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Journal articles on the topic 'Discrete time'

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Published: 4 June 2021
Last updated: 25 May 2024

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1

Zhu, Gaoyan, Lei Xiao, Bingzi Huo, and Peng Xue. "Photonic discrete-time quantum walks [Invited]." Chinese Optics Letters 18, no. 5 (2020): 052701. http://dx.doi.org/10.3788/col202018.052701.

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2

Richman, M. S., T. W. Parks, and R. G. Shenoy. "Discrete-time, discrete-frequency, time-frequency analysis." IEEE Transactions on Signal Processing 46, no. 6 (June 1998): 1517–27. http://dx.doi.org/10.1109/78.678465.

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3

P., Tymoshchuk. "SIMPLIFIED PARALLEL SORTING DISCRETE-TIME NEURAL NETWORK MODEL." Computer systems and network 2, no. 1 (March 23, 2017): 94–101. http://dx.doi.org/10.23939/csn2020.01.094.

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A model of parallel sorting neural network of discrete-time has been proposed. The model is described by system of difference equations and by step functions. The model is based on simplified neural circuit of discrete-time that identifies maximal/minimal values of input data and is described by difference equation and by step functions. A bound from above on a number of iterations required for reaching convergence of search process to steady state is determined. The model does not need a knowledge of change range of input data. In order to use the model a minimal difference between values of input data should be known. The network can process unknown input data with finite values, located in arbitrary unknown finite range. The network is characterized by moderate computational complexity and complexity of software implementation, any finite resolution of input data, speed,. Computing simulation results illustrating efficiency of the network are given. Keywords — Parallel sorting, neural network, difference equation, computational complexity, hardware implementation.
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4

Else, Dominic V., Christopher Monroe, Chetan Nayak, and Norman Y. Yao. "Discrete Time Crystals." Annual Review of Condensed Matter Physics 11, no. 1 (March 10, 2020): 467–99. http://dx.doi.org/10.1146/annurev-conmatphys-031119-050658.

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Experimental advances have allowed for the exploration of nearly isolated quantum many-body systems whose coupling to an external bath is very weak. A particularly interesting class of such systems is those that do not thermalize under their own isolated quantum dynamics. In this review, we highlight the possibility for such systems to exhibit new nonequilibrium phases of matter. In particular, we focus on discrete time crystals, which are many-body phases of matter characterized by a spontaneously broken discrete time-translation symmetry. We give a definition of discrete time crystals from several points of view, emphasizing that they are a nonequilibrium phenomenon that is stabilized by many-body interactions, with no analog in noninteracting systems. We explain the theory behind several proposed models of discrete time crystals, and compare several recent realizations, in different experimental contexts.
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5

Ramakalyan, A., P. Kavitha, and S. Harini Vijayalakshmi. "Discrete-time systems." Resonance 5, no. 4 (April 2000): 91–96. http://dx.doi.org/10.1007/bf02837910.

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6

Ramakalyan, A., P. Kavitha, and S. Harini Vijayalakshmi. "Discrete-time systems." Resonance 5, no. 2 (February 2000): 39–49. http://dx.doi.org/10.1007/bf02838822.

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7

Khan, A. R., F. Mehmood, and M. A. Shaikh. "Обобщение неравенств Островского на временных шкалах." Владикавказский математический журнал 25, no. 3 (September 25, 2023): 98–110. http://dx.doi.org/10.46698/q4172-3323-1923-j.

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The idea of time scales calculus’ theory was initiated and introduced by Hilger (1988) in his PhD thesis order to unify discret and continuous analysis and to expend the discrete and continous theories to cases ``in between''. Since then, mathematical research in this field has exceeded more than 1000 publications and a lot of applications in the fields of science, i.e., operations research, economics, physics, engineering, statistics, finance and biology. Ostrowski proved an inequality to estimate the absolute deviation of a differentiable function from its integral mean. This result was obtained by applying the Montgomery identity. In the present paper we derive a generalization of the Montgomery identity to the various time scale versions such as discrete case, continuous case and the case of quantum calculus, by obtaining this generalization of Montgomery identity we would prove our results about the generalization of the Ostrowski inequalities (without weighted case) to the several time scales such as discrete case, continuous case and the case of quantum calculus and recapture the several published results of different authors of various papers and thus unify corresponding discrete version and continuous version. Similarly we would also derive our results about the generalization of the Ostrowski inequalities (weighted case) to the different time scales such as discrete case and continuous case and recapture the different published results of several authors of various papers and thus unify corresponding discrete version and continuous version. Moreover, we would use our obtained results (without weighted case) to the case of quantum calculus.
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8

Mostafa, El-Sayed M. E. "COMPUTATIONAL DESIGN OF OPTIMAL DISCRETE-TIME OUTPUT FEEDBACK CONTROLLERS." Journal of the Operations Research Society of Japan 51, no. 1 (2008): 15–28. http://dx.doi.org/10.15807/jorsj.51.15.

