# dsphdl.CICDecimator

Decimate signal using CIC filter

## Description

The `dsphdl.CICDecimator`

System object™ decimates an input signal by using a cascaded integrator-comb (CIC) decimation
filter. CIC filters are a class of linear phase finite impulse response (FIR) filters
consisting of a comb part and an integrator part. The CIC decimation filter structure consists
of *N* sections of cascaded integrators, a rate change factor of
*R*, and *N* sections of cascaded comb filters. For more
information about CIC decimation filters, see Algorithms.

The System object supports these combinations of input and output data.

Scalar input and scalar output — Support for fixed and variable decimation rates

Vector input and scalar output — Support for fixed decimation rates only

Vector input and vector output — Support for fixed decimation rates only

The System object provides an architecture suitable for HDL code generation and hardware deployment.

The System object supports real and complex fixed-point inputs.

To filter input data with an HDL-optimized CIC decimation filter, follow these steps:

Create the

`dsphdl.CICDecimator`

object and set its properties.Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

## Creation

### Description

creates an
HDL-optimized CIC decimation filter System object, `cicDecFilt`

= dsphdl.CICDecimator`cicDecFilt`

, with default properties.

creates the filter with properties set using one or more name-value arguments. Enclose
each property name in single quotes.`cicDecFilt`

= dsphdl.CICDecimator(`Name,Value`

)

## Properties

Unless otherwise indicated, properties are *nontunable*, which means you cannot change their
values after calling the object. Objects lock when you call them, and the
`release`

function unlocks them.

If a property is *tunable*, you can change its value at
any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

`DecimationSource`

— Source of decimation factor

`'Property'`

(default) | `'Input port'`

Specify whether the System object operates with a fixed or variable decimation rate.

`'Property'`

— Use a fixed decimation rate specified by the`DecimationFactor`

property.`'Input port'`

— Use a variable decimation rate specified by the`R`

input argument.

For vector inputs, the System object does not support a variable decimation rate.

`DecimationFactor`

— Decimation factor

`2`

(default) | integer from 1 to 2048

Specify the decimation factor as an integer from 1 to 2048. This value gives the rate at which the System object decimates the input.

#### Dependencies

To enable this property, set the `DecimationSource`

property to
`'Property'`

.

`MaxDecimationFactor`

— Upper bound of variable decimation factor

`2`

(default) | integer from 1 to 2048

Specify the upper bound of the range of valid values for the `R`

input argument as an integer from 1 to 2048.

**Note**

For vector inputs, the System object does not support variable decimation.

#### Dependencies

To enable this property, set the `DecimationSource`

property to
`'Input port'`

.

`DifferentialDelay`

— Differential delay

`1`

(default) | `2`

Specify the differential delay of the comb part of the filter as either
`1`

or `2`

cycles.

`NumSections`

— Number of integrator or comb sections

`2`

(default) | `1`

| `3`

| `4`

| `5`

| `6`

Specify the number of sections in either the integrator or the comb part of the System object.

`GainCorrection`

— Output gain compensation

`false`

(default) | `true`

Set this property to `true`

to compensate for the output gain of
the filter.

The latency of the System object varies depending on the type of input, the decimation you specify, the number of sections, and the value of this property. For more information on the latency of the System object, see Latency.

`OutputDataType`

— Data type of output

`'Full precision'`

(default) | `'Same word length as input'`

| `'Minimum section word lengths'`

Choose the data type of the filtered output data.

`'Full precision'`

— The output data type has a word length equal to the input word length plus gain bits.`'Same word length as input'`

— The output data type has a word length equal to the input word length.`'Minimum section word lengths'`

— The output data type uses the word length you specify in the`OutputWordLength`

property. When you choose this option, the System object applies a pruning algorithm internally. For more information about pruning, see Output Data Type.

`OutputWordLength`

— Word length of output

`16`

(default) | integer from 2 to 104

Word length of the output, specified as an integer from 2 to 104.

**Note**

When this value is `2`

, `3`

,
`4`

, `5`

, or `6`

, the
System object can overflow the output data.

#### Dependencies

To enable this property, set the `OutputDataType`

property to
`'Minimum section word lengths'`

.

`ResetInputPort`

— Reset argument

`false`

(default) | `true`

When you set this property to `true`

, the System object expects a `reset`

input argument.

