Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

**MathWorks Machine Translation**

The automated translation of this page is provided by a general purpose third party translator tool.

MathWorks does not warrant, and disclaims all liability for, the accuracy, suitability, or fitness for purpose of the translation.

Display eye diagram of time-domain signals

The eye diagram System object™ displays multiple traces of a modulated signal to produce an eye diagram. You can use the object to reveal the modulation characteristics of the signal, such as the effects of pulse shaping or channel distortions. The eye diagram can measure signal characteristics and plot horizontal and vertical bathtub curves when the jitter and noise comply with the dual-Dirac model [1].

To display the eye diagram of an input signal:

Create a

`comm.EyeDiagram`

object and set the properties of the object.Call

`step`

to display the eye diagram of the signal.

Alternatively, instead of using the `step`

method to perform the
operation defined by the System
object, you can call the object with arguments, as if it were a function. For
example, `y = step(obj,x)`

and `y = obj(x)`

perform
equivalent operations.

`ed = comm.EyeDiagram`

returns an eye diagram object, `ed`

, using the default properties.

`ed = comm.EyeDiagram(Name,Value)`

specifies additional properties
using `Name,Value`

pairs. Unspecified properties have default values.

**Example**:

ed = comm.EyeDiagram('DisplayMode','2D color histogram', ... 'OversamplingMethod','Input interpolation');

`Name`

— Caption to display on the eye diagram window`'Eye Diagram'`

(default) | character vectorName displayed on the eye diagram window, specified as a character vector. Tunable.

`SampleRate`

— Input signal sample rate (Hz)`1`

(default) | positive scalarSample rate of the input signal in Hz, specified as a positive real scalar.

`SamplesPerSymbol`

— Number of samples per symbol`8`

(default) | positive integer scalarNumber of samples per symbol, specified as a positive integer scalar. Tunable.

`SampleOffset`

— Number of samples to omit before plotting the first point`0`

(default) | nonnegative integer scalarNumber of samples to omit before plotting the first point, specified as a
nonnegative integer scalar. To avoid irregular behavior, specify the offset to be less
than the product of `SamplesPerSymbol`

and
`SamplePerTrace`

.

`SymbolsPerTrace`

— Number of symbols per trace`2`

(default) | positive integer scalarNumber of symbols per trace, specified as a positive integer scalar. To obtain eye
measurements and visualize bathtub curves, use the default value of
`2`

. Tunable.

`TracesToDisplay`

— Number of traces to display`40`

(default) | positive integer scalarNumber of traces to display, specified as a positive integer scalar. This property
is available when the `DisplayMode`

property is specified as
`'Line plot'`

. Tunable.

`DisplayMode`

— Eye diagram display mode`'Line plot'`

(default) | `'2D color histogram'`

Eye diagram display mode, specified as `'Line plot'`

or
`'2D color histogram'`

. Tunable.

Specify

`'Line plot'`

to overlay traces by plotting one line for each of the last`TracesToDisplay`

traces.Specify

`'2D color histogram'`

to display a color gradient that shows how often the input matches different time and amplitude values.

`EnableMeasurements`

— Enable eye diagram measurements`false`

(default) | `true`

Enable eye diagram measurements, specified as a logical scalar. Tunable.

`ShowBathtub`

— Enable visualization of bathtub curves`'None'`

(default) | `'Horizontal'`

| `'Vertical'`

| `'Both'`

Enable visualization of bathtub curves, specified as `'None'`

,
`'Horizontal'`

, `'Vertical'`

, or
`'Both'`

. This property is available when
`EnableMeasurements`

is `true`

. Tunable.

`OverlayHistogram`

— Histogram overlay`'None'`

(default) | `'Jitter'`

| `'Noise'`

Histogram overlay, specified as `'None'`

,
`'Jitter'`

, or `'Noise'`

.

To overlay a horizontal histogram on the eye diagram, set this property to

`'Jitter'`

.To overlay a vertical histogram on the eye diagram, set this property to

`'Noise'`

.

