model to be useful, we need some characterization of how large the model error is. In this thesis we consider model errors originating from stochastic (random) disturbances that the system was subject to during the experiment. Stochastic model errors, known as variance-errors, are usually analyzed under the assumption of an infinite number of data. In this context the variance-error can be expressed as a (complicated) function of the spectra (and cross-spectra) of the disturbances and the excitation signals, a description of the true system, and the model structure (i.e., the parametrization of the model). The primary contribution of this thesis is an alternative geometric interpretation of this expression. This geometric approach consists in viewing the asymptotic variance as an orthogonal projection on a vector space that to a large extent is defined from the model structure. This approach is useful in several ways. Primarily, it facilitates structural analysis of how, for example, model structure and model order, and possible feedback mechanisms, affect the variance-error. Moreover, simple upper bounds on the variance-error can be obtained, which are independent of the employed model structure. The accuracy of estimated poles and zeros of linear time-invariant systems can also be analyzed using results closely related to the approach described above. One fundamental conclusion is that the accuracy of estimates of unstable poles and zeros is little affected by the model order, while the accuracy deteriorates fast with the model order for stable poles and zeros. The geometric approach has also shown potential in input design, which treats how the excitation signal (input signal) should be chosen to yield informative experiments. For example, we show cases when the input signal can be chosen so that the variance-error does not depend on the model order or the model structure. Perhaps the most important contribution of this thesis, and of the geometric approach, is the analysis method as such. Hopefully the methodology presented in this work will be useful in future research on the accuracy of identified models; in particular non-linear models and models with multiple inputs and outputs, for which there are relatively few results at present.
QC 20100810
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Myklebust, Andreas. "Closed Loop System Identification of a Torsion System." Thesis, Linköping University, Department of Electrical Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-17531.

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A model is developed for the Quanser torsion system available at Control Systems Research Laboratory at Chulalongkorn University. The torsion system is a laboratory equipment that is designed for the study of position control. It consists of a DC motor that drives three inertial loads that are coupled in series with the motor, and where all components are coupled to each other through torsional springs.

Several nonlinearities are observed and the most significant one is an offset in the input signal, which is compensated for. Experiments are carried out under feedback as the system is marginally stable. Different input signals are tested and used for system identification. Linear black-box state-space models are then identified using PEM, N4SID and a subspace method made for closed-loop identification, where the last two are the most successful ones. PEM is used in a second step and successfully enhances the parameter estimates from the other algorithms.

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Lyzell, Christian. "Initialization Methods for System Identification." Licentiate thesis, Linköping University, Linköping University, Automatic Control, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51688.

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In the system identification community a popular framework for the problem of estimating a parametrized model structure given a sequence of input and output pairs is given by the prediction-error method. This method tries to find the parameters which maximize the prediction capability of the corresponding model via the minimization of some chosen cost function that depends on the prediction error. This optimization problem is often quite complex with several local minima and is commonly solved using a local search algorithm. Thus, it is important to find a good initial estimate for the local search algorithm. This is the main topic of this thesis.

The first problem considered is the regressor selection problem for estimating the order of dynamical systems. The general problem formulation is difficult to solve and the worst case complexity equals the complexity of the exhaustive search of all possible combinations of regressors. To circumvent this complexity, we propose a relaxation of the general formulation as an extension of the nonnegative garrote regularization method. The proposed method provides means to order the regressors via their time lag and a novel algorithmic approach for the \textsc{arx} and \textsc{lpv-arx} case is given.

 

Thereafter, the initialization of linear time-invariant polynomial models is considered. Usually, this problem is solved via some multi-step instrumental variables method. For the estimation of state-space models, which are closely related to the polynomial models via canonical forms, the state of the art estimation method is given by the subspace identification method. It turns out that this method can be easily extended to handle the estimation of polynomial models. The modifications are minor and only involve some intermediate calculations where already available tools can be used. Furthermore, with the proposed method other a priori information about the structure can be readily handled, including a certain class of linear gray-box structures. The proposed extension is not restricted to the discrete-time case and can be used to estimate continuous-time models.

 

The final topic in this thesis is the initialization of discrete-time systems containing polynomial nonlinearities. In the continuous-time case, the tools of differential algebra, especially Ritt's algorithm, have been used to prove that such a model structure is globally identifiable if and only if it can be written as a linear regression model. In particular, this implies that once Ritt's algorithm has been used to rewrite the nonlinear model structure into a linear regression model, the parameter estimation problem becomes trivial. Motivated by the above and the fact that most system identification problems involve sampled data, a version of Ritt's algorithm for the discrete-time case is provided. This algorithm is closely related to the continuous-time version and enables the handling of noise signals without differentiations.

