280 research outputs found
Modeling a Sensor to Improve its Efficacy
Robots rely on sensors to provide them with information about their
surroundings. However, high-quality sensors can be extremely expensive and
cost-prohibitive. Thus many robotic systems must make due with lower-quality
sensors. Here we demonstrate via a case study how modeling a sensor can improve
its efficacy when employed within a Bayesian inferential framework. As a test
bed we employ a robotic arm that is designed to autonomously take its own
measurements using an inexpensive LEGO light sensor to estimate the position
and radius of a white circle on a black field. The light sensor integrates the
light arriving from a spatially distributed region within its field of view
weighted by its Spatial Sensitivity Function (SSF). We demonstrate that by
incorporating an accurate model of the light sensor SSF into the likelihood
function of a Bayesian inference engine, an autonomous system can make improved
inferences about its surroundings. The method presented here is data-based,
fairly general, and made with plug-and play in mind so that it could be
implemented in similar problems.Comment: 18 pages, 8 figures, submitted to the special issue of "Sensors for
Robotics
Towards Identification of Relevant Variables in the observed Aerosol Optical Depth Bias between MODIS and AERONET observations
Measurements made by satellite remote sensing, Moderate Resolution Imaging
Spectroradiometer (MODIS), and globally distributed Aerosol Robotic Network
(AERONET) are compared. Comparison of the two datasets measurements for aerosol
optical depth values show that there are biases between the two data products.
In this paper, we present a general framework towards identifying relevant set
of variables responsible for the observed bias. We present a general framework
to identify the possible factors influencing the bias, which might be
associated with the measurement conditions such as the solar and sensor zenith
angles, the solar and sensor azimuth, scattering angles, and surface
reflectivity at the various measured wavelengths, etc. Specifically, we
performed analysis for remote sensing Aqua-Land data set, and used machine
learning technique, neural network in this case, to perform multivariate
regression between the ground-truth and the training data sets. Finally, we
used mutual information between the observed and the predicted values as the
measure of similarity to identify the most relevant set of variables. The
search is brute force method as we have to consider all possible combinations.
The computations involves a huge number crunching exercise, and we implemented
it by writing a job-parallel program
Maximum Joint Entropy and Information-Based Collaboration of Automated Learning Machines
We are working to develop automated intelligent agents, which can act and
react as learning machines with minimal human intervention. To accomplish this,
an intelligent agent is viewed as a question-asking machine, which is designed
by coupling the processes of inference and inquiry to form a model-based
learning unit. In order to select maximally-informative queries, the
intelligent agent needs to be able to compute the relevance of a question. This
is accomplished by employing the inquiry calculus, which is dual to the
probability calculus, and extends information theory by explicitly requiring
context. Here, we consider the interaction between two question-asking
intelligent agents, and note that there is a potential information redundancy
with respect to the two questions that the agents may choose to pose. We show
that the information redundancy is minimized by maximizing the joint entropy of
the questions, which simultaneously maximizes the relevance of each question
while minimizing the mutual information between them. Maximum joint entropy is
therefore an important principle of information-based collaboration, which
enables intelligent agents to efficiently learn together.Comment: 8 pages, 1 figure, to appear in the proceedings of MaxEnt 2011 held
in Waterloo, Canad
The Spatial Sensitivity Function of a Light Sensor
The Spatial Sensitivity Function (SSF) is used to quantify a detector's
sensitivity to a spatially-distributed input signal. By weighting the incoming
signal with the SSF and integrating, the overall scalar response of the
detector can be estimated. This project focuses on estimating the SSF of a
light intensity sensor consisting of a photodiode. This light sensor has been
used previously in the Knuth Cyberphysics Laboratory on a robotic arm that
performs its own experiments to locate a white circle in a dark field (Knuth et
al., 2007). To use the light sensor to learn about its surroundings, the
robot's inference software must be able to model and predict the light sensor's
response to a hypothesized stimulus. Previous models of the light sensor
