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In this paper, a computationally efficient algorithm is presented for blind phase noise estimation and data detection jointly, based on a sequential Monte Carlo method. The basic idea is to treat the transmitted symbols as “missing data” and draw samples sequentially of them based on the observed signal samples up to time t. This way, the Bayesian estimates of the phase noise and the incoming data...
In many signal processing problems, it is important to estimate the probability that a signal is present in observed data. As opposed to standard Bernoulli experiments where the outcomes of the experiments clearly show when the event occurred, there are many situations where only probabilistic claims can be made about the occurrence of events. Examples of the latter include a variety of problems related...
A new technique for nonlinear state and parameter estimation of the discrete time stochastic volatility models in which the logarithm of the asset return conditional variance follows an autoregressive model has been developed. The Gibbs sampling algorithm is used to construct a Markov-chain simulation tool that reflects both inherent model variability and parameter uncertainty. The proposed chain...
In the context of parametric model-based methods for image processing, this paper deals with image modeling, when the image is considered as a two-dimensional (2-D) random process. The model we are searching on is based on the 2-D Wold-type decomposition. The core of the contribution of this paper is to provide with a new estimation algorithm of the so called “evanescent field” in the 2-D Wold decomposition...
This paper focuses on the pair-matching problem of Two-Dimensional Angle-Of-Arrival (2-D AOA) estimation for the L-shaped array. The authors adopt a modified array configuration in which the azimuth and elevation angles can be exploited independently. Moreover, by such configuration, the Cross Correlation Matrix (CCM) is employed to accomplish pairing-matching between estimated parameters which obtained...
Accurately estimating the failure region of rare events for nanoscale analog circuit blocks under process variations is a challenging task. In this paper, we propose a new statistical rare event analysis method. The new method is based on the iterative failure region locating scheme to reduce the sample counts while still maintains estimation accuracy. We derive the complete formulation for failure...
In this paper, we study the use of historical data from a fleet (i.e., a group) of equipment to improve condition monitoring and health assessment for each individual equipment (within the fleet) for maintenance purposes. In particular, we propose a fleet-based approach to estimate the tool wear of milling machines at arbitrary operating conditions (OCs) using the Monte Carlo sequential importance...
This paper deals with active fingerprinting a.k.a. traitor tracing where a collusion of dishonest users merges their individual versions of a content to yield a pirated copy. The Tardos codes are one of the most powerful tools to fight against such collusion process by identifying the colluders. Instead of studying as usual the necessary and sufficient code length in a theoretical setup, we adopt...
The RANdom SAmple Consensus (RANSAC) algorithm, as a robust parameter estimator, has been widely used to remove gross errors. However, there is less work on analyzing the uncertainty produced by the RANSAC. This paper fills this gap by presenting an uncertainty estimation algorithm for the RANSAC. Based on a thorough analysis on the uncertainty of the model parameters generated during the random hypothesis...
The analog/RF functional test which is based on specification circuit testing is very costly due to lengthy test times and highly sophisticated test equipment. Alternative test measures, extracted by means of Built-in Self Test (BIST) techniques, are a promising approach to replace standard specification-based tests. However, these test measures must be evaluated at the design stage by estimating...
We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D (1, ∞) run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation...
In this paper, we propose a novel subset simulation (SUS) technique to efficiently estimate the rare failure rate for nanoscale circuit blocks (e.g., SRAM, DFF, etc.) in high-dimensional variation space. The key idea of SUS is to express the rare failure probability of a given circuit as the product of several large conditional probabilities by introducing a number of intermediate failure events....
This paper proposes an estimation method of the sub-paths with correlations. In recent years, the process variation may degrade the yield due to the timing error. The timing error is caused by the variation of the clock arrival times of Filp-flops(FFs) and the path-delays between FFs from the expected value on the design. To recover this error, it is important to recognize the condition of the chip,...
Video tracking of abrupt motion is a challenging task in computer vision, especially with abrupt scale change. To deal with the problem efficiently, we proposed a novel tracking algorithm based on Markov Chain Monte Carlo sampling method within Bayesian filtering framework. In our tacking scheme, samples were proposed efficiently using the hybrid model of density grid and distance of sub-regions to...
In this paper, a single antenna interference cancellation (SAIC) by means of sequential importance sampling (SIS) is studied. The contribution of the proposed algorithm is that the maximum a posteriori (MAP) estimates of the transmitted co-channel symbols is recursively computed without explicit channel information, and the joint posterior distribution of the channels is derived. The Symbol error...
Since the introduction of the Relative Gain Array (RGA) by Bristol in 1966, it has received a high level of attention as a practical tool for solving the input-output pairing problem in decentralized control. Moreover, many extensions have been proposed like e.g. for the dynamic case and non-square system matrices. Recently, extensions that provide tools for uncertain parametric process models were...
Line following robots requires image acquisition and processing algorithms for the determination of the line trajectory. The Viterbi algorithm is proposed for the estimation of the line trajectory in this paper. The robustness of this algorithm is verified using the Monte Carlo approach for two distortions types: additive Gaussian noise and false lines sets. The results show possibilities of reliable...
Decoding algorithm in motor Brain Machine Interfaces translates the neural signals to movement parameters. They usually assume the connection between the neural firings and movements to be stationary, which is not true according to the recent studies that observe the time-varying neuron tuning property. This property results from the neural plasticity and motor learning etc., which leads to the degeneration...
In this work, we explore the possibility of applying an Ensemble square root filter (EnSRF) for tracking mobile stations in multi-path wireless networks. The EnSRF applied here is a variant of the Ensemble Kalman filter, that makesuse of the Kalman update formulas within a Monte Carlosetup for estimating the unknown state (position) of the mobile stations. Multipath propagation environments encountered...
Many proposed efficient statistical analysis methods in EMC are limited due to the dimensionality problem; when the number of random variables becomes large the methods can become less efficient than using the established Monte Carlo method. In this paper the univariate and bivariate dimension reduction methods are examined for their applicability and efficiency for statistical EMC analysis. The performance...
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