6,489 research outputs found
Edge-promoting reconstruction of absorption and diffusivity in optical tomography
In optical tomography a physical body is illuminated with near-infrared light
and the resulting outward photon flux is measured at the object boundary. The
goal is to reconstruct internal optical properties of the body, such as
absorption and diffusivity. In this work, it is assumed that the imaged object
is composed of an approximately homogeneous background with clearly
distinguishable embedded inhomogeneities. An algorithm for finding the maximum
a posteriori estimate for the absorption and diffusion coefficients is
introduced assuming an edge-preferring prior and an additive Gaussian
measurement noise model. The method is based on iteratively combining a lagged
diffusivity step and a linearization of the measurement model of diffuse
optical tomography with priorconditioned LSQR. The performance of the
reconstruction technique is tested via three-dimensional numerical experiments
with simulated measurement data.Comment: 18 pages, 6 figure
Device-free Localization using Received Signal Strength Measurements in Radio Frequency Network
Device-free localization (DFL) based on the received signal strength (RSS)
measurements of radio frequency (RF)links is the method using RSS variation due
to the presence of the target to localize the target without attaching any
device. The majority of DFL methods utilize the fact the link will experience
great attenuation when obstructed. Thus that localization accuracy depends on
the model which describes the relationship between RSS loss caused by
obstruction and the position of the target. The existing models is too rough to
explain some phenomenon observed in the experiment measurements. In this paper,
we propose a new model based on diffraction theory in which the target is
modeled as a cylinder instead of a point mass. The proposed model can will
greatly fits the experiment measurements and well explain the cases like link
crossing and walking along the link line. Because the measurement model is
nonlinear, particle filtering tracing is used to recursively give the
approximate Bayesian estimation of the position. The posterior Cramer-Rao lower
bound (PCRLB) of proposed tracking method is also derived. The results of field
experiments with 8 radio sensors and a monitored area of 3.5m 3.5m show that
the tracking error of proposed model is improved by at least 36 percent in the
single target case and 25 percent in the two targets case compared to other
models.Comment: This paper has been withdrawn by the author due to some mistake
A fast Bayesian approach to discrete object detection in astronomical datasets - PowellSnakes I
A new fast Bayesian approach is introduced for the detection of discrete
objects immersed in a diffuse background. This new method, called PowellSnakes,
speeds up traditional Bayesian techniques by: i) replacing the standard form of
the likelihood for the parameters characterizing the discrete objects by an
alternative exact form that is much quicker to evaluate; ii) using a
simultaneous multiple minimization code based on Powell's direction set
algorithm to locate rapidly the local maxima in the posterior; and iii)
deciding whether each located posterior peak corresponds to a real object by
performing a Bayesian model selection using an approximate evidence value based
on a local Gaussian approximation to the peak. The construction of this
Gaussian approximation also provides the covariance matrix of the uncertainties
in the derived parameter values for the object in question. This new approach
provides a speed up in performance by a factor of `hundreds' as compared to
existing Bayesian source extraction methods that use MCMC to explore the
parameter space, such as that presented by Hobson & McLachlan. We illustrate
the capabilities of the method by applying to some simplified toy models.
Furthermore PowellSnakes has the advantage of consistently defining the
threshold for acceptance/rejection based on priors which cannot be said of the
frequentist methods. We present here the first implementation of this technique
(Version-I). Further improvements to this implementation are currently under
investigation and will be published shortly. The application of the method to
realistic simulated Planck observations will be presented in a forthcoming
publication.Comment: 30 pages, 15 figures, revised version with minor changes, accepted
for publication in MNRA
Recognition of activities of daily living
Activities of daily living (ADL) are things we normally do in daily living, including any daily activity such as feeding ourselves, bathing, dressing, grooming, work, homemaking, and leisure. The ability or inability to perform ADLs can be used as a very practical measure of human capability in many types of disorder and disability. Oftentimes in a health care facility, with the help of observations by nurses and self-reporting by residents, professional staff manually collect ADL data and enter data into the system.
Technologies in smart homes can provide some solutions to detecting and monitoring a resident’s ADL. Typically multiple sensors can be deployed, such as surveillance cameras in the smart home environment, and contacted sensors affixed to the resident’s body. Note that the traditional technologies incur costly and laborious sensor deployment, and cause uncomfortable feeling of contacted sensors with increased inconvenience.
This work presents a novel system facilitated via mobile devices to collect and analyze mobile data pertaining to the human users’ ADL. By employing only one smart phone, this system, named ADL recognition system, significantly reduces set-up costs and saves manpower.
It encapsulates rather sophisticated technologies under the hood, such as an agent-based information management platform integrating both the mobile end and the cloud, observer patterns and a time-series based motion analysis mechanism over sensory data. As a single-point deployment system, ADL recognition system provides further benefits that enable the replay of users’ daily ADL routines, in addition to the timely assessment of their life habits
Indoor Geo-location And Tracking Of Mobile Autonomous Robot
The field of robotics has always been one of fascination right from the day of Terminator. Even though we still do not have robots that can actually replicate human action and intelligence, progress is being made in the right direction. Robotic applications range from defense to civilian, in public safety and fire fighting. With the increase in urban-warfare robot tracking inside buildings and in cities form a very important application. The numerous applications range from munitions tracking to replacing soldiers for reconnaissance information. Fire fighters use robots for survey of the affected area. Tracking robots has been limited to the local area under consideration. Decision making is inhibited due to limited local knowledge and approximations have to be made. An effective decision making would involve tracking the robot in earth co-ordinates such as latitude and longitude. GPS signal provides us sufficient and reliable data for such decision making. The main drawback of using GPS is that it is unavailable indoors and also there is signal attenuation outdoors. Indoor geolocation forms the basis of tracking robots inside buildings and other places where GPS signals are unavailable. Indoor geolocation has traditionally been the field of wireless networks using techniques such as low frequency RF signals and ultra-wideband antennas. In this thesis we propose a novel method for achieving geolocation and enable tracking. Geolocation and tracking are achieved by a combination of Gyroscope and encoders together referred to as the Inertial Navigation System (INS). Gyroscopes have been widely used in aerospace applications for stabilizing aircrafts. In our case we use gyroscope as means of determining the heading of the robot. Further, commands can be sent to the robot when it is off balance or off-track. Sensors are inherently error prone; hence the process of geolocation is complicated and limited by the imperfect mathematical modeling of input noise. We make use of Kalman Filter for processing erroneous sensor data, as it provides us a robust and stable algorithm. The error characteristics of the sensors are input to the Kalman Filter and filtered data is obtained. We have performed a large set of experiments, both indoors and outdoors to test the reliability of the system. In outdoors we have used the GPS signal to aid the INS measurements. When indoors we utilize the last known position and extrapolate to obtain the GPS co-ordinates
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