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Scilab Help >> Signal Processing > Convolution - Correlation > conv

# conv

discrete 1-D convolution.

### Syntax

C = conv(A,B [,shape])

### Parameters

- A
a real or complex vector.

- B
a real or complex vector.

- shape
an optional character string with possible values:

`"full"`

,`conv`

computes the full convolution. It is the default value.`"same"`

,`conv`

computes the central part of the convolution of the same size as`A`

.`"valid"`

,`conv`

computes the convolution parts without the zero-padding of`A`

.

- C
a real or complex vector.

### Description

`conv`

uses a straightforward formal
implementation of the one-dimensional convolution equation in
spatial form.

`C=conv(A,B [,shape])`

computes the
one-dimensional convolution of the vectors `A`

and `B`

:

- With
`shape=="full"`

the dimensions of the result`C`

are given by`size(A,'*')+size(B,'*')+1`

. The indices of the center element of`B`

are defined as`floor((size(B,'*')+1)/2)`

. - With
`shape=="same"`

the dimensions of the result`C`

are given by`size(A)`

. The indices of the center element of`B`

are defined as`floor((size(B,'*')+1)/2)`

. - With
`shape=="valid"`

the dimensions of the result`C`

are given by`size(A,'*')-size(B,'*')+1)`

if`and(size(A,'*')-size(B,'*'))>=0`

else`C`

is empty . The indices of the center element of`B`

are defined as`1`

.

Note that convol can be more efficient for large arrays.

### Examples

A=1:10; B=[1 -1]; conv(A,B)

### Used Functions

The conv function is based on the conv2 builtin.

### History

Version | Description |

5.4.0 | Function conv introduced. |

## Comments

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