Designing withProgrammable Logicin an Analog WorldProgrammable logic devicesrevolutionized digital design over 25years ago, promising designers a blankchip to design literally any functionand program it in the field. PLDs canbe low-logic density devices that usenonvolatile sea-of-gates cells calledcomplex programmable logic devices(CPLDs) or they can be high-densitydevices based on SRAM look-up tables
This application note shows how to achieve low-cost, efficient serial configuration for Spartan FPGA designs. The approachrecommended here takes advantage of unused resources in a design, thereby reducing the cost, part count, memory size,and board space associated with the serial configuration circuitry. As a result, neither processor nor PROM needs to be fullydedicated to performing Spartan configuration.In particular, information is provided on how the idle processing time of an on-board controller can be used to loadconfiguration data from an off-board source. As a result, it is possible to upgrade a Spartan design in the field by sending thebitstream over a network.
Field Programmable Gate Arrays (FPGAs) are becoming a critical part of every system design. Many vendors offer many different architectures and processes. Which one is right for your design? How do you design one of these so that it works correctly and functions as you expect in your entire system? These are the questions that this paper sets out to answer.
We would like to welcome you as a user of the Allegro CX, a rugged, handheld fi eld PC for data collection. Developed with the input of data collection professionals worldwide, the Allegro CX is adaptable and versatile for use in a wide variety of data collection environments. The Allegro CX continues to utilize our ergonomic, lightweight design that is standard in our line of Allegro Field PCs. This design makes your Allegro easy to use for extended periods while moving to and from data collection sites in the fi eld.
Calculation of the Differential Impedance of Tracks on FR4 substrates
There is a discrepancy between calculated and measured values of impedance for differential transmission lineson FR4. This is especially noticeable in the case of surface microstrip configurations. The anomaly is shown tobe due to the nature of the substrate material. This needs to be considered as a layered structure of epoxy resinand glass fibre. Calculations, using Boundary Element field methods, show that the distribution of the electricfield within this layered structure determines the apparent dielectric constant and therefore affects theimpedance. Thus FR4 cannot be considered to be uniform dielectric when calculating differential impedance.
The power of programmability gives industrial automation designers a highly efficient， cost-effective alternative to traditional motor control units （MCUs）。 The parallel-processing power， fast computational speeds， and connectivity versatility of Xilinx® FPGAs can accelerate the implementation of advanced motor control algorithms such as Field Oriented Control （FOC）。
Additionally， Xilinx devices lower costs with greater on-chip integration of system components and shorten latencies with high-performance digital signal processing （DSP） that can tackle compute-intensive functions such as PID Controller， Clark/Park transforms， and Space Vector PWM.
The Xilinx Spartan®-6 FPGA Motor Control Development Kit gives designers an ideal starting point for evaluating time-saving， proven， motor-control reference designs. The kit also shortens the process of developing custom control capabilities， with integrated peripheral functions （Ethernet， PowerLink， and PCI® Express）， a motor-control FPGA mezzanine card （FMC） with built-in Texas Instruments motor drivers and high-precision Delta-Sigma modulators， and prototyping support for evaluating alternative front-end circuitry.
This hands-on, one-stop guide delivers the focused, streamlined direction you need to get your Web solutions up and running quickly. Zero in on key ASP.NET configuration details and techniques using quick-reference tables, lists, coding and more.
This a Bayesian ICA algorithm for the linear instantaneous mixing model with additive Gaussian noise [1]. The inference problem is solved by ML-II, i.e. the sources are found by integration over the source posterior and the noise covariance and mixing matrix are found by maximization of the marginal likelihood [1]. The sufficient statistics are estimated by either variational mean field theory with the linear response correction or by adaptive TAP mean field theory [2,3]. The mean field equations are solved by a belief propagation method [4] or sequential iteration. The computational complexity is N M^3, where N is the number of time samples and M the number of sources.
A Java virtual machine instruction consists of an opcode specifying the operation to be performed, followed by zero or more operands embodying values to be operated upon. This chapter gives details about the format of each Java virtual machine instruction and the operation it performs.
