@luizcosta: When I was working in an environment that dealt with state estimation and Kalman filtering, we would usually have a "reality" model that would either synthesize or replay recorded real-world events in real-time (or occasionally scaled real-time) and compare "reality" (as presented to the state estimator) with the estimated state output. By playing many scenarios, you began to see specific scenarios that were handled incorrectly and could then home in on the problem areas to debug the estimator. Is that the sort of thing you are interested in doing?
Truchard will be presented the award at the 2014 Golden Mousetrap Awards ceremony during the co-located events Pacific Design & Manufacturing, MD&M West, WestPack, PLASTEC West, Electronics West, ATX West, and AeroCon.
In a bid to boost the viability of lithium-based electric car batteries, a team at Lawrence Berkeley National Laboratory has developed a chemistry that could possibly double an EV’s driving range while cutting its battery cost in half.
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.