21,366 research outputs found
Daily minimum and maximum temperature simulation over complex terrain
Spatiotemporal simulation of minimum and maximum temperature is a fundamental
requirement for climate impact studies and hydrological or agricultural models.
Particularly over regions with variable orography, these simulations are
difficult to produce due to terrain driven nonstationarity. We develop a
bivariate stochastic model for the spatiotemporal field of minimum and maximum
temperature. The proposed framework splits the bivariate field into two
components of "local climate" and "weather." The local climate component is a
linear model with spatially varying process coefficients capturing the annual
cycle and yielding local climate estimates at all locations, not only those
within the observation network. The weather component spatially correlates the
bivariate simulations, whose matrix-valued covariance function we estimate
using a nonparametric kernel smoother that retains nonnegative definiteness and
allows for substantial nonstationarity across the simulation domain. The
statistical model is augmented with a spatially varying nugget effect to allow
for locally varying small scale variability. Our model is applied to a daily
temperature data set covering the complex terrain of Colorado, USA, and
successfully accommodates substantial temporally varying nonstationarity in
both the direct-covariance and cross-covariance functions.Comment: Published in at http://dx.doi.org/10.1214/12-AOAS602 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
Millirobots are a promising robotic platform for many applications due to
their small size and low manufacturing costs. Legged millirobots, in
particular, can provide increased mobility in complex environments and improved
scaling of obstacles. However, controlling these small, highly dynamic, and
underactuated legged systems is difficult. Hand-engineered controllers can
sometimes control these legged millirobots, but they have difficulties with
dynamic maneuvers and complex terrains. We present an approach for controlling
a real-world legged millirobot that is based on learned neural network models.
Using less than 17 minutes of data, our method can learn a predictive model of
the robot's dynamics that can enable effective gaits to be synthesized on the
fly for following user-specified waypoints on a given terrain. Furthermore, by
leveraging expressive, high-capacity neural network models, our approach allows
for these predictions to be directly conditioned on camera images, endowing the
robot with the ability to predict how different terrains might affect its
dynamics. This enables sample-efficient and effective learning for locomotion
of a dynamic legged millirobot on various terrains, including gravel, turf,
carpet, and styrofoam. Experiment videos can be found at
https://sites.google.com/view/imageconddy
A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles
In recent years, there has been a dramatic increase in the use of unmanned
aerial vehicles (UAVs), particularly for small UAVs, due to their affordable
prices, ease of availability, and ease of operability. Existing and future
applications of UAVs include remote surveillance and monitoring, relief
operations, package delivery, and communication backhaul infrastructure.
Additionally, UAVs are envisioned as an important component of 5G wireless
technology and beyond. The unique application scenarios for UAVs necessitate
accurate air-to-ground (AG) propagation channel models for designing and
evaluating UAV communication links for control/non-payload as well as payload
data transmissions. These AG propagation models have not been investigated in
detail when compared to terrestrial propagation models. In this paper, a
comprehensive survey is provided on available AG channel measurement campaigns,
large and small scale fading channel models, their limitations, and future
research directions for UAV communication scenarios
- …