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This paper introduces a sequentially motivated approach to processing streams of images from datasets with low memory demands. We utilize fuzzy clustering as an incremental dictionary learning scheme and explain how the corresponding membership functions can be subsequently used in encoding features for image patches. We focus on replicating the codebook learning and classification stages from an...
Temporal evolution in the generative distribution of nonstationary sequential data is challenging to model. This paper presents a method for retaining the information in prior distributions of matrix variate dynamic linear models (MVDLMs) as the eigenspace of sequential data evolves. The method starts by constructing sliding windows â" matrices composed of a fixed number of columns...
A non-coherent vector delay/frequency-locked loop architecture for GNSS receivers is proposed. Two dynamics models are considered: PV (position and velocity) and PVA (position, velocity, and acceleration). In contrast with other vector architectures, the proposed approach does not require the estimation of signals amplitudes. Only coarse estimates of the carrier-to-noise ratios are necessary.
This paper proposes a weighted total least squares approach based on both pseudorange and carrier phase measurements. The paper makes use of the weighted total least squares solution to solve the global positioning system (GPS) navigation equation determining the user position. The total least squares estimation considers both measurements vector and observable data matrix errors which common least...
A Gaussian multiple-input multiple-output (MIMO) wiretap channel model is considered, where there exists a transmitter, a legitimate receiver and an eavesdropper each equipped with multiple antennas. The optimality of beamforming for secrecy capacity subject to sum power constraint is studied, and two sufficient conditions for beamforming to be globally optimal are given. The first sufficient condition...
Deployed high-latency anonymous communication systems conceal communication patterns using pool mixes as building blocks. These mixes are known to be vulnerable to Disclosure Attacks that uncover persistent relationships between users. In this paper we study the performance of the Least Squares Disclosure Attack (LSDA), an approach to disclosure rooted in Maximum Likelihood parameter estimation that...
Gaussian Mixture Models (GMMs) are powerful tools for probability density modeling and soft clustering. They are widely used in data mining, signal processing and computer vision. In many applications, we need to estimate the parameters of a GMM from data before working with it. This task can be handled by the Expectation-Maximization algorithm for Gaussian Mixture Models (EM-GMM), which is computationally...
In this paper, frequency-domain subspace-based algorithms are proposed to estimate discrete-time cross-power spectral density (cross-PSD) and auto-power spectral density (auto-PSD) matrices of vector auto-regressive moving-average and moving-average (ARMAMA) models from sampled values of the Welch cross-PSD and auto-PSD estimators on uniform grids of frequencies. The proposed algorithms are shown...
Consensus-based distributed estimation schemes are becoming increasingly popular in sensor networks due to their scalability and fault tolerance capabilities. In a consensus-based state estimation framework, multiple neighboring nodes iteratively communicate with each other, exchanging their own local estimates of a target's state with the goal of converging to a single state estimate over the entire...
In this paper, application-oriented experiment design formulated as a chance constrained problem is investigated. The chance constraint is based on the presumption that the estimated model can be used in an application to achieve a given performance level with a prescribed probability. The aforementioned performance level is dictated by the particular application of interest. The resulting optimization...
As more and more functionalities are packed into a single product, one-response-at-a-time correlation analysis is no longer sufficient to discover critical factors that result in poor qualities or a low yield. Though methodologies of many-to-many correlation analysis have been proposed in the literature, difficulties arise, especially when there exist multi-collinearity effects among variables, to...
This papers concerns an extension of an asymptotic variance expression for Finite Impulse Response (FIR) model based frequency response estimation to the Output Error (OE) system identification case. It is based on the “useful input parametrization” for OE models, in which the Toeplitz covariance matrix structure instrumental in the FIR analysis is extended to OE model input representation after a...
This paper introduces a robust penalty-game approach to deal with the filtering problem for discrete-time Markovian jump linear systems subject to parametric uncertainties. The optimal and sub-optimal solutions provided are based on recursive Riccati equations which do not depend on any auxiliary parameters to be adjusted. A numerical example is shown to illustrate the effectiveness of this new approach.
A suboptimal state estimation algorithm is proposed for a discrete time stochastic multi-mode switched system, with a finite number of modes. The system state is transferred from one mode to the next. The parameters in the different modes are assumed to be known, but the mode-switching is random. The proposed filter is derived by extending the innovations approach to the problem of estimating the...
This paper investigates the small sample-size problem in i-vector based speaker verification systems. The idea of i-vectors is to represent the characteristics of speakers in the factors of a factor analyzer. Because the factor loading matrix defines the possible speakerand channel-variability of i-vectors, it is important to suppress the unwanted channel variability. Linear discriminant analysis...
The use of GPS carrier-phase measurements from multiple receivers can be used in the determination of multiple baseline vectors. This can be used for the high precision determination of a vehicle attitude. The use of these measurements requires the determination of the integer carrier phase ambiguities, which is a problem usually addressed with dual-frequency receivers. However, the high cost of these...
With the expected growth of Machine-to-Machine (M2M) communication, new requirements for future communication systems have to be considered. Traffic patterns in M2M communication fundamentally differ from human based communication. Especially packets in M2M are rather small and transmitted sporadically only. Moreover, nodes for M2M communication are often of reduced functionality which makes complex...
In this paper, we develop a unified Bayesian approach that enables the prediction of binary random events and random scalar fields from heterogeneous data collected by mobile sensor networks with different detectors and sensors. The heterogeneous uncertainties such as different false detection rates and measurement noises are taken into account. This proposed unified approach exploits the statistical...
This paper investigates the asymptotic analysis of the subspace approach for vector ARMA process estimation. To overcome the statistical insufficiency of data, a new and straightforward LMI-based approach is proposed to obtain a positive real covariance model. Numerical results show this approach performs well even if the system poles are very close to the unit circle. Then, an explicit expression...
This paper presents a distributed moving horizon estimator (DMHE) based on dual decomposition. The DMHE is equivalent to a centralized Kalman filter and allows the distributed implementation of any centralized controller. This equivalence is achieved by formulating the estimation problem as a suitable convex optimization problem. The cost function is defined on a sliding window involving a finite...
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