diff --git a/contents/convolutions/1d/1d.md b/contents/convolutions/1d/1d.md
index 3a3ca0cf6..3e663938e 100644
--- a/contents/convolutions/1d/1d.md
+++ b/contents/convolutions/1d/1d.md
@@ -53,7 +53,7 @@ With this in mind, we can almost directly transcribe the discrete equation into
 
 {% method %}
 {% sample lang="jl" %}
-[import:29-48, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:27-46, lang:"julia"](../code/julia/1d_convolution.jl)
 {% endmethod %}
 
 The easiest way to reason about this code is to read it as you might read a textbook.
@@ -184,7 +184,7 @@ Here it is again for clarity:
 
 {% method %}
 {% sample lang="jl" %}
-[import:29-48, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:27-46, lang:"julia"](../code/julia/1d_convolution.jl)
 {% endmethod %}
 
 Here, the main difference between the bounded and unbounded versions is that the output array size is smaller in the bounded case.
@@ -192,14 +192,14 @@ For an unbounded convolution, the function would be called with a the output arr
 
 {% method %}
 {% sample lang="jl" %}
-[import:60-61, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:58-59, lang:"julia"](../code/julia/1d_convolution.jl)
 {% endmethod %}
 
 On the other hand, the bounded call would set the output array size to simply be the length of the signal
 
 {% method %}
 {% sample lang="jl" %}
-[import:63-64, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:61-62, lang:"julia"](../code/julia/1d_convolution.jl)
 {% endmethod %}
 
 Finally, as we mentioned before, it is possible to center bounded convolutions by changing the location where we calculate the each point along the filter.
@@ -207,7 +207,7 @@ This can be done by modifying the following line:
 
 {% method %}
 {% sample lang="jl" %}
-[import:37-37, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:35-35, lang:"julia"](../code/julia/1d_convolution.jl)
 {% endmethod %}
 
 Here, `j` counts from `i-length(filter)` to `i`.
@@ -239,7 +239,7 @@ In code, this typically amounts to using some form of modulus operation, as show
 
 {% method %}
 {% sample lang="jl" %}
-[import:4-27, lang:"julia"](../code/julia/1d_convolution.jl)
+[import:4-25, lang:"julia"](../code/julia/1d_convolution.jl)
 {% endmethod %}
 
 This is essentially the same as before, except for the modulus operations, which allow us to work on a periodic domain.
diff --git a/contents/convolutions/code/julia/1d_convolution.jl b/contents/convolutions/code/julia/1d_convolution.jl
index 78dafc0ed..0019c2496 100644
--- a/contents/convolutions/code/julia/1d_convolution.jl
+++ b/contents/convolutions/code/julia/1d_convolution.jl
@@ -13,9 +13,7 @@ function convolve_cyclic(signal::Array{T, 1},
 
     for i = 1:output_size
         for j = 1:output_size
-            if (mod1(i-j, output_size) <= length(filter))
-                sum += signal[mod1(j, output_size)] * filter[mod1(i-j, output_size)]
-            end
+            sum += get(signal, mod1(j, output_size), 0) * get(filter, mod1(i-j, output_size), 0)
         end
 
         out[i] = sum
@@ -57,8 +55,8 @@ function main()
     normalize!(x)
     normalize!(y)
 
-    # full convolution, output will be the size of x + y
-    full_linear_output = convolve_linear(x, y, length(x) + length(y))
+    # full convolution, output will be the size of x + y - 1
+    full_linear_output = convolve_linear(x, y, length(x) + length(y) - 1)
 
     # simple boundaries
     simple_linear_output = convolve_linear(x, y, length(x))