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Sparse subspace clustering (SSC) is an effective approach to cluster high-dimensional data. However, how to adaptively select the number of clusters/eigenvectors for different data sets, especially when the data are corrupted by noise, is a big challenge in SSC and also an open problem in field of data mining. In this paper, considering the fact that the eigenvectors are robust to noise, we develop...
Compressive spectral clustering (CSC) efficiently leverages graph filter and random sampling techniques to speed up clustering process. However, we find that CSC algorithm suffers from two main problems: i) The direct use of the dichotomy and eigencount techniques for estimating laplacian matrix’s k-th eigenvalue is expensive. ii) The computation of polynomial approximation repeats in each iteration...
This paper formulates and studies the problem of distributed filtering based on randomized gossip strategy in order to estimate the state of a dynamic system via all sensors in a network. First we introduce the randomized gossip algorithm by which the fastest averaging strategy can be obtained for a network with an arbitrary topology. Then we combine the randomized gossip algorithm with the information...
For complex large scale networks, like social networks, it is typically impossible to observe complete information about their topology structure or link weight directly. A recent proposal, the network resonance method, can estimate the eigenvalues and eigenvectors of the Laplacian matrix for representing network structure, by using the resonance phenomena of oscillation dynamics on networks. However,...
We show variants of spectral sparsification routines can preserve the totalspanning tree counts of graphs, which by Kirchhoffs matrix-tree theorem, isequivalent to determinant of a graph Laplacian minor, or equivalently, of any SDDM matrix. Our analyses utilizes this combinatorial connection to bridge between statisticalleverage scores / effective resistances and the analysis of random graphsby [Janson,...
Air traffic and on-board data access services are expected to have tremendous growth, possibly even exponentially in the near future. In order to meet these challenges, Orthogonal Frequency Division Multiplexing (OFDM) can be deployed to enable the increased usage of broadband services on-board. The Doppler effect is one of the major issues in implementing OFDM modulation in aeronautical satellite...
State-of-the-art sensing methods only exploit three dimensions of the spectrum space: frequency, time and geography whereas the angle dimension, that is, spatial spectrum sensing has not been exploited well enough. In this paper, we apply the multiple signal classification (MUSIC) Angle of Arrival (AoA) estimation method into spectrum sensing. Note that MUSIC method needs to know the number of signals,...
In this paper we present a new method for creating polynomial solvers for problems where a (possibly infinite) subset of the solutions are undesirable or uninteresting. These solutions typically arise from simplifications made during modeling, but can also come from degeneracies which are inherent to the geometry of the original problem. The proposed approach extends the standard action matrix method...
This paper proposes a filter-based distributed protocol to realize time synchronization under time-varying clock parameters. The proposed protocol is derived from a first-order controller and is fully distributed, meaning that by relying merely on its local clock readings and reading announcements from its neighbouring sensor nodes, each node in WSNs can dynamically update its virtual clock and bound...
Targets number estimation is an important part to the resolution of unresolved targets. Based on Gerschgorin disks estimation (GDE), we propose a method to estimate unresolved targets number with a single snapshot. The proposed method consists of the virtual image array spatial smoothing (VIASS) and improved Gerschgorin disks estimation (IGDE). A full rank covariance matrix can be obtained while avoiding...
The robustness of adaptive beamforming is relate to the input signal-to-noise ratio (SNR). In order to further improve the performance of input SNR estimation, a modified method of input SNR estimation for robust adaptive beamforming is proposed. Comparatively accurate value of the input SNR can be obtained by the proposed method, especially when the input SNR exceeds 0 dB. When the proposed method...
The most recent estimation algorithms for Phasor Measurement Units (PMUs) rely on increasingly complex signal models. The rationale of this general approach is that such models can support algorithms able to estimate not only amplitude, phase, frequency and rate of change of frequency (ROCOF) of the waveform fundamental component, but also the parameters of possible disturbances, thus reducing their...
We consider the problem of enabling robust range estimation of eigenvalue decomposition (EVD) algorithm for a reliable fixed-point design. The simplicity of fixed-point circuitry has always been so tempting to implement EVD algorithms in fixed-point arithmetic. Working towards an effective fixed-point design, integer bit-width allocation is a significant step which has a crucial impact on accuracy...
This paper presents a new model order selection technique for signal processing applications related to source localization or subspace orthogonal projection techniques in large dimensional regime (Random Matrix Theory) when the noise environment is Complex Elliptically Symmetric (CES) distributed, with unknown scatter matrix. The proposed method consists first in estimating the Toeplitz structure...
In Big Data Processing we typically face very large data sets that are highly structured. To save the computation and storage cost, it is desirable to extract the essence of the data from a reduced number of observations. One example of such a structural constraint is sparsity. If the data possesses a sparse representation in a suitable domain, it can be recovered from a small number of linear projections...
The article proposes a method for estimating the shaft position of a synchronous motor with permanent magnets for the zero and very low speed range. Presented method involves a comparison of obtained shape of current hodograph to the reference pattern using the principal component analysis. The method is based on an analyzing of the high frequency currents, which are excited by high frequency additional...
Electromagnetic (EM) vector sensor arrays can track both the polarisation and direction of arrival (DOA) parameters of the impinging signals. For crossed-dipole linear arrays, due to inherent limitation of the structure, it can only track one DOA parameter and two polarisation parameters. This problem could be solved by extending the geometry to a two-dimensional (2-D) rectangular array so that both...
We focus on the problem of source enumeration in large arrays with relatively few samples, which is solved in this paper by using a statistic of corrected Rao's score test (CRST) via the generalized Bayesian information criterion (GBIC). Under the white noise assumption, the covariance matrix of the noise subspace components of the observations is proportional to an identity matrix, and this structure...
S-Parameters are the most common way to describe electrical behavior of linear electric networks. However, any available S-Parameters that is available for describing a physical system is not perfectly accurate and does not describe it comprehensively. It becomes important to be able to estimate the quality of existing data in order to achieve reliability of the results and conclusions developed based...
Signal processing on graphs becomes popular nowadays due to its great potential in processing the large amount of high dimensional data which exhibit irregular structures. As a signal propagates on a graph, the signal at a later time slot can be thought of as the graph-filtered version of the previous one. Therefore, estimation of the graph filter is important and serves as the building block for...
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