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484
Monte Carlo smoothing for nonlinear time series
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2004
"... We develop methods for performing smoothing computations in general statespace models. The methods rely on a particle representation of the filtering distributions, and their evolution through time using sequential importance sampling and resampling ideas. In particular, novel techniques are pr ..."
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Cited by 153 (16 self)
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are presented for generation of sample realizations of historical state sequences. This is carried out in a forwardfiltering backwardsmoothing procedure which can be viewed as the nonlinear, nonGaussian counterpart of standard Kalman filterbased simulation smoothers in the linear Gaussian case
Finite State Machine based VHDL Implementation of a Median Filter
"... Digital images are often corrupted by impulsive noise also called as salt and pepper noise [1]. It occurs in the form of sharp black or white pixels within the image. An efficient nonlinear filter to reduce such noise is the median filter. The main advantage being the preserving of edges as compare ..."
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Digital images are often corrupted by impulsive noise also called as salt and pepper noise [1]. It occurs in the form of sharp black or white pixels within the image. An efficient nonlinear filter to reduce such noise is the median filter. The main advantage being the preserving of edges
Linear Recursive Filters and Nonlinear Dynamics
 Nonlinearity
, 1996
"... In this paper we investigate the effects of filtering a chaotic time series with a linear IIR filter. Using the Kaplan and Yorke conjecture it has been argued that such filtering can result in an increase in information dimension. Here we show that the filter dynamics induce an extended dynamical sy ..."
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Cited by 4 (0 self)
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In this paper we investigate the effects of filtering a chaotic time series with a linear IIR filter. Using the Kaplan and Yorke conjecture it has been argued that such filtering can result in an increase in information dimension. Here we show that the filter dynamics induce an extended dynamical
Göttfert: Linear filtering of nonlinear shift register sequences
 Proc. of The International Workshop on Coding and Cryptography WCC ’2005
"... Abstract. Nonlinear nstage feedback shiftregister sequences over the finite field Fq of period q n − 1 are investigated under linear operations on sequences. We prove that all members of an easily described class of linear combinations of shifted versions of these sequences possess useful properti ..."
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Cited by 3 (1 self)
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properties for cryptographic applications: large periods, large linear complexities and good distribution properties. They typically also have good maximum order complexity values as has been observed experimentally. A running key generator is introduced based on certain nonlinear feedback shift registers
Structural transformations of probabilistic finite state machines
 International Journal of Control
, 2008
"... Probabilistic finite state machines have recently emerged as a viable tool for modelling and analysis of complex nonlinear dynamical systems. This paper rigorously establishes such models as finite encodings of probability measure spaces defined over symbol strings. The well known Nerode equivalenc ..."
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Cited by 16 (9 self)
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Probabilistic finite state machines have recently emerged as a viable tool for modelling and analysis of complex nonlinear dynamical systems. This paper rigorously establishes such models as finite encodings of probability measure spaces defined over symbol strings. The well known Nerode
Efficient Memorybased Learning for Robot Control
, 1990
"... This dissertation is about the application of machine learning to robot control. A system which has no initial model of the robot/world dynamics should be able to construct such a model using data received through its sensorsan approach which is formalized here as the $AB (StateActionBehaviour) ..."
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Cited by 120 (3 self)
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of the effects of proposed actions and, given a goal behaviour, permitting fast generation of a candidate action. The learning can take place in highdimensional nonlinear control spaces with realvalued ranges of variables. Furthermore, the method avoids a number of shortcomings of earlier learning methods
Wiener chaos and nonlinear filtering
 Appl. Math. Optim
"... Abstract. The paper discusses two algorithms for solving the Zakai equation in the timehomogeneous diffusion filtering model with possible correlation between the state process and the observation noise. Both algorithms rely on the CameronMartin version of the Wiener chaos expansion, so that the a ..."
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Cited by 2 (0 self)
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that the approximate filter is a finite linear combination of the chaos elements generated by the observation process. The coefficients in the expansion depend only on the deterministic dynamics of the state and observation processes. For realtime applications, computing the coefficients in advance improves
On the Linearity of Autonomous Sequential Machines
"... SummaryThis paper presents a method of determining, from Present State Next State Present Output the flow table of an autonomous sequential machine, whether the machine can be realized by a linear circuit which uses the minimum sI SI possible number of secondary variables. The method is based on a ..."
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SummaryThis paper presents a method of determining, from Present State Next State Present Output the flow table of an autonomous sequential machine, whether the machine can be realized by a linear circuit which uses the minimum sI SI possible number of secondary variables. The method is based on a
Design of Wimax Interleaver using Finite State Machine
, 2012
"... ABSTRACT: OFDM is multiplexing technique used in WiMAX standards as it is always challenging to find FPGA solution because of its area and operating frequency. We have developed Interleaver which plays a vital role in improving the performance of FEC (Forward Error Correction Codes) codes in terms o ..."
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of PRBS generator is based on the linear feedback shift register, which consists of ‘n ’ master slave flipflops. The PRBS generator produces a predefined sequence of 1's and 0's, with 1 and 0 occurring with the same probability.
Learning Nonlinear Timescales with Kernel γFilters
, 2008
"... A family of kernel methods, based on the γfilter structure, is presented for nonlinear system identification and time series prediction. The kernel trick allows us to develop the natural nonlinear extension of the (linear) Support Vector Machine (SVM) γfilter [1], but this approach yields a rigi ..."
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A family of kernel methods, based on the γfilter structure, is presented for nonlinear system identification and time series prediction. The kernel trick allows us to develop the natural nonlinear extension of the (linear) Support Vector Machine (SVM) γfilter [1], but this approach yields a
Results 1  10
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484