Lecture01_Intro - Department of Computer Engineering

Report
Hossein Sameti
Department of Computer Engineering
Sharif University of Technology
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Prerequisite:
Lectures:
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Time: Sundays/Tuesdays 9:00 – 10:30
Location:
Lecturer: Hossein Sameti
Email: [email protected]
Tel: 021 6616 6637
Office: Comp. Eng. 706
Office Hours: By appointment
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Address: Courseware
Access to all information (e.g., lecture notes) :
 Password: 763fall92
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Lecture notes and other resources will be available online
(gradually!). Please check the course website regularly
for announcements, lecture notes, solutions, etc.
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Alan V. Oppenheim, Ronald W. Schafer, John
R. Buck, “Discrete-Time Signal Processing”,
Prentice-Hall, 3rd edition, 2009 (ISBN:
0131988425)
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John G. Proakis, Dimitris Manolakis, “Digital Signal Processing:
Principles, Algorithms and Applications”, 4th Edition, Prentice Hall
Steven W. Smith, “The Scientist and Engineer's Guide to Digital
Signal Processing” (available online : http://www.dspguide.com)
Robert D. Strum and Donald E. Kirk,“First Principles of Discrete
Systems and Digital Signal Processing”, Addison Wesley
Richard G. Lyons, “Understanding Digital Signal Processing”,
Pearson Education
Vinay Ingle and John G.Proakis, “Digital Signal Processing Using
MATLAB”, Thomson-Engineering
Matlab Tutorial:
http://users.ece.gatech.edu/mcclella/SPFirst/MatlabMovies/
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By the end of the course, we will develop a solid
understanding of DSP fundamentals including:
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Analog vs. digital signal representation and processing
Why DSP? advantages, limitations and fundamental tradeoffs
Relationship between frequency and time representations
Analysis and processing of signals in the temporal/spatial as well
as in the frequency domain
◦ Practical applications of DSP
◦ Implementation of DSP algorithms using MATLAB
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Discrete-time signals and systems, linear time-invariant (LTI) systems and their
properties
The Z-transform and its application in the analysis of LTI systems
Frequency domain representations including the Discrete-time Fourier
transform (DTFT), Discrete Fourier transform (DFT) and fast Fourier
transform (FFT)
Design of digital filters (FIR, IIR)
Realization of discrete-time systems
Brief introduction to feature extraction and pattern recognition techniques
Sampling and reconstruction , Analog/Digital and Digital/Analog converters
Upsampling / downsampling, multi-rate signal processing
DSP applications including audio signal processing and biomedical data
analysis
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Evaluation:
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Weekly Assignments: 0%
Weekly Quizzes: 15%
Matlab Exercises: 10%
Course Project: 15%
Midterm exam: 20%
Final Exam: 40%
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Assignments will be posted on the course webpage with their due date
At the due date, you are not expected to hand in your solutions, this is the date
they will be discussed in the TA class
TA classes are held on Sundays 12:15 – 13:15 at CE 007
One week after the TA class for each assignment is held, the quiz on the same
chapter is taken
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◦ Learn to identify a problem and propose a solution for solving
that problem
◦ Learn to deal efficiently with a real-world application
◦ Get familiar with the multi-disciplinary nature of DSP
◦ Improve your project management /time management skills by
following strict deadlines
◦ Improve your technical writing skills by writing a project
proposal and a project report
◦ Improve your communication skills by giving an efficient
presentation
◦ Practice team-work during different phases of the project
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The course project constitutes 15% of the course evaluation.
You can choose any topic related to digital signal processing.
However, the topic should be approved by the instructor.
Each group consists of three students (in exceptional cases,
two students). Single-person projects are strongly discouraged
and are subject to the instructor's approval.
Students can not work on the same or very similar research
topics. Email the instructor as soon as you have decided on
your topic, so that your priority is reserved.
Students who need help with choosing a topic for the course
project are encouraged to consult with the instructor.
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Any topic related to DSP is acceptable. However, make sure that the
topic is realistic and the work is doable in a short period.
Some research areas:
◦ Medicine: analysis of biosignals, medical imaging,…
◦ Speech, music, and audio: Speech recognition, speech verification,
music synthesis, music emotion recognition, …
◦ Security: biometrics, surveillance,…
◦ Data management: data storage, compression, and retrieval,…
◦ Financial: financial data analysis
◦ Image processing: image denoising, image manipulation…
◦ Computer/Machine vision: automatic object recognition and
identification,…
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The break-down of the project evaluation is as follows:
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Quality of the project: 40%
Quality of the final report: 25%
Oral presentation: 25%
Project proposal: 10%
Due Dates (strict deadlines):
◦ Project proposal: 5 Aban 1392
◦ Final report: your presentation date
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The project proposal includes a summary of the area you want
to work on, the context, the significance, and the methodology.
Clarify your work plan and schedule and what results are to be
obtained. Please note that
◦ A project proposal should include the title of the project, the name of the
students who are working on the project and a brief description of the project.
◦ Project proposals need to be approved before you start the course project.
◦ Proposals should be returned on the due date in a written format.
◦ Proposals can take up to two pages (single column, single-line spacing). You can
include references and figures if needed.
◦ Proposals will be graded and relevant feedback will be given to you.
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Course project descriptions from previous years and a sample
proposal will be posted on the course webpage later this week. The
sample proposal can be used as the template.
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It is recommended to use available software and Matlab
functions. Visit Matlab Central- File Exchange to find the files or
packages that are developed by other users. However, you should
check these files to make sure that they work properly.
Guidelines for writing clean and efficient code will be posted on
the course website later this week.
Please properly cite the code and the work of others if you
are using them.
If you use others’ code, you need to clearly state your
contribution in your project report.
Your own code should be written in a clear manner and should be
supplied as supplementary material.
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The written report is expected to be 12-15 single-column,
1.5 line spacing pages.
Reports are to be submitted in both soft copy and hard
copy on the due date.
You can provide clearly-explained and organized
supplementary material separately (images, movies, etc).
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The report must follow the guidelines for an IEEE journal/conference
proceedings paper (the template can be found on the course webpage). A
typical report is a single document file that includes the following:
◦ title, author, affiliation
◦ abstract, keywords
◦ introduction (application and problem description, background, related
work/literature survey, short summary of submitted work,...)
◦ description of data, images, etc.
◦ methods (the proposed method, algorithmic details, equations/formula, pseudocode, explanatory figures,...)
◦ results (qualitative and quantitative, figures and tables,...)
◦ summary and conclusions (summarize main contribution, problems encountered,
possible future work,...)
◦ references
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An oral presentation is also required
◦ Each project will be allocated 10 minutes: it includes setting up
the computer and presenting the oral presentation. Questions
and discussion will follow for 2 minutes.
◦ Students are required to attend project presentations.
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Midterm exam accounts for 25% and the final exam accounts
for 40% of the overall mark.
Date of the mid-term: Tuesday 21 Aban at 5 PM
The emphasis of the final exam will be on the topics covered
after the mid-term.
You can bring one piece of paper (double-sided) to the midterm exam and two pieces of paper (double-sided) to the final
exam. You can write anything on it (formulae, figures,
drawings, solutions to the problems, text, poems, etc!).
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Questions or comments?
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