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National Instruments has released a vision module for the PXI platform that provides a high-performance parallel processing architecture for hardware-defined timing, control and image pre-processing. The NI 1483 Camera Link adapter module, in combination with an NI FlexRIO field-programmable gate array (FPGA) board, offers a solution for embedding vision and control algorithms directly on FPGAs which are used to process and analyse an image in real time with little to no CPU intervention. The FPGAs can be used to perform operations by pixel, line and region of interest. They can implement many image processing algorithms that are inherently parallel, including fast Fourier transforms (FFTs), thresholding and filtering.

This book is intended for those who work in or provide components for industries that use digital signal processing (DSP). There is a wide variety of industries that utilize this technology. While the engineers who implement applications using DSP must be very familiar with the technology, there are many others who can benefit from a basic knowledge of its' fundamental principals, which is the goal of this book—to provide a basic tutorial on DSP.

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.

Opus is Octasic's high-performing, ultra low-power, asynchronous DSP technology optimized for basestations, video processing and media gateway solutions. Asynchronous designs deliver similar computing performance to synchronous designs, but use less silicon and less power. No clock tree No state-elements Less sensitive to process and temperature variations

The ARM Cortex™-M4 processor is the latest embedded processor by ARM specifically developed to address digital signal control markets that demand an efficient, easy-to-use blend of control and signal processing capabilities. The combination of high-efficiency signal processing functionality with the low-power, low cost and ease-of-use benefits of the Cortex-M family of processors is designed to satisfy the emerging category of flexible solutions specifically targeting the motor control, automotive, power management, embedded audio and industrial automation markets. The Cortex-M4 processor features extended single-cycle multiply-accumulate (MAC) instructions, optimized SIMD arithmetic, saturating arithmetic instructions and an optional single precision Floating Point Unit (FPU). These features build upon the innovative technology that characterizes the ARM Cortex-M series processors…

4 January 2010—Though flexible devices such as roll-up displays have been promised for several years, their commercialization has been stalled by a missing ingredient: a flexible form of flash memory. But researchers at the University of Tokyo have recently developed an organic, floating-gate nonvolatile memory that behaves like flash memory, which may solve that problem. While silicon-based flash memory is fine for the mass data storage found in cellphones, digital music players, and thumb drives, fabricating it requires high processing temperatures, thus ruling out its production on flexible substrates like plastic. Organic semiconductors, however, can be processed at temperatures well below the melting point of most plastics. What's more, "the cost of flash memory is too high to use in applications that require large arrays of memory," says Tsuyoshi Sekitani, an assistant professor in the University of Tokyo's department of electrical and electronic engineering and one of the researchers who developed the new memory. "But we can print our organic memory on flexible substrates and over large areas using inkjet printers. So costs will be low."

Not so long ago, artists routinely made their own paints using all sorts of odd ingredients: clay, linseed oil, ground-up insects—whatever worked. It was a crude and rather ad hoc process, but the results were used to create some of the greatest paintings in the world. Today I and other scientists are developing our own special paints. We’re not trying to compete with Vermeer or Gauguin, though. We hope to create masterpieces of a more technical nature: optoelectronic components that will make for better photovoltaic cells, imaging sensors, and optical communications equipment. And we’re not mixing and matching ingredients quite so haphazardly. Instead, we’re using our blossoming understanding of the world of nanomaterials to design the constituents of our paints at the molecular level.

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.

Power and energy are key design considerations across a spectrum of computing solutions, from supercomputers and data centers to handheld phones and other mobile computers. A large body of work focuses on managing power and improving energy efficiency. While prior work is easily summarized in two words—"Avoid waste!"—the challenge is figuring out where and why waste happens and determining how to avoid it. In this article, I discuss how, at a general level, many inefficiencies, or waste, stem from the inherent way system architects address the complex trade-offs in the system-design process. I discuss common design practices that lead to power inefficiencies in typical systems and provide an intuitive categorization of high-level approaches to addressing them. The goal is to provide practitioners—whether in systems, packaging, algorithms, user interfaces, or databases—a set of tools, or "recipes," to systematically reason about and optimize power in their respective domains.