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I need a image vision and a FPGA linescan vision 2 modes One mode show the image. Another mode show the linescan mode.
Requirement : 1. 6 input divided into 3 (2 input = 1 output) and 3 will be the output. 2 layer deep fuzzy 2. From the 3 outputs, 1 fuzzy output should be extracted which will show whether it is risk or no risk 3. 3 part model to be transferred to Simulink 4. 3 of the inputs will be in the datafile that will go to the Simulink model and the output will be in an oscilloscope 5. 2 file should be kept in which data paper or plot can be done from 2 files 6. . By combining 3 Simulink models, 3 outputs should be fitted to 1 neural network, 1 output from the neural network will be output that the patient has risk or no risk. 7. Web application should be made for deep neuro fuzzy system
For 10 years, poor FPGA BTC mining implementations, completely missed the big picture with excessively large, slow, power hungry designs. Researchers presented dozens of papers on how to make this better, completely missing the mark. This is your chance to get it right. Read this paper , then and look at their Verilog here to get a good understanding about state of the art FPGA BTC mining with verilog. Then apply that to YOUR FORK of the old standard in with an updated proxy for getwork. Clues follow to make FPGA BTC mining faster, smaller, and lower power, so that you will have REAL bragging rights for the fastest, smallest, lowest power FGPA miners. Goal >10x speed up. 1) The SHA256 compression is seeded with 256 bits of very random constants and forms a large shift register as t...