## Usage

### Syntax

### Description

`[`

filters the input data when `dataOut`

,`validOut`

] = cicDecFilt(`dataIn`

,`validIn`

,`R`

,`reset`

)`reset`

is `false`

and
clears filter internal states when `reset`

is `true`

.
The System object expects the `reset`

argument only when you set the
`ResetInputPort`

property to `true`

. The
`DecimationSource`

property must be set to ```
'Input
port'
```

.

### Input Arguments

`dataIn`

— Input data

scalar | column vector

Specify input data as a scalar or a column vector with a length from 1 to 64. The
input data must be a signed integer or signed fixed point with a word length less than
or equal to 32. The `DecimationFactor`

property must be an integer
multiple of the input frame size.

**Data Types: **`int8`

| `int16`

| `int32`

| `fi`

**Complex Number Support: **Yes

`validIn`

— Indicates valid input data

scalar

Control signal that indicates if the input data is valid. When
`validIn`

is `1`

(`true`

), the
object captures the values from the `dataIn`

argument. When
`validIn`

is `0`

(`false`

), the
object ignores the values from the `dataIn`

argument.

**Data Types: **`logical`

`R`

— Variable decimation rate

scalar

Specify the decimation rate.

The `R`

value must have the data type
`fi(0,12,0)`

and it must be an integer in the range from 1 to the
`MaxDecimationFactor`

property value.

#### Dependencies

To enable this argument, set the `DecimationSource`

property to `'Input port'`

.

**Data Types: **`fi(0,12,0)`

`reset`

— Clears internal states

logical scalar

Control signal that clears internal states. When `reset`

is
`1`

(`true`

), the object stops the current
calculation and clears internal states. When the `reset`

is
`0`

(`false`

) and the input
`valid`

is `1`

(`true`

), the
object captures data for processing.

For more reset considerations, see the Reset Signal section on the Hardware Control Signals page.

#### Dependencies

To enable this argument, set the `ResetInputPort`

property to `true`

.

**Data Types: **`logical`

### Output Arguments

`dataOut`

— CIC-decimated output data

scalar | column vector

CIC-decimated output data, returned as a scalar or a column vector with a length from 1 to 64.

The `OutputDataType`

property sets the data type of this argument. See Output Data Type.

**Data Types: **`int8`

| `int16`

| `int32`

| `fi`

**Complex Number Support: **Yes

`validOut`

— Indicates valid output data

scalar

Control signal that indicates if the output data is valid. When
`validOut`

is `1`

(`true`

), the
object returns valid data from the `dataOut`

argument. When
`validOut`

is `0`

(`false`

),
values from the `dataOut`

argument are not valid.

**Data Types: **`logical`

## Object Functions

To use an object function, specify the
System object as the first input argument. For
example, to release system resources of a System object named `obj`

, use
this syntax:

release(obj)

### Specific to `dsphdl.CICDecimator`

`getLatency` | Latency of CIC decimation filter |

## Examples

### Create CIC Decimation Filter for HDL Code Generation

This example shows how to use a `dsphdl.CICDecimator`

System object™ to filter and downsample data. This object supports scalar and vector inputs. In this example, two functions are provided to work with scalar and vector inputs separately. You can generate HDL code from these functions.

**Generate Frames of Random Input Samples**

Set up workspace variables for the object to use. The object supports fixed and variable decimation rates for scalar inputs and only a fixed decimation rate for vector inputs. The example runs the `HDLCIC_maxR8`

function when you set the scalar variable to `true`

and runs the `HDLCIC_vec`

function when you set the scalar variable to `false`

. For scalar inputs, choose a range of the input `varRValue`

values and set the decimation factor value `R`

to the maximum expected decimation factor. For vector inputs, the input data must be a column vector of size 1 to 64 and `R`

must be an integer multiple of the input frame size.

R = 8; % decimation factor M = 1; % differential delay N = 3; % number of sections scalar = true; % true for scalar; false for vector if scalar varRValue = [2, 4, 5, 6, 7, 8]; vecSize = 1; else varRValue = R; %#ok fac = (factor(R)); vecSize = fac(randi(length(fac),1,1)); end numFrames = length(varRValue); dataSamples = cell(1,numFrames); varRtemp = cell(1,numFrames); framesize = zeros(1,numFrames); refOutput = []; WL = 0; % Word length FL = 0; % Fraction length

