The Experts below are selected from a list of 10086 Experts worldwide ranked by ideXlab platform
J.r. Deller - One of the best experts on this subject based on the ideXlab platform.
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Set-theoretic estimation based on a priori knowledge of the Noise Distribution
IEEE Transactions on Signal Processing, 2000Co-Authors: J.r. Deller, Y.c. TsaiAbstract:A new algorithm for estimation of a linear-in-parameters model is developed and tested by simulation. The method is based on the assumption of independent, identically distributed Noise samples with a triangular density function. Such a Noise model well approximates the symmetrically distributed sources of Noise frequently encountered in practice, and the inclusion of a Distribution assumption allows the computation of a pseudo-mean estimate to complement the set solution. The proposed algorithm recursively incorporates incoming observations with decreasing computational complexity as the number of updates increases. Simulations demonstrate that the algorithm has very favorable convergence rates and estimation accuracy and is very robust to deviations from the assumed Noise properties. Comparisons with other set-theoretic algorithms and with conventional RLS are given.
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ISCAS (3) - Set theoretic estimation through triangular Noise Distribution
ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349), 1999Co-Authors: J.r. DellerAbstract:In most digital signal processing applications, we need to estimate an object from the observations of a physical system which is Noise corrupted. In this paper, we propose a general set theoretic estimation by using a preassumed Noise Distribution. Although Noise is usually unbounded and nonuniformly distributed, we propose using the triangular Distribution to approximate the unknown Noise Distribution. With this approximation, we can construct the local feasible solution sets from the solution space and more local feasible sets can also yield a smaller global feasibility set and more reliable estimates. Besides we can obtain a single estimate from the global feasibility set by using the assumed Distribution. Simulation shows that our scheme cannot only provide a solution set as set theoretic estimation does but also a correct estimate as recursive least-square (RLS) does. The mismatch effects between the assumed triangular and actual Noise Distribution are also studied and are not severe. It is shown that our algorithm converges much faster than the conventional RLS estimation even under the Distribution mismatch.
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Set theoretic estimation through triangular Noise Distribution
1999 IEEE International Symposium on Circuits and Systems (ISCAS), 1999Co-Authors: J.r. DellerAbstract:In most digital signal processing applications, we need to estimate an object from the observations of a physical system which is Noise corrupted. In this paper, we propose a general set theoretic estimation by using a preassumed Noise Distribution. Although Noise is usually unbounded and nonuniformly distributed, we propose using the triangular Distribution to approximate the unknown Noise Distribution. With this approximation, we can construct the local feasible solution sets from the solution space and more local feasible sets can also yield a smaller global feasibility set and more reliable estimates. Besides we can obtain a single estimate from the global feasibility set by using the assumed Distribution. Simulation shows that our scheme cannot only provide a solution set as set theoretic estimation does but also a correct estimate as recursive least-square (RLS) does. The mismatch effects between the assumed triangular and actual Noise Distribution are also studied and are not severe. It is shown that our algorithm converges much faster than the conventional RLS estimation even under the Distribution mismatch.
Ji-feng Zhang - One of the best experts on this subject based on the ideXlab platform.
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RATIONAL MODEL IDENTIFICATION WITH UNKNOWN Noise Distribution USING BINARY DATA
IFAC Proceedings Volumes, 2016Co-Authors: Le Yi Wang, Ji-feng ZhangAbstract:Abstract This paper introduces new methods in system identification with binary-valued output observations. It resolves two key issues, (a) regression structures for identifying a rational model that contain non-smooth nonlinearity, and (b) unknown Noise Distributions arising in practical identification problems. Optimal identification errors, time complexity, and input design are examined in a stochastic information framework.
