Below is a list of current research opportunities. For details and funding contact the professor directly. For additional information, contact Professor A. Banihashemi, the Associate Chair of Graduate Studies.

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Ericsson-Carleton 5G Fellowships

A number of fellowships offered by Ericsson are available to outstanding graduate students at both MASc and PhD levels. In addition to attractive financial support, these fellowships provide excellent opportunities to collaborate with Ericsson on a variety of interesting projects. Whether you are already a graduate student at the SCE Department or still considering our gradate programs, you can apply to the Ericsson-Carleton 5G Fellowships from here. For further information, you can contact your supervisor or the Associate Chair for Graduate Studies (gchair@sce.carleton.ca).

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Project Title: Experiments in software testing from finite state machines: (for MEng Students)

Description: We have developed a framework to automate as much as possible different steps of experiments to evaluate various software testing techniques for finite state machines. The project’s objective is to completement the experiments we have performed with new experimental subjects, i.e., with finite state machines. The work will include: getting familiar with the framework; getting familiar with state-based software testing; getting familiar with specific state machine models; run (replicating) experiments, collecting and analyzing data.

What you will learn: increased software development practice/knowledge/experience; exposure to software testing; exposure to empirical software engineering.

Supervisor: Yvan Labiche

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Project Name: Automatically creating finite state machines for software: (for MEng Students)

Engineering Experiments Description: One of the challenges in software engineering research is the availability of real examples. This is especially true for research in software testing and more specifically when verifying through testing that an implementation (source code) conforms to a finite state machine specification. Researchers have resorted to either simple finite state machines or synthetic finite state machines. This project’s objective is to create an infrastructure so that we can automatically create synthetic finite state machine (graphs) that can then become experimental subjects for empirical research in software testing. The preferred programming language is Java but alternatives can be considered if they make sense.

What you will learn: increased software development practice/knowledge/experience; exposure to software testing; exposure to empirical software engineering.

Supervisor: Yvan Labiche

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Project Name: Measuring test code and application code with software metrics Description: (for MEng Students)

Project Description: A number of metrics have been defined to measure source code quality. The number of lines of code (LOC), usually not counting blank lines nor comment lines is one of them; counting the number of lines of comments is another; yet another one is the well-known cyclomatic complexity which gives a measure of the complexity of a function/method by evaluating the number of alternatives paths/executions of the function/method. Although source code and application code are both code, they have different structures. For instance, test code such as in a JUnit test has a specific structure with a test set up, a tear down and the use of an assert() function; this is not the case of application code. We wish to have an idea of such differences with source code metrics. The work will thus consist of: selecting open source systems; automatically measuring their test code and application code with a commercial software; collecting and analyzing data.

What you will learn: exposure to source code measurement and metrics; increased software development practice/knowledge/experience.

Supervisor: Yvan Labiche

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Project Name: Resource Management on BigData Processing Platforms

Project Description: Research on various issues underlying allocation and scheduling is underway. The focus of attention is parallel processing systems including MapReduce/Hadoop, Spark and specialized stream processing platforms.

Supervisor: Prof. S. Majumdar

Webpage: www.sce.carleton.ca/faculty/majumdar.html

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Project Name: SUSTAIN

Project Description: Modern building automation systems collect and store vast amounts of data in. Tools that facilitate management and visualization of these data are in their infancy. These methods fail to communicate the complexities of building performance and equal opportunities to improve performance. The research focuses on methods and tools for integrating real-time sensor data with occupancy information based on the occupants’ devices and integrating parametric data from the building to construct formal models that can be used for both simulation and control. This research is funded by NSERC strategic grant and supported by Autodesk Research.

Supervisor: Prof. G. Wainer

Webpage: https://sustain.sce.carleton.ca

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Project Name: Modeling and Simulation for the Development of Cyber-Physical Systems (CPS)

Project Description: CPS are built as sets of components interacting with their surrounding environment. These are highly reactive systems, where not only correctness is critical, but also the timing for executing the system tasks. Our research focuses on using a formal Modeling and Simulation methodology for CPS development. The research includes interaction with complex simulation models of the environment.