**Generate Reference Output from dsp.CICDecimator System Object**

Generate frames of random input samples and apply the samples to the `dsp.CICDecimator`

System object. Later in this example, you use the output generated by the System object as reference data for comparison. The System object does not support a variable decimation rate, so you must create and release the object for each change in decimation factor value.

totalsamples = 0; for i = 1:numFrames framesize(i) = varRValue(i)*randi([5 20],1,1); dataSamples{i} = fi(randn(vecSize,framesize(i)),1,16,8); ref_cic = dsp.CICDecimator('DifferentialDelay',M, ... 'NumSections',N, ... 'DecimationFactor',varRValue(i)); refOutput = [refOutput,ref_cic(dataSamples{i}(:)).']; %#ok release(ref_cic); end

**Run Function Containing dsphdl.CICDecimator System Object**

Set the properties of the System object to match the input data parameters and run the function for your input type. These functions operate on a stream of data samples rather than a frame. You can generate HDL code from these functions.

The example uses the `HDLCIC_maxR8`

function for a scalar input.

function [dataOut,validOut] = HDLCIC_maxR8(dataIn,validIn,R) %HDLCIC_maxR8 % Performs CIC decimation with an input decimation factor up to 8. % dataIn is a scalar fixed-point value. % validIn is a logical scalar value. persistent cic8; if isempty(cic8) cic8 = dsphdl.CICDecimator('DecimationSource','Input port', ... 'MaxDecimationFactor',8, ... 'DifferentialDelay',1, ... 'NumSections',3); end [dataOut,validOut] = cic8(dataIn,validIn,R); end

The example uses the `HDLCIC_vec`

function for a vector input.

function [dataOut,validOut] = HDLCIC_vec(dataIn,validIn) %HDLCIC_vec % Performs CIC decimation with an input vector. % dataIn is a fixed-point vector. % validIn is a logical scalar value. persistent cicVec; if isempty(cicVec) cicVec = dsphdl.CICDecimator('DecimationSource','Property', ... 'DecimationFactor',8, ... 'DifferentialDelay',1, ... 'NumSections',3); end [dataOut,validOut] = cicVec(dataIn,validIn); end

To flush the remaining data, run the object by inserting the required number of idle cycles after each frame using the `latency`

variable. For more information, see **Latency**.

Initialize the output to a size large enough to accommodate the output data. The final size is smaller than `totalsamples`

due to decimation.

latency = floor((vecSize - 1)*(N/vecSize)) + 1 + N + (2+(vecSize+1)*N) + 9; dataOut = zeros(1,totalsamples+numFrames*latency); validOut = zeros(1,totalsamples+numFrames*latency); idx=0; for ij = 1:numFrames if scalar % scalar input with variable decimation for ii = 1:length(dataSamples{ij}) idx = idx+1; [dataOut(idx),validOut(idx)] = HDLCIC_maxR8( ... dataSamples{ij}(ii), ... true, ... fi(varRValue(ij),0,12,0)); end for ii = 1:latency idx = idx+1; [dataOut(idx),validOut(idx)] = HDLCIC_maxR8( ... fi(0,1,16,8), ... false, ... fi(varRValue(ij),0,12,0)); end else % vector input with fixed decimation for ii = 1:size(dataSamples{ij},2) %#ok idx = idx+1; [dataOut(idx),validOut(idx)] = HDLCIC_vec( ... dataSamples{ij}(:,ii), ... true); end for ii = 1:latency idx = idx+1; [dataOut(idx),validOut(idx)] = HDLCIC_vec( ... fi(zeros(vecSize,1),1,16,8), ... false); end end end

**Compare Function Output with Reference Data**

Compare the function results against the output from the `dsp.CICDecimator`

object.

cicOutput = dataOut(validOut==1); fprintf('\nCIC Decimator\n'); difference = (abs(cicOutput-refOutput(1:length(cicOutput)))>0); fprintf(['\nTotal number of samples differed between Behavioral ' ... 'and HDL simulation: %d \n'],sum(difference));

CIC Decimator Total number of samples differed between Behavioral and HDL simulation: 0

### Explore Latency of CIC Decimator Object

The latency of the `dsphdl.CICDecimator`

System object™ varies depending on how many integrator and comb sections your filter has, the input vector size, and whether you enable gain correction. Use the `getLatency`

function to find the latency of a particular filter configuration. The latency is the number of cycles between the first valid input and the first valid output, assuming the input is continuously valid.