This property is available when `DisplayMode`

is ```
'2D
color histogram'
```

and `EnableMeasurements`

is
`true`

. Tunable.

`DecisionBoundary`

— Amplitude level threshold`0`

(default) | scalarAmplitude level threshold in V, specified as a scalar. This property separates the
different signaling regions for horizontal (jitter) histograms, and is available when
`EnableMeasurements`

is `true`

. This property is
tunable, but the jitter histograms reset when the property changes.

For non-return-to-zero (NRZ) signals, set `DecisionBoundary`

to
0. For return-to-zero (RZ) signals, set `DecisionBoundary`

to half
the maximum amplitude.

`EyeLevelBoundaries`

— Time range for calculating eye levels`[40 60]`

(default) | vectorTime range for calculating eye levels, specified as a two-element vector. These
values are expressed as a percentage of the symbol duration. This property is available
when `EnableMeasurements`

is `true`

. Tunable.

`RiseFallThresholds`

— Amplitude levels of the rise and fall transitions`[10 90]`

(default) | vectorAmplitude levels of the rise and fall transitions, specified as a two-element
vector. These values are expressed as a percentage of the eye amplitude. This property
is available when `EnableMeasurements`

is `true`

.
This property is tunable but the crossing histograms of the rise and fall thresholds
reset when it is changed.

`Hysteresis`

— Amplitude tolerance of the horizontal crossings`0`

(default) | scalarAmplitude tolerance of the horizontal crossings in V, specified as a scalar.
Increase hysteresis to provide more tolerance to spurious crossings due to noise. This
property is available when `EnableMeasurements`

is
`true`

. This property is tunable, but the jitter and the rise and
fall histograms reset when the property changes.

`BERThreshold`

— BER used for eye measurements`1e-12`

(default) | nonnegative scalar from 0 to 0.5BER used for eye measurements, specified as a nonnegative scalar from 0 to 0.5. The
value is used to make measurements of random jitter, total jitter, horizontal eye
openings, and vertical eye openings. This property is available when
`EnableMeasurements`

is `true`

. Tunable.

`BathtubBER`

— BER values used to calculate openings of bathtub curves`[0.5 10.^-(1:12)]`

| vectorBER values used to calculate openings of bathtub curves, specified as a vector whose
elements range from 0 to 0.5. Horizontal and vertical eye openings are calculated for
each of the values specified by this property. This property is available when
`EnableMeasurements`

is `true`

and
`ShowBathtub`

is `'Both'`

,
`'Horizontal'`

, or `'Vertical'`

. Tunable.

`MeasurementDelay`

— Duration of initial data discarded from measurements`0`

(default) | nonnegative scalarDuration of initial data discarded from measurements in seconds, specified as a
nonnegative scalar. This property is available when
`EnableMeasurements`

is `true`

.

`OversamplingMethod`

— Oversampling method`'None'`

(default) | `'Input interpolation'`

| `'Histogram interpolation'`

Oversampling method, specified as `'None'`

, ```
'Input
interpolation'
```

, or `'Histogram interpolation'`

. This
property is available when `DisplayMode`

is ```
'2D color
histogram'
```

. Tunable.

To plot eye diagrams as quickly as possible, set
`OversamplingMethod`

to `'None'`

. The drawback to
not oversampling is that the plots look pixelated when the number of samples per trace
is small.

To create smoother, less-pixelated plots using a small number of samples per trace,
set `OversamplingMethod`

to`'Input interpolation'`

or `'Histogram interpolation'`

. Input interpolation is the faster
interpolation method and produces good results when the signal-to-noise ratio (SNR) is
high. With a lower SNR, this oversampling method is not recommended because it
introduces a bias to the centers of the histogram ranges. Histogram interpolation is not
as fast as the other techniques, but it provides good results even when the SNR is
low.

`ColorScale`

— Color scale of the histogram`'Linear'`

(default) | `'Logarithmic'`

Color scale of the histogram, specified as `'Linear'`

or
`'Logarithmic'`

. Change this property if certain areas of the
histogram include a disproportionate number of points. Use the
`'Logarithmic'`

option for eye diagrams having sharp peaks, where the
signal repetitively matches specific time and amplitude values. This property is
available when `DisplayMode`

is ```
'2D color
histogram'
```

. Tunable.

