Spectrum Digital

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The various hardware elements of a SCA-compliant radio are referred to as Devices, and they can include general-purpose processors (GPPs), A/Ds, D/As, digital receivers, upconverters, field-programmable gate arrays (FPGAs), DSPs, and other types of equipment.

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Multitarget modeling is an Electronic Control Unit (ECU) development technique that uses Model-Based Design with automatic embedded code generation. With multitarget modeling, algorithm models are adapted, with code automatically generated for a wide variety of embedded DSPs, microprocessors, and microcontrollers. As a result, software can save development teams significant time and effort because they do not have to write, test, and rewrite code for each embedded target device.

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Visteon uses multitarget modeling for developing automotive electronic power train control systems. Their developers' approach relies on the creation of a data dictionary that handles the data types used during model simulation and embedded code generation. With this approach, Visteon engineers can simulate design trade-offs and experiment with various processor schemes for algorithms based on a wide variety of embedded processor options.

The simulation environment helps to determine the effects of data resolution and quantization on system performance prior to software implementation. Once the appropriate embedded processor is selected, code is automatically generated, built, and integrated into the production ECU. The simulation and code generation environment is provided by Simulink® and Real Time Workshop® Embedded Coder tools from The MathWorks.

Multitargeting using a manual approach—challenges The traditional method of supporting a variety of hardware architectures is to develop code manually that is easily ported, and can be changed from one architecture to another. The challenge with this approach is that engineers cannot easily convert their floating-point algorithms into fixed-point designs. The conversion process requires that scale factors and other fixed-point information be provided for all variables and parameters—making it difficult to create and maintain a software layer that provides adequate abstraction between the high-level algorithm and fixed-point software implementation in a manner that is easy to use.

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Design and porting software from floating-point to fixed-point architectures introduces other issues to the development process, including:

Multitargeting using Model-Based Design In the late 1990s, Visteon began to investigate the use of models and automatic code generation for power train applications. This search led the company to move from a manual development approach to Model-Based Design. The change occurred over the past six years as existing hand-coded production modules were gradually converted to Simulink models as needed. Automatic code generation using Real-Time Workshop Embedded Coder was also introduced during that time and used to automatically produce code from the models for implementation in production ECUs.

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The use of Model-Based Design has now gained wide acceptance in the automotive industry, and the virtues of using modeling, simulation, and automatic code generation are well known. As an early adopter of Model-Based Design, Visteon has amassed a large amount of expertise in this area#151;establishing a complete modeling environment that facilitates quick deployment on different hardware architectures using a multitarget, automatic code generation approach. This approach relies on the use of external data dictionaries to constrain the format and structure of the code generated from the model architecture.

Design environment A multitarget model is independent of the data type. Initially the data type used for the model is the maximum word size available (i.e. double-precision real) for the host machine performing the simulation—providing the ideal behavior of the proposed algorithm. If the behavior is satisfactory, target implementation-specific constraints are then added to make the model suitable for a production ECU.

In this approach, the single controller provides a single current that is controlled by a sense resistor at the bottom at the LED string. The system is controlled by an external microcontroller that drives the level shifted drivers that control the shunt FETS. Brightness is controlled by adjusting the percentage of time each FET allows current to flow through its respective LED. The ability to use the processing power of a very inexpensive controller to overcome some of the shortcomings of the LEDs is a significant benefit of this approach. For example Blue LEDs are less efficient and are often run at higher current to achieve brightness balance. This same result can be achieved by appropriately scaling the duty cycles of the FETs to solve this issue. Since it is current density that controls light output, a supplementary method of compensating for brightness imbalances is to place the larger of two LED diodes in parallel where the inherent light of a particular LED color is weak. A circuit has been built to examine this concept and is shown in Figure 4.

The controller in this circuit is a TPS40200 that is used with a pair of parallel current sensing resistors (R11 and R5) to maintain a constant output current across a series string of three LEDs. In this case, the circuit is set up to deliver 700 mA of output current. Each of the three shunt FETs is controlled by a floating driver that can be driven by standard logic, and yet, can control FETs that are at varying voltages above ground.

The motion vector data, the macroblock type, the motion compensated data, and other relevant information need to be communicated to the TE. This is accomplished by having each DSP store this information in the SDRAM using DMA transfers whenever appropriate.

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