Create a `dsphdl.CICDecimator`

System object and request the latency. The default System object filter has two integrator and comb sections, and the gain correction is disabled.

hdlcic = dsphdl.CICDecimator

hdlcic = dsphdl.CICDecimator with properties: DecimationSource: 'Property' DecimationFactor: 2 DifferentialDelay: 1 NumSections: 2 GainCorrection: false Show all properties

L_def = getLatency(hdlcic)

L_def = 5

Modify the filter object so it has three integrator and comb sections. Check the resulting change in latency.

hdlcic.NumSections = 3; L_3sec = getLatency(hdlcic)

L_3sec = 6

Enable the gain correction on the filter object with vector input size 2. Check the resulting change in latency.

hdlcic.GainCorrection = true; vecSize = 2; L_wgain = getLatency(hdlcic,vecSize)

L_wgain = 25

## Algorithms

### CIC Decimation Filter

The transfer function of a CIC decimation filter is

$$H(z)={\left[{\displaystyle \sum _{k=0}^{RM-1}{z}^{-k}}\right]}^{N}=\frac{{\left(1-{z}^{-RM}\right)}^{N}}{{\left(1-{z}^{-1}\right)}^{N}}=\frac{1}{{\left(1-{z}^{-1}\right)}^{N}}\xb7\frac{{\left(1-{z}^{-RM}\right)}^{N}}{1}={H}_{\text{I}}{}^{N}(z)\xb7{H}_{\text{c}}{}^{N}(z).$$

*H*_{I}is the transfer function of the integrator part of the CIC filter.*H*_{C}is the transfer function of the comb part of the CIC filter.*N*is the number of sections. The number of sections in a CIC filter is defined as the number of sections in either the comb part or the integrator part of the filter. This value does not represent the total number of sections throughout the entire filter.*R*is the decimation factor.*M*is the differential delay.

### CIC Filter Structure

The `dsphdl.CICDecimator`

System object has the CIC filter structure shown in this figure. The structure consists of
*N* sections of cascaded integrators, a rate change factor of
*R*, and *N* sections of cascaded comb filters [1].

Designs can put the unit delay in the integrator part of the CIC filter in either the feedforward or feedback path. These two configurations yield an identical filter frequency response. However, the numerical outputs from these two configurations are different due to the latency of the paths. This System object puts the unit delay in the feedforward path of the integrator.

### Fixed and Variable Decimation

The System object downsamples the integrator stage output using *R*, either
based on the fixed decimation rate provided using the `DecimationFactor`

property or
the variable decimation rate provided using the `R`

input argument. At the
downsampler stage, the System object uses a counter to count the valid input samples, which depend on the
decimation rate. Whenever the decimation rate changes, the object resets and starts a new
calculation from the next sample. This mechanism prevents the System object from accumulating invalid values. Then, the System object provides the decimated output to the comb part.

### Gain Correction

The gain of the System object is given by $$Gain={(R\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{x}\text{\hspace{0.17em}}M)}^{N}$$.

*R*is the`DecimationFactor`

property value.*M*is the`DifferentialDelay`

property value.*N*is the`NumSections`

property value.

The System object implements gain correction in two parts: coarse gain and fine gain. In coarse gain correction, the System object calculates the shift value, adds the shift value to the fractional bits to create a numeric type, and then performs a bit-shift left. In fine gain correction, the System object divides the remaining gain with the coarse gain if the gain is not a power of 2 and then multiplies the coarse gain corrected value with the inverse value of fine gain. All possible shift and fine gain values are precalculated and stored in an array before the System object starts processing.

You can modify this equation as $$Gain={2}^{cGain}\text{\hspace{0.17em}}\text{x}\text{\hspace{0.17em}}\text{\hspace{0.17em}}fGain$$, where *cGain* means coarse gain and
*fGain* means fine gain.

$$cGain=floor({\mathrm{log}}_{2}Gain)$$

$$fGain=Gain/{2}^{cGain}=Gain\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{x}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{2}^{-cGain}{}^{}$$

To perform `GainCorrection`

when the
`InterpolationSource`

property is set to ```
'Input
port'
```

