15,074 research outputs found
Particle Detection Algorithms for Complex Plasmas
In complex plasmas, the behavior of freely floating micrometer sized
particles is studied. The particles can be directly visualized and recorded by
digital video cameras. To analyze the dynamics of single particles, reliable
algorithms are required to accurately determine their positions to sub-pixel
accuracy from the recorded images. Typically, straightforward algorithms are
used for this task. Here, we combine the algorithms with common techniques for
image processing. We study several algorithms and pre- and post-processing
methods, and we investigate the impact of the choice of threshold parameters,
including an automatic threshold detection. The results quantitatively show
that each algorithm and method has its own advantage, often depending on the
problem at hand. This knowledge is applicable not only to complex plasmas, but
useful for any kind of comparable image-based particle tracking, e.g. in the
field of colloids or granular matter
Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
Optical microscopy provides rich spatio-temporal information characterizing
in vivo molecular motion. However, effective forces and other parameters used
to summarize molecular motion change over time in live cells due to latent
state changes, e.g., changes induced by dynamic micro-environments,
photobleaching, and other heterogeneity inherent in biological processes. This
study focuses on techniques for analyzing Single Particle Tracking (SPT) data
experiencing abrupt state changes. We demonstrate the approach on GFP tagged
chromatids experiencing metaphase in yeast cells and probe the effective forces
resulting from dynamic interactions that reflect the sum of a number of
physical phenomena. State changes are induced by factors such as microtubule
dynamics exerting force through the centromere, thermal polymer fluctuations,
etc. Simulations are used to demonstrate the relevance of the approach in more
general SPT data analyses. Refined force estimates are obtained by adopting and
modifying a nonparametric Bayesian modeling technique, the Hierarchical
Dirichlet Process Switching Linear Dynamical System (HDP-SLDS), for SPT
applications. The HDP-SLDS method shows promise in systematically identifying
dynamical regime changes induced by unobserved state changes when the number of
underlying states is unknown in advance (a common problem in SPT applications).
We expand on the relevance of the HDP-SLDS approach, review the relevant
background of Hierarchical Dirichlet Processes, show how to map discrete time
HDP-SLDS models to classic SPT models, and discuss limitations of the approach.
In addition, we demonstrate new computational techniques for tuning
hyperparameters and for checking the statistical consistency of model
assumptions directly against individual experimental trajectories; the
techniques circumvent the need for "ground-truth" and subjective information.Comment: 25 pages, 6 figures. Differs only typographically from PLoS One
publication available freely as an open-access article at
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.013763
Incremental low rank noise reduction for robust infrared tracking of body temperature during medical imaging
Thermal imagery for monitoring of body temperature provides a powerful tool to decrease
health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging).
The presented approach discusses an experiment to simulate radiology conditions with infrared
imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system
uses an incremental low-rank noise reduction applying incremental singular value decomposition
(SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary.
Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal
database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera.
This dataset is created to verify the robustness of our method with respect to motion-artifacts and in
presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the
infrared images in the dataset and was able to successfully measure and track the ROI continuously
(100% detecting and tracking the temperature of participants), and provided considerable robustness
against noise (unchanged accuracy even in 20% additive noise), which shows promising performanc
Reasoning About Liquids via Closed-Loop Simulation
Simulators are powerful tools for reasoning about a robot's interactions with
its environment. However, when simulations diverge from reality, that reasoning
becomes less useful. In this paper, we show how to close the loop between
liquid simulation and real-time perception. We use observations of liquids to
correct errors when tracking the liquid's state in a simulator. Our results
show that closed-loop simulation is an effective way to prevent large
divergence between the simulated and real liquid states. As a direct
consequence of this, our method can enable reasoning about liquids that would
otherwise be infeasible due to large divergences, such as reasoning about
occluded liquid.Comment: Robotics: Science & Systems (RSS), July 12-16, 2017. Cambridge, MA,
US
Particulate airborne impurities
The cumulative effects of air pollutants are of principal concern in research on environmental protection in Sweden. Post-industrial society has imposed many limits on emitted air pollutants, yet the number of reports on the negative effects from them is increasing, largely due to human activity in the form of industrial emissions and increased traffic flows. Rising concerns over the health effects from airborne particulate matter (PM) stem from in vitro, in vivo, and cohort studies revealing effects of mostly negative nature.
