20,449 research outputs found
Towards Structural Testing of Superconductor Electronics
Many of the semiconductor technologies are already\ud
facing limitations while new-generation data and\ud
telecommunication systems are implemented. Although in\ud
its infancy, superconductor electronics (SCE) is capable of\ud
handling some of these high-end tasks. We have started a\ud
defect-oriented test methodology for SCE, so that reliable\ud
systems can be implemented in this technology. In this\ud
paper, the details of the study on the Rapid Single-Flux\ud
Quantum (RSFQ) process are presented. We present\ud
common defects in the SCE processes and corresponding\ud
test methodologies to detect them. The (measurement)\ud
results prove that we are able to detect possible random\ud
defects for statistical purposes in yield analysis. This\ud
paper also presents possible test methodologies for RSFQ\ud
circuits based on defect oriented testing (DOT)
Multi-q Pattern Classification of Polarization Curves
Several experimental measurements are expressed in the form of
one-dimensional profiles, for which there is a scarcity of methodologies able
to classify the pertinence of a given result to a specific group. The
polarization curves that evaluate the corrosion kinetics of electrodes in
corrosive media are an application where the behavior is chiefly analyzed from
profiles. Polarization curves are indeed a classic method to determine the
global kinetics of metallic electrodes, but the strong nonlinearity from
different metals and alloys can overlap and the discrimination becomes a
challenging problem. Moreover, even finding a typical curve from replicated
tests requires subjective judgement. In this paper we used the so-called
multi-q approach based on the Tsallis statistics in a classification engine to
separate multiple polarization curve profiles of two stainless steels. We
collected 48 experimental polarization curves in aqueous chloride medium of two
stainless steel types, with different resistance against localized corrosion.
Multi-q pattern analysis was then carried out on a wide potential range, from
cathodic up to anodic regions. An excellent classification rate was obtained,
at a success rate of 90%, 80%, and 83% for low (cathodic), high (anodic), and
both potential ranges, respectively, using only 2% of the original profile
data. These results show the potential of the proposed approach towards
efficient, robust, systematic and automatic classification of highly non-linear
profile curves.Comment: 12 pages, 7 figure
Mining Fix Patterns for FindBugs Violations
In this paper, we first collect and track a large number of fixed and unfixed
violations across revisions of software.
The empirical analyses reveal that there are discrepancies in the
distributions of violations that are detected and those that are fixed, in
terms of occurrences, spread and categories, which can provide insights into
prioritizing violations.
To automatically identify patterns in violations and their fixes, we propose
an approach that utilizes convolutional neural networks to learn features and
clustering to regroup similar instances. We then evaluate the usefulness of the
identified fix patterns by applying them to unfixed violations.
The results show that developers will accept and merge a majority (69/116) of
fixes generated from the inferred fix patterns. It is also noteworthy that the
yielded patterns are applicable to four real bugs in the Defects4J major
benchmark for software testing and automated repair.Comment: Accepted for IEEE Transactions on Software Engineerin
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An Assessment of PIER Electric Grid Research 2003-2014 White Paper
This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of Californiaâs California Institute for Energy and the Environment, from 2003-2014
A New 3-D automated computational method to evaluate in-stent neointimal hyperplasia in in-vivo intravascular optical coherence tomography pullbacks
Abstract. Detection of stent struts imaged in vivo by optical coherence
tomography (OCT) after percutaneous coronary interventions (PCI) and
quantification of in-stent neointimal hyperplasia (NIH) are important.
In this paper, we present a new computational method to facilitate the
physician in this endeavor to assess and compare new (drug-eluting)
stents. We developed a new algorithm for stent strut detection and utilized
splines to reconstruct the lumen and stent boundaries which provide
automatic measurements of NIH thickness, lumen and stent area. Our
original approach is based on the detection of stent struts unique characteristics:
bright reflection and shadow behind. Furthermore, we present
for the first time to our knowledge a rotation correction method applied
across OCT cross-section images for 3D reconstruction and visualization
of reconstructed lumen and stent boundaries for further analysis in
the longitudinal dimension of the coronary artery. Our experiments over
OCT cross-sections taken from 7 patients presenting varying degrees of
NIH after PCI illustrate a good agreement between the computer method
and expert evaluations: Bland-Altmann analysis revealed a mean difference
for lumen cross-section area of 0.11 ± 0.70mm2 and for the stent
cross-section area of 0.10 ± 1.28mm2
Automatic learning of gait signatures for people identification
This work targets people identification in video based on the way they walk
(i.e. gait). While classical methods typically derive gait signatures from
sequences of binary silhouettes, in this work we explore the use of
convolutional neural networks (CNN) for learning high-level descriptors from
low-level motion features (i.e. optical flow components). We carry out a
thorough experimental evaluation of the proposed CNN architecture on the
challenging TUM-GAID dataset. The experimental results indicate that using
spatio-temporal cuboids of optical flow as input data for CNN allows to obtain
state-of-the-art results on the gait task with an image resolution eight times
lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN)
for gait recognition. Data and code:
http://www.uco.es/~in1majim/research/cnngaitof.htm
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