This lecture series looks pretty interesting from reading the topic summaries. Slides aren't online yet as I write this - it would be interesting if the material covers when and whether the data reduction algorithms should mimic the operation of biological systems (e.g. human/animal eye) vs. preprocessing the raw dataset in a machine-only paradigm, i.e. one where the biological system approach might end up abtracting away or losing information from raw sensor data that a machine-only paradigm could make good use of.
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For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This discussion will examine what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.