34,641 research outputs found
A Novel Generic Framework for Track Fitting in Complex Detector Systems
This paper presents a novel framework for track fitting which is usable in a
wide range of experiments, independent of the specific event topology, detector
setup, or magnetic field arrangement. This goal is achieved through a
completely modular design. Fitting algorithms are implemented as
interchangeable modules. At present, the framework contains a validated Kalman
filter. Track parameterizations and the routines required to extrapolate the
track parameters and their covariance matrices through the experiment are also
implemented as interchangeable modules. Different track parameterizations and
extrapolation routines can be used simultaneously for fitting of the same
physical track. Representations of detector hits are the third modular
ingredient to the framework. The hit dimensionality and orientation of planar
tracking detectors are not restricted. Tracking information from detectors
which do not measure the passage of particles in a fixed physical detector
plane, e.g. drift chambers or TPCs, is used without any simplifications. The
concept is implemented in a light-weight C++ library called GENFIT, which is
available as free software
Dictionary Learning-based Inpainting on Triangular Meshes
The problem of inpainting consists of filling missing or damaged regions in
images and videos in such a way that the filling pattern does not produce
artifacts that deviate from the original data. In addition to restoring the
missing data, the inpainting technique can also be used to remove undesired
objects. In this work, we address the problem of inpainting on surfaces through
a new method based on dictionary learning and sparse coding. Our method learns
the dictionary through the subdivision of the mesh into patches and rebuilds
the mesh via a method of reconstruction inspired by the Non-local Means method
on the computed sparse codes. One of the advantages of our method is that it is
capable of filling the missing regions and simultaneously removes noise and
enhances important features of the mesh. Moreover, the inpainting result is
globally coherent as the representation based on the dictionaries captures all
the geometric information in the transformed domain. We present two variations
of the method: a direct one, in which the model is reconstructed and restored
directly from the representation in the transformed domain and a second one,
adaptive, in which the missing regions are recreated iteratively through the
successive propagation of the sparse code computed in the hole boundaries,
which guides the local reconstructions. The second method produces better
results for large regions because the sparse codes of the patches are adapted
according to the sparse codes of the boundary patches. Finally, we present and
analyze experimental results that demonstrate the performance of our method
compared to the literature
Computational polarimetric microwave imaging
We propose a polarimetric microwave imaging technique that exploits recent
advances in computational imaging. We utilize a frequency-diverse cavity-backed
metasurface, allowing us to demonstrate high-resolution polarimetric imaging
using a single transceiver and frequency sweep over the operational microwave
bandwidth. The frequency-diverse metasurface imager greatly simplifies the
system architecture compared with active arrays and other conventional
microwave imaging approaches. We further develop the theoretical framework for
computational polarimetric imaging and validate the approach experimentally
using a multi-modal leaky cavity. The scalar approximation for the interaction
between the radiated waves and the target---often applied in microwave
computational imaging schemes---is thus extended to retrieve the susceptibility
tensors, and hence providing additional information about the targets.
Computational polarimetry has relevance for existing systems in the field that
extract polarimetric imagery, and particular for ground observation. A growing
number of short-range microwave imaging applications can also notably benefit
from computational polarimetry, particularly for imaging objects that are
difficult to reconstruct when assuming scalar estimations.Comment: 17 pages, 15 figure
Gravity-flow dominated sedimentation on the Buda paleoslope (Hungary): Record of Late Eocene continental escape of the Bakony unit
The Upper Eocene sequence of the Buda Hills consists of fluvial and shallow marine conglomerates, sandstones, bioclastic shallow-water limestone, marlstone and pelagic Globigerina marl. The succession illustrates rapid, overall subsidence of the area, from terrestrial environments to bathyal depths. Sedimentation occurred on slopes situated on the flanks of synsedimentary basement antiforms. Vertical growth of antiforms caused progressive tilting of beds, layerparallel extension by boudinage and faulting, and induced redeposition by mass flow. Antiforms are localised in the dextral Budaörs shear zone and in the Buda
imbricate stack, which accommodated the dextral displacement. The latter is underlain by blind reverse faults probably merging into a detachment tault at shallow
depths. These structures were formed by WNW-ESE
oriented compression and NNE-SSW directed tension. The morphological expression of the imbricate stack is the SE-facing Buda slope.
The Bakony unit, while "escaping" from the Alps, was bordered by a northern sinistral and a southern dextral
shear zone. Synsedimentary tectonics in the Buda Hills demonstrates the style of deformation inside the escaping block, close to the southern border zone. Tectonically controlled sedimentation suggests that escape tectonics was active as early as Late Eocene time
Reconstructing the Traffic State by Fusion of Heterogeneous Data
We present an advanced interpolation method for estimating smooth
spatiotemporal profiles for local highway traffic variables such as flow, speed
and density. The method is based on stationary detector data as typically
collected by traffic control centres, and may be augmented by floating car data
or other traffic information. The resulting profiles display transitions
between free and congested traffic in great detail, as well as fine structures
such as stop-and-go waves. We establish the accuracy and robustness of the
method and demonstrate three potential applications: 1. compensation for gaps
in data caused by detector failure; 2. separation of noise from dynamic traffic
information; and 3. the fusion of floating car data with stationary detector
data.Comment: For more information see http://www.mtreiber.de or
http://www.akesting.d
Scalable wavelet-based coding of irregular meshes with interactive region-of-interest support
This paper proposes a novel functionality in wavelet-based irregular mesh coding, which is interactive region-of-interest (ROI) support. The proposed approach enables the user to define the arbitrary ROIs at the decoder side and to prioritize and decode these regions at arbitrarily high-granularity levels. In this context, a novel adaptive wavelet transform for irregular meshes is proposed, which enables: 1) varying the resolution across the surface at arbitrarily fine-granularity levels and 2) dynamic tiling, which adapts the tile sizes to the local sampling densities at each resolution level. The proposed tiling approach enables a rate-distortion-optimal distribution of rate across spatial regions. When limiting the highest resolution ROI to the visible regions, the fine granularity of the proposed adaptive wavelet transform reduces the required amount of graphics memory by up to 50%. Furthermore, the required graphics memory for an arbitrary small ROI becomes negligible compared to rendering without ROI support, independent of any tiling decisions. Random access is provided by a novel dynamic tiling approach, which proves to be particularly beneficial for large models of over 10(6) similar to 10(7) vertices. The experiments show that the dynamic tiling introduces a limited lossless rate penalty compared to an equivalent codec without ROI support. Additionally, rate savings up to 85% are observed while decoding ROIs of tens of thousands of vertices
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