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Although signal processing is usually associated with digital signal processors, it is becoming increasingly evident that FPGAs are taking over as the platform of choice in the implementation of high-performance, high-precision signal processing. For many such applications, the choice generally boils down to using either a single FPGA, a FPGA with an associated DSP processor or a farm of DSP processors.

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.

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.

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.

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.

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

High Level Synthesis Architectural Optimization Basics In part 1 of this article we introduced basic filter bank theory and used the Synplify DSP High Level Synthesis (HLS) tool to implement an example filter bank into three alternative architectures. In part 2 we dive deeper into these three architectures to better understand how these filters work. We will also examine the HLS optimizations we applied and the resulting benefits. Example Filter Bank Review Before we proceed, let's quickly review our filter bank example. Our example, shown in Figure 1, is a size 16 DFT filter bank. The color scheme shows the sample rate change where a 16 MHz input sample rate (red) has been chosen and the output sample rate is downsampled by 16 (green).

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.

DSP core licensor Ceva Inc. is due to unveil a software-programmable multimedia video processor architecture at the Mobile World Congress in Barcelona next week. The multicore architecture, called MM3000, which comes complete with C compilers, power management provision and an RTOS/multithreading scheduler is intended to be able to process any and all video codecs up to the highest resolutions and frame rates currently available as well as future codecs for things like 3-D video.

While the new multicore system on chip (SoC) signal-processing architecture announced by Texas Instruments this week at Mobile World Congress hits all the right notes with respect to what's needed in next-generation basestation designs, it rings a bit hollow given how sketchy the architectural details remain when contrasted with more 'real' announcements from the likes of Freescale. For sure, the requirements of next-generation basestations will push all architectures to their limits and beyond. Balancing lower power and lower cost with increasingly parallel, math-intensive processing to meet multiuser demands for high-data-rate data in 3GPP Long Term Evolution (LTE) Release 8 all-IP networks is not going to be easy, especially with the introduction of MIMO, beam forming, OFDMA and many other enhancements engineered to maximize spectral efficiency.

NXP BV (Eindhoven, The Netherlands) has said it will demonstrate a microcontroller based on the ARM Cortex-M4 at the Embedded Systems Conference in San Jose, California. NXP was one of the first companies to license the Cortex-M4 processor core and a chip has been implemented using a low-leakage 90-nm process technology. This enables performance in excess of 150-MHz clock frequency, NXP said. NXP has added proprietary power-down techniques to reduce power consumption. The ESC Silicon Valley demo will show that a 7-channel audio graphic equalizer application processing 32-bit precision audio data requires only 12 MHz of CPU bandwidth using the Cortex-M4 DSP extensions, and 60 MIPs without. The core includes DSP extensions not usually found inside a microcontroller and NXP's implementation are aimed at a broad set of applications including motor control, digital power control and embedded audio.

Length-match your traces to within 100 mils. Or is it 10 mils? Or should you go down to 1 mil? Should you include the lengths of the vias? How about the lengths of resistors? Understanding the origin of length-matching requirements, coupled with some rudimentary signal integrity analysis, can help answer these questions.   Determining length requirements requires an understanding of flight time, electrical length vs. physical length, loading and signal quality. Those elements are vital in determining what the length really needs to be, as well as in determining the allowable trade-offs to meet system timing goals.

There are severe flaws within the Harmonic-Balance and SPICE programs now widely used. Mentioned as far back as within an abstract of Session WSO at the 2008 IEEE International Microwave Symposium: "Even though nonlinear circuit-analysis software has been in use for many years, users still have difficulty obtaining valid results with existing methods.  Recognized problems include poor accuracy, convergence difficulties, long simulation times, and unstable results (i.e., results that vary greatly with minor changes in parameters).  These problems are encountered in both harmonic-balance and time-domain simulations."

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.

Field programmable gate arrays (FPGAs) present an efficient and inexpensive alternative when it comes to implementing complete embedded systems along with important peripheral functions. The reconfigurable logic circuitry of an FPGA offers tremendous flexibility. A lesser known feature is that the outputs of a digital FPGA also permit various analogue applications.

In high-end audio equipment, careful selection of resistors is one of the best ways to avoid or minimize noise and distortion in the signal path. This paper describes the noise generation in resistors manufactured using the various available resistor technologies and quantifies the noise insertion typical for each type.