diff --git a/Notebooks/Chap10/10_1_1D_Convolution.ipynb b/Notebooks/Chap10/10_1_1D_Convolution.ipynb
index 1529713..a5a889d 100644
--- a/Notebooks/Chap10/10_1_1D_Convolution.ipynb
+++ b/Notebooks/Chap10/10_1_1D_Convolution.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPHUNRkJMI5LujaxIXNV60m",
+      "authorship_tag": "ABX9TyPTidpnPhn4O5QF011gt0cz",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -85,10 +85,10 @@
       "cell_type": "code",
       "source": [
         "# Now let's define a zero-padded convolution operation\n",
-        "# with a convolution kernel size of 3, a stride of 1, and a dilation of 0\n",
+        "# with a convolution kernel size of 3, a stride of 1, and a dilation of 1\n",
         "# as in figure 10.2a-c.  Write it yourself, don't call a library routine!\n",
         "# Don't forget that Python arrays are indexed from zero, not from 1 as in the book figures\n",
-        "def conv_3_1_0_zp(x_in, omega):\n",
+        "def conv_3_1_1_zp(x_in, omega):\n",
         "    x_out = np.zeros_like(x_in)\n",
         "    # TODO -- write this function\n",
         "    # replace this line\n",
@@ -119,7 +119,7 @@
       "source": [
         "\n",
         "omega = [0.33,0.33,0.33]\n",
-        "h = conv_3_1_0_zp(x, omega)\n",
+        "h = conv_3_1_1_zp(x, omega)\n",
         "\n",
         "# Check that you have computed this correctly\n",
         "print(f\"Sum of output is {np.sum(h):3.3}, should be 71.1\")\n",
@@ -155,7 +155,7 @@
       "source": [
         "\n",
         "omega = [-0.5,0,0.5]\n",
-        "h2 = conv_3_1_0_zp(x, omega)\n",
+        "h2 = conv_3_1_1_zp(x, omega)\n",
         "\n",
         "# Draw the signal\n",
         "fig,ax = plt.subplots()\n",
@@ -187,9 +187,9 @@
       "cell_type": "code",
       "source": [
         "# Now let's define a zero-padded convolution operation\n",
-        "# with a convolution kernel size of 3, a stride of 2, and a dilation of 0\n",
-        "# as in figure 10.2a-c.  Write it yourself, don't call a library routine!\n",
-        "def conv_3_2_0_zp(x_in, omega):\n",
+        "# with a convolution kernel size of 3, a stride of 2, and a dilation of 1\n",
+        "# as in figure 10.3a-b.  Write it yourself, don't call a library routine!\n",
+        "def conv_3_2_1_zp(x_in, omega):\n",
         "    x_out = np.zeros(int(np.ceil(len(x_in)/2)))\n",
         "    # TODO -- write this function\n",
         "    # replace this line\n",
@@ -209,7 +209,7 @@
       "cell_type": "code",
       "source": [
         "omega = [0.33,0.33,0.33]\n",
-        "h3 = conv_3_2_0_zp(x, omega)\n",
+        "h3 = conv_3_2_1_zp(x, omega)\n",
         "\n",
         "# If you have done this right, the output length should be six and it should\n",
         "# contain every other value from the original convolution with stride 1\n",
@@ -226,9 +226,9 @@
       "cell_type": "code",
       "source": [
         "# Now let's define a zero-padded convolution operation\n",
-        "# with a convolution kernel size of 5, a stride of 1, and a dilation of 0\n",
-        "# as in figure 10.2a-c.  Write it yourself, don't call a library routine!\n",
-        "def conv_5_1_0_zp(x_in, omega):\n",
+        "# with a convolution kernel size of 5, a stride of 1, and a dilation of 1\n",
+        "# as in figure 10.3c.  Write it yourself, don't call a library routine!\n",
+        "def conv_5_1_1_zp(x_in, omega):\n",
         "    x_out = np.zeros_like(x_in)\n",
         "    # TODO -- write this function\n",
         "    # replace this line\n",
@@ -249,7 +249,7 @@
       "source": [
         "\n",
         "omega2 = [0.2, 0.2, 0.2, 0.2, 0.2]\n",
-        "h4 = conv_5_1_0_zp(x, omega2)\n",
+        "h4 = conv_5_1_1_zp(x, omega2)\n",
         "\n",
         "# Check that you have computed this correctly\n",
         "print(f\"Sum of output is {np.sum(h4):3.3}, should be 69.6\")\n",
@@ -273,10 +273,10 @@
       "cell_type": "code",
       "source": [
         "# Finally let's define a zero-padded convolution operation\n",
-        "# with a convolution kernel size of 3, a stride of 1, and a dilation of 1\n",
-        "# as in figure 10.2a-c.  Write it yourself, don't call a library routine!\n",
+        "# with a convolution kernel size of 3, a stride of 1, and a dilation of 2\n",
+        "# as in figure 10.3d.  Write it yourself, don't call a library routine!\n",
         "# Don't forget that Python arrays are indexed from zero, not from 1 as in the book figures\n",
-        "def conv_3_1_1_zp(x_in, omega):\n",
+        "def conv_3_1_2_zp(x_in, omega):\n",
         "    x_out = np.zeros_like(x_in)\n",
         "    # TODO -- write this function\n",
         "    # replace this line\n",
@@ -295,7 +295,7 @@
       "cell_type": "code",
       "source": [
         "omega = [0.33,0.33,0.33]\n",
-        "h5 = conv_3_1_1_zp(x, omega)\n",
+        "h5 = conv_3_1_2_zp(x, omega)\n",
         "\n",
         "# Check that you have computed this correctly\n",
         "print(f\"Sum of output is {np.sum(h5):3.3}, should be 68.3\")\n",
@@ -328,7 +328,7 @@
       "cell_type": "code",
       "source": [
         "# Compute matrix in figure 10.4 d\n",
-        "def get_conv_mat_3_1_0_zp(n_out, omega):\n",
+        "def get_conv_mat_3_1_1_zp(n_out, omega):\n",
         "  omega_mat = np.zeros((n_out,n_out))\n",
         "  # TODO Fill in this matix\n",
         "  # Replace this line:\n",
@@ -349,11 +349,11 @@
       "source": [
         "# Run original convolution\n",
         "omega = np.array([-1.0,0.5,-0.2])\n",
-        "h6 = conv_3_1_0_zp(x, omega)\n",
+        "h6 = conv_3_1_1_zp(x, omega)\n",
         "print(h6)\n",
         "\n",
         "# If you have done this right, you should get the same answer\n",
-        "omega_mat = get_conv_mat_3_1_0_zp(len(x), omega)\n",
+        "omega_mat = get_conv_mat_3_1_1_zp(len(x), omega)\n",
         "h7 = np.matmul(omega_mat, x)\n",
         "print(h7)\n"
       ],