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Barenthin, Märta. "On input design in system identification for control." Licentiate thesis, KTH, School of Electrical Engineering (EES), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4000.

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There are many aspects to consider when designing system identification experiments in control applications. Input design is one important issue. This thesis considers input design both for identification of linear time-invariant models and for stability validation.

Models obtained from system identification experiments are uncertain due to noise present in measurements. The input spectrum can be used to shape the model quality. A key tool in input design is to introduce a linear parametrization of the spectrum. With this parametrization a number of optimal input design problems can be formulated as convex optimization programs. An Achilles' heel in input design is that the solution depends on the system itself, and this problem can be handled by iterative procedures where the input design is based on a model of the system. Benefits of optimal input design are quantified for typical industrial applications. The result shows that the experiment time can be substantially shortened and that the input power can be reduced.

Another contribution of the thesis is a procedure where input design is connected to robust control. For a certain system structure with uncertain parameters, it is shown that the existence of a feedback controller that guarantees a given performance specification can be formulated as a convex optimization program. Furthermore, a method for input design for multivariable systems is proposed. The constraint on the model quality is transformed to a linear matrix inequality using a separation of graphs theorem. The result indicates that in order to obtain a model suitable for control design, it is important to increase the power of the input in the low-gain direction of the system relative to the power in the high-gain direction.

A critical issue when validating closed-loop stability is to obtain an accurate estimate of the maximum gain of the system. This problem boils down to finding the input signal that maximizes the gain. Procedures for gain estimation of nonlinear systems are proposed and compared. One approach uses a model of the system to design the optimal input. In other approaches, no model is required, and the system itself determines the optimal input sequence in repeated experiments.

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Lindstrom, Tedric R. "Using automatic identification system technology to improve maritime border security." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44607.

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Approved for public release; distribution is unlimited
Our coastal waters are the United States’ most open and vulnerable borders. This vast maritime domain harbors critical threats from terrorism, criminal activities, and natural disasters. Maritime borders pose significant security challenges, as nefarious entities have used small boats to conduct illegal activities for years, and they continue to do so today. Illegal drugs, money, weapons, and migrants flow both directions across our maritime borders, as vessels can quickly complete these transits without detection. To what extent could Automatic Identification System (AIS) technology improve border security against the small vessel threat? This thesis provides an overview of existing AIS systems and reviews the maritime border security effectiveness and cost benefit impacts of potential AIS equipment carriage requirements. We compared and analyzed policy options and reviewed implementation issues and concerns. Our conclusions are that the Department of Homeland Security should implement a regulation for all vessels, regardless of size, to install and broadcast Class A or Class B AIS when conducting international voyages. The proposed regulation would expand the existing Small Vessel Reporting System to a mandatory program wherein mariners are required to preregister and file float plans prior to conducting an international voyage. This proposed action provides direct support to three of the five basic homeland security missions: prevent terrorism and enhance security, secure and manage our borders, and enforce and administer our immigration laws.
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Elias, Jorge Rodrigo Marreiros Duarte. "Sistema de apoio à navegação baseado em automatic identification system." Master's thesis, Faculdade de Ciências e Tecnologia, 2010. http://hdl.handle.net/10362/6019.

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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e Computadores
A segurança marítima é indispensável para as embarcações de transporte de mercadorias e de passageiros devido ao intenso tráfego e aos perigos inerentes ao mar. Este projecto nasceu da necessidade da TRANSTEJO e SOFLUSA terem a bordo dos navios de transporte de passageiros da sua frota um sistema de apoio à navegação e em terra uma ferramenta que ajude à gestão da frota. Foram desenvolvidas duas aplicações para cumprir com os objectivos pretendidos: o SINAIS, sistema de apoio à navegação de bordo que serve para a monitorização da viagem e registo de dados de viagem; o MAIS, uma ferramenta de monitorização do tráfego marítimo e análise dos registos de viagem. Ambas as aplicações recolhem os dados de navegação a partir do sistema de partilha de informações náuticas Automatic Identification System. Foi ainda desenvolvida uma aplicação de suporte, o Calibrador de Mapas, para referenciar geograficamente e preparar as cartas náuticas para o SINAIS e para o MAIS. O SINAIS foi instalado num navio da Soflusa, tendo-se verificado ser um importante complemento aos restantes equipamentos de bordo. O MAIS foi testado como ferramenta de gestão de frotas pela Soflusa e Transtejo. Ambas as aplicações permitem o aumento da segurança marítima nas embarcações.
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Qin, Tiantian. "Identification of Reliable Cues for an Automatic Deception Detection System." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/194385.

