Generalized Eigenvector Problem Research Articles

Ameliorated PGC demodulation scheme using Taubin least squares fitting of ellipse in fiber optic interferometric sensors

In this paper, we propose an ameliorated phase generated carrier (PGC) demodulation scheme using Taubin least squares (LS) fitting of ellipse to eliminate the nonlinear errors. Taubin LS has carefully chosen a normalization matrix and the ellipse fitting parameters are directly obtained by solving the generalized eigenvector problem, leading to a high accuracy result. Quadrature signals are extracted from the signal fundamental and the sidebands of the fundamental of the carrier, and then corrected by the ellipse fitting algorithm (EFA) based on Taubin LS. A combined internal modulation is used to ensure the proper operation of the EFA under small signals. And the desired phase signal is extracted from the corrected quadrature signals by the differential-cross-multiplying method and evaluated by the total harmonic distortion. Experimental results show that the proposed scheme is superior and highly stable, it has a phase resolution of 3.16 μrad/√Hz, a large dynamic range of 120 dB @ 1 KHz and a good linearity of better than 99.99%.

Efficient Dynamic Latent Variable Analysis for High-Dimensional Time Series Data

Dynamic-inner canonical correlation analysis (DiCCA) extracts dynamic latent variables from high-dimensional time series data with a descending order of predictability in terms of $R^2$ . The reduced dimensional latent variables with rank-ordered predictability capture the dynamic features in the data, leading to easy interpretation and visualization. In this article, numerically efficient algorithms for DiCCA are developed to extract dynamic latent components from high-dimensional time series data. The numerically improved DiCCA algorithms avoid repeatedly inverting a covariance matrix inside the iteration loop of the numerical DiCCA algorithms. A further improvement using singular value decomposition converts the generalized eigenvector problem into a standard eigenvector problem for the DiCCA solution. Another improvement in model efficiency in this article is the dynamic model compaction of the extracted latent scores using autoregressive integrated moving average (ARIMA) models. Integrating factors, if existed in the latent variable scores, are made explicit in the ARIMA models. Numerical tests on two industrial datasets are provided to illustrate the improvements.

Deterministic Guarantees for Burer‐Monteiro Factorizations of Smooth Semidefinite Programs

AbstractWe consider semidefinite programs (SDPs) with equality constraints. The variable to be optimized is a positive semidefinite matrix X of size n. Following the Burer‐Monteiro approach, we optimize a factor Y of size n × p instead, such that X = YYT. This ensures positive semidefiniteness at no cost and can reduce the dimension of the problem if p is small, but results in a nonconvex optimization problem with a quadratic cost function and quadratic equality constraints in Y. In this paper, we show that if the set of constraints on Y regularly defines a smooth manifold, then, despite nonconvexity, first‐ and second‐order necessary optimality conditions are also sufficient, provided p is large enough. For smaller values of p, we show a similar result holds for almost all (linear) cost functions. Under those conditions, a global optimum Y maps to a global optimum X = YYT of the SDP. We deduce old and new consequences for SDP relaxations of the generalized eigenvector problem, the trust‐region subproblem, and quadratic optimization over several spheres, as well as for the Max‐Cut and Orthogonal‐Cut SDPs, which are common relaxations in stochastic block modeling and synchronization of rotations. © 2019 Wiley Periodicals, Inc.

Confidential Multicasting Assisted by Multi-Hop Multi-Antenna DF Relays in the Presence of Multiple Eavesdroppers

In this paper, we consider confidential multicasting in multi-hop multi-antenna decode-and-forward (DF) relay networks, where a source sends a secure common message to multiple destinations in the presence of multiple eavesdroppers with the help of a multi-antenna DF relay located at each hop. Under an overall power constraint, we first show that the optimization problem for relay beamformer design and power allocation to maximize the achievable multicast secrecy rate in a two-hop multi-antenna relay network can be solved by using semidefinite relaxation and bisection technique. Further, we also propose a suboptimal iterative sequential update scheme for a multi-hop relay network including more than two hops. At each iteration, the relay beamformer at each hop is updated sequentially by solving an extended generalized eigenvector problem for a multiple-destination multiple-eavesdropper case, and the power allocation is updated by using the bisection technique with the feasibility problem of linear programming. Numerical results are presented to verify the multicast secrecy rates achieved by the proposed schemes in multi-hop multi-antenna DF relay networks.

Low-Complexity Distributed Beamforming for Relay Networks With Real-Valued Implementation

The distributed beamforming problem for amplify-and-forward relay networks is studied. Maximizing output SNR (signal-to-noise ratio) for distributed beamforming can be considered as a generalized eigenvector problem (GEP) and the principal eigenvector and its eigenvalue can be derived with a standard closed-form solution. In this paper, four classes of beamforming algorithms are derived based on different design criteria and constraints, including maximizing output SNR subject to a constraint on the total transmitted signal power, minimizing the total transmitted signal power subject to certain level of output SNR, minimizing the relay node number subject to constraints on the total signal power and output SNR, and a robust algorithm to deal with channel estimation errors. All of the algorithms have a low computational complexity due to the proposed real-valued implementation.

Generalized eigenvector problem for Hermitian Toeplitz matrices and its application to beamforming

The generalized eigenvector problem (GEP) for Hermitian Toeplitz matrices is studied and some properties related to its eigenvectors and the associated eigenfilters are derived. Zero locations of the eigenfilters are also investigated and all of the results are applied to the maximum SINR (signal-to-interference-plus-noise ratio) beamforming problem based on ULAs (uniform linear arrays), since maximizing output SINR can be formulated as a generalized eigenvector problem where the matrix pair consisting of the desired signal correlation matrix and interference plus noise correlation matrix. Theoretical analysis based on a three-element ULA is provided, supported by simulations.

Low Complexity Non-Iterative Coordinated Beamforming in 2-User Broadcast Channels

We propose a new non-iterative coordinated beamforming scheme to obtain full multiplexing gain in 2-user MIMO systems. In order to find the beamforming and combining matrices, we solve a generalized eigenvector problem and describe how to find generalized eigenvectors according to the Gaussian broadcast channels. Selected simulation results show that the proposed method yields the same sum-rate performance as the iterative coordinated beamforming method, while maintaining lower complexity by non-iterative computation of the beamforming and combining matrices. We also show that the proposed method can easily exploit selective gain by choosing the best combination of generalized eigenvectors.

Learning Multimodal Dictionaries

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

Calculation of transmission zeros using QZ techniques

The computation of various ‘system zeros’ is investigated through application of QZ-type algorithms for the nonsymmetric generalized eigenvalue problem. Such algorithms use unitary similarities to efficiently reduce the problem to one where the zeros may be determined in a useful, accurate, and dependable manner. Recent reliable and sophisticated analysis and software (specifically, EISPACK) developed by numerical linear algebra specialists is used. EISPACK, moreover, is widely available and can be applied directly to the transmission zero problem. Examples and timing estimates are given and the associated generalized eigenvector problem is noted with its application to the computation of supremal ( A,B)-invariant and controllability subspaces.