This application note covers the design considerations of a system using the performance
features of the LogiCORE™ IP Advanced eXtensible Interface (AXI) Interconnect core. The
design focuses on high system throughput through the AXI Interconnect core with F
MAX
and
area optimizations in certain portions of the design.
The design uses five AXI video direct memory access (VDMA) engines to simultaneously move
10 streams (five transmit video streams and five receive video streams), each in 1920 x 1080p
format, 60 Hz refresh rate, and up to 32 data bits per pixel. Each VDMA is driven from a video
test pattern generator (TPG) with a video timing controller (VTC) block to set up the necessary
video timing signals. Data read by each AXI VDMA is sent to a common on-screen display
(OSD) core capable of multiplexing or overlaying multiple video streams to a single output video
stream. The output of the OSD core drives the DVI video display interface on the board.
Performance monitor blocks are added to capture performance data. All 10 video streams
moved by the AXI VDMA blocks are buffered through a shared DDR3 SDRAM memory and are
controlled by a MicroBlaze™ processor.
The reference system is targeted for the Virtex-6 XC6VLX240TFF1156-1 FPGA on the
Xilinx® ML605 Rev D evaluation board
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.
The practice of enterprise application development has benefited from the emergence of many new enabling technologies. Multi-tiered object-oriented platforms, such as Java and .NET, have become commonplace.
This model simulates a CDMA2000 1xRTT Forward link (between Base Station and Mobile Station). In particular, it simulates the Radio Configuration 3 of a Forward Fundamental channel. The block CDMA2k: Initial settings allows you to set different parameters such as data rate, Power Control SubChannel insertion rate, spreading code index, QOSF index and the channel model.
This an adaptive receiver for a direct-sequence spread spectrum (DS-SS) system over an AWGN channel. The adaptive receiver block is modified from the LMS adaptive filter block in DSP Blockset. For DS-SS signal reception, the adaptive filter needs to have multi-rate operation. The input sample rate is equal to chip rate and the output is at symbol rate. Two rates are related by PG, processing gain
The CD Audio sample allows some non-SCSI2 CD ROMs to support audio operations by intercepting the relevant audio ioctls and translating them into the command block(s) expected by the non-compliant cdroms. It supports Plug and Play and Power Management, and is 64-bit compliant.
The purpose of this computer program is to allow the user to construct, train and test differenttypes of artificial neural networks. By implementing the concepts of templates, inheritance andderived classes from C++ object oriented programming, the necessity for declaring multiple largestructures and duplicate attributes is reduced. Utilizing dynamic binding and memory allocationafforded by C++, the user can choose to develop four separate types of neural networks:
