Signal conditioning can prepare a sensor’s output for digitization. In a previous series, we looked at the analog-to-digital converter (ADCs) and sources of error that occur within the device. Of course, errors can creep in upstream of the ADC along the analog signal chain as the signal to be digitized is acquired and conditioned. Q: […]
Understanding ADC specs and architectures: part 5
ENOB describes an analog-to-digital converter’s performance with respect to total noise and distortion. In the earlier parts of this series on analog-to-digital converters (ADCs), we looked at the basics (part 1); gain error, offset error, and differential nonlinearity (part 2); and integral nonlinearity (part 3); and then we looked at some ADC topologies and introduced […]
Understanding ADC specs and architectures: part 4
The AC performance of an analog-to-digital converter depends on its architecture. In part 3 of this series, we discussed the integral nonlinearity (INL) error of an analog-to-digital converter (ADC), noting that gain, offset, and INL error all contribute to the total unadjusted error. This metric provides an overall view of an ADC’s DC performance. Q: What about the AC […]
Understanding ADC specs and architectures: part 3
Integral nonlinearity tracks the cumulative effects of an ADC’s differential nonlinearity. In part 2 of this series, we discussed several sources of error in an analog-to-digital converter (ADC), including gain, offset, missing-code error, and differential nonlinearity (DNL). We concluded with an illustration of a waveform with varying levels of DNL superimposed on the staircase representing […]
Understanding ADC specs and architectures: part 2
Specifications such as gain error, offset error, and differential nonlinearity help define an analog-to-digital converter’s performance. In part 1 of this series, we discussed an ideal analog-to-digital converter (ADC), noting that it would have infinite resolution and bandwidth. Then we looked at the real world of practical inverters and how their resolution, expressed in a […]
Understanding ADC specs and architectures: part 1
Analog-to-digital converters are the heart of most test equipment, setting the stage for the digital processing of analog signals. Several posts over the past year or so have involved digital signal processing. For example, we have covered the fast Fourier transform (FFT), the inverse FFT, and discrete convolution. To perform these operations on real-world signals, […]
How to use convolution to implement filters: part 4
A windowed sinc function can implement a low-pass filter, and a two-dimensional convolutional filter can blur or sharpen images. In part 3 of this series, we introduced a low-pass filter based on the Sinc function and described the need for windowing to compensate for sampling and truncation. Q: How can we apply this filter? A: […]
How to use convolution to implement filters: part 3
A windowed sinc filter outperforms a moving-average filter in the frequency domain. In part 2 of this series, we described a type of convolution filter called the moving-average filter, and we demonstrated that it is effective at removing Gaussian white noise in the time domain but performs poorly in the frequency domain. Q: Do all […]
How to use convolution to implement filters: part 2
A moving-average filter can address white noise in the time domain but performs poorly in the frequency domain. In part 1 of this series, we defined convolution, denoted by the * symbol, and looked at a simple geometrical example of how it operates to produce a new function y(t) from two given functions, f(t) and […]
How to use convolution to implement filters: part 1
Convolution is used in a variety of signal-processing applications, including time-domain-waveform filtering. In a recent series on the inverse fast Fourier transform (FFT), we concluded with a mention of convolution and its application to filtering. Convolution Q: What is convolution? A: Convolution, denoted by * symbol, combines two functions to form a third function in […]
