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A study guide for students preparing for the first exam in ece714/814 fall 2009, focusing on discrete time signal processing. The guide covers key concepts from chapters 1, 2, and 3, including the relationship between time-domain and frequency-domain representations, periodicity and symmetry of fourier spectra, and the dft. Students are encouraged to understand the meaning of various parameters and relationships between them, as well as the differences between periodic discrete time signals and dft frequency domain representations.
Typology: Exams
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The first exam will be given in class on Wednesday, October 28. It will be given closed book and closed notes. The exam will emphasize visualization and basic understanding rather than memorizing equations or performing computations. However, a little computing (adding, subtracting, multiplying, dividing) will be necessary. You are welcome to bring a simple calculator (but not a laptop computer), although I will try to choose numbers that can be manipulated without one (e.g. 10/2 = ?).
The following items are intended to help you in preparing for the exam. This should give a good idea of what to emphasize, although it is not guaranteed to be absolutely inclusive (since I haven’t made the test up yet). Also, I can’t guarantee to ask about all of these things, given that the exam is only 50 minutes in duration.
Chapter 1 (and Assignment #1). Discrete Sequences and Systems
This chapter was very introductory, so there isn’t a lot to consider. However, you should definitely be comfortable with:
Chapter 2 (and DTFT notes and Assignment #2). Periodic Sampling (and the DTFT)
This chapter appears to simply be about creating discrete time signals by sampling continuous time signals, however, with the added class material on the DTFT, it addresses the most fundamental frequency domain properties of discrete time signals, which impact absolutely everything that is done in DSP.
Chapter 3 (and power spectral estimation class notes, and Assignments #3 and #4). The DFT
This material is also fundamental to DSP in that the DFT is the classic way to estimate the Fourier spectra of discrete time signals (to estimate the DTFT), and, since the inverse DFT exists, it is an important tool for moving back and forth between the time domain and the frequency domain used in many design and analysis techniques. In general, you should look at and make sure you understand every figure from figure 3-1 through 3-
The DFT introduces a new frequency variable, which is an integer index “m”, and the overall transform size “N”. You should understand the relationship between the DFT frequency index m, the transform size N, and the variables discussed in the context of the DTFT: t, n, ts, f, fs, and w. For example, given the sampling rate and transform size, you should be able to specify the frequency in Hz (or the period) of the sinusoidal component associated with a particular value of the index m.
You should understand the relationships between the DFT, the DTFT, and the DFS (see the on-line notes on the interpretations of the DFT).
You should understand the relationships between the periodic discrete time signals in figures 3-2, and 3- 3 and the DFT frequency domain representation in figure 3-4 (which in this case can be interpreted as the DFS).
You should understand the differences in the relationships between the periodic discrete time signals and the DFT frequency domain representations in figures 3-7 contrasted to figure 3-8.
Figure 3-10 shows a segment of the magnitude of the DFT of three different discrete time sinusoidal signals (in parts a, b, c).
You should understand the use of finite length window functions (figures 3-15 and 3-16) when using the DFT as an approximation of the DTFT. How does the Fourier spectrum of the finite length window function impact the resulting estimate of the DTFT?
You should understand the use of “zero padding” (figure 3-21) when using the DFT as an approximation of the DTFT. How do the added zeros impact the resulting estimate of the DTFT?
You should at least know the terms “periodogram” and “spectrogram”, and in what sense they are different and in what sense they are closely related. You should understand the relationship between the periodogram and the DFT.