`ColorFading`

— Color fading`false`

(default) | `true`

Color fading, specified as a logical scalar. To fade the points in the display as
the interval of time after they are first plotted increases, set this property to
`true`

. This animation resembles an oscilloscope. This property is
available when `DisplayMode`

is ```
'Line
plot'
```

. Tunable.

`ShowImaginaryEye`

— Show imaginary signal component`false`

(default) | `true`

Show imaginary signal component, specified as a logical scalar. To view the
imaginary or quadrature component of the input signal, set this property to
`true`

. This property is available when
`EnableMeasurements`

is `false`

. Tunable.

`YLimits`

— Y-axis limits`[-1.1 1.1]`

(default) | two-element vectorY-axis limits of the eye diagram in V, specified as a two-element vector. The first
element corresponds to *ymin* and the second to
*ymax*. The second element must be greater than the first.
Tunable.

`ShowGrid`

— Enable grid display`false`

(default) | `true`

Enable grid display on eye diagram, specified as a logical scalar. To display a grid
on the eye diagram, set this property to `true`

. Tunable.

`Position`

— Scope window positionvector

Scope window position in pixels, specified as a four-element vector of the form
`[left bottom width height]`

. Tunable.

hide | Hide scope window |

horizontalBathtub | Horizontal bathtub curve |

jitterHistogram | Jitter histogram |

measurements | Measure eye diagram parameters |

noiseHistogram | Noise histogram |

reset | Reset states of eye diagram object |

show | Make scope window visible |

step | Plot eye diagram of input signal |

verticalBathtub | Vertical bathtub curve |

Common to All System Objects | |
---|---|

`release` | Allow System object property value changes |

Specify the sample rate and the number of output samples per symbol parameters.

fs = 1000; sps = 4;

Create transmit filter and eye diagram objects.

txfilter = comm.RaisedCosineTransmitFilter('OutputSamplesPerSymbol',sps); ed = comm.EyeDiagram('SampleRate',fs*sps,'SamplesPerSymbol',sps);

Generate random symbols and apply QPSK modulation. Then filter the modulated signal and display the eye diagram.

data = randi([0 3],1000,1); modSig = pskmod(data,4,pi/4); txSig = txfilter(modSig); ed(txSig)

Show the effects of different interpolation methods on 2-D histograms for different signal-to-noise ratio (SNR) conditions.

Create GMSK modulator and eye diagram System objects. Specify that the eye diagram displays using a 2-D color histogram and plots the real and imaginary signals.

gmsk = comm.GMSKModulator('PulseLength',3); ed = comm.EyeDiagram('DisplayMode','2D color histogram', ... 'ShowImaginaryEye',true,'YLimits',[-2 2]);

Generate bipolar data and apply GMSK modulation.

d = 2*randi([0 1],1e4,1)-1; x = gmsk(d);

%Pass the signal through an AWGN channel having a 25 dB SNR and with a fixed seed for repeatable results. randStream = RandStream('mt19937ar','Seed',5489); y = awgn(x,25,'measured',randStream);

Display the eye diagram.

ed(y)

For a small number of samples per trace (16), the lack of interpolation causes piecewise-continuous behavior.

To compensate for the piecewise-continuous behavior, set the `OversamplingMethod`

property to `'Input interpolation'`

. Reset the object and display the eye diagram.

```
ed.OversamplingMethod = 'Input interpolation';
reset(ed)
ed(y)
```

The interpolation smooths the eye diagram.

Now pass the GMSK-modulated signal through an AWGN channel having a 10 dB SNR. Display the eye diagram.

```
y = awgn(x,10,'measured',randStream);
reset(ed)
ed(y)
```

The vertical striping is the result of input interpolation, which has limited accuracy in low-SNR conditions.

Set the `OversamplingMethod`

property to `'Histogram interpolation'`

. Plot the eye diagram.

