489 research outputs found
Interpolation and Extrapolation of Toeplitz Matrices via Optimal Mass Transport
In this work, we propose a novel method for quantifying distances between
Toeplitz structured covariance matrices. By exploiting the spectral
representation of Toeplitz matrices, the proposed distance measure is defined
based on an optimal mass transport problem in the spectral domain. This may
then be interpreted in the covariance domain, suggesting a natural way of
interpolating and extrapolating Toeplitz matrices, such that the positive
semi-definiteness and the Toeplitz structure of these matrices are preserved.
The proposed distance measure is also shown to be contractive with respect to
both additive and multiplicative noise, and thereby allows for a quantification
of the decreased distance between signals when these are corrupted by noise.
Finally, we illustrate how this approach can be used for several applications
in signal processing. In particular, we consider interpolation and
extrapolation of Toeplitz matrices, as well as clustering problems and tracking
of slowly varying stochastic processes
Sum-of-Squares approach to feedback control of laminar wake flows
A novel nonlinear feedback control design methodology for incompressible
fluid flows aiming at the optimisation of long-time averages of flow quantities
is presented. It applies to reduced-order finite-dimensional models of fluid
flows, expressed as a set of first-order nonlinear ordinary differential
equations with the right-hand side being a polynomial function in the state
variables and in the controls. The key idea, first discussed in Chernyshenko et
al. 2014, Philos. T. Roy. Soc. 372(2020), is that the difficulties of treating
and optimising long-time averages of a cost are relaxed by using the
upper/lower bounds of such averages as the objective function. In this setting,
control design reduces to finding a feedback controller that optimises the
bound, subject to a polynomial inequality constraint involving the cost
function, the nonlinear system, the controller itself and a tunable polynomial
function. A numerically tractable approach to the solution of such optimisation
problems, based on Sum-of-Squares techniques and semidefinite programming, is
proposed.
To showcase the methodology, the mitigation of the fluctuation kinetic energy
in the unsteady wake behind a circular cylinder in the laminar regime at
Re=100, via controlled angular motions of the surface, is numerically
investigated. A compact reduced-order model that resolves the long-term
behaviour of the fluid flow and the effects of actuation, is derived using
Proper Orthogonal Decomposition and Galerkin projection. In a full-information
setting, feedback controllers are then designed to reduce the long-time average
of the kinetic energy associated with the limit cycle. These controllers are
then implemented in direct numerical simulations of the actuated flow. Control
performance, energy efficiency, and physical control mechanisms identified are
analysed. Key elements, implications and future work are discussed
H^∞-Optimal Fractional Delay Filters
Fractional delay filters are digital filters to delay discrete-time signals by a fraction of the sampling period. Since the delay is fractional, the intersample behavior of the original analog signal becomes crucial. In contrast to the conventional designs based on the Shannon sampling theorem with the band-limiting hypothesis, the present paper proposes a new approach based on the modern sampled-data optimization that aims at restoring the intersample behavior beyond the Nyquist frequency. By using the lifting transform or continuous-time blocking the design problem is equivalently reduced to a discrete-time optimization, which can be effectively solved by numerical computation softwares. Moreover, a closed-form solution is obtained under an assumption on the original analog signals. Design examples are given to illustrate the advantage of the proposed method
Convex searches for discrete-time Zames-Falb multipliers
In this paper we develop and analyse convex searches for Zames--Falb
multipliers. We present two different approaches: Infinite Impulse Response
(IIR) and Finite Impulse Response (FIR) multipliers. The set of FIR multipliers
is complete in that any IIR multipliers can be phase-substituted by an
arbitrarily large order FIR multiplier. We show that searches in discrete-time
for FIR multipliers are effective even for large orders. As expected, the
numerical results provide the best -stability results in the
literature for slope-restricted nonlinearities. Finally, we demonstrate that
the discrete-time search can provide an effective method to find suitable
continuous-time multipliers.Comment: 12 page
Fault Detection for Cyber-Physical Systems: Smart Grid case
The problem of fault detection and isolation in
cyber-physical systems is growing in importance following the
trend to have an ubiquitous presence of sensors and actuators
with network capabilities in power networks and other areas. In
this context, attacks to power systems or other vital components
providing basic needs might either present a serious threat or
at least cost a lot of resources. In this paper, we tackle the
problem of having an intruder corrupting a smart grid in two
different scenarios: a centralized detector for a portion of the
network and a fully distributed solution that only has limited
neighbor information. For both cases, differences in strategies
using Set-Valued Observers are discussed and theoretical results
regarding a bound on the maximum magnitude of the attacker’s
signal are provided. Performance is assessed through simulation,
illustrating, in particular, the detection time for various
types of faults in IEEE testbed scenarios
Design and Analysis of A New Illumination Invariant Human Face Recognition System
In this dissertation we propose the design and analysis of a new illumination invariant face recognition system. We show that the multiscale analysis of facial structure and features of face images leads to superior recognition rates for images under varying illumination. We assume that an image I ( x,y ) is a black box consisting of a combination of illumination and reflectance. A new approximation is proposed to enhance the illumination removal phase. As illumination resides in the low-frequency part of images, a high-performance multiresolution transformation is employed to accurately separate the frequency contents of input images. The procedure is followed by a fine-tuning process. After extracting a mask, feature vector is formed and the principal component analysis (PCA) is used for dimensionality reduction which is then proceeded by the extreme learning machine (ELM) as a classifier. We then analyze the effect of the frequency selectivity of subbands of the transformation on the performance of the proposed face recognition system. In fact, we first propose a method to tune the characteristics of a multiresolution transformation, and then analyze how these specifications may affect the recognition rate. In addition, we show that the proposed face recognition system can be further improved in terms of the computational time and accuracy. The motivation for this progress is related to the fact that although illumination mostly lies in the low-frequency part of images, these low-frequency components may have low- or high-resonance nature. Therefore, for the first time, we introduce the resonance based analysis of face images rather than the traditional frequency domain approaches. We found that energy selectivity of the subbands of the resonance based decomposition can lead to superior results with less computational complexity. The method is free of any prior information about the face shape. It is systematic and can be applied separately on each image. Several experiments are performed employing the well known databases such as the Yale B, Extended-Yale B, CMU-PIE, FERET, AT&T, and LFW. Illustrative examples are given and the results confirm the effectiveness of the method compared to the current results in the literature
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