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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.

In then the past few years we have seen multiprocessing systems become more mainstream, in fact most modern personal computer CPUs now feature symmetric multiprocessing systems (SMP), where multiple instantiations of the same processor share the processing burden of the applications running on the PC. While SMPs are quite common today, we typically have not seen a shift towards multiprocessing in embedded computing. However, a new type of embedded design technique gives engineers the freedom to intelligently distribute processing functions across a digital subsystem. This article will look at an example of the distributed processing technique using Cypress Semiconductor's PSoC 3 and PSoC 5 architectures, which consist of a main CPU (in this case an 8051 or ARM Cortex M3), a DMA engine, and array of Universal Digital Blocks (UDB).

The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.

In case you haven’t realized it, the new trend in computer chip technology is multi-core. This is where most of the speed improvements moving forward will come from on our computers. To take full advantage of this however it is necessary to design your applications using Parallel Programming practices, also known as "parallelism". In today’s episode, we will meet with Stephen Toub, who will share with us some of the overarching concepts associated with parallelism, and some of the ways we are trying to empower developers to develop applications to take advantage of it.

The first part of this white paper explores the basic concepts behind HDMI, the markets it serves and its leadership role in multimedia interfaces. This is followed by a tutorial on the new capabilities of HDMI 1.4 and their role in providing a richer, more straightforward user experience. Next, we'll explore a series of user case scenarios that illustrate how the HEAC feature can simplify cabling requirements between digital home multimedia devices. The last portion of this paper discusses the architectural considerations and technical details involved with incorporating the Ethernet and Sony/Philips Digital Interconnect Format (S/PDIF) standards into the HDMI system-on-chips (SoCs) to support the HEAC feature.

When considering the resolution required for an A/D converter (ADC) integrated in a microcontroller (MCU), embedded systems designers must balance cost and performance. Higher ADC resolution implies higher-cost MCUs, but in some cases you can use other features in the MCU to enhance the ADC performance via software. That approach lets you achieve higher resolution using an inexpensive integrated ADC. Here's how to use of oversampling to achieve extra bits of resolution for an ADC integrated in an MCU.