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The consumer handheld market is growing by leaps and bounds. With more processing power and increased support for more applications, portable products are cross-pollinating with traditional computing systems even as the product life cycle has decreased considerably in this market segment. As a result, especially in this era of economic slowdown, it is imperative that new products meet the time-to-market window to gain maximum acceptance. A decrease in product life cycles requires a reduced development cycle and an increased emphasis on reusability and reprogrammability. The emerging handheld market is also seeing interesting trends in which each individual device in a family has lower volumes but there is more customization across the series of devices, effectively upping the total unit volumes. The key challenge then becomes how to develop a system that is widely reusable and also customizable. These requirements have led designers increasingly to turn to the FPGA for handheld-product development. The FPGA has become more powerful and feature-rich, while gate counts, area and frequency have increased. FPGA development and turnaround cycles are considerably shorter than those of custom ASICs, and the added advantage of reprogrammability can make the FPGA a more compelling solution for handheld embedded systems.

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.

In separate keynote addresses in Silicon Valley, two Internet pioneers gave different takes on the future of the network of networks Monday (May 24). Vint Cerf, co-developer of the Net's TCP/IP protocol, shared his thoughts with developers of the smart electric grid, seen as a massive embedded extension to the Internet. Larry Roberts, who helped launch the forerunner of the Internet, explained his ideas for remedying the growing congestion that plagues the Web today. Smart grid developers can learn from the lessons of the Internet, Cerf told attendees at Connectivity Week here. They should follow the model of the Net in creating a layered architecture with plenty of room for flexibility in areas that cannot be anticipated today, he said in a keynote at Connectivity Week.

Earlier in this series, we touched on one problem that can arise when sampling an analog signal, namely the problem of aliasing. There are three other issues with signal sampling to which we now turn our attention: digitization effects, finite register length effects, and oversampling. So far, weve assumed that all of the signals were measuring are continuous analog values; i.e., our measurements are completely accurate. Even in the cases in which we have noise, the underlying assumption is that the measurement itself, for example the noisy sensor output voltage, is known precisely.

We’re all familiar with the general idea of a filter: it removes something that we don’t want from something we do want. Coffee filters that pass the liquid coffee but retain the grounds or air filters that pass clean air but trap the dust and other pollutants are two common examples of mechanical filters in everyday life. That same concept can be applied to noisy electrical signals to pass through the “true” signal of interest while blocking the undesirable noise signal. Looking at Figure 2.5c below, imagine for a moment that the signal of interest is in the lower-frequency region and that the noise signal is in the higher-frequency region. Ideally, we’d like to be able to get rid of that high-frequency noise, leaving just the signal component that we want.

In earlier articles on intelligent sensor design, we saw how valuable they can be to both end users and those who manufacture and sell them. It’s now time to delve more deeply into what it takes to make intelligent sensors work.   The first step in that journey is to develop a solid, intuitive understanding of the principles of digital signal processing(DSP). Unlike many introductory DSP articles and texts, the focus here will be on presenting and using the important concepts rather than deriving them, for the simple reason that addressing the subject in depth is a book-sized, not a chapter-sized, project.

While the topic of timing has already been raised in previous parts in this series, the discussion here will be expanded to include the execution and response time of the software functions. When discussing timing in embedded software, there are typically two types of timing requirements, rate of execution and response time. Rate of execution deals with the event-to-event timing within a software function. It can be the timing between changes in an output, time between samples of an input, or some combination of both.

Carnegie Mellon has taught its robotic snake to climb trees, though one hopes it won’t start offering your spouse apples. “Uncle Sam” (presumably named for its red, white, and blue markings) is a snake robot built from modular pieces. The latest in a line of ‘modsnakes’ from Carnegie Mellon’s Biorobotics Lab, Uncle Sam can move in a variety of different ways including rolling, wiggling, and side-winding. It can also wrap itself around a pole and climb vertically, which comes in handy when scaling a tree. You have to watch this thing in action. There is something incredibly life-like, and eerie, about the way it scales the tree outdoors and then looks around with its camera ‘eye’. Projects like Uncle Sam show how life-mimicking machines could revolutionize robotics in the near future.

With the consumer demand for richer content and its resultant , increasing high data bandwidth continuing to drive communications systems, coding for error control has become extraordinarily important. One way to improve the bit error rate (BER), while maintaining high data reliability, is to use an error correction technique like the Viterbi algorithm. Originally conceived by Andrew Viterbi as an error-correction scheme for noisy digital communication, the Viterbi algorithm provides an efficient method for forward error correction (FEC) that improves channel reliability. Today, it is used in many digital communications systems in applications as diverse as CDMA and GSM digital cellular, dial-up modems, satellite, deep-space communications and 802.11 wireless LANs. It is also commonly used in speech recognition, keyword spotting and computational linguistics.

Let’s face it. Most of the gear you use at work or play has multicore processors in it. Your laptop has them (the CPU itself has two cores, and the dedicated graphics processor has many more). That game console in the living room has still more, and even a high-end smartphone typically has a CPU and graphics core on a single chip. Out of sight but definitely not out of mind–particularly if they cease working–are the servers and high-throughput network routers, all which have numerous multicore processors in them. The multiple cores in these devices work in concert to provide quick responses to user queries or to manage the smooth flow of data throughout the office.