The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
R.a. Gopinath - One of the best experts on this subject based on the ideXlab platform.
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Thoughts on least squared-error optimal windows
IEEE Transactions on Signal Processing, 1996Co-Authors: R.a. GopinathAbstract:Recently, a simple and versatile method for the design of linear phaser FIR filters with spline Transition bands and optimal in a least-squared sense was introduced. The following question is raised: Given an arbitrary window, say, for example, a Hamming window, does there exist a Transition Function (like the spline Function above) such that the Hamming window is least-squares optimal? A related question is the following: Given a Transition Function, does there exist a window sequence w(n) such that the least squared optimal FIR filter is given by g(n)w(n)? This correspondence shows that all windows have associated Transition Functions that make them least-squared optimal. For every window, there exists a Transition Function that makes it superoptimal.
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Some thoughts on least squared error optimal windows
Proceedings of IEEE International Symposium on Circuits and Systems - ISCAS '94, 1994Co-Authors: R.a. GopinathAbstract:Windowing methods give simple designs of FIR filters. Window designs are generally not considered optimal in any meaningful sense. A typical desired FIR lowpass filter is specified by giving the passband, stopband and Transitionband responses of the filter. A particular class of Transition responses viz., spline Transition Functions, give rise to windows that are optimal in a least squared sense. Do all Transition Functions have associated windows that are least squared optimal? More importantly, given a window does there exist a (meaningful) Transition Function with respect to which the window is least squared optimal? In trying to answer the second question this note also characterizes all possible lowpass extensions of a given sequence and exhibits the unique minimum norm extension. This is used to show that a given finitely supported window w/sub b/(n), and a desired response with prescribed Transition band edges (but no Transition Function) there exist infinitely many Transition Functions with respect to which the windowed FIR filter (obtained by windowing an ideal frequency response) is least squared optimal. The unique minimum norm lowpass extension of w/sub b/(n) is used to exhibit a particular Transition Function with an extremal property. Since it is desirable to have a monotone Transition Function we ask the question: can a given window be least squared optimal with respect to a monotone desired Transition response? This question leads to a few open problems on non-negative definite lowpass extensions of sequences.
Oliver Stein - One of the best experts on this subject based on the ideXlab platform.
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The role of the Transition Function in a continuum model for kinetic roughening and coarsening in thin films
Materials Science in Semiconductor Processing, 2001Co-Authors: Oliver SteinAbstract:Abstract In the model for kinetic roughening and coarsening in high-temperature super-conducting thin films that Ortiz et al. suggest in (J. Mech. Phys. Solids 47(1999) 697.) a Transition Function is used which takes the energetics of the boundary layer at the film/substrate interface into account. Apart from having some basic properties, this Function can be chosen quite arbitrarily. We show that certain decay and concavity properties of this Function have a major impact on the film growth. In particular, a modified decay can circumvent the occurrence of negative film heights which is predicted in the original model, and a change in curvature alters stability properties of the film. These observations not only underscore the importance of the Transition Function in the model, but they also suggest to estimate the actual Transition Function from experimental data or from atomistic simulation.
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The role of the Transition Function in a continuum model for kinetic roughening and coarsening in thin films
Materials Science in Semiconductor Processing, 2001Co-Authors: Oliver SteinAbstract:In the model for kinetic roughening and coarsening in high-temperature super-conducting thin films that Ortiz et al. suggest in (J. Mech. Phys. Solids 47(1999) 697.) a Transition Function is used which takes the energetics of the boundary layer at the film/substrate interface into account. Apart from having some basic properties, this Function can be chosen quite arbitrarily. We show that certain decay and concavity properties of this Function have a major impact on the film growth. In particular, a modified decay can circumvent the occurrence of negative film heights which is predicted in the original model, and a change in curvature alters stability properties of the film. These observations not only underscore the importance of the Transition Function in the model, but they also suggest to estimate the actual Transition Function from experimental data or from atomistic simulation. ?? 2001 Elsevier Science Ltd. All rights reserved.
Yishay Mansour - One of the best experts on this subject based on the ideXlab platform.
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online stochastic shortest path with bandit feedback and unknown Transition Function
Neural Information Processing Systems, 2019Co-Authors: Aviv Rosenberg, Yishay MansourAbstract:We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss Function can change arbitrarily between episodes. The Transition Function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss Function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability $\beta > 0$ under any policy, we give a regret bound of $\tilde{O} ( L|X|\sqrt{|A|T} / \beta )$, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. When this assumption is removed we give a regret bound of $\tilde{O} ( L^{3/2} |X| |A|^{1/4} T^{3/4})$, that holds for an arbitrary Transition Function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown Transition Function.
