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In this paper, the principle of normalized minimum-sum (NMS) polar decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, performances approach to that of the sum-product (SP) algorithm can be achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation results for successive...
The method of analytic combinatorics (AC) is a unified approach to multiple object tracking that encodes joint probability distributions into probability generating functionals (PGFLs). PGFLs characterize distributions exactly. A high level view of the tracking applications of PGFLs is outlined in this paper. Assignment models in well-known filters are modeled as products of PGFLs. MHT and multiBernoulli...
In this paper, the principle of density evolution (DE) combined with the normalized minimum-sum (NMS) decoding process is explored. It is demonstrated that with one properly chosen parameters for NMS algorithm, then almost the same behaviour of sum-product (SP) algorithm is achieved. As well, the complexity reduction is realized by calculating a linear function instead of nonlinear function. Simulation...
With the development of manufacture technology, the multi-level cell (MLC) technique dramatically increases the storage density of NAND flash memory. As the result, cell-to-cell interference (CCI) becomes more serious and hence causes an increase in the raw bit error rate of data stored in the cells. Recently, low-density parity-check (LDPC) codes have appeared to be a promising solution to combat...
Communication systems subjected to strong impulse noise are prone to performance degradation when the impulse occurrence is neglected in the decoding process: turbo decoders are likely to exhibit error propagation because the decision-making is dictated by excessive samples corrupted by impulses when the conventional decision metric, which is based on the assumption of additive white Gaussian noise,...
Epileptic seizure detection using EEGs is a heavy workload of traditional visual inspection for diagnosing epilepsy. Therefore, more and more research on automatic seizure detection have been developed in recent years. The appropriate feature extraction method and efficient classifier are recognized to be crucial in the successful realization. In this paper, we first create a novel feature extraction...
Shannon observed that the normal distribution has maximal entropy among distributions with a density function and a given variance. This sparked a significant body of research in statistics, broadly concerned with goodness-of-fit estimators based on Shannon entropy for a variety of distributions and, in particular, normality testing. The present paper proposes to use compression algorithms and other...
We give a general unified method that can be used for L1 closeness testing of a wide range of university structured distribution families. More specifically, we design a sample optimal and computationally efficient algorithm for testing the equivalence of two unknown (potentially arbitrary) university distributions under the Ak-distance metric: Given sample access to distributions with density functions...
A new field of Computing with Words (CWW) intends to “compute” with phrases that involve linguistic numbers and relations between them such as “about five applies” and “close to six PM”. CWW includes answering arithmetic question such as: What is the number of apples that John obtained if he bought about 5 apples two times today? The common sense answer is “about 10 apples”. However the numeric values...
In this paper, we derive closed-form capacity expressions for a low complexity bit-interleaved coded modulation system with uniform inputs in a Rayleigh fading channel with additive white Gaussian noise. Additionally, we include pilot-symbol assisted channel estimation in our considerations. Finding a closed-form solution is enabled by assuming quantization and that the decoder has no channel state...
This paper describes several fast algorithms for approximation of the maximum entropy estimate of probability density functions on the basis of a finite number of sampled data. The proposed algorithms are compared with the exact maximum entropy estimate in terms of approximation accuracy and computational efficiency. Some application examples are given.
A non-parametric probability density function (pdf) estimation technique is presented. The estimation consists in approximating the unknown pdf by a network of Gaussian Radial Basis Functions (GRBFs). Complexity analysis is introduced in order to select the optimal number of GRBFs. Results obtained on real data show the potentiality of this technique.
A numerical experiment is performed using a Shooting and Bouncing Rays based solver on eight finite clusters of eight perfectly electrically conductive canonical target geometries where each finite cluster volume includes certain amount of randomly translated and rotated target of one kind. The best probability density function that best fits to the obtained mono-static radar cross section distribution...
A new sparse kernel density estimator is introduced. Our main contribution is to develop a recursive algorithm for the selection of significant kernels one at time using the minimum integrated square error (MISE) criterion for both kernel selection. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate that the...
In a recent work [1], we have introduced a probabilistic formulation for the model validation problem to provide a unifying framework for (in)validating nonlinear deterministic and stochastic models, in both discrete and continuous time. As an extension to that work, this paper provides rigorous performance bounds for the model validation algorithms presented in [1]. Further, it is shown that the...
In this paper we propose a probabilistic method for fusing depth maps in real time for wide-baseline situation. We treat the depth map fusion as a problem of probability density function (pdf) estimation. The original point cloud, instead of the reprojected depth map, is used to estimate the pdf, and a mathematical expectation computation method is proposed to reduce the complexity of the method....
The main problem of stochastic nonlinear model predictive control (SNMPC) is that the equations for state prediction and calculation of the expected reward are in general not solvable in closed form. A popular approach is to approximate the occurring continuous probability density functions by a discrete density representation, which allows an analytical solution of the SNMPC equations. In this paper,...
In this paper, we propose a novel scheme for dynamically reducing the computational complexity of MVC. Our scheme exploits the coding mode correlation available in the 3D-neighborhood (i.e., spatial, temporal, and view) along with the rate-distortion properties of the neighboring Macroblocks. Our scheme incorporates a multi-level mode decision process based on a mode-ranking mechanism that categorizes...
We study new modulation classification methods for M-ary CPM signals based on the average likelihood ratio test (ALRT) approach. It is well-known that the M-ary CPM signal can be decomposed into the superposition of multiple PAM waveforms. In this paper, we apply the decomposed PAM waveforms for CPM classification through ALRT. However, to detect the CPM signals costs a high complexity because the...
Bayesian estimation for nonlinear systems is still a challenging problem, as in general the type of the true probability density changes and the complexity increases over time. Hence, approximations of the occurring equations and/or of the underlying probability density functions are inevitable. In this paper, we propose an approximation of the conditional densities by wavelet expansions. This kind...
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