convolution

I want to numerically calculate several convolutions like where the x , y , z , w functions are given in the below code: t = linspace(-100,100,10000); x = t.*exp(-t.^2); y = exp(-4*t.^2).*cos(t); z = (t-2)/((t-2).^2+3^2); w = exp(-3*t.^2).*exp(2i*t); u = conv(conv(conv(x,y),z),w); plot(t,u) % ??? - if we want to convolute N functions, what range should t span? Is this the most efficient way to calculate and plot multiple convolutions? Is it generally better to numerically integrate the functions for each convolution? Edit: This is the plot of the real part of my convolution, u vs t : whereas

I want to simulate an interpolator in MATLAB using upsampling followed by a low pass filter. First I have up-sampled my signal by introducing 0's. Now I want to apply a low pass filter in order to interpolate. I have designed the following filter: The filter is exactly 1/8 of the normalized frequency because I need to downsample afterward. (it's a specific excersise to upsample interpolate and downsample in this particular order.) However when I apply this filter to my data using the function filter(myfilter, data) the following signal is generated: I really don't know what is happening to my

I have written the following code to develop a Homomorphic Filter. I think (I am not sure though) the color images are being filtered well. In case of Grayscale images, Why is the kernel always Green? Also, the filter was supposed to be sharpening the image. But, its not doing so. What could have possibly gone wrong? . . Source Code: Here is the Github repository. public class HomomorphicFilter { public HomoMorphicKernel Kernel = null; public bool IsPadded { get; set; } public int Width { get; set; } public int Height { get; set; } public double RH { get; set; } public double RL { get; set; }

I'm trying to implement a lightweight (minimal library dependencies) version of a TensorFlow graph in c++ and I'm trying to use Eigen Tensor objects to perform the graphs operations. Right now I'm stuck trying to use the Eigen Tensor.convolve() method to try and replicate the behaviour of TensorFlow's Conv2D operation. To keep things simple my initial Conv2D operation has no padding and strides of one. The input to convolutional layer is a 51x51x1 tensor which is being convolved with a filter bank of size 3x3x1x16. In tensorflow this generates an output tensor of size 49x49x16. Setting up this

I have been using the following functional API for an image classification task using CNN: def create_model(X_train, X_test): visible = Input(shape=(X_train.shape[0], X_train.shape[1], 1)) conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible) hidden1 = Dense(10, activation='relu')(pool2) output = Dense(1, activation='sigmoid')(hidden1) model = Model(inputs = visible, outputs = output) model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy']) return model X_tr = np.reshape(X_train, (1,X_train.shape[0], X_train.shape[1], 1)) X_te = np.reshape(X_test, (1,X_test