treated it as a point sensor and ignored its spatial characteristics. Here we
propose a parametric approach where the SSF is described by a mixture of
Gaussians (MOG). By performing controlled calibration experiments with known
stimulus inputs, we used nested sampling to estimate the SSF of the light
sensor using an MOG model with the number of Gaussians ranging from one to
five. By comparing the evidence computed for each MOG model, we found that one
Gaussian is sufficient to describe the SSF to the accuracy we require. Future
work will involve incorporating this more accurate SSF into the Bayesian
machine learning software for the robotic system and studying how this detailed
information about the properties of the light sensor will improve robot's
ability to learn.Comment: Published in MaxEnt 200
Steady state, relaxation and first-passage properties of a run-and-tumble particle in one-dimension
We investigate the motion of a run-and-tumble particle (RTP) in one
dimension. We find the exact probability distribution of the particle with and
without diffusion on the infinite line, as well as in a finite interval. In the
infinite domain, this probability distribution approaches a Gaussian form in
the long-time limit, as in the case of a regular Brownian particle. At
intermediate times, this distribution exhibits unexpected multi-modal forms. In
a finite domain, the probability distribution reaches a steady state form with
peaks at the boundaries, in contrast to a Brownian particle. We also study the
relaxation to the steady state analytically. Finally we compute the survival
probability of the RTP in a semi-infinite domain. In the finite interval, we
compute the exit probability and the associated exit times. We provide
numerical verifications of our analytical results
Knowledge and awareness of emergency contraception among women undergoing medical termination of pregnancy in a tertiary care hospital in Andaman and Nicobar Islands
Background: Emergency contraception (EC) is a safe and cost effective measure, which when used judiciously can avoid unintended pregnancies. This is especially relevant in our country where both population control and unsafe abortions are a serious concern. For any contraceptive to be effective, the general population must have a proper knowledge and a positive attitude towards it. Women who come for medical termination of unwanted pregnancy are the best representatives of this and our study aimed to assess the knowledge and awareness of EC in this group.Methods: It was a descriptive, cross-sectional study conducted using a predesigned questionnaire among 100 women who came for medical termination of pregnancy at G. B. Pant Hospital, Port Blair. Questions were asked to evaluate their awareness, knowledge and attitude towards EC.Results: Out of 100 women, only 26 % had heard about EC, mostly from mass media, and seven of these 26 women had correct knowledge of timing of using EC. Four women had used EC before, whereas among others, fear of menstrual irregularity was the major deterrent. A significant association was found between education level and occupation with knowledge of EC.Conclusions: Although EC is a potent tool to avoid unwanted pregnancies, ignorance and misconceptions regarding EC is unfortunately still prevalent among those who would benefit the most from it. Medical personnel and health workers should be more proactive to propagate benefits of EC and encourage its use, as they are usually the first point of contact to the target population
Saving the Greater Adjutant Stork by Changing Perceptions and Linking to Assamese Traditions in India
https://ojs.ethnobiology.org/index.php/ebl/article/view/1648https://ojs.ethnobiology.org/index.php/ebl/article/view/164
Bayesian Evidence and Model Selection
In this paper we review the concepts of Bayesian evidence and Bayes factors,
also known as log odds ratios, and their application to model selection. The
theory is presented along with a discussion of analytic, approximate and
numerical techniques. Specific attention is paid to the Laplace approximation,
variational Bayes, importance sampling, thermodynamic integration, and nested
sampling and its recent variants. Analogies to statistical physics, from which
many of these techniques originate, are discussed in order to provide readers
with deeper insights that may lead to new techniques. The utility of Bayesian
model testing in the domain sciences is demonstrated by presenting four
specific practical examples considered within the context of signal processing
in the areas of signal detection, sensor characterization, scientific model
selection and molecular force characterization.Comment: Arxiv version consists of 58 pages and 9 figures. Features theory,
numerical methods and four application
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