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Identification of Sensor Thresholds and Noise Distribution Functions
System Identification with Quantized Observations, 2010Co-Authors: Le Yi Wang, Ji-feng Zhang, Yanlong ZhaoAbstract:The developments in the early chapters rely on the knowledge of the Distribution function F· or its inverse, as well as the threshold C. However, in many applications, the Noise Distributions are not known, or only limited information is available. On the other hand, input–output data from the system contain information about the Noise Distribution. By viewing unknown Distributions and system parameters jointly as uncertainties, we develop a methodology of joint identification.
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joint identification of plant rational models and Noise Distribution functions using binary valued observations
Automatica, 2006Co-Authors: Le Yi Wang, Ji-feng ZhangAbstract:System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearities, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which Noise Distribution functions are unknown. Convergence of parameter estimates is established. Recursive algorithms for joint identification and their key properties are further developed.
K. Taki - One of the best experts on this subject based on the ideXlab platform.
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a built in technique for probing power supply and ground Noise Distribution within large scale digital integrated circuits
IEEE Journal of Solid-state Circuits, 2005Co-Authors: M. Nagata, T. Okumoto, K. TakiAbstract:Design of Noise detector circuits as compact as standard logic cells is proposed. High-density large-scale digital integrated circuits that embed such built-in Noise detectors enable in-depth characterization of dynamic power supply and ground Noises. Dependence of power supply and ground voltage drops on the location of active cell rows within 1.8-V standard cell-based digital circuits are consistently measured by 1.8- and 2.5-V built-in detectors fabricated in a 0.18-/spl mu/m CMOS triple-well technology. Measurements also show that ground Noise Distribution is distinctively more localized than power supply counterparts due to the presence of a substrate.
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A built-in technique for probing power-supply Noise Distribution within large-scale digital integrated circuits
2004 Symposium on VLSI Circuits. Digest of Technical Papers (IEEE Cat. No.04CH37525), 2004Co-Authors: T. Okumoto, M. Nagata, K. TakiAbstract:Noise detector circuits as compact as standard logic cells for being embedded within a high-density large-scale digital circuit enable in-depth characterization of dynamic power-supply and ground Noises. Voltage drops at the locations of active cell rows within 1.8-V standard cell based digital circuits are consistently measured by 1.8-V and 2.5-V built-in detectors in a 0.18-/spl mu/m CMOS triple well technology. Measurements show that the ground-Noise Distribution is distinctively more localized than the power-supply counterpart due to the presence of a substrate.
Y.c. Tsai - One of the best experts on this subject based on the ideXlab platform.
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Set-theoretic estimation based on a priori knowledge of the Noise Distribution
IEEE Transactions on Signal Processing, 2000Co-Authors: J.r. Deller, Y.c. TsaiAbstract:A new algorithm for estimation of a linear-in-parameters model is developed and tested by simulation. The method is based on the assumption of independent, identically distributed Noise samples with a triangular density function. Such a Noise model well approximates the symmetrically distributed sources of Noise frequently encountered in practice, and the inclusion of a Distribution assumption allows the computation of a pseudo-mean estimate to complement the set solution. The proposed algorithm recursively incorporates incoming observations with decreasing computational complexity as the number of updates increases. Simulations demonstrate that the algorithm has very favorable convergence rates and estimation accuracy and is very robust to deviations from the assumed Noise properties. Comparisons with other set-theoretic algorithms and with conventional RLS are given.
Keigo Hirakawa - One of the best experts on this subject based on the ideXlab platform.
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Quantile analysis of image sensor Noise Distribution
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015Co-Authors: Jiachao Zhang, Keigo HirakawaAbstract:This paper describes a study aimed at comparing the real image sensor Noise Distribution to the models of Noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor Noise behavior. Noise model mismatch would likely result in image denoising that undersmoothes real sensor data.
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ICASSP - Quantile analysis of image sensor Noise Distribution
2015 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2015Co-Authors: Jiachao Zhang, Keigo HirakawaAbstract:This paper describes a study aimed at comparing the real image sensor Noise Distribution to the models of Noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor Noise behavior. Noise model mismatch would likely result in image denoising that undersmoothes real sensor data.
