Signal Processing Blockset™

Mean - Find mean value of input or sequence of inputs

Library

Statistics

dspstat3

Description

The Mean block computes the mean of each row or column of the input, along vectors of a specified dimension of the input, or of the entire input. The Mean block can also track the mean value in a sequence of inputs over a period of time. The Running mean parameter selects between basic operation and running operation.

The Mean block accepts real and complex fixed-point and floating-point inputs.

Basic Operation

When you do not select the Running mean check box, the block computes the mean value in each row or column of the input, along vectors of a specified dimension of the input, or of the entire input at each individual sample time. Each element in the output array y is the mean value of the corresponding column, row, vector, or entire input. The output array y depends on the setting of the Find the mean value over parameter. For example, consider a 3-dimensional input signal of size M-by-N-by-P:

• Entire input — The output at each sample time is a scalar that contains the mean value of the M-by-N-by-P input matrix. In this mode, the block output is always sample based.

  y = mean(u(:))					% Equivalent MATLAB code 

• Each row — The output at each sample time consists of an M-by-1-by-P array, where each element contains the mean value of each vector over the second dimension of the input. For an input that is an M-by-N matrix, the output at each sample time is an M-by-1 column vector. In this mode, the frame status of the output is the same as that of the input.

  y = mean(u,2)					% Equivalent MATLAB code 

• Each column — The output at each sample time consists of a 1-by-N-by-P array, where each element contains the mean value of each vector over the first dimension of the input. For an input that is an M-by-N matrix, the output at each sample time is a 1-by-N row vector. In this mode, the frame status of the output is the same as that of the input.

  y = mean(u)					% Equivalent MATLAB code 

  For convenience, length-M 1-D vector inputs are treated as M-by-1 column vectors when the block is in this mode. Sample-based length-M row vector inputs are also treated as M-by-1 column vectors when the Treat sample-based row input as a column check box is selected.

• Specified dimension — The output at each sample time depends on Dimension. If Dimension is set to 1, the output is the same as when you select Each column. If Dimension is set to 2, the output is the same as when you select Each row. If Dimension is set to 3, the output at each sample time is an M-by-N matrix containing the mean value of each vector over the third dimension of the input. In this mode, the frame status of the output is the same as that of the input.

  y = mean(u,Dimension)					% Equivalent MATLAB code 

The mean of a complex input is computed independently for the real and imaginary components, as shown in the next figure.

Running Operation

When the Running mean check box is selected, the block tracks the mean value of each channel in a time sequence of inputs. For sample-based M-by-N inputs, the output is a sample-based M-by-N array with each element y_ij containing the mean value of the elements u_ij for all inputs since the last reset. For frame-based M-by-N inputs, the output is a frame-based M-by-N matrix with each element y_ij containing the mean of the values in the jth column of all inputs since the last reset, up to and including element u_ij of the current input.

N-D signals cannot be frame based. When the block is set to Running mode, each element of the N-D signal is treated as a separate channel. There are channels, where d_i is the size of the ith dimension.

Resetting the Running Mean

The block resets the running mean whenever a reset event is detected at the optional Rst port. The reset sample time must be a positive integer multiple of the input sample time.

When the block is reset for sample-based inputs, the running mean for each channel is initialized to the value in the corresponding channel of the current input. For frame-based inputs, the running mean for each channel is initialized to the earliest value in each channel of the current input.

You specify the reset event by the Reset port parameter:

• None disables the Rst port.

• Rising edge — Triggers a reset operation when the Rst input does one of the following:

  • Rises from a negative value to a positive value or zero

  • Rises from zero to a positive value, where the rise is not a continuation of a rise from a negative value to zero (see the following figure)

    

• Falling edge — Triggers a reset operation when the Rst input does one of the following:

  • Falls from a positive value to a negative value or zero

  • Falls from zero to a negative value, where the fall is not a continuation of a fall from a positive value to zero (see the following figure)

    

• Either edge — Triggers a reset operation when the Rst input is a Rising edge or Falling edge (as described earlier)

• Non-zero sample — Triggers a reset operation at each sample time that the Rst input is not zero

Note   When running simulations in the Simulink® MultiTasking mode, reset signals have a one-sample latency. Therefore, when the block detects a reset event, there is a one-sample delay at the reset port rate before the block applies the reset. For more information on latency and the Simulink tasking modes, see Excess Algorithmic Delay (Tasking Latency) and Models with Multiple Sample Rates in the Real-Time Workshop® User's Guide.

ROI Processing

To calculate the statistical value within a particular region of interest (ROI) of the input, select the Enable ROI processing check box. This option is only available when the Find the mean value over parameter is set to Entire input and the Running mean check box is not selected. ROI processing is only supported for 2-D inputs.

Note   Full ROI processing is only available to users who have a Video and Image Processing Blockset™ license. If you only have a Signal Processing Blockset™ license, you can still use ROI processing, but are limited to the ROI type Rectangles.

Use the ROI type parameter to specify whether the ROI is a rectangle, line, label matrix, or binary mask. A binary mask is a binary image that enables you to specify which pixels to highlight, or select. In a label matrix, pixels equal to 0 represent the background, pixels equal to 1 represent the first object, pixels equal to 2 represent the second object, and so on. When the ROI type parameter is set to Label matrix, the Label and Label Numbers ports appear on the block. Use the Label Numbers port to specify the objects in the label matrix for which the block calculates statistics. The input to this port must be a vector of scalar values that correspond to the labeled regions in the label matrix. For more information about the format of the input to the ROI port when the ROI is a rectangle or a line, see the Draw Shapes block reference page.

