15,305 research outputs found
Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques
Among all animals, mosquitoes are responsible for the most deaths worldwide.
Interestingly, not all types of mosquitoes spread diseases, but rather, a
select few alone are competent enough to do so. In the case of any disease
outbreak, an important first step is surveillance of vectors (i.e., those
mosquitoes capable of spreading diseases). To do this today, public health
workers lay several mosquito traps in the area of interest. Hundreds of
mosquitoes will get trapped. Naturally, among these hundreds, taxonomists have
to identify only the vectors to gauge their density. This process today is
manual, requires complex expertise/ training, and is based on visual inspection
of each trapped specimen under a microscope. It is long, stressful and
self-limiting. This paper presents an innovative solution to this problem. Our
technique assumes the presence of an embedded camera (similar to those in
smart-phones) that can take pictures of trapped mosquitoes. Our techniques
proposed here will then process these images to automatically classify the
genus and species type. Our CNN model based on Inception-ResNet V2 and Transfer
Learning yielded an overall accuracy of 80% in classifying mosquitoes when
trained on 25,867 images of 250 trapped mosquito vector specimens captured via
many smart-phone cameras. In particular, the accuracy of our model in
classifying Aedes aegypti and Anopheles stephensi mosquitoes (both of which are
deadly vectors) is amongst the highest. We present important lessons learned
and practical impact of our techniques towards the end of the paper
Robot Autonomy for Surgery
Autonomous surgery involves having surgical tasks performed by a robot
operating under its own will, with partial or no human involvement. There are
several important advantages of automation in surgery, which include increasing
precision of care due to sub-millimeter robot control, real-time utilization of
biosignals for interventional care, improvements to surgical efficiency and
execution, and computer-aided guidance under various medical imaging and
sensing modalities. While these methods may displace some tasks of surgical
teams and individual surgeons, they also present new capabilities in
interventions that are too difficult or go beyond the skills of a human. In
this chapter, we provide an overview of robot autonomy in commercial use and in
research, and present some of the challenges faced in developing autonomous
surgical robots
Using machine learning techniques to automate sky survey catalog generation
We describe the application of machine classification techniques to the development of an automated tool for the reduction of a large scientific data set. The 2nd Palomar Observatory Sky Survey provides comprehensive photographic coverage of the northern celestial hemisphere. The photographic plates are being digitized into images containing on the order of 10(exp 7) galaxies and 10(exp 8) stars. Since the size of this data set precludes manual analysis and classification of objects, our approach is to develop a software system which integrates independently developed techniques for image processing and data classification. Image processing routines are applied to identify and measure features of sky objects. Selected features are used to determine the classification of each object. GID3* and O-BTree, two inductive learning techniques, are used to automatically learn classification decision trees from examples. We describe the techniques used, the details of our specific application, and the initial encouraging results which indicate that our approach is well-suited to the problem. The benefits of the approach are increased data reduction throughput, consistency of classification, and the automated derivation of classification rules that will form an objective, examinable basis for classifying sky objects. Furthermore, astronomers will be freed from the tedium of an intensely visual task to pursue more challenging analysis and interpretation problems given automatically cataloged data
The Design and Operation of The Keck Observatory Archive
The Infrared Processing and Analysis Center (IPAC) and the W. M. Keck
Observatory (WMKO) operate an archive for the Keck Observatory. At the end of
2013, KOA completed the ingestion of data from all eight active observatory
instruments. KOA will continue to ingest all newly obtained observations, at an
anticipated volume of 4 TB per year. The data are transmitted electronically
from WMKO to IPAC for storage and curation. Access to data is governed by a
data use policy, and approximately two-thirds of the data in the archive are
public.Comment: 12 pages, 4 figs, 4 tables. Presented at Software and
Cyberinfrastructure for Astronomy III, SPIE Astronomical Telescopes +
Instrumentation 2014. June 2014, Montreal, Canad
From XML to XML: The why and how of making the biodiversity literature accessible to researchers
We present the ABLE document collection, which consists of a set of annotated volumes of the Bulletin of the British Museum (Natural History). These follow our work on automating the markup of scanned copies of the biodiversity literature, for the purpose of supporting working taxonomists. We consider an enhanced TEI XML markup language, which is used as an intermediate stage in translating from the initial XML obtained from Optical Character Recognition to the target taXMLit. The intermediate representation allows additional information from external sources such as a taxonomic thesaurus to be incorporated before the final translation into taXMLit
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