Project 1: Title: Video monitoring of newborns in low resource

Project 1:
Title: Video monitoring of newborns in low resource countries.
Tittel: Bildebehandling i forbindelse med videoovervåking av nyfødte i lav-ressurs land.
This is a part of a larger project for improving newborn survival in low resource countries and will be
conducted in collaboration with Stavanger Acute Medicine Foundation for Education and Research
(SAFER) and Laerdal Global Health in Stavanger and India. Newborn mortality is a major challenge in
these countries, and the hypothesis is that it is possible to use a simple and cheap camera for video
monitoring of challenged babies the first days or weeks of their lives to constant monitor the heart
beat frequency as well as the respiratory frequency by means of image processing techniques.
Recent publications in the image processing community have demonstrated an interesting potential
in detecting respiratory frequency and heart beat frequency from video of humans, and these
techniques will be investigated, implemented and adapted for our purpose. The project will be
conducted within a multi-disciplinary collaborative network. After a preliminary study of video of
human skin, video monitoring of babies will be conducted at a hospital in Tanzania to provide real
world data.
The applicant should have a master’s degree with specialization in signal- and image processing,
pattern recognition, and/or mathematics. Contact: Professor Kjersti Engan, e-mail:
[email protected]
Supervisors: Professor Kjersti Engan and professor Trygve Eftesøl
---------------------------------------------------------------------------------------------------------------------------------Project 2:
Designing Fault-Tolerant Autonomous Systems with Supervisors
Tittel: Konstruksjon av feil-tolerante autonome systemer med overordnet styring
“Designing Fault-Tolerant Autonomous Systems with Supervisors”
There is a great deal of research done on fault-tolerant systems, adaptive systems, intelligent
systems, and so on, to minimize material damages and to save human lives if and when system
failures occur. In this PhD research, a new approach will be proposed that takes a holistic view,
starting from the system analysis for any probable failures, and finalizing with the adaptive
supervisors that chooses appropriate control actions based on the situation.
Supervisor (or supervisory control) is a new methodology for automatic control of discrete event
systems. Despite its simple mathematical background and closeness of its models to the real-life
systems, acceptance of supervisory control as a feedback control technique is low; studies point out
that the main reason for this low acceptance is the lack of tools that are easy to use [3].
This research work will involve Petri nets based supervisors only; references [1-3] present basics of
Petri net and references [4-5] present Petri net based supervisors in detail. There are supervisors
based on other methodologies too, like finite automata based supervisors. Finite automata based
supervisors that have been devised and successfully applied for some industrial control systems [67]. However the fact that models based on automata can easily grow to infinite size makes this
approach impractical for many practical problems [5]. Unlike automata, Petri net do not suffer from
the infinite model size. Petri net models present simple graphical view for the user and also simple
linear algebraic tools for computations; hence, Petri net are more suitable for developing supervisory
control of discrete event systems [5].
T. Murata. “Petri Nets: Properties, Analysis, and Applications”. Proceedings of the IEEE, 77
(4), pp. 541-580, 1989
M. Silva, E. Teruel, and J. M. Colom. “Linear Algebraic Techniques for the Analysis of P/T Net
Systems”. In Lectures in Petri Nets I: Basic Models. Springer, 1998
J. M. Colom. “Some suggestions for future research”. L20 – Lecture notes for the PhD course
on Petri nets, UPC, Barcelona, 14-25 May 2012
M. V. Iordache and P. J. Antsaklis. Supervisory control of concurrent systems: a Petri net
structural approach. Boston: Birkhauser, 2006
J. O. Moody and P. J. Antsaklis. Supervisory Control of Discrete Event Systems Using Petri
Nets. Springer verlag, 1998
P. Ramadge and W. Wonham. “The control of discrete event systems. In Discrete event
dynamic systems”, Proceedings of the IEEE. v77 i1. 81-98, 1989
W. Wonham and P. Ramadge. “On the supermal controllable sublanguage of a given
language”, SIAM Journal on Control and Optimization, v.25 n.3, p.637-659, May 1, 1987
Veiledere: Reggie Davidrajuh og Tormod Drengstig - KONTAKTINFO
Project 3:
Semantic Entity Search / Semantisk Entity Søk
Semantic search refers to the idea that the search engine understands the
concepts, meaning and intent behind the query that the user enters into the
search box, and provides rich and focused responses (as opposed to merely a
list of documents). Entities, such as people, organizations or products, play
a central role in this context; they reflect the way humans think and organize
information. We can observe that major search engines (like Google or Apple's
SIRI) are becoming "smarter" day by day in recognizing specific types of
objects (for example, locations, events or celebrities); yet, true semantic
search has still a long way to go.
