Overall Structure
A thesis proposal in the project base (my.itu.dk) is composed of three key parts:
- Problem formulation
- Method
- What will be delivered
The two latter parts are quite straightforward. What will be delivered is a report. The method is experimental. You will design and implement software that you will evaluate.
The problem formulation should be organized in two parts. The first part is a brief description of the context of the project. What is it about? The second part is the crucial part of the thesis proposal: Intended Learning Outcomes.
Intended Learning Outcomes
If you get back to the course base on my.itu.dk, every course you have taken had some Intended Learning Outcomes, which describe what students should be able to do at the end of the course. In the case of a project proposal, the Intended Learning Outcomes (ILOs) are a form of contract that formulate your overall goals. The first objective is to protect you. Your supervisor should not move the goal post a few months into the project. The second objective is to set up the ambition level for the project. The censor will check the ILOs when assessing your project. ILOs should be ambitious enough, so that you get 12 if you fulfill all of them. But they should be reasonable, so that you do not fail to complete them.
Please make sure to read carefully the following document. We strongly recommend that you use Bloom taxonomy to write your ILOs.
Example 1
The intended learning outcomes for this project are:
- Explain the goals and specify the requirements of the MANA project.
- Survey the MANA deployment in Greenland including hardware, software and design decisions.
- Design, implement and evaluate in the lab an accurate replica of the system currently deployed in Greenland.
- Analyze in the lab the failover protocol deployed in Greenland.
Example 2
Problem Formulation:
Heat pumps are predicted a bright future in danish homes. The projected evolution of gas, oil and electricity prices make heat pumps an interesting option for home owners. In addition, heat pumps are interesting for utility companies in the context of the Smart Grid, as they could be controlled to curb peak energy demand. A problem however, is that heat pumps have not evolved much regarding intelligent house control, external data sources and Internet connectivity. In particular, the way they are controlled is primarily based on simple and local data. Normally is the heat pump controlled on the combination of a timer and some rules (e.g., the temperature in a given room is above a predefined threshold).
The goal with this project is to design, implement and evaluate an adaptive energy controller that autonomously takes decision on when and how to activate one or more heat pumps based on (a) specification from the user, (b) various data feeds including sensors/meters inside the house and external data sources (e.g., electricity price signal, weather predictions). Such an energy controller should adapt heat exchange to optimize a given utility function defined by the utility company (e.g., the controller should optimize wind-based el-usage) while conforming to the specifications from the user.
The intended learning outcomes for this project are:
- Analyze and compare existing heat pump controllers
- Analyze the constraints on an adaptive energy controller (e.g., RAM footprint, CPU characteristics, power consumption)
- Analyze and describe the embedded Linux environment chosen for the implementation of the adaptive energy controller
- Analyze and describe a set of relevant data feeds for an adaptive energy controller
- Design, implement and evaluate an adaptive energy controller
Method: The method is experimental. We will design, implement and evaluate a prototype.
What will be handed in: A report containing system design, results and discussion.