3,021 research outputs found
Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks
Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector
Scalable Semantic Matching of Queries to Ads in Sponsored Search Advertising
Sponsored search represents a major source of revenue for web search engines.
This popular advertising model brings a unique possibility for advertisers to
target users' immediate intent communicated through a search query, usually by
displaying their ads alongside organic search results for queries deemed
relevant to their products or services. However, due to a large number of
unique queries it is challenging for advertisers to identify all such relevant
queries. For this reason search engines often provide a service of advanced
matching, which automatically finds additional relevant queries for advertisers
to bid on. We present a novel advanced matching approach based on the idea of
semantic embeddings of queries and ads. The embeddings were learned using a
large data set of user search sessions, consisting of search queries, clicked
ads and search links, while utilizing contextual information such as dwell time
and skipped ads. To address the large-scale nature of our problem, both in
terms of data and vocabulary size, we propose a novel distributed algorithm for
training of the embeddings. Finally, we present an approach for overcoming a
cold-start problem associated with new ads and queries. We report results of
editorial evaluation and online tests on actual search traffic. The results
show that our approach significantly outperforms baselines in terms of
relevance, coverage, and incremental revenue. Lastly, we open-source learned
query embeddings to be used by researchers in computational advertising and
related fields.Comment: 10 pages, 4 figures, 39th International ACM SIGIR Conference on
Research and Development in Information Retrieval, SIGIR 2016, Pisa, Ital
Conditioned spin and charge dynamics of a single electron quantum dot
In this article we describe the incoherent and coherent spin and charge
dynamics of a single electron quantum dot. We use a stochastic master equation
to model the state of the system, as inferred by an observer with access to
only the measurement signal. Measurements obtained during an interval of time
contribute, by a past quantum state analysis, to our knowledge about the system
at any time within that interval. Such analysis permits precise estimation
of physical parameters, and we propose and test a modification of the classical
Baum-Welch parameter re-estimation method to systems driven by both coherent
and incoherent processes.Comment: 9 pages, 9 figure
Space-Time Sampling for Network Observability
Designing sparse sampling strategies is one of the important components in
having resilient estimation and control in networked systems as they make
network design problems more cost-effective due to their reduced sampling
requirements and less fragile to where and when samples are collected. It is
shown that under what conditions taking coarse samples from a network will
contain the same amount of information as a more finer set of samples. Our goal
is to estimate initial condition of linear time-invariant networks using a set
of noisy measurements. The observability condition is reformulated as the frame
condition, where one can easily trace location and time stamps of each sample.
We compare estimation quality of various sampling strategies using estimation
measures, which depend on spectrum of the corresponding frame operators. Using
properties of the minimal polynomial of the state matrix, deterministic and
randomized methods are suggested to construct observability frames. Intrinsic
tradeoffs assert that collecting samples from fewer subsystems dictates taking
more samples (in average) per subsystem. Three scalable algorithms are
developed to generate sparse space-time sampling strategies with explicit error
bounds.Comment: Submitted to IEEE TAC (Revised Version
Thermal Modeling of Additive Manufacturing Using Graph Theory: Validation with Directed Energy Deposition
Metal additive manufacturing (AM/3D printing) offers unparalleled advantages over conventional manufacturing, including greater design freedom and a lower lead time. However, the use of AM parts in safety-critical industries, such as aerospace and biomedical, is limited by the tendency of the process to create flaws that can lead to sudden failure during use. The root cause of flaw formation in metal AM parts, such as porosity and deformation, is linked to the temperature inside the part during the process, called the thermal history. The thermal history is a function of the process parameters and part design.
Consequently, the first step towards ensuring consistent part quality in metal AM is to understand how and why the process parameters and part geometry influence the thermal history. Given the current lack of scientific insight into the causal design-process-thermal physics link that governs part quality, AM practitioners resort to expensive and time-consuming trial-and-error tests to optimize part geometry and process parameters.
An approach to reduce extensive empirical testing is to identify the viable process parameters and part geometry combinations through rapid thermal simulations. However, a major barrier that deters physics-based design and process optimization efforts in AM is the prohibitive computational burden of existing finite element-based thermal modeling.
