79,108 research outputs found
Visual, Motor and Attentional Influences on Proprioceptive Contributions to Perception of Hand Path Rectilinearity during Reaching
We examined how proprioceptive contributions to perception of hand path straightness are influenced by visual, motor and attentional sources of performance variability during horizontal planar reaching. Subjects held the handle of a robot that constrained goal-directed movements of the hand to the paths of controlled curvature. Subjects attempted to detect the presence of hand path curvature during both active (subject driven) and passive (robot driven) movements that either required active muscle force production or not. Subjects were less able to discriminate curved from straight paths when actively reaching for a target versus when the robot moved their hand through the same curved paths. This effect was especially evident during robot-driven movements requiring concurrent activation of lengthening but not shortening muscles. Subjects were less likely to report curvature and were more variable in reporting when movements appeared straight in a novel “visual channel” condition previously shown to block adaptive updating of motor commands in response to deviations from a straight-line hand path. Similarly, compromised performance was obtained when subjects simultaneously performed a distracting secondary task (key pressing with the contralateral hand). The effects compounded when these last two treatments were combined. It is concluded that environmental, intrinsic and attentional factors all impact the ability to detect deviations from a rectilinear hand path during goal-directed movement by decreasing proprioceptive contributions to limb state estimation. In contrast, response variability increased only in experimental conditions thought to impose additional attentional demands on the observer. Implications of these results for perception and other sensorimotor behaviors are discussed
Improved detection of small atom numbers through image processing
We demonstrate improved detection of small trapped atomic ensembles through
advanced post-processing and optimal analysis of absorption images. A fringe
removal algorithm reduces imaging noise to the fundamental photon-shot-noise
level and proves beneficial even in the absence of fringes. A
maximum-likelihood estimator is then derived for optimal atom-number estimation
and is applied to real experimental data to measure the population differences
and intrinsic atom shot-noise between spatially separated ensembles each
comprising between 10 and 2000 atoms. The combined techniques improve our
signal-to-noise by a factor of 3, to a minimum resolvable population difference
of 17 atoms, close to our ultimate detection limit.Comment: 4 pages, 3 figure
Statistical Mechanics and Visual Signal Processing
The nervous system solves a wide variety of problems in signal processing. In
many cases the performance of the nervous system is so good that it apporaches
fundamental physical limits, such as the limits imposed by diffraction and
photon shot noise in vision. In this paper we show how to use the language of
statistical field theory to address and solve problems in signal processing,
that is problems in which one must estimate some aspect of the environment from
the data in an array of sensors. In the field theory formulation the optimal
estimator can be written as an expectation value in an ensemble where the input
data act as external field. Problems at low signal-to-noise ratio can be solved
in perturbation theory, while high signal-to-noise ratios are treated with a
saddle-point approximation. These ideas are illustrated in detail by an example
of visual motion estimation which is chosen to model a problem solved by the
fly's brain. In this problem the optimal estimator has a rich structure,
adapting to various parameters of the environment such as the mean-square
contrast and the correlation time of contrast fluctuations. This structure is
in qualitative accord with existing measurements on motion sensitive neurons in
the fly's brain, and we argue that the adaptive properties of the optimal
estimator may help resolve conlficts among different interpretations of these
data. Finally we propose some crucial direct tests of the adaptive behavior.Comment: 34pp, LaTeX, PUPT-143
Trajectory Reconstruction Techniques for Evaluation of ATC Systems
This paper is focused on trajectory reconstruction techniques for evaluating ATC systems, using real data of recorded opportunity traffic. We analyze different alternatives for this problem, from traditional interpolation approaches based on curve fitting to our proposed schemes based on modeling regular motion patterns with optimal smoothers. The extraction of trajectory features such as motion type (or mode of flight), maneuvers profile, geometric parameters, etc., allows a more accurate computation of the curve and the detailed evaluation of the data processors used in the ATC centre. Different alternatives will be compared with some performance results obtained with simulated and real data sets
Environmental boundary tracking and estimation using multiple autonomous vehicles
In this paper, we develop a framework for environmental
boundary tracking and estimation by considering the
boundary as a hidden Markov model (HMM) with separated
observations collected from multiple sensing vehicles. For each
vehicle, a tracking algorithm is developed based on Page’s
cumulative sum algorithm (CUSUM), a method for change-point
detection, so that individual vehicles can autonomously
track the boundary in a density field with measurement noise.
Based on the data collected from sensing vehicles and prior
knowledge of the dynamic model of boundary evolvement, we
estimate the boundary by solving an optimization problem, in
which prediction and current observation are considered in the
cost function. Examples and simulation results are presented
to verify the efficiency of this approach
Optimisation of the T-square sampling method to estimate population sizes.
Population size and density estimates are needed to plan resource requirements and plan health related interventions. Sampling frames are not always available necessitating surveys using non-standard household sampling methods. These surveys are time-consuming, difficult to validate, and their implementation could be optimised. Here, we discuss an example of an optimisation procedure for rapid population estimation using T-Square sampling which has been used recently to estimate population sizes in emergencies. A two-stage process was proposed to optimise the T-Square method wherein the first stage optimises the sample size and the second stage optimises the pathway connecting the sampling points. The proposed procedure yields an optimal solution if the distribution of households is described by a spatially homogeneous Poisson process and can be sub-optimal otherwise. This research provides the first step in exploring how optimisation techniques could be applied to survey designs thereby providing more timely and accurate information for planning interventions
Efficient Evaluation of the Number of False Alarm Criterion
This paper proposes a method for computing efficiently the significance of a
parametric pattern inside a binary image. On the one hand, a-contrario
strategies avoid the user involvement for tuning detection thresholds, and
allow one to account fairly for different pattern sizes. On the other hand,
a-contrario criteria become intractable when the pattern complexity in terms of
parametrization increases. In this work, we introduce a strategy which relies
on the use of a cumulative space of reduced dimensionality, derived from the
coupling of a classic (Hough) cumulative space with an integral histogram
trick. This space allows us to store partial computations which are required by
the a-contrario criterion, and to evaluate the significance with a lower
computational cost than by following a straightforward approach. The method is
illustrated on synthetic examples on patterns with various parametrizations up
to five dimensions. In order to demonstrate how to apply this generic concept
in a real scenario, we consider a difficult crack detection task in still
images, which has been addressed in the literature with various local and
global detection strategies. We model cracks as bounded segments, detected by
the proposed a-contrario criterion, which allow us to introduce additional
spatial constraints based on their relative alignment. On this application, the
proposed strategy yields state-of the-art results, and underlines its potential
for handling complex pattern detection tasks
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