# 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:

1. Create the `dsphdl.CICDecimator` object and set its properties.

2. Call the object with arguments, as if it were a function.

## Creation

### Syntax

``cicDecFilt = dsphdl.CICDecimator``
``cicDecFilt = dsphdl.CICDecimator(Name,Value)``

### Description

````cicDecFilt = dsphdl.CICDecimator` creates an HDL-optimized CIC decimation filter System object, `cicDecFilt`, with default properties.```

example

````cicDecFilt = dsphdl.CICDecimator(Name,Value)` creates the filter with properties set using one or more name-value arguments. Enclose each property name in single quotes.```

## Properties

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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.

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.

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'`.

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'`.

Specify the differential delay of the comb part of the filter as either `1` or `2` cycles.

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

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.

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.

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'`.

When you set this property to `true`, the System object expects a `reset` input argument.

## Usage

### Syntax

``[dataOut,validOut] = cicDecFilt(dataIn,validIn)``
``[dataOut,validOut] = cicDecFilt(dataIn,validIn,R)``
``[dataOut,validOut] = cicDecFilt(dataIn,validIn,reset)``
``[dataOut,validOut] = cicDecFilt(dataIn,validIn,R,reset)``

### Description

````[dataOut,validOut] = cicDecFilt(dataIn,validIn)` filters and decimates the input data using a fixed decimation factor only when `validIn` is `true`.```
````[dataOut,validOut] = cicDecFilt(dataIn,validIn,R)` filters the input data using the specified variable decimation factor `R`. The `DecimationSource` property must be set to `'Input port'`.```
````[dataOut,validOut] = cicDecFilt(dataIn,validIn,reset)` filters the input data when `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`.```
````[dataOut,validOut] = cicDecFilt(dataIn,validIn,R,reset)` filters the input data when `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

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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

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`

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)`

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

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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

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)`

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 `getLatency` Latency of CIC decimation filter
 `step` Run System object algorithm `release` Release resources and allow changes to System object property values and input characteristics `reset` Reset internal states of System object

## Examples

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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 ```

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 ```

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## 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.

## Version History

Introduced in R2019b

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