, the System object sets the output data type configured with the maximum decimation rate and
bit-shifts left for all the values under the maximum decimation rate. The bit-shift value is
equal to $$Maximum\text{}gain\text{}-\text{}{\mathrm{log}}_{2}(current\text{}gain)$$.

### Output Data Type

This section explains how the System object determines the output data type. For example, consider a filter with
`DecimationFactor`

, `DifferentialDelay`

, and
`NumSections`

values of 8, 1, and 3, respectively, with an input width
of 16 bits.

The output word length is calculated as $${B}_{\text{Out}}=\text{\hspace{0.17em}}\text{\hspace{0.17em}}{B}_{\text{In}}+\text{\hspace{0.17em}}\text{\hspace{0.17em}}[{\mathrm{log}}_{2}(Gain)]$$.

$$Gain={(R\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{x}\text{\hspace{0.17em}}M)}^{N}$$

*B*_{In}is the input word length.*B*_{Out}is the output word length.

When you set the `OutputDataType`

property to ```
'Full
precision'
```

, the System object returns data with a word length of 25 bits, by adding nine gain bits to the
input word length.

When you set the `OutputDataType`

property to ```
'Same word
length as input'
```

, the object outputs data with a word length of 16, which is the
same length as the input word length. The internal integrator and comb stages use the
full-precision data type with 25 bits.

