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One of the biggest commercial applications of spintronics in computing to date has been the use of giant magnetoresistance (GMR), the material phenomenon that makes possible the huge storage capacity of today’s hard disk drives. in the awarding of the 2007 Nobel Prize in Physics, GMR was cited as the first big commercial application for nanotechnology. But extending the commercial application of spintronic-enabled systems beyond read heads for HDDs has proven to be a difficult task. One need only look at the seemingly endless travails of NVE Corporation, which in its financial results still shows it greatest revenue growth in contract research as opposed to product sales. While recent research from a team of researchers at Ohio State University and the University of Hamburg in Germany may not turn around the fortunes of spintronics in the short term, it does provide a way to better characterize the spin of electrons and thereby promises better ways of exploiting it for electronics applications. The researchers are reporting in Nature Nanotechnology that they have for the first time been able to create images of the spin direction of electrons.

In traditional short-haul microwave transmission (that is, lIne-of-sight microwave transmissions operatIng In the 18 GHz and 23 GHz radio bands),RF design engIneers typically are concerned with signal aspects such as fade margIns, signal reflections, multipath signals, and so forth. Like an accountant seekIng to balance a fInancial spreadsheet, an RF design engIneer normally creates an RF budget table, expressed In decibels (dB), In order to establish a wireless design. Aspects like transmit power and antenna gaIn are registered In the assets (or plus) column, and free space attenuation, antenna alignment, and atmospheric losses are noted In the liabilities (or mInus) column. The goal is to achieve a positive net signal strength adequate to support the wireless path(s) called for In the design.

Ever since graphene was first produced in a lab at the University of Manchester in 2004, researchers around the world have been fascinated with its potential in electronics applications. Graphene possessed all the benefits of carbon nanotubes (CNTs), namely its charged-carrier mobility, but it didn’t have any of the down sides, such as CNTs’ need for different processing techniques than silicon and the intrinsic difficulty of creating interconnects for CNTs. But all was not easy for applying graphene to electronics applications. One of the fundamental problems for graphene was its lack of a band gap, which left it with a very low on-off ratio measured at about 10 as compared to in the 100s for silicon. Now this fundamental hurdle has been overcome. Based on research led by Phaedon Avouris at IBM’s IBM T.J. Watson Research Center, Yorktown Heights, New York, IBM is reporting that they have created a significant band gap in graphene.

I remember when, a decade ago, Kevin Warwick, a professor at the University of Reading, in the U.K., implanted a radio chip in his own arm. The feat caused quite a stir. The implant allowed him to operate doors, lights, and computers without touching anything. On a second version of the project he could even control an electric wheelchair and produce artificial sensations in his brain using the implanted chip. Warwick had become, in his own words, a cyborg. The idea of a cyborg -- a human-machine hybrid -- is common in science fiction and although the term dates back to the 1960s it still generates a lot of curiosity. I often hear people asking, When will we become cyborgs? When will humans and machines merge? Although some researchers might have specific time frames in mind, I think a better answer is: It's already happening. When we look back at the history of technology, we tend to see distinct periods -- before the PC and after the PC, before the internet and after the internet, and so forth -- but in reality most technological advances unfold slowly and gradually. That's particularly true with the technologies that are allowing us to modify and enhance our bodies.

Verification of algorithm-intensive systems is a long, costly process. Studies show that the majority of flaws in embedded systems are introduced at the specification stage, but are not detected until late in the development process. These flaws are the dominant cause of project delays and a major contributor to engineering costs. For algorithm-intensive systems —including systems with communications, audio, video, imaging, and navigation functions— these delays and costs are exploding as system complexity increases. It doesn't have to be this way. Many designers of algorithm-intensive systems already have the tools they need to get verification under control. Engineers can use these same tools to build system models that help them find and correct problems earlier in the development process. This can not only reduce verification time, but also improves the performance of their designs. in this article, we'll explain three practical approaches to early verification that make this possible. First, let's examine why the current algorithm verification process is inefficient and error-prone. in a typical workflow, designs start with algorithm developers, who pass the design to hardware and software teams using specification documents.