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9

FRYZ, Mykhailo, and Bogdana MLYNKO. "DISCRETE-TIME CONDITIONAL LINEAR RANDOM PROCESSES AND THEIR PROPERTIES." Herald of Khmelnytskyi National University. Technical sciences 309, no. 3 (May 26, 2022): 7–12. http://dx.doi.org/10.31891/2307-5732-2022-309-3-7-12.

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Continuous-time conditional linear random process is represented as a stochastic integral of a random kernel driven by a process with independent increments. Such processes are used in the problems of mathematical modelling, computer simulation, and processing of stochastic signals, the physical nature of which generates them to be represented as the sum of many random impulses that occur at Poisson moments. Impulses are stochastically dependent functions, in contrast to another well-known mathematical model which is a linear random process, that has a similar structure but is represented as the sum of a large amount of independent random impulses that occur at Poisson moments of time. The application areas of these models are mathematical modelling, computer simulation, and processing of electroencephalographic signals, cardio signals, resource consumption processes (such as electricity consumption, water consumption, gas consumption), radar signals, etc. A discrete-time conditional linear random process has been defined in the paper, the relationships with corresponding continuous-time model has been shown. According to the given definition the discrete-time conditional linear random process can be considered as an output of linear digital filter with random parameters on the input of the white noise which is infinitely divisible distributed. Moment functions of first and second order have been analyzed. In particular, the expressions for mathematical expectation, variance and covariance function have been obtained. The results can be utilized to study the probabilistic characteristics of the investigated information stochastic signals, which will depend on the properties of the corresponding kernel and white noise. In particular, the conditions for the process to be wide-sense stationary have been represented.
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10

Peyrin, F., and R. Prost. "A unified definition for the discrete-time, discrete-frequency, and discrete-time/Frequency Wigner distributions." IEEE Transactions on Acoustics, Speech, and Signal Processing 34, no. 4 (August 1986): 858–67. http://dx.doi.org/10.1109/tassp.1986.1164880.

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11

Richard, C. "Time-frequency-based detection using discrete-time discrete-frequency Wigner distributions." IEEE Transactions on Signal Processing 50, no. 9 (September 2002): 2170–76. http://dx.doi.org/10.1109/tsp.2002.801927.

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12

Aggoun, L., and L. Benkherouf. "Filtering of discrete-time systems hidden in discrete-time random measures." Mathematical and Computer Modelling 35, no. 3-4 (February 2002): 273–82. http://dx.doi.org/10.1016/s0895-7177(01)00164-9.

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13

Zhu, J., L. S. Shieh, and R. E. Yates. "Fitting continuous-time and discrete-time models using discrete-time data and their applications." Applied Mathematical Modelling 9, no. 2 (April 1985): 93–98. http://dx.doi.org/10.1016/0307-904x(85)90119-2.

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14

Vermunt, Jeroen K., Rolf Langeheine, and Ulf Bockenholt. "Discrete-Time Discrete-State Latent Markov Models with Time-Constant and Time-Varying Covariates." Journal of Educational and Behavioral Statistics 24, no. 2 (June 1999): 179–207. http://dx.doi.org/10.3102/10769986024002179.

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15

Vermunt, Jeroen K., Rolf Langeheine, and Ulf Bockenholt. "Discrete-Time Discrete-State Latent Markov Models with Time-Constant and Time-Varying Covariates." Journal of Educational and Behavioral Statistics 24, no. 2 (1999): 179. http://dx.doi.org/10.2307/1165200.

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16

Jakobsson, A., S. V. Andersen, and S. R. Alty. "Time-updating discrete-time ‘analytic’ signals." Electronics Letters 40, no. 3 (2004): 205. http://dx.doi.org/10.1049/el:20040148.

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17

Ponomarev, V. A., O. V. Ponomareva, and N. V. Ponomareva. "Discrete Time Inversion and Parametric Discrete Fourier Inversion." Intellekt. Sist. Proizv. 14, no. 4 (January 30, 2017): 25. http://dx.doi.org/10.22213/2410-9304-2016-4-25-31.

