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Let Fig. 325 show a portion of an analog signal voltage v t , where we ll assume v t is limited to values between 0 and 8 volts. The regular intervals of time on the horizontal axis can represent seconds, milliseconds, or microseconds, as the case may be. Now consider Fig. 326, which, let us assume, shows IDEAL SAMPLING of v t , which means that the amplitude or height of each sampling pulse represents the exact value of the corresponding amplitude of v t at that instant. In such a case it would be theoretically possible to completely recover the original analog signal v t from the sample pulses; this assumes, of course, that the basic requirement of the sampling theorem, regarding sampling rate, is satis ed. If the pulse samples in Fig. 326 are used to amplitude-modulate a high-frequency carrier so as to be transmitted by wireless, we have what is called pulse-amplitude modulation, abbreviated PAM. The advantage of PAM is that it is ideally suited for use in what is called timedivision-multiplex, a system which allows several di erent signals to be sent together over the same transmission system, using the same carrier frequency. The disadvantages of

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PAM are that it requires increased bandwidth with no basic reduction in the e ect of noise (in comparison with analog transmission) Another disadvantage is that a PAM system must be free of amplitude distortion, a requirement that complicates the design of PAM systems as compared with simple present or not-present pulse systems The just-mentioned shortcomings of PAM can be overcome by using what is called pulse-code modulation, abbreviated PCM An outline of this remarkably e ective system is as follows, in which, as before, v t will denote the original ANALOG signal it is desired to transmit in PCM form The rst step in a PCM system is the same as in PAM; that is, the ANALOG signal must be SAMPLED as in Figs 325 and 326 The second step in forming a PCM signal is to LIMIT THE NUMBER OF DIFFERENT AMPLITUDES the sampling pulses can have.

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This is called quantizing the sampled signal, and is accomplished by using a quantizer circuit at the output of the sampling circuit Thus, at the output of the quantizer the amplitude of each sample pulse represents a RANGE of possible values of v t , instead of an unlimited number of amplitudes as in Fig 326 Only a quantized signal, which has an exact (integral) number of di erent values, can be converted into a PCM signal In Fig 326 it s possible for a sample pulse to have ANY amplitude in the range from 0 to 8 volts Now suppose the pulses of Fig 326 are fed into a quantizer circuit that is capable of sorting the pulse amplitudes into, let us say, 8 di erent ranges, in accordance with the table to the left of Fig 327 If we now redraw Fig.

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How to create qr code based on cell value in Excel ? - ExtendOffice
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Users can refer to the following steps to generate QR Code barcode images in Microsoft Excel documents. Open a new Excel spreadsheet, move to "Add-Ins" tab, and click "Insert Barcode". Choose a cell, select " QRCode " barcode symbology, and input valid data.

But because it was developed with Caucasians in mind, it is also fundamentally flawed African Americans, Hispanics, Asians, American Indians, and others complain that their traditional foods and ways of eating were forgotten when the Daily Food Guide Pyramid was built For example, the majority of the ethnic populations in this country are to various degrees lactose-intolerant (they cannot consume dairy products) but the government s pyramid identifies dairy products as a major source of protein and the only good source of calcium The fact is, the use of dairy products is typically quite low in Asian populations, where calcium is obtained instead from soybean products, seaweed, and cruciferous vegetables These and other foods, including spinach, turnip greens and other leafy vegetables, nuts, seeds, whole grains, and beans, are just as good and in many cases better sources of calcium than dairy products..

326 in accordance with the table, the result is Fig 327..

Samples, Populations, Distributions, and Variables The Basic Statistics The Mean The Median The Mode Variance and Standard Deviation The Regression Equation The Correlation Coefficient The Entire Stats Class Graphing Data Scaling Data The Graphs Class The Graphs final and Instance Variables The Graphs Constructor The paint( ) method The bargraph( ) Method The scatter( ) Method The regplot( ) Method A Statistics Application The StatsWin Constructor The itemStateChanged( ) Handler The actionPerformed( ) Method The shutdown( ) Method The createMenu( ) Method The DataWin Class Putting Together the Pieces Creating a Simple Statistical Applet Some Things to Try ..

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