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In this paper, we propose a parameter estimation method for nonlinear state-space models based on the variational Bayes. We show that the variational posterior distribution of the hidden states corresponds to a posterior distribution of the states of an augmented nonlinear state-space model. From this, we can obtain the variational posterior distribution of the hidden states by implementing a variety...
A promising direction in deep learning research is to learn representations and simultaneously discover cluster structure in unlabeled data by optimizing a discriminative loss function. Contrary to supervised deep learning, this line of research is in its infancy and the design and optimization of a suitable loss function with the aim of training deep neural networks for clustering is still an open...
In the satellite formation task, due to a satellite failure or formation task change, it is necessary to reconstruct the formation. In the process of reconfiguration of satellites, satellites anti-collision research is one of the most important links. In this paper, by collapsing the initial covariance matrix of the satellite, the collision probability density function is integrated in the region...
This paper considers the problem of range-based decentralized localization in wireless sensor networks when the impulsive measurement noise is present. We develop a robust localization estimator requiring no a priori knowledge of the noise distribution. The approach to robust localization presented here follows the concept of M-estimation and is implemented in a decentralized manner thus suiting the...
In this paper, the cellular mobile communication system with microdiversity and macrodiversity reception operating over Gamma shadowed Weibull multipath fading environment is analyzed. Macrodiversity selection combining (SC) receiver reduces Gamma long term fading effects and three microdiversity SC receivers mitigate Weibull short term fading effects on the system performance. The useful closed form...
A maximum likelihood (ML) approach is presented for estimating the mean of radar cross section (RCS) of a Swerling target in the presence of false measurements and its numerical solution methods are discussed. The ML approaches are based on the maximum likelihood probabilistic data association (ML-PDA) formulation. The numerical solution methods are evaluated through Monte Carlo simulations in terms...
The underwater acoustic communication is characterized with multipath channel affected by the reflection signal from the surface and the bottom, and the ambient noise of the underwater environment which depend on the condition where the measurement has been conducted. This paper presents a performance analysis of BPSK system in the underwater acoustic channel environment with the additive Laplacian...
The focus of this paper is on the design of input shapers for systems with uncertainties in the parameters of the vibratory modes which need to be attenuated. A probabilistic framework is proposed for the design of the robust input shaper, when the uncertain modal parameters are characterized by probability density functions. A convex chance constrained optimization problem is posed to determine the...
This paper presents an all-digital background blind calibration technique for the capacitor mismatch problem in SAR ADCs. It utilizes the redundancy offered using a sub-radix-2 DAC architecture to blindly estimate the mismatch and the assigned weight for each comparator decision. The weights are estimated by building partial histogram windows for the comparator decision vectors. To remove the dependency...
Photovoltaics (PV) are considered as one of the most promising renewable energy source for Singapore. This paper proposes an optimization strategy for a distribution grid that includes PV. The objective is to provide optimal grid operation for seamless integration of distributed generation (DG). A novel approach for grid reconfiguration considering a probabilistic statistical model for the solar irradiance...
Future self-driving cars and current ones with advanced driver assistance systems are expected to interact with other traffic participants, which often are multiple other vehicles. To facilitate the motion planning of the autonomously controlled vehicle in collision avoidance, individual object vehicles with closeness in positions and velocities can be grouped as a single extended moving object. However,...
In the growing context of Machine Type Communications (MTC), synthesized by the concept of Internet of Things (IoT), the need of an increased coverage pushes the industrial companies to develop efficient transmission techniques. Especially, the 3rd Generation Partnership Project (3GPP) already standardized evolutions of the 2G and 4G networks to include IoT features. These new standards share the...
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...
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