BCS
(Ottawa) Rideau Section
Postgraduate
Presentations
16
January 2007
Summary
of Abstracts and Presenters:
Title: Haptic Data Compression for Real-time Haptic-enabled
Virtual Surgical Training Applications
Name: Nizar Sakr
Abstract: Haptic (sense of touch) and telehaptic (sense of touch over
a network) systems are expected to become the next dimension of human-computer
interaction. This increased interest in haptic systems in recent years is due
to its wide range of applications, which include military, medical, industrial,
educational, and consumer markets. In the undertaken project, novel haptic
compression techniques are derived to reduce haptic data traffic in networked
surgical training applications while preserving the immersiveness of the
haptic-enabled virtual environment. These methods are also applied to compress
voluminous haptic data files typically produced during a haptic session in
order to be later analyzed or replayed.
A psychophysical model that takes into account human haptic perceptibility is
exploited to compress (or remove) data that is perceptually insignificant.
Adaptive prediction techniques are derived in order to remove any data that may
be predicted from previously coded data samples. Finally, a number of
experiments are performed to evaluate the impact of compression on a
haptic-enabled virtual surgery training simulations.
Bio: Nizar Sakr received the B.A.Sc. in Computer Engineering
(Summa cum laude) and the M.A.Sc. in Electrical Engineering from the University
of Ottawa, Ottawa, ON, Canada in 2004 and 2006, respectively. He is currently
pursuing the Ph.D. degree in Electrical Engineering at the School of
Information Technology and Engineering at the University of Ottawa. His current
research interests include image and signal processing, haptic data processing,
embedded systems design and computational intelligence (fuzzy logic and neural
networks). He is a student member of IEEE. He is also the Vice Chair of the
IEEE Computational Intelligence Society - Ottawa chapter.
Title: Using Secondary Knowledge to Support Decision Tree
Classification of Retrospective Clinical Data
Name: William Elazmeh
Abstract: Retrospective clinical data presents many challenges for
data mining and machine learning. The transcription of patient records from
paper charts and subsequent manipulation of data often results in high volumes
of noise as well as in a loss of other important information. In addition, such
datasets often fail to represent expert medical knowledge and reasoning in any
explicit manner. In this research we describe applying data mining methods to
retrospective clinical data to build a prediction model for asthma exacerbation
severity for pediatric patients in the emergency department. Difficulties in
building such a model forced us to investigate alternative strategies for
analyzing and processing retrospective data.
We present our methodology and show experimental results that
demonstrate some advantages and some limitations of our approach.
Bio: Mr. Elazmeh earned his Master's and Bachelor's
degrees in Computer science at the University of Ottawa prior to holding
several teaching positions as a faculty lecturer at the University of Alberta
as well as the University of Guelph, Ontario. His research interests are
focused on developing methods for evaluating machine learning algorithms, in
particular, he studies the relationship between classification and ranking. Mr.
Elazmeh aims to enhance the use of methods developed by the machine learning
community in medical applications by elevating their performance measures.
Title: Developing Ad Hoc Routing Protocols using Generative
Programming
Name: Pedro E. Villanueva-Peña
Abstract: Routing in mobile ad hoc networks (MANETs), where network
topology is highly dynamic, is not a trivial task. Many protocols have been
proposed but only four of them have reached RFC status. Simulation is the tool
of choice to test and analyze routing protocols, however, its credibility has
decreased due to simulations being poorly performed and their inaccurate match
with the results obtained from real test-bed deployments. On the other hand,
the constantly increasing network requirements in terms of bandwidth,
robustness, reliability and quality of service for a broad range of
multiplatform scenarios demand for fast development and implementation of
routing protocols that satisfy specific user requirements. Generative
Programming is an attractive solution that makes use of reusable components and
is also powered with the knowledge to automatically assemble them. This talk
discusses the problem of developing ad hoc routing protocols, proposes an
approach to automate the development process and presents the GP-Pro protocol
generator, which is based on Generative Programming, for automatic generation
of ad hoc routing protocols according to user specifications. GP-Pro is
designed with the explicit goal of generating a large number of different
protocols ready for deployment.
Bio: Pedro E. Villanueva-Peña graduated (with honours) from the National Autonomous University of Mexico in 2001, with
the BCS degree in Applied Mathematics and Computer Science. In 2002, he
received the M.Sc. degree in Engineering from the University of Sheffield, UK.
He was an associated researcher at the Mexican Valley University from 1998 to
2000 and he has worked in software development for firms such as Ford Motors
Company. Since 2004, he has been doing his PhD in Engineering at the University
of Ottawa. His research interest is on mobile wireless networks, mainly in the
area of routing protocols. Recently, he has been exploring the applicability of
automated software development to the field of ad hoc networks.
