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A digital pattern generator is a piece of electronic test equipment or software used to generate digital electronic stimuli. Digital electronics stimuli are a specific kind of electrical waveform varying between two conventional voltages that correspond to two logic states ("low state" and "high state", "0" and "1").
ATPG (acronym for both automatic test pattern generation and automatic test pattern generator) is an electronic design automation method or technology used to find an input (or test) sequence that, when applied to a digital circuit, enables automatic test equipment to distinguish between the correct circuit behavior and the faulty circuit behavior caused by defects.
Valentina is an open source pattern drafting software tool, designed to be the foundation of a new stack of open source tools to remake the garment industry. [5] The program was named after mother of the founder Roman Telezhinsky, a cutter by profession, who gave him the idea for the project.
A logic signal generator or data pattern generator or digital pattern generator produces logic signals—that is, logical 1s and 0s in the form of conventional voltage levels. The usual voltage standards are LVTTL and LVCMOS. It is different from a "pulse/pattern generator", which refers to signal generators able to generate logic pulses with ...
DK-Audio (also known as DK-Technologies) subsequently released the PT5300, which superseded the PT5230, accepting all of its pattern generator modules and included many new options and features developed by DK-Audio. It was the last physical pattern generator directly descending from the original PM5544 to generate the Philips circle pattern.
A look at how different mapping techniques reveal different voting patterns 11/5 2013 Election Results Live returns with real-time historical and demographic scatterplots
The software is designed to detect faces and other patterns in images, with the aim of automatically classifying images. [10] However, once trained, the network can also be run in reverse, being asked to adjust the original image slightly so that a given output neuron (e.g. the one for faces or certain animals) yields a higher confidence score.
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