When you set the `OutputDataType`

property to ```
'Minimum
section word lengths'
```

and the `OutputWordLength`

property to
`16`

, the System object returns data with a word length of 16 bits. In this case, the object changes
the bit width at each stage, based on the pruning algorithm.

If the `OutputWordLength`

property value is less than the number of
bits requested at the output, the least significant bits (LSBs) at the earlier stages are
pruned. The Hogenauer algorithm provides the number of LSBs to discard at each stage. This
algorithm minimizes the loss of information in the output data [1].

### Latency

The latency of the System object varies depending on the type of input, the decimation you specify, the number
of sections, and the value of the `GainCorrection`

property. This table
shows the latency of the System object. *N* is the number of sections and *vecLen*
is the length of the vector.

Input Data | Output Data | Decimation Type | Gain Correction | Latency in Clock Cycles |
---|---|---|---|---|

Scalar | Scalar | Fixed | `off` | 3 + N. When R = 1, 2 +
N. |

`on` | 3 + N + 9. When R = 1, 2 +
N + 9. | |||

Scalar | Scalar | Variable | `off` | 4 + N. When R =
1, 3 + _{max}N. |

`on` | 4 + N + 9. When
R = 1, 3 + _{max}N +
9. | |||

Vector | Scalar | Fixed | `off` | `floor` ((vecLen – 1) x
(N/vecLen)) + 1 + N + (2
+ (vecLen + 1) x N |

`on` | `floor` ((vecLen – 1) x
(N/vecLen)) + 1 + N + (2
+ (vecLen + 1) x N) + 9 | |||

Vector | Vector | Fixed | `off` | `floor` ((vecLen – 1) x
(N/vecLen)) + 1 + N + (2
+ (vecLen + 1) x N |

`on` | `floor` ((vecLen – 1) x
(N/vecLen)) + 1 + N + (2
+ (vecLen + 1) x N) + 9 |

**Note**

For vector inputs, the System object does not support variable decimation.

**Scalar Input**

This section shows the output of the System object for a scalar input with different *R*,
*M*, and *N* values.

This figure shows the output of the System object with the default configuration, that is, with a fixed decimation rate and
`DecimationFactor`

, `DifferentialDelay`

, and
`NumSections`

values of 2, 1, and 2, respectively. The System object returns valid output data at every second cycle based on the fixed `DecimationFactor`

value of 2.
The latency of the System object is 5 clock cycles, calculated as 3 + *N*.

This figure shows the output of the System object with a fixed decimation rate, `DecimationFactor`

,
`DifferentialDelay`

, and `NumSections`

values of
8, 1, and 3, respectively, and `GainCorrection`

set to
`true`

. The System object returns valid output data at every eighth cycle based on the fixed `DecimationFactor`

value of 8.
The latency of the object is 15 clock cycles and is calculated as 3 + *N*
+ 9.

This figure shows the output of the System object for variable `R`

values of 2, 4, and 8 along with *M* and
*N* values of 1 and 3. The `GainCorrection`

property
is set to `false`

. The System object returns valid output data at the second, fourth, and eighth cycles
corresponding to the `R`

values 2, 4, and 8, respectively. The
System object accepts `R`

argument value changes only when the input
`validIn`

is `1`

(`true`

). The
latency of the System object is 7 clock cycles, calculated as 4 + *N*.

**Vector Input**

This section shows the output of the System object for a vector input with different *R*,
*M*, and *N* values.

This figure shows the output of the System object for a two-element column vector input with the default configuration, that
is, with a fixed decimation rate and `DecimationFactor`

,
`DifferentialDelay`

, and `NumSections`

values of
2, 1, and 2, respectively. The latency of the object is 12 clock cycles.

This figure shows the output of the System object for an eight-element column vector input with a fixed decimation rate,
*R*, *M*, and *N* values of 8, 1, and
3, respectively, and `GainCorrection`

set to `true`

.
The latency of the object is 44 clock cycles.

### Performance

The performance of the synthesized HDL code varies with your target and synthesis options. It also varies based on the input data type.

This table shows the resource and performance data synthesis results of the object for a
scalar input of type `fixdt(1,16,0)`

with fixed and variable decimation
rates and for a two-element column vector input with fixed decimation rate
`DecimationFactor`

, `DifferentialDelay`

, and
`NumSections`

values are 2, 1, and 2, respectively. The generated HDL
is targeted to the Xilinx^{®}
Zynq^{®}-7000 ZC706 Evaluation Board.

Input Data | Decimation Type | Slice LUTs | Slice Registers | Maximum Frequency in MHz |
---|---|---|---|---|

Scalar | Fixed rate | 101 | 166 | 711.74 |

Variable rate | 206 | 186 | 441.70 | |

Vector | Fixed rate | 218 | 627 | 624.61 |

The resources and frequencies vary based on the type of input data and the
values of *R*, *M*, and *N*, as well as
other properties. Using a vector input can increase the throughput, however, doing so also
increases the number of hardware resources that the System object uses.

## References

[1] Hogenauer, E. “An Economical Class
of Digital Filters for Decimation and Interpolation.” *IEEE
Transactions on Acoustics, Speech, and Signal Processing* 29, no. 2 (April 1981):
155–62. https://doi.org/10.1109/TASSP.1981.1163535.

## Extended Capabilities

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

This System object supports C/C++ code generation for accelerating MATLAB^{®} simulations, and for DPI component generation.

### HDL Code Generation

Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

`double`

and `single`

data
types are supported for simulation, but not for HDL code generation.

To generate HDL code from predefined System objects, see HDL Code Generation from Viterbi Decoder System Object (HDL Coder).

## Version History

**Introduced in R2019b**

### R2022a: Moved to DSP HDL Toolbox from DSP System Toolbox

Before R2022a, this System object was named `dsp.HDLCICDecimation`

and was included in the
DSP System Toolbox™.

### R2022a: Changes to decimation factor arguments

You can now set the decimation factor to

`1`

. In previous releases, a decimation factor of`1`

was invalid.`ResetIn`

property is renamed to`ResetInputPort`

.

Configuration | Before R2022a | After 2022a |
---|---|---|

Variable decimation factor | Select the `VariableDownsample` property and set the
`DecimationFactor` parameter to the maximum expected decimation
factor. | Set the `DecimationSource` property to ```
Input
port
``` and set the `MaxDecimationFactor` property to
the maximum expected decimation factor. The `decimFactor ` port
is renamed to `R` . |

Fixed decimation factor | Clear the `VariableDownsample` property and set the
`DecimationFactor` property to the desired decimation
factor. | Set the `DecimationSource` property to
`Property` and set the `DecimationFactor`
property to the desired decimation factor. |

### R2021b: High-throughput interface

The object accepts and returns a column vector of elements that represent samples in time. The input vector can contain up to 64 samples. When you use frame-based input, you must use a fixed decimation factor.

### R2020a: Gain correction and programmable decimation factor

You can optionally enable automatic gain correction. You can also specify the decimation factor as an input argument.

## Open Example

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You can also select a web site from the following list:

## How to Get Best Site Performance

Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.

### Americas

- América Latina (Español)
- Canada (English)
- United States (English)

### Europe

- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)

- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)