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An automatic deception detection system (ADDS) is to detect deceptive human behavior with machine extractable evidences (i.e., cues). One of the most prominent challenges for building a ADDS is the availability of reliable cues. This study represents one of the first attempts to address the system's reliability by identifying the set of reliable cues in order to improve the system performance (detection accuracy).This study addresses two critical challenges of existing machine cues, irreproducibility and inconsistency. First, in order to mitigate the irreproducibility, the study introduces a set of machine measurable cues to estimate the commonality of related machine cues. These more reproducible cues are referred to as the macro cues which can be applied for automatic pattern recognition. Second, in order to address the consistency, the study separates cues based on the controllability, and defines the strategic cues as those can easily be manipulated by deceivers during interaction. The strategic cues fluctuate during deception and thus are less consistently reliable as predictors for the ADDS. On the contrary, the nonstrategic cues are more consistent. This study also considers other moderator effects that influencing the ADDS performance: time and the condition of interviewer's immediacy (ERIMD).The macro cues are automatically estimated from the micro cues based on the predefined relational models. The empirical data support the relationship models between macro and micro cues. Results show that macro cues mitigate the irreproducibility problem by reducing the variability in the single cues. However, the results also show that using macro cues as predictors in the discriminant analysis does not perform better than micro cues, and thus imply the needs to adjust weights of important components when constructing the macro cues. In terms of the consistent cues, results show that the nonstrategic cues are relatively more consistent than strategic ones in ADDS performance. Furthermore, the study suggests that particular detection methods must be tailored according to the feature of strategic and nonstrategic cues. The findings have many potential implications. One is to use the macro cues to recognize the dynamic patterns in deceptive behaviors. Specifically, truthtellers increase the certainty, immediacy, and tend to decrease the cognitive load; but deceivers behave the opposite. The other is to rely on the characteristics of strategic cues to manipulate the communication environment to improve the ADDS performance. This concept is also referred to as the Proactive Deception Detection (PDD). In the current study, the interviewer's immediacy is a controllable environment factor for PDD. The high ERIMD increase the system performance because it has higher overhead added to the deceptive behavior to trigger more abnormal cues. In sum, methods and results of this study have multiple impacts in information assurance and human-computer interaction.
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Stefani, Massimo <1989&gt. "An object detection system for automatic document reorientation and identification." Master's Degree Thesis, Università Ca' Foscari Venezia, 2020. http://hdl.handle.net/10579/16234.

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In this document, we present an object detection system for italian documents. Unlike similar systems which use a single deep-learning solution, this system employs different solutions for a fast and accurated detection. The first is an image segmentation module which process an acquired-scanner image and find every important artificats. The second is a custom CNN for detect every artificat's rotation and then use the information for set the document to the upright (this is important for read the document-text content). The third is a simple CNN for detect each upright element. We present the algorithm used in the first part and the training methods for both types of networks. We also perform analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting documents.
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Books on the topic "Automatic Identification System"

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Fuzzy control and identification. Hoboken, N.J: Wiley, 2010.

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Petre, Stoica, ed. System identification. Hemel Hempstead: Prentice-Hall, 1989.

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1950-, Chase Rory L., ed. Automatic identification: An IFS executive briefing. Bedford, England: IFS Publications, 1988.

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Mulley, Raymond. Control system documentation: Applying symbols and identification. Research Triangle Park, N.C: Instrument Society of America, 1994.

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Kadour, Najim, ed. Advanced process identification and control. New York: M. Dekker, 2002.

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Sung, Su Whan. Process identification and PID control. Singapore: Wiley, 2009.

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System identification and robust control: A case study approach. London: Springer, 1996.

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Tøffner-Clausen, Steen. System Identification and Robust Control: A Case Study Approach. London: Springer London, 1996.

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1955-, Gu Guoxiang, ed. Control-oriented system identification: An H [infinity] approach. New York: John Wiley & Sons, 2000.