Supervisor: Prof. G. Wainer

Webpage: http://arslab.sce.carleton.ca/

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Project Name: Discrete-Event Methodologies for Modeling and Simulation (M&S)

Project Description: M&S is now one of the pillars of science and engineering. Formal M&S provides even better results as the software artifacts can be built faster and safer. In particular, the DEVS formalism, on generic dynamic systems concepts, is suitable to deal with these issues. The goal of this research is to expand DEVS theoretical framework for advanced simulation models. From the practical standpoint, we are developing a set of tools that can be applied to develop complex simulations. These tools can be applied to a wide range of applications, ranging from mobile communication, environmental issues, traffic control, or emergency planning, between others.

Supervisor: Prof. G. Wainer

Webpage: http://arslab.sce.carleton.ca/

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Project: Algorithm development for advanced signal/image processing and learning

Project Description:  Advanced Sensors Signal Processing Lab has several projects in  detection, estimation, tracking and classification. Application areas include autonomous systems, biomedical systems and public safety.

Supervisor: Prof. S. Rajan

Website: https://www.sce.carleton.ca/faculty/rajan

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Project Name: Machine Learning applied to Network Management

Project Description: The goal of the project is to apply machine learning techniques to logs of network management traces, and to extract rules that can automate some of the more repetitive tasks of a human operator. The provided data may need to be cleaned and pre-processed in order to be usable by a machine learning algorithm. The choice of the algorithm is up to you, but needs to be justified given the nature of the data. Relevant metrics should be used to assess the performance of your algorithm

Pre-requisite: students must have taken, and excelled at, one of the machine learning or pattern classification courses offered in the program.

Supervisor: Prof. B. Esfandiari

Website: https://carleton.ca/nmai/

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Project Name: Ultrasound imaging and measurements for biomedical applications.

Project Description: R&D on ultrasonic sensors, methods, system and signal processing for tissue characterization, biomedical monitoring and diagnosis. The projects include (but not limited to): muscle monitoring and characterization, cardiovascular monitoring including artery diameter tracking and measurement, ultrasound elastography.

Supervisor: Prof. Yuu Ono

Website: www.sce.carleton.ca/faculty/ono

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Project Name: Formal Security Modeling and Analysis for Software-Dependent Systems

Project Description: The CyberSEA Research Lab has open positions in developing formal security modeling and analysis frameworks in support of security evaluation and assurance for software-dependent systems.

Supervisor: Prof. J. Jaskolka

Website: https://carleton.ca/jaskolka/

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Project Name: System-Level Security Evaluation Methods

Project Description: The CyberSEA Research Lab has open positions in establishing system-level security evaluation methods, techniques, metrics, and measures for understanding and mitigating the risks to system assets posed by identified security vulnerabilities.

Supervisor: Prof. J. Jaskolka

Website: https://carleton.ca/jaskolka/

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Project Name: Incremental Development of Security Assurance Cases

Project Description: The CyberSEA Research Lab has open positions in advancing techniques to support the management, evaluation, and presentation of sufficient evidence for developing incremental security assurance cases.

Supervisor: Prof. J. Jaskolka

Website: https://carleton.ca/jaskolka/

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Project Name: Machine learning for biomedical informatics

Project description: The cuBIC lab has several ongoing projects applying machine learning to problems in biomedical informatics. These include both medical informatics (e.g. patient monitoring in the NICU) and bioinformatics (e.g. protein/RNA analysis).

Supervisor: Prof. J. Green

Website: www.sce.carleton.ca/faculty/green

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Project Name: Image processing for biomedical applications.

Project Description: The Bioprinting & Imaging lab has projects focused image processing and feature detection. These projects include understanding optical microscopy imaging and how the information extracted from these images can be used to answer biological questions (e.g. disease progression).

Supervisor: Prof. L. Guidolin

Website: https://carleton.ca/sce/people/mostaco-guidolin/

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