406 research outputs found
Using Crash Detection and Alert Beacons on an Electronic Device to Increase Survival of Snow Immersion Victims
This publication describes systems and techniques for crash detection on an electronic device to trigger transmitting alert beacons with the electronic device. The alert beacons may be received by nearby electronic devices to alert the users of those electronic devices of a snow immersion victim. A crash is detected by monitoring one or more sensors on an electronic device for a tumbling or inversion of the electronic device that indicates a snow immersion accident. Additionally, location and weather may also be monitored to detect a snow immersion accident and support a crash detection. After a crash is detected, a user of the electronic device may be provided with a notification to dismiss triggering alert beacons. If the user does not dismiss the notification, the electronic device may trigger transmitting alert beacons to alert others via wireless signal that the user has been in a snow immersion accident. The alerts may be provided via a peer-to-peer system tagged with location information of the snow immersion accident. The alerts can assist others in locating the snow immersion accident victim more quickly
A New Color Image Encryption Scheme Using CML and a Fractional-Order Chaotic System
Funding: This research was jointly supported by the National Natural Science Foundation of China (No. 61004006, http://www.nsfc.gov.cn), China Postdoctoral Science Foundation(No. 2013M530181, http://res.chinapostdoctor.org.cn/BshWeb/index.shtml), the Natural Science Foundation of Henan Province, China (No. 13230010254, http://www.hnkjt.gov.cn/, Program for Science & Technology Innovation Talents in Universities of Henan Province, China (Grant No 14HASTIT042, http://rcloud.edu.cn), the Foundation for University Young Key Teacher Program of Henan Province, China (No. 2011GGJS-025, http://www.haedu.gov.cn/), Shanghai Postdoctoral Scientific Program (No. 13R21410600, http://www.21cnhr.gov.cn/doctorarea/), the Science & Technology Project Plan of Archives Bureau of Henan Province (No. 2012-X-62, http://www.hada.gov.cn/) and the Natural Science Foundation of Educational Committee of Henan Province, China (No. 13A520082, http://www.haedu.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
Correction of image radial distortion based on division model
This paper presents an approach for estimating and then removing image radial distortion. It works on
a single image and does not require a special calibration. The approach is extremely useful in many applications,
particularly those where human-made environments contain abundant lines. A division model is applied, in which
a straight line in the distorted image is treated as a circular arc. LevenbergâMarquardt (LM) iterative nonlinear
least squares method is adopted to calculate the arcâs parameters. Then âTaubin fitâ is applied to obtain the initial
guess of the arcâs parameters which works as the initial input to the LM iteration. This dramatically improves the
convergence rate in the LM process to obtain the required parameters for correcting image radial distortion.
Hough entropy, as a measure, has achieved the quantitative evaluation of the estimated distortion based on
the probability distribution in one-dimensional θ Hough space. The experimental results on both synthetic
and real images have demonstrated that the proposed method can robustly estimate and then remove
image radial distortion with high accurac
3DPortraitGAN: Learning One-Quarter Headshot 3D GANs from a Single-View Portrait Dataset with Diverse Body Poses
3D-aware face generators are typically trained on 2D real-life face image
datasets that primarily consist of near-frontal face data, and as such, they
are unable to construct one-quarter headshot 3D portraits with complete head,
neck, and shoulder geometry. Two reasons account for this issue: First,
existing facial recognition methods struggle with extracting facial data
captured from large camera angles or back views. Second, it is challenging to
learn a distribution of 3D portraits covering the one-quarter headshot region
from single-view data due to significant geometric deformation caused by
diverse body poses. To this end, we first create the dataset
360{\deg}-Portrait-HQ (360{\deg}PHQ for short) which consists of high-quality
single-view real portraits annotated with a variety of camera parameters (the
yaw angles span the entire 360{\deg} range) and body poses. We then propose
3DPortraitGAN, the first 3D-aware one-quarter headshot portrait generator that
learns a canonical 3D avatar distribution from the 360{\deg}PHQ dataset with
body pose self-learning. Our model can generate view-consistent portrait images
from all camera angles with a canonical one-quarter headshot 3D representation.
Our experiments show that the proposed framework can accurately predict
portrait body poses and generate view-consistent, realistic portrait images
with complete geometry from all camera angles
Correlation of Influenza Virus Excess Mortality with Antigenic Variation: Application to Rapid Estimation of Influenza Mortality Burden
The variants of human influenza virus have caused, and continue to cause, substantial morbidity and mortality. Timely and accurate assessment of their impact on human death is invaluable for influenza planning but presents a substantial challenge, as current approaches rely mostly on intensive and unbiased influenza surveillance. In this study, by proposing a novel host-virus interaction model, we have established a positive correlation between the excess mortalities caused by viral strains of distinct antigenicity and their antigenic distances to their previous strains for each (sub)type of seasonal influenza viruses. Based on this relationship, we further develop a method to rapidly assess the mortality burden of influenza A(H1N1) virus by accurately predicting the antigenic distance between A(H1N1) strains. Rapid estimation of influenza mortality burden for new seasonal strains should help formulate a cost-effective response for influenza control and prevention
Suppressing electron disorder-induced heating of ultracold neutral plasma via optical lattice
Disorder-induced heating (DIH) prevents ultracold neutral plasma into
electron strong coupling regime. Here we propose a scheme to suppress
electronic DIH via optical lattice. We simulate the evolution dynamics of
ultracold neutral plasma constrained by three-dimensional optical lattice using
classical molecular dynamics method. The results show that for experimentally
achievable condition, electronic DIH is suppressed by a factor of 1.3, and the
Coulomb coupling strength can reach to 0.8 which is approaching the strong
coupling regime. Suppressing electronic DIH via optical lattice may pave a way
for the research of electronic strongly coupled plasma
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