Browsing by Subject "Gaussian distribution"

Now showing 1 - 7 of 7

Open Access
Çok-bileşenli sinyallerin analizi için destek bölge uyarlamalı hermite-gauss açılımı
(IEEE, 2011-04) Alp, Yaşar Kemal; Arıkan, Orhan; Özertem, U.
Zaman-frekans destek bölgesi orijin etrafında dairesel bir alana uyan bir sinyal bileşeni için, Hermite-Gauss açılımı en az sayıda taban fonsiyonu kullanarak en iyi temsili oluşturur. Ancak, orijinden uzakta ve dairesel olmayan zaman-frekans destek noklarına sahip sinyal bileşenleri için Hermite-Gauss açılımının direk uygulanması, çok fazla sayıda Hermite-Gauss fonksiyonunun kullanımını gerektirir. Bu da, eğer ölçüm sinyali gürültü altında kaydedilmişse ya da birçok sinyal bileşeni içeriyorsa, başarısız bileşen kestirimlerine neden olur. Bu problemi çözmek için sinyal bileşenlerinin destek bölgelerini bulup, zaman-frekans düzleminde orijin civarında, dairesel bir bölgeye oturtan ve bu sayede en az sayıda Hermite-Gauss fonksiyonu kullanarak sinyal bileşenlerini başarılı bir şekilde kestiren, tamamen otomatikleştirilmiş bir önişleme yöntemi önermekteyiz. Önişlemenin ardından, kestirilen bileşenlere ters dönüşümler uygulanarak destek bölgeleri eski yerlerine taşınır
Open Access
Image histogram thresholding using Gaussian kernel density estimation (English)
(IEEE, 2013) Suhre, Alexander; Çetin, A. Enis
In this article, image histogram thresholding is carried out using the likelihood of a mixture of Gaussians. In the proposed approach, a prob ability density function (PDF) of the histogram is computed using Gaussian kernel density estimation in an iterative manner. The threshold is found by iteratively computing a mixture of Gaussians for the two clusters. This process is aborted when the current bin is assigned to a different cluster than its predecessor. The method does not envolve an exhaustive search. Visual examples of our segmentation versus Otsu's thresholding method are presented. © 2013 IEEE.
Open Access
Implementing the Han-Kobayashi scheme using low density parity check codes over Gaussian interference channels
(Institute of Electrical and Electronics Engineers Inc., 2015) Sharifi S.; Tanc, A. K.; Duman, T. M.
We focus on Gaussian interference channels (GICs) and study the Han-Kobayashi coding strategy for the two-user case with the objective of designing implementable (explicit) channel codes. Specifically, low-density parity-check codes are adopted for use over the channel, their benefits are studied, and suitable codes are designed. Iterative joint decoding is used at the receivers, where independent and identically distributed channel adapters are used to prove that log-likelihood-ratios exchanged among the nodes of the Tanner graph enjoy symmetry when BPSK or QPSK with Gray coding is employed. This property is exploited in the proposed code optimization algorithm adopting a random perturbation technique. Code optimization and convergence threshold computations are carried out for different GICs employing finite constellations by tracking the average mutual information. Furthermore, stability conditions for the admissible degree distributions under strong and weak interference levels are determined. Via examples, it is observed that the optimized codes using BPSK or QPSK with Gray coding operate close to the capacity boundary for strong interference. For the case of weak interference, it is shown that nontrivial rate pairs are achievable via the newly designed codes, which are not possible by single user codes with time sharing. Performance of the designed codes is also studied for finite block lengths through simulations of specific codes picked with the optimized degree distributions with random constructions, where, for one instance, the results are compared with those of some structured designs. © 1972-2012 IEEE.
Open Access
Scalar diffraction field calculation from curved surfaces via Gaussian beam decomposition
(Optical Society of America, 2012-06-29) Şahin, E.; Onural, L.
We introduce a local signal decomposition method for the analysis of three-dimensional (3D) diffraction fields involving curved surfaces. We decompose a given field on a two-dimensional curved surface into a sum of properly shifted and modulated Gaussian-shaped elementary signals. Then we write the 3D diffraction field as a sum of Gaussian beams, each of which corresponds to a modulated Gaussian window function on the curved surface. The Gaussian beams are propagated according to a derived approximate expression that is based on the Rayleigh-Sommerfeld diffraction model. We assume that the given curved surface is smooth enough that the Gaussian window functions on it can be treated as written on planar patches. For the surfaces that satisfy this assumption, the simulation results show that the proposed method produces quite accurate 3D field solutions.
