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Monte Carlo integration is a numerical integration method using random numbers. The speed of convergence of the Monte Carlo integration can be faster by using appropriate chaotic random numbers generated by one-dimensional chaotic maps. This paper discusses the efficiency of Monte Carlo integration using chaotic random numbers generated by tent maps with a uniform distribution.
The Evolutionary algorithm (EA) for researching parameters of nonlinear system is a rapidly growing field of identification. This can owe to the importance of EA for both the theoretical field and the engineering community. However, the identification of the nonlinear system is still a knotty problem, especially when heavy-tailed noises exists. Compared to classical identification methods, EA has...
The α-stable distribution is highly intractable for inference because of the lack of a closed form density function in the general case. However, it is well-established that the α-stable distribution admits a Poisson series representation (PSR) in which the terms of the series are a function of the arrival times of a unit rate Poisson process. In our previous work, we have shown how to carry out inference...
Consider a stochastic system composed of multiple subsystems, each subsystem with binary outputs. Based on the test data from both the subsystems and the full system, the goal is to estimate the parameters of the whole system. Beyond the interest of full system mean parameter, this paper studies the identification of the system parameters under a more general setting, where the full system can follow...
Deep convolutional neural networks (DCNN's) have shown great value in approaching highly challenging problems in image classification. Based on the successes of DCNNs in scene classification and object detection and localization it is natural to consider whether they would be effective for much simpler computer vision tasks. Our work involves the application of a DCNN to the relatively simple task...
This paper studies the random-coding union (RCU) bound to the error probability in quasi-static fading channels. An asymptotic expansion and a normal approximation to the RCU bound suggest that the error probability converges to the outage probability as 1/n, where n is the codeword blocklength. We particularize our results for Rayleigh fading, and compare them with the conventional normal approximation.
The complex multivariate generalized Gaussian distribution (CMGGD) is a flexible parametrized distribution suitable for a variety of applications. Previous work in this area is either limited to the univariate case or, in the multivariate case, restricts the complex vectors, unjustifiably, to be circular. In both cases, algorithms for parameter estimation also suffer from convergence or accuracy limitations...
The EM algorithm is a novel numerical method to obtain maximum likelihood estimates and is often used for practical calculations. However, many of maximum likelihood estimation problems are nonconvex, and it is known that the EM algorithm fails to give the optimal estimates by being trapped by local optima. In order to deal with this difficulty, we propose a deterministic quantum annealing EM algorithm...
We propose new median type estimators of the quadratic trend parameter of location based on the second order differences of observations. Asymptotic normality and strong consistency of the estimators are proved. The estimators have a high breakdown point and high asymptotic efficiency in the Gaussian case, and do not depend on the slope and intercept parameters or their estimates. The simulation results...
In this paper, Bayesian parameter estimation through the consideration of the Maximum A Posteriori (MAP) criterion is revisited under the prism of the Expectation-Maximization (EM) algorithm. By incorporating a sparsity-promoting penalty term in the cost function of the estimation problem through the use of an appropriate prior distribution, we show how the EM algorithm can be used to efficiently...
This paper presents a new approach to statistically characterize the variability of intermodulation distortion of nonlinear RF circuits in response to uncertainty in the design parameters. The proposed approach is built upon two ideas. The first idea is a moment-based computation of the Volterra Kernels. The second idea is derived from the recently reported decoupled formulation of the Hermite-based...
Dynamics of non-stationary processes that follow the MaxEnt principle for differential entropy is considered. A set of equations describing the dynamics of probability density function (pdf) for such processes is proposed. Equations are derived based on the Speed-Gradient principle originated in the control theory. The uniqueness of the limit pdf and asymptotic convergence of pdf are examined under...
This paper considers universal lossless variable-length source coding problem and deals with one of the fundamental limits and pointwise asymptotics of the Bayes code for stationary ergodic finite order Markov sources. As investigation of the fundamental limits, we show upper and lower bounds of the minimum rate such that the probability which exceeds it is less than ϵ ∈ (0, 1). Furthermore, we prove...
The equalization of constant amplitude signals is considered in the scope of this paper. A criterion based on the probability density function (pdf) of the signal of interest is proposed. The objective is to derive a suitable soft-decision scheme, more robust than the classical CMA algorithm that ensures recover ability of the signal.
For the current advanced technology nodes, an accurate, yet fast reliability analysis is needed at design time, to enable the comparison between different circuit architectures, and thus a reliability-aware design and synthesis process. To this end we propose a reliability assessment framework that is able to estimate more accurately the circuit reliability and which can be applied to large-scale...
This paper presents a binarization approach to degraded document images, which is based on Gaussian Markov Random Field (GMRF) model. The energy function with the single-site and pair-site clique potential functions is formulated for the GMRF. The parameters of the potential functions are estimated by expectation-maximization (EM) algorithm, without necessity of training process. Experiments on different...
The error probability performance of convolutional codes are mostly evaluated by computer simulations, and few studies have been made for exact error probability of convolutional codes. In [1], the moments of decision variable are derived by a recurrence relation for maximum a-posteriori probability (MAP) decoding of 4-state convolutional code. However, due to the convergence problem of moment techniques,...
Global optimization problems are relevant in many fields (e.g., control systems, operations research, economics). There are many approaches to solving these problems. One particular approach is modelbased methods, which are a class of random search methods. A model-based method iteratively updates its probability density function. At each step, additional weight is given to solution subspaces that...
This letter proposed a prior probability-based scheme for spectrum sensing in Cognitive Radio. While conventional schemes perform spectrum sensing independently from block to block, the proposed scheme enables sequence spectrum sensing with utilization of the statistics of licensed band occupancy. We further investigate the convergence of the sensing performance and show that the proposed scheme is...
For the performance evaluation of convolutional codes, computer simulations are widely used, and few studies have been made for exact error probability of convolutional codes of more than 4-state. In this paper, an analytical approach is presented for moments of decision variables for a maximum a-posteriori probability (MAP) decoding. The moments are derived by recurrence relations. An application...
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