Examples of binary data in the following topics:
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- The information on these discs are read by a computer in the form of binary data.
- The data is stored either by a stamping machine or laser and is read when the data is illuminated by a laser diode in the disc drive.
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- Suppose we have discrete data, not a continuous function.
- In particular, suppose we have data $f_k$ recorded at locations $x_k$ .
- Now we will compute the coefficients in such a way that $p$ interpolates (i.e., fits exactly) the data at each $x_k$ :
- $f_n$ are the data and $c_k$ are the harmonic coefficients of a trigonometric function that interpolates the data.
- You should get the same result, but it will take dramatically longer than Mathematica would for 100 data points.
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- For instance, consider the data shown in Figure 4.1.
- Think of how you might correct the data for this drift.
- Data courtesy of Dr.
- This is known as an estimate of the power spectrum of the data.
- On the right is the power spectrum of these data.
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- Natural satellites, often called moons (see ), are celestial bodies that orbit a larger body call a primary (often planet, though there are binary asteroids, too).
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- Think of the DFT coefficients $c_k$ and the data points $f_n$ as being elements of vectors $\mathbf{c}$ and $\mathbf{f}$ .
- There are $N$ coefficients and $N$ data so both $\mathbf{c}$ and $\mathbf{f}$ are elements of $R^N$ .
- $N$ is fixed, that's just the number of data points.
- Now you may well ask: what happens if we use fewer Fourier coefficients than we have data?
- That corresponds to having fewer unknowns (the coefficients) than data.
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- In other words the value of $x$ that minimizes the sum of squares of the errors is just the mean of the data.
- In other words, find a linear combination of the columns of $A$ that is as close as possible in a least squares sense to the data.
- Now $A$$A \mathbf{x_{ls}}$ applied to the least squares solution is the approximation to the data from within the column space.
- So $A \mathbf{x_{ls}}$ is precisely the projection of the data $\mathbf{y}$ onto the column space:
- The generalized inverse projects the data onto the column space of $A$ .
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- Depending on what trends in your data you want to showcase, you can choose one of a variety of thematic maps.
- Cartograms distort the shape of each region to represent the magnitude of its data point.
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- The rainbow pattern that appears is a result of the light being interfered by the pits and lands on the disc that hold the data. shows this effect.
- Film used to be used to record the data, but that was inconvenient because it had to be replaced often.
- In this method, the detector collects data at a single fixed angle at a time.
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- (It was a running joke that any theory of atomic and molecular spectra could be destroyed by throwing a book of data at it, so complex were the spectra.)
- While the formula in the wavelengths equation was just a recipe designed to fit data and was not based on physical principles, it did imply a deeper meaning.
- Experimentally, the spectra were well established, an equation was found to fit the experimental data, but the theoretical foundation was missing.
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- Quantitative data on the effects of ionizing radiation on human health are relatively limited compared to other medical conditions because of the low number of cases to date and because of the stochastic nature of some of the effects.
- Stochastic effects can only be measured through large epidemiological studies in which enough data have been collected to remove confounding factors such as smoking habits and other lifestyle factors.
- The richest source of high-quality data is the study of Japanese atomic bomb survivors.