Academic literature on the topic 'Sequential identification'

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Journal articles on the topic "Sequential identification"

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Larsson, Roger, and Martin Enqvist. "Sequential Aerodynamic Model Parameter Identification." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1413–18. http://dx.doi.org/10.3182/20120711-3-be-2027.00293.

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Perlini, Arthur H., and Andrew D. Silvaggio. "Eyewitness Misidentification: Single vs Double-Blind Comparison of Photospread Administration." Psychological Reports 100, no. 1 (February 2007): 247–56. http://dx.doi.org/10.2466/pr0.100.1.247-256.

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Interactive effects of photospread procedures (blind administration or has knowledge of suspect identity) and presentation techniques (sequential or simultaneous photospreads) on false eyewitness identification rates were examined. Of 117 subjects (57 men and 60 women), 87 served as a witness sample and 30 as a photospread administrator sample. Witnesses viewed a 20-sec. mock robbery video prior to viewing a photospread of six photographs either simultaneously or sequentially. Half of the administrators, prior to displaying the photospreads, were made aware of the photograph of the designated suspect (single-blind condition). Consistent with previous findings, sequential presentation was associated with both lower overall false identification rates as well as lower suspect designated-misidentification rates. In each case, there was evidence that administrators' knowledge increased false identification rates more in simultaneous than in sequential presentation conditions. Implications for administrations by police investigators are discussed.
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Rai, Alka, and Benjamin Nandy. "Employer brand to leverage employees’ intention to stay through sequential mediation model: evidence from Indian power sector." International Journal of Energy Sector Management 15, no. 3 (March 1, 2021): 551–65. http://dx.doi.org/10.1108/ijesm-10-2019-0024.

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Purpose This study aims to explain the linkage between employer branding and employee retention; a sequential mediation is hypothesized, where it is proposed that the relationship between employer branding and employee retention is sequentially mediated by person–organization fit (P-O fit) and organizational identification. Design/methodology/approach The sample belongs to 224 executive-level employees of the Indian power sector organization. The sequential mediation model is tested by using SPSS macro command of Preacher and Hayes. Findings The findings established that the relationship between employer brand and employees’ intention to stay is sequentially mediated by P-O fit and organizational identification. Practical implications The findings emphasize the role of employer brand on constructs such as P-O fit, organizational identification and intention to stay. In addition, the established mechanism emphasizes the role of P-O fit to realize the benefits such as organizational identification and employee retention. Originality/value Internal branding efforts may have a major impact on workforce attitude and behavior including engagement, job performance and retention. “Yet studies of the positive impact of employer brand on employee attitudes and behaviors, or of the factors that shape employer brand, are rare” (Charbonnier-Voirin et al., 2017, p. 2). Along the line of such gap, this study has taken up to test the unexplored sequential mediation mechanism between employer brand and employees’ intention to stay through P-O fit and organizational identification.
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Hwang, D. M., and C. T. Kelley. "Sequential Quadratic Programming for Parameter Identification Problems." IFAC Proceedings Volumes 22, no. 4 (June 1989): 259–63. http://dx.doi.org/10.1016/s1474-6670(17)53554-5.

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Yun, Chung-Bang, Hyeong-Jin Lee, and Chang-Gun Lee. "Sequential Prediction-Error Method for Structural Identification." Journal of Engineering Mechanics 123, no. 2 (February 1997): 115–22. http://dx.doi.org/10.1061/(asce)0733-9399(1997)123:2(115).

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Br�sio, Ana S. R., Andrey Romanenko, and Nat�rcia C. P. Fernandes. "Using Sequential Quadratic Programming for System Identification." Applied Mathematics & Information Sciences 9, no. 1 (January 1, 2015): 19–26. http://dx.doi.org/10.12785/amis/090103.

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Lin, F. B., Z. P. Bažant, J. C. Chern, and A. H. Marchertas. "Concrete model with normality and sequential identification." Computers & Structures 26, no. 6 (January 1987): 1011–25. http://dx.doi.org/10.1016/0045-7949(87)90118-0.

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Chen, Lin. "Graph Isomorphism and Identification Matrices: Sequential Algorithms." Journal of Computer and System Sciences 59, no. 3 (December 1999): 450–75. http://dx.doi.org/10.1006/jcss.1999.1642.