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1927-, Sinha N. K., and Prasada Rao Ganti 1942-, eds. Identification of continuous-time systems: Methodology and computer implementation. Dordrecht: Kluwer Academic, 1991.

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Book chapters on the topic "Automatic Identification System"

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Castle, Jennifer L., and David F. Hendry. "Automatic Selection for Non-linear Models." In System Identification, Environmental Modelling, and Control System Design, 229–50. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1_12.

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Ludwig, Rafael, and Johannes Gerstmayr. "Automatic Parameter Identification for Mechatronic Systems." In Multibody System Dynamics, Robotics and Control, 193–212. Vienna: Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-1289-2_12.

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Chittaragi, Nagaratna B., Asavari Limaye, N. T. Chandana, B. Annappa, and Shashidhar G. Koolagudi. "Automatic Text-Independent Kannada Dialect Identification System." In Advances in Intelligent Systems and Computing, 79–87. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3338-5_8.

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Fernández-Valdivia, J., P. Castillo, and A. Gómez-Barcina. "Use of Image Analysis for Automatic Recognition of Nematodes." In Nematode Identification and Expert System Technology, 293–99. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-9016-9_28.

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Soorajkumar, R., G. N. Girish, Pravin B. Ramteke, Shreyas S. Joshi, and Shashidhar G. Koolagudi. "Text-Independent Automatic Accent Identification System for Kannada Language." In Proceedings of the International Conference on Data Engineering and Communication Technology, 411–18. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1678-3_40.

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Steidel, Matthias, Arne Lamm, Sebastian Feuerstack, and Axel Hahn. "Correcting the Destination Information in Automatic Identification System Messages." In Business Information Systems Workshops, 496–507. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36691-9_42.

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Pawar, Mahesh Y., and Shrinivas Mahajan. "Automatic Tonic (Shruti) Identification System for Indian Classical Music." In Advances in Intelligent Systems and Computing, 733–42. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3600-3_70.

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Tichavska, Miluše, Francisco Cabrera, Beatriz Tovar, and Víctor Araña. "Use of the Automatic Identification System in Academic Research." In Computer Aided Systems Theory – EUROCAST 2015, 33–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27340-2_5.

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Gong, Cheng, and Wenjun Fan. "A New Automatic Train Identification System Based on RFID." In Lecture Notes in Computer Science, 463–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24396-7_37.

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Chen, Yuehui, Bo Yang, and Jin Zhou. "Automatic Design of Hierarchical RBF Networks for System Identification." In Lecture Notes in Computer Science, 1191–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-36668-3_159.

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Conference papers on the topic "Automatic Identification System"

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Savic, M., E. Acosta, and S. K. Gupta. "An automatic language identification system." In [Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1991. http://dx.doi.org/10.1109/icassp.1991.150462.

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Li, C. James, and Tung-Yung Huang. "Nonlinear System Identification by Automatic Localized Modeling." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-0233.

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Abstract This paper describes two automated methods to identify binary and continuous systems from their operating data. Both methods employ data clustering and localized modeling. For binary functions, the Karnaugh map is employed to transform an arbitrarily high dimensional distribution into a two-dimensional one where an elliptical clustering algorithm can be performed to identify a set of local neural net models to approximate the data. For real continuous functions, the input space is partitioned into regions that are small enough that a simple neural network can approximate the data well in each region. The partitioning method is inspired by an automatic mesh generation algorithm for Finite Element Method (FEM). A 7-bit Exclusive-OR (XOR), a Van der Pol oscillator, and a pendulum, respectively, are used to test the two proposed algorithms, and they are found to be satisfactory in generating models that can simulate the systems well.
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Thumwarin, Pitak. "An Automatic System for Firearm Identification." In 2008 International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2008. http://dx.doi.org/10.1109/iscit.2008.4700162.

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Youssif, A. A., E. A. Sarhan, and W. H. El Behaidy. "Development of automatic speaker identification system." In Proceedings of the Twenty-First National Radio Science Conference. IEEE, 2004. http://dx.doi.org/10.1109/nrsc.2004.240502.

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Chuan, Zun Liang, Choong-Yeun Liong, Abdul Aziz Jemain, and Nor Azura Md Ghani. "An efficient automatic firearm identification system." In PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES. AIP Publishing LLC, 2014. http://dx.doi.org/10.1063/1.4882634.

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Sekkate, Sara, Mohammed Khalil, and Abdellah Adib. "An improved automatic aircraft identification system." In 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2016. http://dx.doi.org/10.1109/wincom.2016.7777189.

