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- #Optisystem matalab data transfer verification
- #Optisystem matalab data transfer code
- #Optisystem matalab data transfer windows
Optimum soft-decision decoding of linear block codes for AWGN channel Generic capacity equation for discrete memoryless channel (DMC)Ĭapacity over binary symmetric channel (BSC)Ĭapacity over binary erasure channel (BEC)Ĭonstrained capacity of discrete input continuous output memoryless AWGN channelĮrror-detection and error-correction capability Unconstrained capacity for bandlimited AWGN channel Part II Channel Capacity and Coding Theory Generating multiple sequences of correlated random variables using Cholesky decomposition Generating two sequences of correlated random variables Random Variables - Simulating Probabilistic Systems Method 3: Using FFT to compute convolutionĬhoosing a filter : FIR or IIR : understanding the design perspective Multiplication of polynomials and linear convolution Representing single variable polynomial functions Polynomials, convolution and Toeplitz matrices
#Optisystem matalab data transfer verification
Reconstructing the time domain signal from the frequency domain samplesĬomputation of power of a signal - simulation and verification Representing the signal in frequency domain using FFT Obtaining magnitude and phase information from FFT Some observations on FFTShift and IFFTShift
#Optisystem matalab data transfer code
We have provided the training and test code script for BSCP method in (‘/CNN-model’), and you can specify the input data set just generated in Data Preparation.įinally, the prediction accuracy and loss wil be preserved in a MATALAB data format.Interpreting FFT results - complex DFT, frequency bins and FFTShift Then, the file “indiantest.m” in BSCP(‘/generatedata’) will generate standard training and test sets through selecting different number of bands(just fetched before). After that, you will get an 200*1 array and all the bands are ranked from top to bottom based on the amount of information contained. In order to get the ideal bands, we have provided code to rank different bands in BSCP(‘/channelpruning’). ( ) or just clone the repo and the data set with MATLAB data format called “Indian_pines_corrected.mat “ is put in BSCP(‘/data’) We apply our proposed method BSCP on one typical data set, Indian Pines which is widely used as HSI. For example we use keras, which is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, to build our 1-d CNN. In order to make fully use of our code ,some guidelines are listedġ You will need to import some necessary libraries in Sublime Text 3 to run the code.
#Optisystem matalab data transfer windows
Besides the code has been modified and tested on Windows 10(圆4) operation system with MATLAB2017a and Sublime Text 3. This experiment was on a basic hardware condition(an Intel Core i5-7700K 4.2GHz CPU and a NVIDIA GeForce GTX 1080 Ti GPU). At the same time, our algorithm presents similar classification results using less arguments compared with BSCNN+( ), and outperforms GLBS( ) Therefore BSCP as a method of band selection can reduce the dimension effectively. However redundant spectrum information is usually considered unnecessary. HSI has proved that it contains a large amount of information which can be utlized to analyse different images. #Band Selection for Hyperspectral Image Classification via channel Puning IntroductionīSCPis an algorithm for HSI classification and band selection.