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Lai, Tao, Ting-Hua Yi, and Hong-Nan Li. "Parametric study on sequential deconvolution for force identification." Journal of Sound and Vibration 377 (September 2016): 76–89. http://dx.doi.org/10.1016/j.jsv.2016.05.013.

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Choi, J. "Sequential loop closing identification of multivariable process models." Computers & Chemical Engineering 24, no. 2-7 (July 15, 2000): 809–14. http://dx.doi.org/10.1016/s0098-1354(00)00386-0.

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Dissertations / Theses on the topic "Sequential identification"

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Kuznetsova, Yulia. "Analysis and Evaluation of Sequential Redundancy Identification Algorithms." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-51105.

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This thesis has a goal of analysing different methods used for identifying redundant faults in synchronous sequential circuits as a part of reducing the complexity of ATPG algorithms and minimizing the test sets. It starts with an overview of various faults which occur in digital circuits of different types and moves on to the common testing methods used for fault detection. As it is not possible to perform an exhaustive search in order to detect every possible fault in any given circuit due to time and power consumption issues, there are certain needs for minimizing the set of tests which detects the existing faults. Therefore discovering the untestable and redundant faults is so important when testing. The overview of both classical and novel methods for detecting untestable and redundant faults is presented followed by the analysis of the results and the benefits each of these methods promises.
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Flowe, Heather D. "The effect of lineup member similarity on recognition accuracy in simultaneous and sequential lineups." Diss., Connected to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2005. http://wwwlib.umi.com/cr/ucsd/fullcit?p3189995.

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Thesis (Ph. D.)--University of California, San Diego, 2005.
Title from first page of PDF file (viewed March 1, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references ( p. 113-116).
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Topp, Lisa Dawn. "An evaluation of eyewitness decision making strategies for simultaneous and sequential lineups." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2008. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Annis, Jeffrey Scott. "A Model of Positive Sequential Dependencies in Judgments of Frequency." Scholar Commons, 2013. http://scholarcommons.usf.edu/etd/4626.

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Positive sequential dependencies occur when the response on the current trial n is positively correlated with the response on trial n-1. This was recently observed in a Judgment of Frequency (JOF) task (Malmberg and Annis, 2011). A model of positive sequential dependencies was developed in the REM framework (Shiffrin & Steyvers, 1997) by assuming that features that represent the current test item in a retrieval cue carry over from the previous retrieval cue. To assess the model, we sought a set of data that allows us to distinguish between frequency similarity and item similarity. Therefore, we chose to use a JOF task in which we manipulated the item similarity of the stimuli by presenting either landscape photos (high similarity), or photos of everyday objects such as shoes, cars, etc (low similarity). Similarity was modeled by assuming either that the item representations share a proportion of features or by assuming that the exemplars from different stimulus classes vary in the distinctiveness or diagnosticity. The model fits indicated that the best way to model similarity was to assume that items share a proportions of features.
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YU, CHENGGANG. "A SUB-GROUPING METHODOLOGY AND NON-PARAMETRIC SEQUENTIAL RATIO TEST FOR SIGNAL VALIDATION." University of Cincinnati / OhioLINK, 2002. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1022258703.

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Batra, Sushil Baker Erich J. Lee Myeongwoo. "Identification of phenotypes in Caenorabhditis elegans on the basis of sequence similarity." Waco, Tex. : Baylor University, 2009. http://hdl.handle.net/2104/5325.

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Baghaee, Sajjad. "Identification And Localization On A Wireless Magnetic Sensor Network." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614447/index.pdf.

