1,382 research outputs found
The 'Paris-end' of town? Urban typology through machine learning
The confluence of recent advances in availability of geospatial information,
computing power, and artificial intelligence offers new opportunities to
understand how and where our cities differ or are alike. Departing from a
traditional `top-down' analysis of urban design features, this project analyses
millions of images of urban form (consisting of street view, satellite imagery,
and street maps) to find shared characteristics. A (novel) neural network-based
framework is trained with imagery from the largest 1692 cities in the world and
the resulting models are used to compare within-city locations from Melbourne
and Sydney to determine the closest connections between these areas and their
international comparators. This work demonstrates a new, consistent, and
objective method to begin to understand the relationship between cities and
their health, transport, and environmental consequences of their design. The
results show specific advantages and disadvantages using each type of imagery.
Neural networks trained with map imagery will be highly influenced by the mix
of roads, public transport, and green and blue space as well as the structure
of these elements. The colours of natural and built features stand out as
dominant characteristics in satellite imagery. The use of street view imagery
will emphasise the features of a human scaled visual geography of streetscapes.
Finally, and perhaps most importantly, this research also answers the age-old
question, ``Is there really a `Paris-end' to your city?''
Identifying safe intersection design through unsupervised feature extraction from satellite imagery
The World Health Organization has listed the design of safer intersections as
a key intervention to reduce global road trauma. This article presents the
first study to systematically analyze the design of all intersections in a
large country, based on aerial imagery and deep learning. Approximately 900,000
satellite images were downloaded for all intersections in Australia and
customized computer vision techniques emphasized the road infrastructure. A
deep autoencoder extracted high-level features, including the intersection's
type, size, shape, lane markings, and complexity, which were used to cluster
similar designs. An Australian telematics data set linked infrastructure design
to driving behaviors captured during 66 million kilometers of driving. This
showed more frequent hard acceleration events (per vehicle) at four- than
three-way intersections, relatively low hard deceleration frequencies at
T-intersections, and consistently low average speeds on roundabouts. Overall,
domain-specific feature extraction enabled the identification of infrastructure
improvements that could result in safer driving behaviors, potentially reducing
road trauma.Comment: 16 pages, 10 figures. Computer-Aided Civil and Infrastructure
Engineering (2020
Flora and vegetation of greenstone formations of the Yilgarn Craton: south-west Ravensthorpe Greenstone Belt
ABSTRACT A quadrat-based survey of the flora of the south-west region of the Ravensthorpe Greenstone Belt identified 321 taxa, including six taxa of conservation significance and three weed species. All of the conservation-listed taxa were known to the area. Range extensions were recorded for three taxa and additional collections of two Austrostipa species currently under taxonomic description were made. Six community types were derived from statistical classification of the 50 quadrats. These community types were similar to those described from other parts of the Ravensthorpe Greenstone Belt. As with the other greenstone belts of the Yilgarn Craton, soil chemical parameters and site physical characteristics were influential in delineating community types. Only a small portion of the study area is in the conservation estate; mining and exploration pressures remain the primary threat to this species-rich area
A picogram and nanometer scale photonic crystal opto-mechanical cavity
We describe the design, fabrication, and measurement of a cavity
opto-mechanical system consisting of two nanobeams of silicon nitride in the
near-field of each other, forming a so-called "zipper" cavity. A photonic
crystal patterning is applied to the nanobeams to localize optical and
mechanical energy to the same cubic-micron-scale volume. The picrogram-scale
mass of the structure, along with the strong per-photon optical gradient force,
results in a giant optical spring effect. In addition, a novel damping regime
is explored in which the small heat capacity of the zipper cavity results in
blue-detuned opto-mechanical damping.Comment: 15 pages, 4 figure
Characterization of radiation pressure and thermal effects in a nanoscale optomechanical cavity
Optical forces in guided-wave nanostructures have recently been proposed as
an effective means of mechanically actuating and tuning optical components. In
this work, we study the properties of a photonic crystal optomechanical cavity
consisting of a pair of patterned silicon nitride nanobeams. Internal stresses
in the stoichiometric silicon nitride thin-film are used to produce inter-beam
slot-gaps ranging from 560 to 40nm. A general pump-probe measurement scheme is
described which determines, self-consistently, the contributions of
thermo-mechanical, thermo-optic, and radiation pressure effects. For devices
with 40nm slot-gap, the optical gradient force is measured to be 134fN per
cavity photon for the strongly coupled symmetric cavity supermode, producing a
static cavity tuning greater than five times that of either the parasitic
thermo-mechanical or thermo-optic effects.Comment: 6 pages, 4 figure
Optical and mechanical design of a "zipper" photonic crystal optomechanical cavity
Design of a doubly-clamped beam structure capable of localizing mechanical
and optical energy at the nanoscale is presented. The optical design is based
upon photonic crystal concepts in which patterning of a nanoscale-cross-section
beam can result in strong optical localization to an effective optical mode
volume of 0.2 cubic wavelengths ((\lambda_{c})^3). By placing two identical
nanobeams within the near field of each other, strong optomechanical coupling
can be realized for differential motion between the beams. Current designs for
thin film silicon nitride beams at a wavelength of 1.5 microns indicate that
such structures can simultaneously realize an optical Q-factor of 7x10^6,
motional mass m~40 picograms, mechanical mode frequency ~170 MHz, and an
optomechanical coupling factor (g_{OM}=d\omega_{c}/dx = \omega_{c}/L_{OM}) with
effective length L_{OM} ~ \lambda = 1.5 microns.Comment: 16 pages, 10 figure
Peripheral vasoconstriction influences thenar oxygen saturation as measured by near-infrared spectroscopy
Purpose: Near-infrared spectroscopy has been used as a noninvasive monitoring tool for tissue oxygen saturation (StO2) in acutely ill patients. This study aimed to investigate whether local vasoconstriction induced by body surface cooling significantly influences thenar StO2 as measured by InSpectra model 650. Methods: Eight healthy individuals (age 26 ± 6 years) participated in the study. Using a cooling blanket, we aimed to cool the entire body surface to induce vasoconstriction in the skin without any changes in central temperature. Thenar StO2 was noninvasively measured during a 3-min vascular occlusion test using InSpectra model 650 with a 15-mm probe. Measurements were analyzed for resting StO2 values, rate of StO2 desaturation (RdecStO2, %/min), and rate of StO2 recovery (RincStO2, %/s) before, during, and after skin cooling. Measurements also included heart rate (HR), mean arterial pressure (MAP), cardiac output (CO), stroke volume (SV), capillary refill time (CRT), forearm-to-fingertip skintemperature gradient (Tskin-diff), perfusion index (PI), and tissue hemoglobin index (THI). Results: In all subjects MAP, CO, SV, and core temperature did not change during the procedure. Skin cooling resulted in a significant decrease in StO2 from 82% (80-87) to 72% (70-77) (P\0.05) and in RincStO2 from 3.0%/s (2.8-3.3) to 1.7%/s (1.1-2.0) (P\0.05). Similar changes in CRT, Tskin-diff, and PI were also observed: from 2.5 s (2.0-3.0) to 8.5 s (7.2-11.0) (P\0.05), from 1.0 (-1.6-1.8) to 3.1 (P\0.05), and from 10.0% (9.1-11.7) to 2.5
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