827 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
Matrix-valued Monge-Kantorovich Optimal Mass Transport
We formulate an optimal transport problem for matrix-valued density
functions. This is pertinent in the spectral analysis of multivariable
time-series. The "mass" represents energy at various frequencies whereas, in
addition to a usual transportation cost across frequencies, a cost of rotation
is also taken into account. We show that it is natural to seek the
transportation plan in the tensor product of the spaces for the two
matrix-valued marginals. In contrast to the classical Monge-Kantorovich
setting, the transportation plan is no longer supported on a thin zero-measure
set.Comment: 11 page
Analyzing eyebrow region for morphed image detection
Facial images in passports are designated as primary identifiers for the
verification of travelers according to the International Civil Aviation
Organization (ICAO). Hence, it is important to ascertain the sanctity of the
facial images stored in the electronic Machine-Readable Travel Document
(eMRTD). With the introduction of automated border control (ABC) systems that
rely on face recognition for the verification of travelers, it is even more
crucial to have a system to ensure that the image stored in the eMRTD is free
from any alteration that can hinder or abuse the normal working of a facial
recognition system. One such attack against these systems is the face-morphing
attack. Even though many techniques exist to detect morphed images, morphing
algorithms are also improving to evade these detections. In this work, we
analyze the eyebrow region for morphed image detection. The proposed method is
based on analyzing the frequency content of the eyebrow region. The method was
evaluated on two datasets that each consisted of morphed images created using
two algorithms. The findings suggest that the proposed method can serve as a
valuable tool in morphed image detection, and can be used in various
applications where image authenticity is critical
Conal Distances Between Rational Spectral Densities
This paper generalizes Thompson and Hilbert
metrics to the space of spectral densities. The resulting
complete metric space has the differentiable structure of a
Finsler manifold with explicit geodesics. The corresponding distances are filtering invariant, can be computed efficiently, and admit geodesic paths that preserve rationality; these are properties of fundamental importance in many
engineering applications.European Research Counci
Analysis of Heat Transfer on Turbulence Generating Ribs using Dynamic Mode Decomposition
Ducts with turbulence-promoting ribs are common in heat transfer applications. This study uses a recent modal extraction technique called Dynamic Mode Decomposition (DMD) to determine mode shapes of the spatially and temporally complex flowfield inside a ribbed duct. One subject missing from current literature is a method of directly linking a mode to a certain engineering quantity of interest. Presented is a generalized methodology for producing such a link utilizing the data from the DMD analysis. Theory suggests exciting the modes which are identified may cause the flow to change in such a way to promote the quantity of interest, in this case, heat transfer. This theory is tested by contouring the walls of the duct by the extracted mode shapes. The test procedure is taken from an industrial perspective. An initial, unmodified geometry provides a baseline for comparison to later contoured models. The initial case is run as a steady-state Reynolds-Averaged Navier-Stokes model. Large-Eddy Simulation generates the necessary data for the DMD analysis. Several mode shapes extracted from the flow are applied to the duct walls and run again in the RANS model, then compared to the baseline, and their relative performance examined
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PHOTOTHERMAL AND PHOTOCHEMICAL STRATEGIES FOR LIGHTINDUCED SHAPE-MORPHING OF SOFT MATERIALS
Engineering materials with the capability to transform energy from photons into mechanical work is an outstanding technical challenge with implications across myriad disciplines. Despite decades of work in this area, comprehensive understanding of how to prescribe shape change and work output in photoactive systems remains limited. To this end, this dissertation explores strategies to assemble photothermal and photochemical moieties in soft material systems to fabricate photoaddressable devices capable of specific shape changes upon illumination. Chapters 2 and 3 describe a methodology for spatially patterning plasmonic nanoparticles in liquid crystal elastomer fibers and sheets to specify local photothermally-induced strain profiles. Using this platform, devices capable of deployment into specific 3D configurations in response to both waveguided light and flood illumination are demonstrated. Next, to circumvent the inherent limitation of approaches based on photothermal effects, two new strategies for shape programming azobenzene-containing materials are explored for athermal photoactuation. In Chapter 4, a new material platform is presented that uses azobenzene incorporated into the backbone of polymers to modulate crystallinity on-demand via photoisomerization for next-generation shape memory systems. Next, host-guest cyclodextrin-azobenzene systems are shown in Chapter 5 to enable robust, re-programmable shape changes in hydrogels. Lastly, in Chapter 6 an outlook for the future of the field and an identification of areas in need of further study are presented
Face morphing detection in the presence of printing/scanning and heterogeneous image sources
Face morphing represents nowadays a big security threat in the context of
electronic identity documents as well as an interesting challenge for
researchers in the field of face recognition. Despite of the good performance
obtained by state-of-the-art approaches on digital images, no satisfactory
solutions have been identified so far to deal with cross-database testing and
printed-scanned images (typically used in many countries for document issuing).
In this work, novel approaches are proposed to train Deep Neural Networks for
morphing detection: in particular generation of simulated printed-scanned
images together with other data augmentation strategies and pre-training on
large face recognition datasets, allowed to reach state-of-the-art accuracy on
challenging datasets from heterogeneous image sources
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