264 research outputs found
Federated AI for building AI Solutions across Multiple Agencies
The different sets of regulations existing for differ-ent agencies within the
government make the task of creating AI enabled solutions in government
dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different
agencies, which could be a significant impediment to training AI models. We
discuss the challenges that exist in environments where data cannot be freely
shared and assess tech-nologies which can be used to work around these
challenges. We present results on building AI models using the concept of
federated AI, which al-lows creation of models without moving the training data
around.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and
Public Sector, Arlington, Virginia, US
Recursive Estimation of Orientation Based on the Bingham Distribution
Directional estimation is a common problem in many tracking applications.
Traditional filters such as the Kalman filter perform poorly because they fail
to take the periodic nature of the problem into account. We present a recursive
filter for directional data based on the Bingham distribution in two
dimensions. The proposed filter can be applied to circular filtering problems
with 180 degree symmetry, i.e., rotations by 180 degrees cannot be
distinguished. It is easily implemented using standard numerical techniques and
suitable for real-time applications. The presented approach is extensible to
quaternions, which allow tracking arbitrary three-dimensional orientations. We
evaluate our filter in a challenging scenario and compare it to a traditional
Kalman filtering approach
Unscented Orientation Estimation Based on the Bingham Distribution
Orientation estimation for 3D objects is a common problem that is usually
tackled with traditional nonlinear filtering techniques such as the extended
Kalman filter (EKF) or the unscented Kalman filter (UKF). Most of these
techniques assume Gaussian distributions to account for system noise and
uncertain measurements. This distributional assumption does not consider the
periodic nature of pose and orientation uncertainty. We propose a filter that
considers the periodicity of the orientation estimation problem in its
distributional assumption. This is achieved by making use of the Bingham
distribution, which is defined on the hypersphere and thus inherently more
suitable to periodic problems. Furthermore, handling of non-trivial system
functions is done using deterministic sampling in an efficient way. A
deterministic sampling scheme reminiscent of the UKF is proposed for the
nonlinear manifold of orientations. It is the first deterministic sampling
scheme that truly reflects the nonlinear manifold of the orientation
Extrinisic Calibration of a Camera-Arm System Through Rotation Identification
Determining extrinsic calibration parameters is a necessity in any robotic
system composed of actuators and cameras. Once a system is outside the lab
environment, parameters must be determined without relying on outside artifacts
such as calibration targets. We propose a method that relies on structured
motion of an observed arm to recover extrinsic calibration parameters. Our
method combines known arm kinematics with observations of conics in the image
plane to calculate maximum-likelihood estimates for calibration extrinsics.
This method is validated in simulation and tested against a real-world model,
yielding results consistent with ruler-based estimates. Our method shows
promise for estimating the pose of a camera relative to an articulated arm's
end effector without requiring tedious measurements or external artifacts.
Index Terms: robotics, hand-eye problem, self-calibration, structure from
motio
Nose Heat: Exploring Stress-induced Nasal Thermal Variability through Mobile Thermal Imaging
Automatically monitoring and quantifying stress-induced thermal dynamic
information in real-world settings is an extremely important but challenging
problem. In this paper, we explore whether we can use mobile thermal imaging to
measure the rich physiological cues of mental stress that can be deduced from a
person's nose temperature. To answer this question we build i) a framework for
monitoring nasal thermal variable patterns continuously and ii) a novel set of
thermal variability metrics to capture a richness of the dynamic information.
We evaluated our approach in a series of studies including laboratory-based
psychosocial stress-induction tasks and real-world factory settings. We
demonstrate our approach has the potential for assessing stress responses
beyond controlled laboratory settings
Gaussianity and the Kalman Filter: A Simple Yet Complicated Relationship
One of the most common misconceptions made about the Kalman filter when applied to linear systems is that it requires an assumption that all error and noise processes are Gaussian. This misconception has frequently led to the Kalman filter being dismissed in favor of complicated and/or purely heuristic approaches that are supposedly ``more general'' in that they can be applied to problems involving non-Gaussian noise. The fact is that the Kalman filter provides rigorous and optimal performance guarantees that do not rely on any distribution assumptions beyond mean and error covariance information. These guarantees even apply to use of the Kalman update formula when applied with nonlinear models, as long as its other required assumptions are satisfied. Here we discuss misconceptions about its generality that are often found and reinforced in the literature, especially outside the traditional fields of estimation and control
Information measures in distributed multitarget tracking
In this paper, we consider the role that different information measures play in the problem of decentralised multi-target tracking. In many sensor networks, it is not possible to maintain the full joint probability distribution and so suboptimal algorithms must be used. We use a distributed form of the Probability Hypothesis Density (PHD) filter based on a generalisation of covariance intersection known as exponential mixture densities (EMDs). However, EMD-based fusion must be actively controlled to optimise the relative weights placed on different information sources. We explore the performance consequences of using different information measures to optimise the update. By considering approaches that minimise absolute information (entropy and Rényi entropy) or equalise divergence (Kullback-Leibler Divergence and Rényi Divergence), we show that the divergence measures are both simpler and easier to work with. Furthermore, in our simulation scenario, the performance is very similar with all the information measures considered, suggesting that the simpler measures can be used. © 2011 IEEE
Attention-Based Applications in Extended Reality to Support Autistic Users: A Systematic Review
With the rising prevalence of autism diagnoses, it is essential for research
to understand how to leverage technology to support the diverse nature of
autistic traits. While traditional interventions focused on technology for
medical cure and rehabilitation, recent research aims to understand how
technology can accommodate each unique situation in an efficient and engaging
way. Extended reality (XR) technology has been shown to be effective in
improving attention in autistic users given that it is more engaging and
motivating than other traditional mediums. Here, we conducted a systematic
review of 59 research articles that explored the role of attention in XR
interventions for autistic users. We systematically analyzed demographics,
study design and findings, including autism screening and attention measurement
methods. Furthermore, given methodological inconsistencies in the literature,
we systematically synthesize methods and protocols including screening tools,
physiological and behavioral cues of autism and XR tasks. While there is
substantial evidence for the effectiveness of using XR in attention-based
interventions for autism to support autistic traits, we have identified three
principal research gaps that provide promising research directions to examine
how autistic populations interact with XR. First, our findings highlight the
disproportionate geographic locations of autism studies and underrepresentation
of autistic adults, evidence of gender disparity, and presence of individuals
diagnosed with co-occurring conditions across studies. Second, many studies
used an assortment of standardized and novel tasks and self-report assessments
with limited tested reliability. Lastly, the research lacks evidence of
performance maintenance and transferability.Comment: [Accepted version] K. Wang, S. J. Julier and Y. Cho, "Attention-Based
Applications in Extended Reality to Support Autistic Users: A Systematic
Review," in IEEE Access, vol. 10, pp. 15574-15593, 2022, doi:
10.1109/ACCESS.2022.314772
- …