Phd thesis in computer networks

13. Include a title on your proposal. I'm amazed at how often the title is left for the end of the student's writing and then somehow forgotten when the proposal is prepared for the committee. A good proposal has a good title and it is the first thing to help the reader begin to understand the nature of your work. Use it wisely! Work on your title early in the process and revisit it often. It's easy for a reader to identify those proposals where the title has been focused upon by the student. Preparing a good title means:

    ...having the most important words appear toward the beginning of your title,

    ...limiting the use of ambiguous or confusing words,

    ..breaking your title up into a title and subtitle when you have too many words, and

    ...including key words that will help researchers in the future find your work.
14. It's important that your research proposal be organized around a set of questions that will guide your research. When selecting these guiding questions try to write them so that they frame your research and put it into perspective with other research. These questions must serve to establish the link between your research and other research that has preceded you. Your research questions should clearly show the relationship of your research to your field of study. Don't be carried away at this point and make your questions too narrow. You must start with broad relational questions.

A PhD will provide you advancement in your career, usually a hefty salary differential, and prestige. You should thoroughly research career prospects before you commit to a program: some fields, such as the humanities, are increasingly glutted with PhDs, making it near impossible to land a job at all, especially if you are seeking a job as a professor. Other fields are expanding. Logically, if a field is growing in its need for research, and is connected with powerful policy decision-making, then a PhD in that field will be an asset. PhDs in economics, finance, marketing, and development studies, just to name a few, are solid bets for future job and salary prospects.

We compare multiple synthesis techniques to one another as well as the real data that they seek to replicate. We also introduce learned synthesis techniques that either train models better than the most realistic graphical methods used by standard rendering packages or else approach their fidelity using far less computation. We accomplish this by learning shading of geometry as well as denoising the results of low sample Monte Carlo image synthesis. Our major contributions are (i) a dataset that allows comparison of real and synthetic versions of the same scene, (ii) an augmented data representation that boosts the stability of learning, and (iii) three different partially differentiable rendering techniques where lighting, denoising and shading are learned. Finally we are able to generate datasets that can outperform full global illumination rendering and approach the performance of training on real data.

Phd thesis in computer networks

phd thesis in computer networks

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