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This study focused on using magnetic sensors for localization and identification of targets with a wireless sensor network (WSN). A wireless sensor network with MICAz motes was set up utilizing a centralized tree-based system. The MTS310, which is equipped with a 2-axis magnetic sensor was used as the sensor board on MICAz motes. The use of magnetic sensors in wireless sensor networks is a topic that has gained limited attention in comparison to that of other sensors. Research has generally focused on the detection of large ferromagnetic targets (e.g., cars and airplanes). Moreover, the changes in the magnetic field intensity measured by the sensor have been used to obtain simple information, such as target direction or whether or not the target has passed a certain point. This work aims at understanding the sensing limitations of magnetic sensors by considering small-scale targets moving within a 30 cm radius. Four heavy iron bars were used as test targets in this study. Target detection, identification and sequential localization were accomplished using the Minimum Euclidean Distance (MED) method. The results show the accuracy of this method for this job. Different forms of sensor sensing region discretization were considered. Target identification was done on the boundaries of sensing regions. Different gateways were selected as entrance point for identification point and the results of them were compared with each other. An online ILS system was implemented and continuous movements of the ferromagnetic objects were monitored. The undesirable factors which affect the measurements were discussed and techniques to reduce or eliminate faulty measurements are presented. A magnetic sensor orientation detector and set/reset strap have been designed and fabricated. Orthogonal Matching Pursuit (OMP) algorithm was proposed for multiple sensors multiple target case in ILS systems as a future work. This study can then be used to design energy-efficient, intelligent magnetic sensor networks
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Meyer, Marcus. "Parameter identification problems for elastic large deformations - Part I: model and solution of the inverse problem." Universitätsbibliothek Chemnitz, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200901869.

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In this paper we discuss the identification of parameter functions in material models for elastic large deformations. A model of the the forward problem is given, where the displacement of a deformed material is found as the solution of a n onlinear PDE. Here, the crucial point is the definition of the 2nd Piola-Kirchhoff stress tensor by using several material laws including a number of material parameters. In the main part of the paper we consider the identification of such parameters from measured displacements, where the inverse problem is given as an optimal control problem. We introduce a solution of the identification problem with Lagrange and SQP methods. The presented algorithm is applied to linear elastic material with large deformations.
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Lindsten, Fredrik. "Particle filters and Markov chains for learning of dynamical systems." Doctoral thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97692.

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Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies.
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Ru, Jifeng. "Adaptive estimation and detection techniques with applications." ScholarWorks@UNO, 2005. http://louisdl.louislibraries.org/u?/NOD,285.

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Thesis (Ph. D.)--University of New Orleans, 2005.
Title from electronic submission form. "A dissertation ... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering and Applied Science"--Dissertation t.p. Vita. Includes bibliographical references.
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Books on the topic "Sequential identification"

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Liu, G. P. Stable sequential identification of continuous nonlinear dynamical systems by growing RBF networks. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1994.

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Brendstrup, Bjarne. Nonparametric identification and estimation of multi-unit, sequential, oral, ascending-price auctions with asymmetric bidders. Milan: Fondazione Eni Enrico Mattei, 2004.

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Cameron, April A. Cross-racial identification of a caucasian by an aboriginal: using a sequential photo line-up versus a simultaneous line-up. Sudbury, Ont: Laurentian University, 2006.

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Donkin, Chris, Babette Rae, Andrew Heathcote, and Scott D. Brown. Why Is Accurately Labeling Simple Magnitudes So Hard? A Past, Present, and Future Look at Simple Perceptual Judgment. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.6.

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Absolute identification is a deceptively simple task that has been the focus of empirical investigation and theoretical speculation for more than half a century. Since Miller’s (1956) seminal paper the puzzle of why people are severely limited in their capacity to accurately perform absolute identification has endured. Despite the apparent simplicity of absolute identification, many complicated and robust effects are observed in both response latency and accuracy, including capacity limitations, strong sequential effects and effects of the position of a stimulus within the set. Constructing a comprehensive theoretical account of these benchmark effects has proven difficult, and existing accounts all have shortcomings. We review classical empirical findings, as well as some newer findings that challenge existing theories. We then discuss a variety of theories, with a focus on the most recent proposals, make some broad conclusions about general classes of models, and discuss the challenges ahead for each class.
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Kronenberger, William G., and David B. Pisoni. Neurocognitive Functioning in Deaf Children with Cochlear Implants. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190880545.003.0016.

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Cochlear implantation restores some attributes of hearing and spoken language to prelingually deaf children. However, reduced access to auditory and spoken-language experiences for children with cochlear implants can alter the development of downstream neurocognitive functions such as sequential processing and self-regulatory language skills, which are critical building blocks for executive functioning. Executive functioning is the active regulation of cognitive, behavioral, and emotional processes in the service of planned, organized, controlled, goal-driven behavior. This chapter presents findings from two primary lines of research on the development of executive functioning in prelingually deaf, early implanted children with cochlear implants. The first is identification of specific executive function domains that are at risk for delay in children with cochlear implants compared to hearing children. The second is reciprocal influences of executive function and spoken-language skills throughout development in children and adolescents with cochlear implants.
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Book chapters on the topic "Sequential identification"

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Liu, G. P. "Sequential Nonlinear Identification." In Nonlinear Identification and Control, 27–52. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0345-5_2.