This project aims to develop a theoretically sound and computationally
efficient framework for entity-oriented information access: the search and
discovery of entities and relationships between entities. A key element to a
successful approach is the combination of massive volumes of structured and
unstructured information from the Document Web and the Data Web, respectively.
Successful candidates will be expected to conduct research, design, develop,
and deploy state-of-art, scalable information retrieval, information
extraction and machine learning techniques for innovative entity-oriented
search applications. The project will include both theoretical and empirical
explorations, where lab-based results will be evaluated in 'live' environments
with real users.
Qualifications: M.Sc. in Computer Science, Computational Linguistics,
Mathematics or related fields by the appointment date. Good written and spoken
command of English.
Research experience or a track record of project based work, demonstrable
interest in the domain, solid programming skills (particularly Java), and
experience in manipulating and analyzing large data sets (esp. using Hadoop)
are a clear plus.
Supervisor: Associate professor Krisztian Balog
Project 4:
Intelligent real-time industrial operations using Big-data Analytics
(Intelligent sant-tid industrielle operasjoner ved å bruke Big-data analytikk):
The project aims to research and develop a new monitoring control system that in real-time
guarantee a risk reduction to zero tolerance in industrial operations (e.g. in oil and gas, smart grids).
The new monitoring system will combine several methods from different knowledge domains into a
unique robust platform that can monitor complex life-critical systems guaranteeing zero tolerance
for errors. The main domains to be combined are Ontology/semantics, Model Checking, Adaptive
Control, Artificial Intelligence and Big Data analysis.
Professor Chunming Rong (chunming.ron[email protected])
Co-supervisor: Rui Esteves
Project 5:
Smart Cities using Big-data Analytics
(Smart by anvendelser ved å bruke Big-data analytikk):
Big data describes datasets whose volume, velocity, variety and complexity exceed ability of
commonly used software tools to capture, process, store, manage, and analyse them. It requires
efficient analytics methods in a world where data availability is exploding. We shall explore the
potential applications in relation to making the daily life of citizens easier.
Professor Chunming Rong [email protected]
Co-supervisor: Mohsen Assadi / Erdal Cayirci
----------------------------------------------------------------------------------------------------------------------------------Project 6:
Security, and Privacy in Big-data and Cloud Services
(Sikkerhet, personvern i big-data og tjenester i nettsky):
Accountable organizations ensure that obligations to protect data are observed by all who process
the data, irrespective of where that processing occurs. We need accountability in the cloud because
there is a higher risk to privacy and security that is a magnification of issues faced in subcontracting
and offshoring, with some new threats and with the cloud’s dynamism rendering inappropriate most
traditional mechanisms for establishing trust and regulatory control.
Professor Chunming Rong ([email protected])
Co-supervisor: Erdal Cayirci / Janne Hagen
-------------------------------------------------------------------------------------------------------------------------------------Project 7:
Software Defined Networking (SDN) and Network Virtualization
(Software definerte nettverk (SDN) og nettverk virtualisering):
Software Defined Networking (SDN) and Network Virtualization are trending research topics in
computer science and networking disciplines. In SDN capable networks, the control plane is
separated from the forwarding plane and a single control plane can control several forwarding
planes. This approach yields to more control and better fault tolerance in networks. In the era of
Cloud Computing, Network Virtualization is an essential component of today's data center
networking. SDN can provide the functionality for building reliable virtual networks in data centers.