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Рассмотрено применение фундаментального понятия инверсии дискретного времени в векторном анализе сигналов, заданных на конечных интервалах. Исследована взаимосвязь параметрического дискретного преобразование Фурье действительной последовательности с параметрическим дискретным преобразованием Фурье соответствующих ей последовательностей с циклической инверсией дискретного времени и с линейной инверсией дискретного времени. Полученные результаты могут быть использованы при рассмотрении как теоретических, так и практических вопросов цифровой обработки сигналов, например при устранении нелинейности фазочастотных характеристик фильтров с импульсной характеристикой бесконечной длины.
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18

Wang, Pei. "Discrete Lorentz symmetry and discrete time translational symmetry." New Journal of Physics 20, no. 2 (February 16, 2018): 023042. http://dx.doi.org/10.1088/1367-2630/aaaa17.

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19

Jang, Sophia R. J., and Azmy S. Ackleh. "Discrete-time, discrete stage-structured predator–prey models." Journal of Difference Equations and Applications 11, no. 4-5 (April 2005): 399–413. http://dx.doi.org/10.1080/10236190412331335454.

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20

Mazzola, Claudio. "Can discrete time make continuous space look discrete?" European Journal for Philosophy of Science 4, no. 1 (September 10, 2013): 19–30. http://dx.doi.org/10.1007/s13194-013-0072-3.

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21

Nelatury, S. R., and B. G. Mobasseri. "Synthesis of discrete-time discrete-frequency Wigner distribution." IEEE Signal Processing Letters 10, no. 8 (August 2003): 221–24. http://dx.doi.org/10.1109/lsp.2003.814391.

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22

Maltseva, Anastasia, and Volker Reitmann. "Bifurcations of invariant measures in discrete-time parameter dependent cocycles." Mathematica Bohemica 140, no. 2 (2015): 205–13. http://dx.doi.org/10.21136/mb.2015.144326.

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23

de Assis dos Santos Neves, Francisco, Roberto Feliciano Dias Filho, Felipe C. Camboim, Marcelo Cabral Cavalcanti, and Emilio J. Bueno. "Discrete-time Sliding Mode Direct Power Control For Threephase Rectifiers." Eletrônica de Potência 15, no. 2 (May 1, 2010): 77–85. http://dx.doi.org/10.18618/rep.2010.2.077085.

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24

Gitizadeh, R., I. Yaesh, and J. Z. Ben-Asher. "Discrete-Time Optimal Guidance." Journal of Guidance, Control, and Dynamics 22, no. 1 (January 1999): 171–75. http://dx.doi.org/10.2514/2.7622.

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25

Inoue, Rei, and Kazuhiro Hikami. "LatticeWCurrents with Discrete Time." Journal of the Physical Society of Japan 68, no. 2 (February 15, 1999): 386–90. http://dx.doi.org/10.1143/jpsj.68.386.

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26

Yao, Norman Y., Chetan Nayak, Leon Balents, and Michael P. Zaletel. "Classical discrete time crystals." Nature Physics 16, no. 4 (February 10, 2020): 438–47. http://dx.doi.org/10.1038/s41567-019-0782-3.

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27

Yin, G. G., and Q. Zhang. "Discrete-Time Markov Chains." IEEE Transactions on Automatic Control 51, no. 6 (June 2006): 1080–81. http://dx.doi.org/10.1109/tac.2006.876962.

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28

Fotopoulos, Stergios B. "Discrete-Time Dynamic Models." Technometrics 43, no. 1 (February 2001): 110–11. http://dx.doi.org/10.1198/tech.2001.s567.

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29

Mitkowski, Wojciech, Waldemar Bauer, and Marta Zagórowska. "Discrete-time feedback stabilization." Archives of Control Sciences 27, no. 2 (June 1, 2017): 309–22. http://dx.doi.org/10.1515/acsc-2017-0020.

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Abstract This paper presents an algorithm for designing dynamic compensator for infinitedimensional systems with bounded input and bounded output operators using finite dimensional approximation. The proposed method was then implemented in order to find the control function for thin rod heating process. The optimal sampling time was found depending on discrete output measurements.
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30

Bender, Carl M., Kimball A. Milton, David H. Sharp, L. M. Simmons, and Richard Stong. "Discrete-time quantum mechanics." Physical Review D 32, no. 6 (September 15, 1985): 1476–85. http://dx.doi.org/10.1103/physrevd.32.1476.

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31

Ortigueira, Manuel D., Fernando J. V. Coito, and Juan J. Trujillo. "Discrete-time differential systems." Signal Processing 107 (February 2015): 198–217. http://dx.doi.org/10.1016/j.sigpro.2014.03.004.

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32

Doukhan, Paul, Adam Jakubowski, Silvia R. C. Lopes, and Donatas Surgailis. "Discrete-time trawl processes." Stochastic Processes and their Applications 129, no. 4 (April 2019): 1326–48. http://dx.doi.org/10.1016/j.spa.2018.05.004.

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33

Zagalak, Petr. "Discrete-time control systems." Automatica 33, no. 12 (December 1997): 2281–82. http://dx.doi.org/10.1016/s0005-1098(97)00139-8.