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Fernandez, Marcel, and Miguel Soriano. "Identification Algorithms for Sequential Traitor Tracing." In Progress in Cryptology - INDOCRYPT 2004, 414–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30556-9_32.

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Verde, Cristina, and Jorge Rojas. "Recursive Scheme for Sequential Leaks’ Identification." In Applied Condition Monitoring, 125–45. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55944-5_7.

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Jaber, Mohamad, and Chadi Barakat. "Enhancing Application Identification by Means of Sequential Testing." In NETWORKING 2009, 287–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01399-7_23.

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Kastler, Bruno. "MRI Sequential Segmental Analysis and Identification of Anomalies." In MRI of Cardiovascular Malformations, 41–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-30702-0_3.

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Zhu, Fangqi, Qilian Liang, and Zhen Zhong. "Sequential Modeling for Polyps Identification from the Vocal Data." In Lecture Notes in Electrical Engineering, 945–54. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6504-1_113.

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Homem, Rafael A., Thierry Le Bihan, Manda Yu, and Gary J. Loake. "Identification of S-Nitrosothiols by the Sequential Cysteine Blocking Technique." In Methods in Molecular Biology, 163–74. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3600-7_14.

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Yun, C.-B., C.-G. Lee, and H.-N. Cho. "Identification of Structural Dynamic Systems by Sequential Prediction Error Method." In Computational Stochastic Mechanics, 139–48. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3692-1_13.

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Klein, Rebecca, Thomas Schuster, and Anne Wald. "Sequential Subspace Optimization for Recovering Stored Energy Functions in Hyperelastic Materials from Time-Dependent Data." In Time-dependent Problems in Imaging and Parameter Identification, 165–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-57784-1_6.

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Wang, Xue, Qian He, and Dongyang Cai. "Sequential LOUD Test for Genuine and Dummy Warhead Identification Using MIMO Radar." In Lecture Notes in Electrical Engineering, 187–97. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08991-1_20.

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Conference papers on the topic "Sequential identification"

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Dubois, C., and M. Davy. "Harmonic tracking using sequential monte carlo." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628795.

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Yang, Jung-Min, and Seong Woo Kwak. "A Combined State Recovery Controller for Two Independent Asynchronous Sequential Machines." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.799-031.

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Jenkins, John, William Nick, Kaushik Roy, Albert Esterline, and Joel Bloch. "Author identification using Sequential Minimal Optimization." In SoutheastCon 2016. IEEE, 2016. http://dx.doi.org/10.1109/secon.2016.7506654.

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Rangarajan, R., R. Raich, and A. O. Hero. "Sequential design of experiments for a rayleigh inverse scattering problem." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628670.

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Ng, W., J. Li, S. Godsill, and J. Vermaak. "Tracking variable number of targets using sequential monte carlo methods." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628794.

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Vemula, M., and P. M. Djuric. "Multisensor fusion for target tracking using sequential monte carlo methods." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628797.

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Gautier, M., and C. Presse. "Sequential identification of base parameters of robots." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240408.

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Niu, Wenjuan, Zhendong Niu, Shiping Tang, Zhi Huang, Wei Wang, Xi Li, and Yaxin Chen. "A learning resource recommendation method combining user sequential interaction with collaborative filtering." In Modelling, Identification and Control / 827: Computational Intelligence. Calgary,AB,Canada: ACTAPRESS, 2015. http://dx.doi.org/10.2316/p.2015.827-015.

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"Session MA4b: New trends in sequential system identification." In 2010 44th Asilomar Conference on Signals, Systems and Computers. IEEE, 2010. http://dx.doi.org/10.1109/acssc.2010.5757466.

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Fang, Fang, and Chunming Ye. "Hybrid Traffic Identification Based on Sequential Pattern Mining." In 2018 5th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2018. http://dx.doi.org/10.1109/icisce.2018.00060.

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