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We develop a model-free technique to identify weak sources within dense sensor arrays using graph clustering. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial coherence matrix of a wave field as a matrix whose support is a connectivity matrix of a graph with...
This paper proposes a new TDOA estimation based on phase-voting cross correlation and circular standard deviation. Based on phase delay and kernel function, the proposed method generates a probability density function (PDF) of TDOA for each frequency bin. TDOA estimate is determined by voting the PDFs generated for all frequency bins. Peak positions of the bin-wise PDFs for the target signal are concentrated...
In this paper, a novel PC based approach to model the impact of both aleatory (random) and epistemic (ignorance based) uncertainty on the performance of carbon nanotube (CNT) interconnects is presented. The key feature of this approach is a dimension fusion strategy whereby the aleatory and epistemic uncertainty in a model parameter is collectively represented using a single mixed variable. Thereafter,...
Currently the number of applications where the data generation function is not known has been growing, making necessary the use of non-parametric estimation techniques to describe such model. Therefore, relevant questions emerge regarding the quality of the model that represents some dataset and how to quantify this quality. This article aims to evaluate some of the measurements presented in the literature...
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...
A novel solution of the inverse Frobenius-Perron problem for constructing semi-Markov chaotic maps with prescribed statistical properties is presented. The proposed solution uses recursive Markov state disaggregation to construct an ergodic map with a piecewise constant invariant density function that approximates an arbitrary probability distribution over a compact interval. The solution is novel...
The power-line communication (PLC) channel causes information-bearing signals to be affected by impulsive noise and the effects of the multipath fading. To mitigate these effects, we propose the employment of non-binary turbo codes, since non-binary error-correcting codes generally promise an enhanced performance in such harsh environments. In this paper, we investigate the performance of non-binary...
The modeling of speech can be used for speech synthesis and speech recognition. We present a speech analysis method based on pole-zero modeling of speech with mixed block sparse and Gaussian excitation. By using a pole-zero model, instead of the all-pole model, a better spectral fitting can be expected. Moreover, motivated by the block sparse glottal flow excitation during voiced speech and the white...
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized...
In this paper, we apply probability density function (PDF) projection to arrive at an exact closed-form expression for the marginal distribution of the visible data of a restricted Boltzmann machine (RBM) without requiring integrating over the distribution of the hidden variables or needing to know the partition function. We express the visible data marginal as a projected PDF based on a set of sufficient...
This paper addresses the problem of multichannel audio source separation in under-determined convolutive mixtures. We target a semi-blind scenario assuming that the mixing filters are known. The convolutive mixing process is exactly modeled using the time-domain impulse responses of the mixing filters. We propose a Student's t time-frequency source model based on non-negative matrix factorization...
In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.
Jitter is a critical factor to the performance of highspeed signal links. Jitter can be modeled as a random process. Both the probability density function (PDF) and the spectral characteristics of the jitter are important for evaluating the impact to the channel performance. The concept of numerical conditional probability density function (NCPDF) and a new statistical method called FastBER are proposed...
Statistical link analysis and link budget calculation is the essential part for current high-speed system design. Because of the difficulty of low bit-error rate (BER) simulation at SPICE transient solver, several methods were proposed to simulate low BER in a short time. Single-bit response (SBR) method is the most basic method; it uses SBR to construct the arbitrary waveform under the assumption...
We investigate the estimation of normalisation constants of probability distributions using nonlinear importance sampling (IS). This is a problem that involves the solution of complicated multidimensional integrals and, in general, does not admit a closed-form solution or approximation. It is especially relevant for Bayesian model assessment problems, where the normalisation constant of the posterior...
Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling (IS) and Markov Chain Monte Carlo (MCMC) approaches. Different parallel MCMC chains provide the location parameters of the proposal probability density functions...
Cognitive radio (CR) has been identified as an enabling technology toward meeting the high spectrum utilization efficiency demand in future internet-of-things (IoT) systems. Development of new spectrum sensing schemes better suited to CR-based IoT networks, which are typically heterogeneous with perfect network-wide synchronization difficult to achieve, is thus rather crucial. Motivated by the low-complexity...
Structural Health Monitoring utilizes sensor network system embedded within structure to evaluate the size and location of structural damage, and perform the remaining useful life prediction and reliability assessment. Piezoelectric (PZT) and intelligent coating monitoring (ICM) sensors are two types of sensors which have been reported for efficient damage detection in real engineering fields. For...
Aiming at the ordering problem of the key spare parts whose life is consistent with Weibull distribution, an optimization method based on residual life prediction is proposed considering both residual life and the lead time. Firstly, the Weibull distribution parameters of the component life are estimated according to the historical data of the airline and the state change information of the part on...
The space-based precipitation products are commonly used for regional and/or global hydrologic modelling and climate studies. However, the accuracy of onboard satellite measurements is limited due to the spatial-temporal sampling limitations, especially for extreme events such as very heavy or light rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation...
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