29,518 research outputs found
Interactive Camera Network Design using a Virtual Reality Interface
Traditional literature on camera network design focuses on constructing
automated algorithms. These require problem specific input from experts in
order to produce their output. The nature of the required input is highly
unintuitive leading to an unpractical workflow for human operators. In this
work we focus on developing a virtual reality user interface allowing human
operators to manually design camera networks in an intuitive manner. From real
world practical examples we conclude that the camera networks designed using
this interface are highly competitive with, or superior to those generated by
automated algorithms, but the associated workflow is much more intuitive and
simple. The competitiveness of the human-generated camera networks is
remarkable because the structure of the optimization problem is a well known
combinatorial NP-hard problem. These results indicate that human operators can
be used in challenging geometrical combinatorial optimization problems given an
intuitive visualization of the problem.Comment: 11 pages, 8 figure
Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset.
Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1-10 cameras), and (2) by total season length (1-365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40-128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
The Multi-Object, Fiber-Fed Spectrographs for SDSS and the Baryon Oscillation Spectroscopic Survey
We present the design and performance of the multi-object fiber spectrographs
for the Sloan Digital Sky Survey (SDSS) and their upgrade for the Baryon
Oscillation Spectroscopic Survey (BOSS). Originally commissioned in Fall 1999
on the 2.5-m aperture Sloan Telescope at Apache Point Observatory, the
spectrographs produced more than 1.5 million spectra for the SDSS and SDSS-II
surveys, enabling a wide variety of Galactic and extra-galactic science
including the first observation of baryon acoustic oscillations in 2005. The
spectrographs were upgraded in 2009 and are currently in use for BOSS, the
flagship survey of the third-generation SDSS-III project. BOSS will measure
redshifts of 1.35 million massive galaxies to redshift 0.7 and Lyman-alpha
absorption of 160,000 high redshift quasars over 10,000 square degrees of sky,
making percent level measurements of the absolute cosmic distance scale of the
Universe and placing tight constraints on the equation of state of dark energy.
The twin multi-object fiber spectrographs utilize a simple optical layout
with reflective collimators, gratings, all-refractive cameras, and
state-of-the-art CCD detectors to produce hundreds of spectra simultaneously in
two channels over a bandpass covering the near ultraviolet to the near
infrared, with a resolving power R = \lambda/FWHM ~ 2000. Building on proven
heritage, the spectrographs were upgraded for BOSS with volume-phase
holographic gratings and modern CCD detectors, improving the peak throughput by
nearly a factor of two, extending the bandpass to cover 360 < \lambda < 1000
nm, and increasing the number of fibers from 640 to 1000 per exposure. In this
paper we describe the original SDSS spectrograph design and the upgrades
implemented for BOSS, and document the predicted and measured performances.Comment: 43 pages, 42 figures, revised according to referee report and
accepted by AJ. Provides background for the instrument responsible for SDSS
and BOSS spectra. 4th in a series of survey technical papers released in
Summer 2012, including arXiv:1207.7137 (DR9), arXiv:1207.7326 (Spectral
Classification), and arXiv:1208.0022 (BOSS Overview
- …