The objective of this thesis is to understand the causal effect of process parameters on the temperature distribution in AM parts using the theory of heat dissipation on graphs (graph theory). We develop and apply a novel graph theory-based computational thermal modeling approach for predicting the thermal history of titanium alloy parts made using the directed energy deposition metal AM process. As an example of the results obtained for one of the three test parts studied in this work, the temperature trends predicted by the graph theory approach had error ~11% compared to experimental trends. Moreover, the graph theory simulation was obtained within 9 minutes, which is less than the 25 minutes required to print the part.
Advisors: Prahalada K. Rao and Kevin D. Col
Methods of space radiation dose analysis with applications to manned space systems
The full potential of state-of-the-art space radiation dose analysis for manned missions has not been exploited. Point doses have been overemphasized, and the critical dose to the bone marrow has been only crudely approximated, despite the existence of detailed man models and computer codes for dose integration in complex geometries. The method presented makes it practical to account for the geometrical detail of the astronaut as well as the vehicle. Discussed are the major assumptions involved and the concept of applying the results of detailed proton dose analysis to the real-time interpretation of on-board dosimetric measurements
The UTMOST Survey for Magnetars, Intermittent pulsars, RRATs and FRBs I: System description and overview
We describe the ongoing `Survey for Magnetars, Intermittent pulsars, Rotating
radio transients and Fast radio bursts' (SMIRF), performed using the newly
refurbished UTMOST telescope. SMIRF repeatedly sweeps the southern Galactic
plane performing real-time periodicity and single-pulse searches, and is the
first survey of its kind carried out with an interferometer. SMIRF is
facilitated by a robotic scheduler which is capable of fully autonomous
commensal operations. We report on the SMIRF observational parameters, the data
analysis methods, the survey's sensitivities to pulsars, techniques to mitigate
radio frequency interference and present some early survey results. UTMOST's
wide field of view permits a full sweep of the Galactic plane to be performed
every fortnight, two orders of magnitude faster than previous surveys. In the
six months of operations from January to June 2018, we have performed
sweeps of the Galactic plane with SMIRF. Notable blind re-detections include
the magnetar PSR J16224950, the RRAT PSR J09413942 and the eclipsing
pulsar PSR J17482446A. We also report the discovery of a new pulsar, PSR
J170554. Our follow-up of this pulsar with the UTMOST and Parkes telescopes
at an average flux limit of mJy and mJy respectively,
categorizes this as an intermittent pulsar with a high nulling fraction of Comment: Submitted to MNRAS, comments welcom
Pulsar-black hole binaries: prospects for new gravity tests with future radio telescopes
The anticipated discovery of a pulsar in orbit with a black hole is expected
to provide a unique laboratory for black hole physics and gravity. In this
context, the next generation of radio telescopes, like the Five-hundred-metre
Aperture Spherical radio Telescope (FAST) and the Square Kilometre Array (SKA),
with their unprecedented sensitivity, will play a key role. In this paper, we
investigate the capability of future radio telescopes to probe the spacetime of
a black hole and test gravity theories, by timing a pulsar orbiting a
stellar-mass-black-hole (SBH). Based on mock data simulations, we show that a
few years of timing observations of a sufficiently compact pulsar-SBH (PSR-SBH)
system with future radio telescopes would allow precise measurements of the
black hole mass and spin. A measurement precision of one per cent can be
expected for the spin. Measuring the quadrupole moment of the black hole,
needed to test GR's no-hair theorem, requires extreme system configurations
with compact orbits and a large SBH mass. Additionally, we show that a PSR-SBH
system can lead to greatly improved constraints on alternative gravity theories
even if they predict black holes (practically) identical to GR's. This is
demonstrated for a specific class of scalar-tensor theories. Finally, we
investigate the requirements for searching for PSR-SBH systems. It is shown
that the high sensitivity of the next generation of radio telescopes is key for
discovering compact PSR-SBH systems, as it will allow for sufficiently short
survey integration times.Comment: 20 pages, 11 figures, 1 table, accepted for publication in MNRA
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