Simulation tools in the engineering classroom are a good thing, given the ubiquity of these tools in modern engineering practice. When I was in school a decade ago, there was minimal coverage of simulation techniques. In school, finite-element analysis was still considered a specialized topic for graduate students, while in industry, it was already well-established as a regular part of the design process.
While it's good to see students being introduced to simulation tools early on (especially in core courses such as circuit analysis, rather than specialized courses focused on computational methods), it's also important for students to understand the limits of a given simulation. They need to learn not to believe things just because they see them on a screen.
I hope that the use of MapleSim in this class is not intended to replace a more traditional electronics lab. It should not be an alternative to building the circuits on a breadboard. That's an inductive learning approach that has been around for a long time.
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.