```
ed.OversamplingMethod = 'Histogram interpolation';
reset(ed)
ed(y)
```

The eye diagram plot now renders accurately because the histogram interpolation method works for all SNR values. This method results in increased execution time.

Visualize the eye diagram of a dual-dirac input signal. Compute eye measurements, and visualize horizontal and vertical bathtub curves. Overlay the horizontal (jitter) histogram.

Specify the sample rate, the samples per symbol, and the number of traces.

fs = 10000; sps = 200; numTraces = 2000;

Create an eye diagram object having these properties:

2-D color histogram display

Logarithmic color scale

Jitter histogram overlay

Horizontal and vertical bathtub curves

Y-axis limits of [-1.3 1.3]

Increased window height

ed = comm.EyeDiagram('SampleRate',fs,'SamplesPerSymbol',sps,'SampleOffset',sps/2, ... 'DisplayMode','2D color histogram','ColorScale','Logarithmic', ... 'EnableMeasurements',true,'OverlayHistogram','Jitter', ... 'ShowBathtub','Both','YLimits', [-1.3 1.3]); ed.Position = ed.Position + [0 0 0 120];

Generate a waveform having dual-dirac and random jitter. Specify 3 ms rise and fall times.

src = commsrc.pattern('SamplesPerSymbol',sps,'RiseTime',3e-3,'FallTime', 3e-3); src.Jitter = commsrc.combinedjitter('RandomJitter','on','DiracJitter','on', ... 'DiracDelta',[-10e-04 10e-04],'RandomStd',5e-4);

Generate two symbols for each trace.

symbols = src.generate(numTraces*2);

Process the data in packets of 40e3 symbols, add noise, and display the eye diagram.

for idx = 1:(numTraces-1)/100 x = symbols(1+(idx-1)*100*2*sps:idx*100*2*sps); % Read 40,000 symbols y = awgn(x,30); % Add noise ed(y); % Display eye diagram end

Display the eye diagram for a waveform having dual-dirac and random jitter. Plot the jitter and noise histograms.

Specify the sample rate, the samples per symbol, and the number of traces parameters.

fs = 1000; sps = 200; numTraces = 1000;

Create an eye diagram object.

ed = comm.EyeDiagram('SampleRate',fs,'SamplesPerSymbol',sps,'SampleOffset',sps/2, ... 'DisplayMode','2D color histogram','ColorScale','Logarithmic', ... 'EnableMeasurements',true,'YLimits',[-1.2 1.2]);

Generate a waveform having dual-dirac and random jitter. Specify 3 ms rise and fall times.

src = commsrc.pattern('SamplesPerSymbol',sps,'RiseTime',3e-3,'FallTime', 3e-3); src.Jitter = commsrc.combinedjitter('RandomJitter','on','DiracJitter','on', ... 'DiracDelta',[-10e-04 10e-04],'RandomStd',5e-4);

Generate two symbols for each trace.

x = src.generate(numTraces*2);

Pass the signal through an AWGN channel with a fixed seed for repeatable results.

randStream = RandStream('mt19937ar','Seed',5489); y = awgn(x,30,'measured',randStream); ed(y)

Calculate the jitter histogram count for each bin by using the `jitterHistogram`

method. Plot the histogram.

jbins = jitterHistogram(ed); plot(jbins)

Calculate the noise histogram count for each bin by using the `noiseHistogram`

method. Plot the histogram.

nbins = noiseHistogram(ed); plot(nbins)

Display the eye diagram for a waveform having dual-dirac and random jitter. Generate and plot the horizontal and vertical bathtub curves.

Specify the sample rate, the samples per symbol, and the number of traces parameters.

fs = 1000; sps = 200; numTraces = 1000;

Create an eye diagram object.

ed = comm.EyeDiagram('SampleRate',fs,'SamplesPerSymbol',sps,'SampleOffset',sps/2, ... 'DisplayMode','2D color histogram','ColorScale','Logarithmic', ... 'EnableMeasurements',true,'ShowBathtub','Both','YLimits',[-1.2 1.2]);

Generate a waveform having dual-dirac and random jitter. Specify 3 ms rise and fall times.