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Mon, Su Larb, Thi Thi Zin, Pyke Tin, and Ikuo Kobayashi. "Video-based Automatic Cattle Identification System." In 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE). IEEE, 2022. http://dx.doi.org/10.1109/gcce56475.2022.10014109.

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Iwamoto, Takashi. "Radiometric Identification of Emitters in the Automatic Identification System." In MILCOM 2013 - 2013 IEEE Military Communications Conference. IEEE, 2013. http://dx.doi.org/10.1109/milcom.2013.83.

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Li, Chonghua. "Automatic vehicle identification (AVI) system based on RFID." In 2010 International Conference on Anti-Counterfeiting, Security and Identification (2010 ASID). IEEE, 2010. http://dx.doi.org/10.1109/icasid.2010.5551336.

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Hall, John, Jordan Lee, Joseph Benin, Christopher Armstrong, and Henry Owen. "IEEE 1609 Influenced Automatic Identification System (AIS)." In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). IEEE, 2015. http://dx.doi.org/10.1109/vtcspring.2015.7145867.

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Reports on the topic "Automatic Identification System"

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Kress, Marin, Patricia DiJoseph, Patrick Donohue, and Kenneth Mitchell. Automatic Identification System Analysis Package user guide. Engineer Research and Development Center (U.S.), July 2022. http://dx.doi.org/10.21079/11681/44783.

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Abstract:
The Automatic Identification System Analysis Package (AISAP) enables acquisition, statistical analysis, and visualization of Automatic Identification System (AIS) data from historic vessel position reports. The web-based AISAP software allows the users to choose which data they want to examine for a specific geographic area, time period, and vessel type(s). Built-in features provide vessel characteristics, arrival and departure information within a geofenced area, vessel travel time between two locations, vessel track line plots, and relative density plots of AIS data reports. AISAP accesses the Nationwide Automatic Identification System database hosted by the United States Coast Guard. This user manual provides training exercises for users to follow to familiarize themselves with AISAP procedures and workflows. These training exercises also provide examples of AISAP products.
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Roberts, II, Gaspari Robert G., Webster Gregory, MacKay Matthew R., Mall Donald M., Nelson Warren O., and Mark E. Automatic Identification System (AIS) Collection and Reach-back System: System Description. Fort Belvoir, VA: Defense Technical Information Center, August 2014. http://dx.doi.org/10.21236/ada611418.

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Wendelberger, James, Elizabeth Kelly, and Kimberly Kaufeld. Automatic Potential Crack Identification from Wide Area Measurement System (WAMS) Data. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1764869.

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Scully, Brandan. Tidal analysis and arrival process mining using Automatic Identification System (AIS) data. Coastal and Hydraulics Laboratory (U.S.), February 2017. http://dx.doi.org/10.21079/11681/21465.

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Day, Christopher. Roadway System Assessment Using Bluetooth-Based Automatic Vehicle Identification Travel Time Data. Purdue University, December 2012. http://dx.doi.org/10.5703/1288284314988.

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Wendelberger, James, Elizabeth Kelly, Kimberly Kaufeld, Michael Martinez-Rodriguez, and Emmanual Perez. Automatic Potential Crack Identification from Wide Area Measurement System (WAMS) Data with Software Demonstration. Office of Scientific and Technical Information (OSTI), February 2021. http://dx.doi.org/10.2172/1766954.

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Schultz, Martin, and Scott Bourne. Using Automatic Identification System (AIS) data to assess collision and grounding risk in U.S. coastal ports. Engineer Research and Development Center (U.S.), July 2019. http://dx.doi.org/10.21079/11681/33243.

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Schultz, Martin, and Scott Bourne. Using Automatic Identification System (AIS) data to assess collision and grounding risk in U.S. coastal ports. Engineer Research and Development Center (U.S.), July 2019. http://dx.doi.org/10.21079/11681/33343.

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DiJoseph, Patricia, Kenneth Mitchell, Brian Tetreault, and Jonathan Marshall. Inland Marine Transportation System travel time atlas via Automatic Identification System (AIS) data : Ohio River, Upper Mississippi River, and Illinois River. Engineer Research and Development Center (U.S.), September 2019. http://dx.doi.org/10.21079/11681/34083.

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Thomas, Michael J. Combining Facial Recognition, Automatic License Plate Readers and Closed Circuit Television to Create an Interstate Identification System for Wanted Subjects. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1009302.

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