Open Access
Source and filter estimation for Throat-Microphone speech enhancement
(Institute of Electrical and Electronics Engineers Inc., 2016) Turan, M. A. T.; Erzin, E.
In this paper, we propose a new statistical enhancement system for throat microphone recordings through source and filter separation. Throat microphones (TM) are skin-attached piezoelectric sensors that can capture speech sound signals in the form of tissue vibrations. Due to their limited bandwidth, TM recorded speech suffers from intelligibility and naturalness. In this paper, we investigate learning phone-dependent Gaussian mixture model (GMM)-based statistical mappings using parallel recordings of acoustic microphone (AM) and TM for enhancement of the spectral envelope and excitation signals of the TM speech. The proposed mappings address the phone-dependent variability of tissue conduction with TM recordings. While the spectral envelope mapping estimates the line spectral frequency (LSF) representation of AM from TM recordings, the excitation mapping is constructed based on the spectral energy difference (SED) of AM and TM excitation signals. The excitation enhancement is modeled as an estimation of the SED features from the TM signal. The proposed enhancement system is evaluated using both objective and subjective tests. Objective evaluations are performed with the log-spectral distortion (LSD), the wideband perceptual evaluation of speech quality (PESQ) and mean-squared error (MSE) metrics. Subjective evaluations are performed with an A/B comparison test. Experimental results indicate that the proposed phone-dependent mappings exhibit enhancements over phone-independent mappings. Furthermore enhancement of the TM excitation through statistical mappings of the SED features introduces significant objective and subjective performance improvements to the enhancement of TM recordings. ©2015 IEEE.
Open Access
Time-frequency analysis of signals using support adaptive Hermite-Gaussian expansions
(Elsevier, 2012-05-18) Alp, Y. K.; Arıkan, Orhan
Since Hermite-Gaussian (HG) functions provide an orthonormal basis with the most compact time-frequency supports (TFSs), they are ideally suited for time-frequency component analysis of finite energy signals. For a signal component whose TFS tightly fits into a circular region around the origin, HG function expansion provides optimal representation by using the fewest number of basis functions. However, for signal components whose TFS has a non-circular shape away from the origin, straight forward expansions require excessively large number of HGs resulting to noise fitting. Furthermore, for closely spaced signal components with non-circular TFSs, direct application of HG expansion cannot provide reliable estimates to the individual signal components. To alleviate these problems, by using expectation maximization (EM) iterations, we propose a fully automated pre-processing technique which identifies and transforms TFSs of individual signal components to circular regions centered around the origin so that reliable signal estimates for the signal components can be obtained. The HG expansion order for each signal component is determined by using a robust estimation technique. Then, the estimated components are post-processed to transform their TFSs back to their original positions. The proposed technique can be used to analyze signals with overlapping components as long as the overlapped supports of the components have an area smaller than the effective support of a Gaussian atom which has the smallest time-bandwidth product. It is shown that if the area of the overlap region is larger than this threshold, the components cannot be uniquely identified. Obtained results on the synthetic and real signals demonstrate the effectiveness for the proposed time-frequency analysis technique under severe noise cases.
Open Access
Unsupervised classification of remotely sensed images using Gaussian mixture models and particle swarm optimization
(IEEE, 2010) Arı, Çağlar; Aksoy, Selim
Gaussian mixture models (GMM) are widely used for un-supervised classification applications in remote sensing. Expectation-Maximization (EM) is the standard algorithm employed to estimate the parameters of these models. However, such iterative optimization methods can easily get trapped into local maxima. Researchers use population-based stochastic search algorithms to obtain better estimates. We present a novel particle swarm optimization-based algorithm for maximum likelihood estimation of Gaussian mixture models. The proposed approach provides solutions for important problems in effective application of population-based algorithms to the clustering problem. We present a new parametrization for arbitrary covariance matrices that allows independent updating of individual parameters during the search process. We also describe an optimization formulation for identifying the correspondence relations between different parameter orderings of candidate solutions. Experiments on a hyperspectral image show better clustering results compared to the commonly used EM algorithm for estimating GMMs. © 2010 IEEE.