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NeurIPS - Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
2019Co-Authors: Aviv Rosenberg, Yishay MansourAbstract:We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss Function can change arbitrarily between episodes. The Transition Function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss Function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability $\beta > 0$ under any policy, we give a regret bound of $\tilde{O} ( L|X|\sqrt{|A|T} / \beta )$, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. When this assumption is removed we give a regret bound of $\tilde{O} ( L^{3/2} |X| |A|^{1/4} T^{3/4})$, that holds for an arbitrary Transition Function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown Transition Function.
Fumihiko Asano - One of the best experts on this subject based on the ideXlab platform.
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Stability analysis of limit cycle walking in traversing steps based on semianalytical solution of Transition Function of state error
2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2015Co-Authors: Fumihiko AsanoAbstract:This paper analyzes the gait properties of a limit-cycle walker that achieves constraint on impact posture in traversing steps. First, we develop the mathematical model of a simple walker and describe the problem formulation. Second, we analytically derive the Transition Function of state error for the stance phase according to our method. The Function derived is a semianalytical solution because it includes an unknown parameter. We then conduct numerical simulations to examine the accuracy of the semianalytical solution of the Transition Function obtained through comparison with the numerical solution. We also discuss the changes in the state error when the walker traverses small steps.
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AIM - Stability analysis of limit cycle walking in traversing steps based on semianalytical solution of Transition Function of state error
2015 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), 2015Co-Authors: Fumihiko AsanoAbstract:This paper analyzes the gait properties of a limitcycle walker that achieves constraint on impact posture in traversing steps. First, we develop the mathematical model of a simple walker and describe the problem formulation. Second, we analytically derive the Transition Function of state error for the stance phase according to our method. The Function derived is a semianalytical solution because it includes an unknown parameter. We then conduct numerical simulations to examine the accuracy of the semianalytical solution of the Transition Function obtained through comparison with the numerical solution. We also discuss the changes in the state error when the walker traverses small steps.
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IROS - Analytical solution to Transition Function of state error in 1-DOF semi-passive dynamic walking
2013 IEEE RSJ International Conference on Intelligent Robots and Systems, 2013Co-Authors: Fumihiko AsanoAbstract:In this paper, we derive the analytical solution to the Transition Function of the state error in 1-DOF semi-passive dynamic walking for understanding how the gait stability changes according to acceleration or deceleration. We introduce the model of an active rimless wheel (RW) as the simplest walker for analysis and linearize the equation of motion incorporating a simple control torque. Through mathematical investigations, we finally derive the analytical solution to the Transition Function of the state error for the stance phase as a Function only of the control parameters. We discuss the accuracy of the solution obtained through comparison with the values numerically-integrated in the linearized and the nonlinear walking models.
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Analytical solution to Transition Function of state error in 1-DOF semi-passive dynamic walking
2013 IEEE RSJ International Conference on Intelligent Robots and Systems, 2013Co-Authors: Fumihiko AsanoAbstract:In this paper, we derive the analytical solution to the Transition Function of the state error in 1-DOF semi-passive dynamic walking for understanding how the gait stability changes according to acceleration or deceleration. We introduce the model of an active rimless wheel (RW) as the simplest walker for analysis and linearize the equation of motion incorporating a simple control torque. Through mathematical investigations, we finally derive the analytical solution to the Transition Function of the state error for the stance phase as a Function only of the control parameters. We discuss the accuracy of the solution obtained through comparison with the values numerically-integrated in the linearized and the nonlinear walking models.
Aviv Rosenberg - One of the best experts on this subject based on the ideXlab platform.
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online stochastic shortest path with bandit feedback and unknown Transition Function
Neural Information Processing Systems, 2019Co-Authors: Aviv Rosenberg, Yishay MansourAbstract:We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss Function can change arbitrarily between episodes. The Transition Function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss Function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability $\beta > 0$ under any policy, we give a regret bound of $\tilde{O} ( L|X|\sqrt{|A|T} / \beta )$, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. When this assumption is removed we give a regret bound of $\tilde{O} ( L^{3/2} |X| |A|^{1/4} T^{3/4})$, that holds for an arbitrary Transition Function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown Transition Function.
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NeurIPS - Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function
2019Co-Authors: Aviv Rosenberg, Yishay MansourAbstract:We consider online learning in episodic loop-free Markov decision processes (MDPs), where the loss Function can change arbitrarily between episodes. The Transition Function is fixed but unknown to the learner, and the learner only observes bandit feedback (not the entire loss Function). For this problem we develop no-regret algorithms that perform asymptotically as well as the best stationary policy in hindsight. Assuming that all states are reachable with probability $\beta > 0$ under any policy, we give a regret bound of $\tilde{O} ( L|X|\sqrt{|A|T} / \beta )$, where $T$ is the number of episodes, $X$ is the state space, $A$ is the action space, and $L$ is the length of each episode. When this assumption is removed we give a regret bound of $\tilde{O} ( L^{3/2} |X| |A|^{1/4} T^{3/4})$, that holds for an arbitrary Transition Function. To our knowledge these are the first algorithms that in our setting handle both bandit feedback and an unknown Transition Function.
