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[Part 2 explains how ADCs and DACs introduce noise through quantization errors, offset errors, and other "DC" errors.]
Introduction
A block diagram of a typical sampled data DSP system is shown in Figure 2-1. Prior to the actual analog-to-digital conversion, the analog signal usually passes through some sort of signal conditioning circuitry, which performs such functions as amplification, attenuation, and filtering. The low-pass/band-pass filter is required to remove unwanted signals outside the bandwidth of interest and prevent aliasing.
Figure 2-1: Fundamental Sampled Data System.
The system shown in Figure 2-1 is a real-time system, i.e., the signal to the ADC is continuously sampled at a rate equal to fs, and the ADC presents a new sample to the DSP at this rate. In order to maintain real-time operation, the DSP must perform all its required computation within the sampling interval, 1/fs, and present an output sample to the DAC before arrival of the next sample from the ADC. An example of a typical DSP function would be a digital filter.
In the case of FFT analysis, a block of data is first transferred to the DSP memory. The FFT is calculated at the same time a new block of data is transferred into the memory, in order to maintain real-time operation. The DSP must calculate the FFT during the data transfer interval so it will be ready to process the next block of data.
Note that the DAC is required only if the DSP data must be converted back into an analog signal (as would be the case in a voice-band or audio application, for example). There are many applications where the signal remains entirely in digital format after the initial A/D conversion. Similarly, there are applications where the DSP is solely responsible for generating the signal to the DAC, such as in CD player electronics. If a DAC is used, it must be followed by an analog anti-imaging filter to remove the image frequencies.
There are two key concepts involved in the actual analog-to-digital and digital-to-analog conversion process: discrete time sampling and finite amplitude resolution due to quantization. An understanding of these concepts is vital to DSP applications.
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