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This book concentrates on improving the prediction of a vehicle’s
future trajectory, particularly on non-straight paths. Having an
accurate prediction of where a vehicle is heading is crucial for
the system to reliably determine possible path intersections of
more than one vehicle at the same time. The US DOT will be
mandating that all vehicle manufacturers begin implementing V2V and
V2I systems, so very soon collision avoidance systems will no
longer rely on line of sight sensors, but instead will be able to
take into account another vehicle’s spatial movements to
determine if the future trajectories of the vehicles will intersect
at the same time. Furthermore, the book introduces the reader to
some improvements when predicting the future trajectory of a
vehicle and presents a novel temporary solution on how to speed up
the implementation of such V2V collision avoidance systems.
Additionally, it evaluates whether smartphones can be used for
trajectory predictions, in an attempt to populate a V2V collision
avoidance system faster than a vehicle manufacturer can.
This book describes recent radiotherapy technologies including
tools for measuring target position during radiotherapy and
tracking-based delivery systems. This book presents a
customized prediction of respiratory motion with clustering from
multiple patient interactions. The proposed method contributes to
the improvement of patient treatments by considering breathing
pattern for the accurate dose calculation in radiotherapy systems.
Real-time tumor-tracking, where the prediction of irregularities
becomes relevant, has yet to be clinically established. The
statistical quantitative modeling for irregular breathing
classification, in which commercial respiration traces are
retrospectively categorized into several classes based on breathing
pattern are discussed as well. The proposed statistical
classification may provide clinical advantages to adjust the dose
rate before and during the external beam radiotherapy for
minimizing the safety margin. In the first chapter following the
Introduction to this book, we review three prediction
approaches of respiratory motion: model-based methods, model-free
heuristic learning algorithms, and hybrid methods. In the following
chapter, we present a phantom study—prediction of human motion
with distributed body sensors—using a Polhemus Liberty AC
magnetic tracker. Next we describe respiratory motion estimation
with hybrid implementation of extended Kalman filter. The given
method assigns the recurrent neural network the role of the
predictor and the extended Kalman filter the role of the corrector.
After that, we present customized prediction of respiratory motion
with clustering from multiple patient interactions. For the
customized prediction, we construct the clustering based on
breathing patterns of multiple patients using the feature selection
metrics that are composed of a variety of breathing features. We
have evaluated the new algorithm by comparing the prediction
overshoot and the tracking estimation value. The experimental
results of 448 patients’ breathing patterns validated the
proposed irregular breathing classifier in the last chapter.
Augmented reality (AR) systems are often used to superimpose
virtual objects or information on a scene to improve situational
awareness. Delays in the display system or inaccurate registration
of objects destroy the sense of immersion a user experiences when
using AR systems. AC electromagnetic trackers are ideal for these
applications when combined with head orientation prediction to
compensate for display system delays. Unfortunately, these trackers
do not perform well in environments that contain conductive or
ferrous materials due to magnetic field distortion without
expensive calibration techniques. In our work we focus on both the
prediction and distortion compensation aspects of this application,
developing a "small footprint" predictive filter for display lag
compensation and a simplified calibration system for AC magnetic
trackers. In the first phase of our study we presented a novel
method of tracking angular head velocity from quaternion
orientation using an Extended Kalman Filter in both single model
(DQEKF) and multiple model (MMDQ) implementations. In the second
phase of our work we have developed a new method of mapping the
magnetic field generated by the tracker without high precision
measurement equipment. This method uses simple fixtures with
multiple sensors in a rigid geometry to collect magnetic field data
in the tracking volume. We have developed a new algorithm to
process the collected data and generate a map of the magnetic field
distortion that can be used to compensate distorted measurement
data. Table of Contents: List of Tables / Preface / Acknowledgments
/ Delta Quaternion Extended Kalman Filter / Multiple Model Delta
Quaternion Filter / Interpolation Volume Calibration / Conclusion /
References / Authors' Biographies
This book describes recent radiotherapy technologies including
tools for measuring target position during radiotherapy and
tracking-based delivery systems. This book presents a customized
prediction of respiratory motion with clustering from multiple
patient interactions. The proposed method contributes to the
improvement of patient treatments by considering breathing pattern
for the accurate dose calculation in radiotherapy systems.
Real-time tumor-tracking, where the prediction of irregularities
becomes relevant, has yet to be clinically established. The
statistical quantitative modeling for irregular breathing
classification, in which commercial respiration traces are
retrospectively categorized into several classes based on breathing
pattern are discussed as well. The proposed statistical
classification may provide clinical advantages to adjust the dose
rate before and during the external beam radiotherapy for
minimizing the safety margin. In the first chapter following the
Introduction to this book, we review three prediction approaches of
respiratory motion: model-based methods, model-free heuristic
learning algorithms, and hybrid methods. In the following chapter,
we present a phantom study-prediction of human motion with
distributed body sensors-using a Polhemus Liberty AC magnetic
tracker. Next we describe respiratory motion estimation with hybrid
implementation of extended Kalman filter. The given method assigns
the recurrent neural network the role of the predictor and the
extended Kalman filter the role of the corrector. After that, we
present customized prediction of respiratory motion with clustering
from multiple patient interactions. For the customized prediction,
we construct the clustering based on breathing patterns of multiple
patients using the feature selection metrics that are composed of a
variety of breathing features. We have evaluated the new algorithm
by comparing the prediction overshoot and the tracking estimation
value. The experimental results of 448 patients' breathing patterns
validated the proposed irregular breathing classifier in the last
chapter.
This book concentrates on improving the prediction of a vehicle's
future trajectory, particularly on non-straight paths. Having an
accurate prediction of where a vehicle is heading is crucial for
the system to reliably determine possible path intersections of
more than one vehicle at the same time. The US DOT will be
mandating that all vehicle manufacturers begin implementing V2V and
V2I systems, so very soon collision avoidance systems will no
longer rely on line of sight sensors, but instead will be able to
take into account another vehicle's spatial movements to determine
if the future trajectories of the vehicles will intersect at the
same time. Furthermore, the book introduces the reader to some
improvements when predicting the future trajectory of a vehicle and
presents a novel temporary solution on how to speed up the
implementation of such V2V collision avoidance systems.
Additionally, it evaluates whether smartphones can be used for
trajectory predictions, in an attempt to populate a V2V collision
avoidance system faster than a vehicle manufacturer can.
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