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34

Verriest, Erik I., and W. Steven Gray. "Discrete Time Nonlinear Balancing." IFAC Proceedings Volumes 34, no. 6 (July 2001): 475–80. http://dx.doi.org/10.1016/s1474-6670(17)35221-7.

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35

Cohen, Daniel, Arie Kaufman, Reuven Bakalash, and Samuel Bergman. "Real time discrete shading." Visual Computer 6, no. 1 (January 1990): 16–27. http://dx.doi.org/10.1007/bf01902626.

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36

Sanchez, Tonametl, Denis Efimov, Andrey Polyakov, and Jaime A. Moreno. "Homogeneous Discrete-Time Approximation." IFAC-PapersOnLine 52, no. 16 (2019): 19–24. http://dx.doi.org/10.1016/j.ifacol.2019.11.749.

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37

Halme, A. "Discrete-time control systems." Automatica 25, no. 5 (September 1989): 788–89. http://dx.doi.org/10.1016/0005-1098(89)90039-3.

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38

Norton, J. P. "Discrete-time control systems." Chemical Engineering Science 43, no. 5 (1988): 1218. http://dx.doi.org/10.1016/0009-2509(88)85088-7.

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39

Bender, C. M., L. R. Mead, and K. A. Milton. "Discrete time quantum mechanics." Computers & Mathematics with Applications 28, no. 10-12 (November 1994): 279–317. http://dx.doi.org/10.1016/0898-1221(94)00198-7.

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40

Baeten, J. C. M., and J. A. Bergstra. "Discrete time process algebra." Formal Aspects of Computing 8, no. 2 (March 1996): 188–208. http://dx.doi.org/10.1007/bf01214556.

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41

Henley, E. M. "Discrete space-time symmetries." Czechoslovak Journal of Physics 43, no. 3-4 (March 1993): 289–308. http://dx.doi.org/10.1007/bf01589848.

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42

Borodin, Alexei, and Ivan Corwin. "Discrete Time q-TASEPs." International Mathematics Research Notices 2015, no. 2 (October 11, 2013): 499–537. http://dx.doi.org/10.1093/imrn/rnt206.

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43

Wan, Eric A. "Discrete time neural networks." Applied Intelligence 3, no. 1 (February 1993): 91–105. http://dx.doi.org/10.1007/bf00871724.

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44

Suris, Yuri B. "Discrete time Toda systems." Journal of Physics A: Mathematical and Theoretical 51, no. 33 (July 5, 2018): 333001. http://dx.doi.org/10.1088/1751-8121/aacbdc.

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45

Yassine, H. M. "Exact discrete time synthesis." IEE Proceedings G (Electronic Circuits and Systems) 133, no. 4 (1986): 209. http://dx.doi.org/10.1049/ip-g-1.1986.0034.

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46

Bou-hamad, Imad, Denis Larocque, Hatem Ben-Ameur, Louise C. Mâsse, Frank Vitaro, and Richard E. Tremblay. "Discrete-time survival trees." Canadian Journal of Statistics 37, no. 1 (March 2009): 17–32. http://dx.doi.org/10.1002/cjs.10007.

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47

Weiß, Christian H. "Discrete-Valued Time Series." Entropy 25, no. 12 (November 23, 2023): 1576. http://dx.doi.org/10.3390/e25121576.

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Time series are sequentially observed data in which important information about the phenomenon under consideration is contained not only in the individual observations themselves, but also in the way these observations follow one another [...]
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48

Bulinskaya, E. V. "Discrete-Time Insurance Models." Moscow University Mathematics Bulletin 78, no. 6 (December 2023): 298–308. http://dx.doi.org/10.3103/s0027132223060025.

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49

Bouchard, Bruno, and Jean-François Chassagneux. "Discrete-time approximation for continuously and discretely reflected BSDEs." Stochastic Processes and their Applications 118, no. 12 (December 2008): 2269–93. http://dx.doi.org/10.1016/j.spa.2007.12.007.

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50

Chassagneux, Jean-François. "A discrete-time approximation for doubly reflected BSDEs." Advances in Applied Probability 41, no. 1 (March 2009): 101–30. http://dx.doi.org/10.1239/aap/1240319578.

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We study the discrete-time approximation of doubly reflected backward stochastic differential equations (BSDEs) in a multidimensional setting. As in Ma and Zhang (2005) or Bouchard and Chassagneux (2008), we introduce the discretely reflected counterpart of these equations. We then provide representation formulae which allow us to obtain new regularity results. We also propose an Euler scheme type approximation and give new convergence results for both discretely and continuously reflected BSDEs.
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