Thomas Edison promised that he
would build a small invention every 10 days and a big invention every six
months. This brings to mind the engineering grand
challenges of today. Solving these challenges - from making solar energy
economical to engineering better medicine - will make our lives better, and we
can't afford to take our time finding resolutions. Imagine if today's engineers
could deliver Edison's level of innovation with the vast amount of technology
access we have in this modern age. To make this happen, it's important that
vendors and technology providers create productivity platforms to build complex
systems but without the need for large teams. Today's engineers and scientists
need to be able to not only make high-performance embedded system prototypes to
vet new ideas and prove new approaches efficiently, but also deploy
high-performance control systems for their needs without outsourcing.
The right kinds of tools can empower a much smaller team of
domain experts to work together to solve real problems. Domain experts
understand today's real-world challenges, so why not enable them to solve them
as well? Edison invented and improved upon objects that transformed our world.
It wasn't about academic or theoretical discussion; it was about making the
world a better place. Whether he invented it himself or worked with others,
almost all of his inventions are things we still use in some form today.
To make this kind of impact today
requires the right level of software abstraction and hardware platform
integration. Users need a controllable level of abstraction that is appropriate
for their knowledge and needs, as well as the ability to control the low-level
details and visualize the high-level structure of what they are creating.
Graphical system design is a productive approach to this need. We've found that
domain experts using this approach are strongly motivated and more capable of
solving the real-world problems they're experiencing because they learn about
the parameters of a problem while solving it.
Domain experts, like the engineers at OptiMedica Corp., are
using NI tools to develop a highly accurate device capable of using patterns to
automate the delivery of laser pulses that treat retinal diseases. Their new
innovation has proven to reduce treatment duration as well as patient
discomfort. Another example, demonstrated by Neptune Technologies, is a scaled
version of a home water system with distributed water meters. Smart Meters are
the future of utility conservation and allow real-time monitoring of water, gas
or electric consumption. Finally, Siliken Renewable Energy is using graphical
system design to optimize the solar panel production process, enabling
engineers to help harness our abundant sunlight resource and address escalating
environmental and energy concerns.
These examples are encouraging displays of the possibility of
being able to deliver on the "Return of Edison" - empowered innovators inventing
real solutions to some of the toughest problems we are seeing today and into
tomorrow. We should all be inspired to return to the Edison-like era of
innovation and true engineering problem solving.
Dr. James Truchard is president, CEO and co-founder of National Instruments. He holds a doctorate in electrical engineering as well as bachelor's and master's degrees in physics.
Are they robots or androids? We're not exactly sure. Each talking, gesturing Geminoid looks exactly like a real individual, starting with their creator, professor Hiroshi Ishiguro of Osaka University in Japan.
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.