Full insight into the health effects from PM can only be achieved through practical investigation of the mode of toxicity from distinct types of particles and requires techniques for their identification, monitoring, and the production of model fractions for health studies.
To this effect, comprehensive collection and chemical analysis of particulates at the origin of emission was performed in order to provide clearer insight into the nature of the particulates at exposure and add detail to aid risk assessment. Methods of capturing particles and analyzing their chemical nature were devised using scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS).
Furthermore, taking the approach of in vitro cytotoxicity testing, nanoparticles of types typical to automotive emissions, were synthesized and extensively characterized using SEM-EDS, X-ray diffraction (XRD), transmission electron microscopy (TEM),dynamic light scattering (DLS), and nanoparticle tracking analysis (NTA). The produced model magnetite and palladium nanoparticles were found to induce toxicity in human pulmonary epithelial cells (A549 and PBEC) as well as impact severely on immunological and renal cells (221 B- and 293T-cells) in a dose-dependent manner
Unsupervised automatic tracking of thermal changes in human body
An automated system for detecting and tracking of the thermal fluctuation in human body is addressed. It applies
HSV based k-means clustering which initialized and controlled the points which lie on the ROI boundary. Afterward a
particle filter tracked the targeted ROI in the thermal video stream. There were six subjects have voluntarily
participated on these experiments. For simulating the hot spots occur during the some medical tests a controllable
heater utilized close to the subjects body. The results indicated promising accuracy of the proposed approach for
tracking the hot spots. However, there were some approximations (e.g. the transmittance of the atmosphere and
emissivity of the fabric) which can be neglected because of independency of the proposed approach for these
parameters. The approach can track the heating spots efficiently considering the movement in the subjects which
provided a confidence of considerable robustness against motion-artifact usually occurs in the medical tests
The NASA SBIR product catalog
The purpose of this catalog is to assist small business firms in making the community aware of products emerging from their efforts in the Small Business Innovation Research (SBIR) program. It contains descriptions of some products that have advanced into Phase 3 and others that are identified as prospective products. Both lists of products in this catalog are based on information supplied by NASA SBIR contractors in responding to an invitation to be represented in this document. Generally, all products suggested by the small firms were included in order to meet the goals of information exchange for SBIR results. Of the 444 SBIR contractors NASA queried, 137 provided information on 219 products. The catalog presents the product information in the technology areas listed in the table of contents. Within each area, the products are listed in alphabetical order by product name and are given identifying numbers. Also included is an alphabetical listing of the companies that have products described. This listing cross-references the product list and provides information on the business activity of each firm. In addition, there are three indexes: one a list of firms by states, one that lists the products according to NASA Centers that managed the SBIR projects, and one that lists the products by the relevant Technical Topics utilized in NASA's annual program solicitation under which each SBIR project was selected
Automated assessment and tracking of human body thermal variations using unsupervised clustering
The presented approach addresses a review of the overheating that occurs during radiological examinations, such as magnetic resonance imaging, and a series of thermal experiments to determine a thermally suitable fabric material that should be used for radiological gowns. Moreover, an automatic system for detecting and tracking of the thermal fluctuation is presented. It applies hue-saturated-value-based kernelled k-means clustering, which initializes and controls the points that lie on the region-of-interest (ROI) boundary. Afterward, a particle filter tracks the targeted ROI during the video sequence independently of previous locations of overheating spots. The proposed approach was tested during experiments and under conditions very similar to those used during real radiology exams. Six subjects have voluntarily participated in these experiments. To simulate the hot spots occurring during radiology, a controllable heat source was utilized near the subject’s body. The results indicate promising accuracy for the proposed approach to track hot spots. Some approximations were used regarding the transmittance of the atmosphere, and emissivity of the fabric could be neglected because of the independence of the proposed approach for these parameters. The approach can track the heating spots continuously and correctly, even for moving subjects, and provides considerable robustness against motion artifact, which occurs during most medical radiology procedures
- …