For rectangular ROIs, use the ROI portion to process parameter to specify whether to calculate the statistical value for the entire ROI or just the ROI perimeter.

Use the Output parameter to specify the block output. The block can output separate statistical values for each ROI or the statistical value for all specified ROIs. This parameter is not available if, for the ROI type parameter, you select Binary mask.

If, for the ROI type parameter, you select Rectangles or Lines, the Output flag indicating if ROI is within image bounds check box appears in the dialog box. If you select this check box, the Flag port appears on the block. The following tables describe the Flag port output based on the block parameters.

Output = Individual statistics for each ROI

Output = Single statistic for all ROIs

If the ROI is partially outside the image, the block only computes the statistical values for the portion of the ROI that is within the image.

If, for the ROI type parameter, you select Label matrix, the Output flag indicating if input label numbers are valid check box appears in the dialog box. If you select this check box, the Flag port appears on the block. The following tables describe the Flag port output based on the block parameters.

Output = Individual statistics for each ROI

Output = Single statistic for all ROIs

Fixed-Point Data Types

The following diagram shows the data types used within the Mean block for fixed-point signals.

You can set the accumulator and output data types in the block dialog, as discussed in Dialog Box.

Examples

The Mean block in the following model calculates the running mean of a frame-based 3-by-2 (two-channel) matrix input, u. The running mean is reset at t=2 by an impulse to the block's Rst port.

The Mean block has the following settings:

• Running mean = Select this check box

• Reset port = Non-zero sample

The Signal From Workspace block has the following settings:

• Signal = dsp_examples_u

• Sample time = 1/3

• Samples per frame = 3

where

dsp_examples_u = [6 1 3 -7 2 5 8 0 -1 -3 2 1;1 3 9 2 4 1 6 2 5 0 4 17]' 

The Discrete Impulse block has the following settings:

• Delay (samples) = 2

• Sample time = 1

• Samples per frame = 1

The block's operation is shown in the next figure.

Dialog Box

The Main pane of the Mean block dialog appears as follows.

Running mean

Enables running operation when selected.

Reset port

Determines the reset event that causes the block to reset the running mean. The rate of the reset signal must be a positive integer multiple of the rate of the data signal input. This parameter is enabled only when you set the Running mean parameter. For more information, see Resetting the Running Mean.

Find the mean value over

Specify whether to find the mean value along rows, columns, entire input, or the dimension specified in the Dimension parameter. For more information, see Basic Operation.

Treat sample-based row input as a column

Select to treat sample-based length-M row vector inputs as M-by-1 column vectors. This parameter is only visible when the Find the mean value over parameter is set to Each column.

Dimension

Specify the dimension (one-based value) of the input signal, over which the mean is computed. The value of this parameter cannot exceed the number of dimensions in the input signal. This parameter is only visible when the Find the mean value over parameter is set to Specified dimension.

Enable ROI Processing

Select this check box to calculate the statistical value within a particular region of each image. This parameter is only available when the Find the mean value over parameter is set to Entire input, and the block is not in running mode.

Note   Full ROI processing is only available to users who have a Video and Image Processing Blockset license. If you only have a Signal Processing Blockset license, you can still use ROI processing, but are limited to the ROI type Rectangles.

ROI type

Specify the type of ROI you want to use. Your choices are Rectangles, Lines, Label matrix, or Binary mask.

ROI portion to process

Specify whether you want to calculate the statistical value for the entire ROI or just the ROI perimeter. This parameter is only visible if, for the ROI type parameter, you specify Rectangles.

Output

Specify the block output. The block can output a vector of separate statistical values for each ROI or a scalar value that represents the statistical value for all the specified ROIs. This parameter is not available if, for the ROI type parameter, you select Binary mask.

Output flag indicating if ROI is within image bounds

If you select this check box, the Flag port appears on the block. For a description of the Flag port output, see the tables in ROI Processing. This parameter is visible if, for the ROI type parameter, you select Rectangles or Lines.

Output flag indicating if label numbers are valid

If you select this check box, the Flag port appears on the block. For a description of the Flag port output, see the tables in ROI Processing. This parameter is visible if, for the ROI type parameter, you select Label matrix.

The Fixed-point pane of the Mean block dialog appears as follows.

Rounding mode

Select the rounding mode for fixed-point operations.

Overflow mode

Select the overflow mode for fixed-point operations.

Accumulator

Use this parameter to specify the accumulator word and fraction lengths:

• When you select Same as input, these characteristics match those of the input to the block.

• When you select Binary point scaling, you can enter the word length and the fraction length of the accumulator, in bits.

• When you select Slope and bias scaling, you can enter the word length, in bits, and the slope of the accumulator. This block requires power-of-two slope and a bias of zero.

Output

Choose how you specify the output word length and fraction length:

• When you select Same as accumulator, these characteristics match those of the accumulator.

• When you select Same as input, these characteristics match those of the input to the block.

• When you select Binary point scaling, you can enter the word length and the fraction length of the output, in bits.

• When you select Slope and bias scaling, you can enter the word length, in bits, and the slope of the output. This block requires power-of-two slope and a bias of zero.

Lock scaling against changes by the autoscaling tool

Select this parameter to prevent any fixed-point scaling you specify in this block mask from being overridden by the autoscaling feature of the Fixed-Point Tool. See the fxptdlg reference page for more information.

Supported Data Types

See Also

Maximum

Median