There are a few simulation tools for testing SDN controllers and analyzing virtual network. Open
vSwitch is an example, which is implemented in software and can be used for limited experiments.
Several vendors are working on OpenFlow capable hardware. Testing with physical hardware and
developing new solutions on top of them, provide us real-world experience. Furthermore, it can lead
us to be one of the few research institutes working on this topic.
Professor Chunming Rong ([email protected])
Co-supervisor: Erdal Cayirci
-------------------------------------------------------------------------------------------------------------------------------------------Project 8:
Norsk tittel: Skalerbare distribuerte geografiske informasjonssystemer
Title: Scalable distributed geographic information systems
In order to store all the geodata that is being generated today, and to satisfy the query load that
some of these systems receive, the data needs to be stored in a distributed system. Traditional
spatial indexing methods such as R-trees are not designed for use in distributed systems. One idea is
to design a query system or index for geographical data that is scalable in the same manner as NoSQL
systems. That is, use a distributed hashing technique (possibly modified versions of Grid files or
BANG files) to query geographical information. This should ideally work for all the typical
geographical objects: point features, line features and region features. Grid Files and BANG files as
they are now work best for point features and are poorly suited for line and region features.
Another challenge in such a system is to ensure consistency among replicas of the objects given that
many geographical objects are more complex than the objects typically stored in cloud computing
systems, and have topological relationships or other relationships that need to be maintained or that
the user can use in queries.
Example applications:
GPS-based car navigation systems that use updated traffic information
Web-map applications (OpenStreetMap data may be downloaded and used to test the
Environment monitoring systems. These may additionally require storing fields and
uncertain objects.
Generating vague regions from people’s conceptions of places
Data mining on animal tracking data from applications such as the previous sheep tracking
Data mining on indoor position data
Supervisor: Førsteamanuensis Erlend Tøssebro
---------------------------------------------------------------------------------------------------------------------------Project 9:
Topic: Predictive Big Data Analytics in Data Centers, Smart Energy, or Sensor Networks
Tema: Prediktiv Stor Data Analyse i Datasentre, Smart Energy, eller Sensor-nettverk
Predictive analytics are the key technology behind success of Internet, financial, energy, retail,
healthcare and many other companies. They allow predicting trends in the most important factors
for company operations like: user behavior, stock prices, operation risks, etc.
Major type of data that is used for prediction is so called time series data. The most of time series
data comes from physical (e.g.
temperature, pressure) or virtual sensors (e.g. price level, network utilization).
Current trend in predictive analytics is the increase in data amount to be analyzed. Common belief is
that being able to handle more data is the most important factor in improving prediction accuracy.
The project will focus on developing systems, algorithms, and analytic approaches that make it
possible to quickly analyze large and complex data sets to distill new knowledge and insights from
these data.
Technologies of interest include: Hadoop, R (and Rhipe), OpenTSDB and other. The particular focus
will be put on one of the domains: Data Centers, Smart Energy, or Sensors Networks
The project will be conducted in connection with Strategic Collaboration on Big Data Analysis and
Communication between Purdue University and University of Stavanger. Student is expected to stay
up to 6 months at Purdue University (appropriate funding will be provided).
Supervisors: Associate professor Tomasz Wiktor Wlodarczyk, [email protected], phone
+47 832061 and professor Thomas J.
Hacker, Purdue University
----------------------------------------------------------------------------------------------------------------------Project 10:
Title: Sparse representation of signals and images using
overcomplete dictionaries
Norsk tittel: Glissen representasjon av signal og bilder med overkomplette matriser.
Sparse representations and learned dictionaries are hot topics in
the signal and image processing community. This approach has
successfully been used in several applications the recent years,
including compressed sensing, image compression, restoration, noise
removal, texture classification and segmentation, and signal
classification in general. We have long experience on dictionary
learning used in different applications. In this project we want to
continue this work and look into new applications where we can
exploit this signal model.
Expected background: M.Sc. in electrical engineering, with signal
and image processing subjects in the curriculum, or a strong
mathematical background.
Supervisor: Associated professor Karl Skretting and professor Kjersti Engan
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