src = commsrc.pattern('SamplesPerSymbol',sps,'RiseTime',3e-3,'FallTime', 3e-3); src.Jitter = commsrc.combinedjitter('RandomJitter','on','DiracJitter','on', ... 'DiracDelta',[-5e-04 5e-04],'RandomStd',2e-4);

Generate two symbols for each trace.

x = src.generate(numTraces*2);

Pass the signal through an AWGN channel with a fixed seed for repeatable results.

randStream = RandStream('mt19937ar','Seed',5489); y = awgn(x,30,'measured',randStream);

Display the eye diagram.

ed(y)

Generate the horizontal bathtub data for the eye diagram. Plot the curve.

hb = horizontalBathtub(ed) semilogy([hb.LeftThreshold],[hb.BER],'b',[hb.RightThreshold],[hb.BER],'b') grid

hb = 1x13 struct array with fields: BER LeftThreshold RightThreshold

Generate the vertical bathtub data for the eye diagram. Plot the curve.

vb = verticalBathtub(ed) semilogx([vb.BER],[vb.LowerThreshold],'b',[vb.BER],[vb.UpperThreshold],'b') grid

vb = 1x13 struct array with fields: BER UpperThreshold LowerThreshold

Create a combined jitter object having random jitter with a 2e-4 standard deviation.

jtr = commsrc.combinedjitter('RandomJitter','on','RandomStd',2e-4);

Generate an NRZ signal having random jitter and 3 ms rise and fall times.

genNRZ = commsrc.pattern('Jitter',jtr,'RiseTime',3e-3,'FallTime',3e-3); x = generate(genNRZ,2000);

Pass the signal through an AWGN channel with fixed seed for repeatable results.

randStream = RandStream('mt19937ar','Seed',5489); y = awgn(x,30,'measured',randStream);

Create an eye diagram object. Enable the measurements.

ed = comm.EyeDiagram('SamplesPerSymbol',genNRZ.SamplesPerSymbol, ... 'SampleRate',genNRZ.SamplingFrequency,'SampleOffset',genNRZ.SamplesPerSymbol/2, ... 'EnableMeasurements',true,'DisplayMode','2D color histogram', ... 'OversamplingMethod','Input interpolation','ColorScale','Logarithmic','YLimits',[-1.2 1.2]);

To compute the rise and fall times, determine the rise and fall thresholds from the eye level and eye amplitude measurements. Plot the eye diagram to calculate these parameters.

ed(y)

Pass the signal through the eye diagram object again to measure the rise and fall times.

ed(y) hide(ed)

Display the data by using the `measurements`

method.

eyestats = measurements(ed); riseTime = eyestats.RiseTime fallTime = eyestats.FallTime

riseTime = 0.0030 fallTime = 0.0030

The measured values match the 3 ms specification.

To open the measurements panel, click on the **Eye Measurements**
button or select Tools > Measurements > Eye Measurements from the toolbar menu.

- For amplitude measurements, at least one bin per vertical histogram must reach 10 hits before the measurement is taken, ensuring higher accuracy.
- For time measurements, at least one bin per horizontal histogram must reach 10 hits before the measurement is taken.
- When an eye crossing time measurement falls within the [-0.5/Fs, 0) seconds interval, the time measurement wraps to the end of the eye diagram, i.e., the measurement wraps by 2*Ts seconds (where Ts is the symbol time). For a complex signal case, the analyze method issues a warning if the crossing time measurement of the in-phase branch wraps while that of the quadrature branch does not (or vice versa). To avoid the time-wrapping or a warning, add a half-symbol duration delay to the current value in the MeasurementDelay property of the eye diagram object. This additional delay repositions the eye in the approximate center of the scope.

`Eye Levels`

— Amplitude level used to represent data bitsEye level is the amplitude level used to represent data bits. For the displayed NRZ signal, the levels are –1 V and +1 V. The eye levels are calculated by averaging the 2-D histogram within the eye level boundaries.

`Eye Amplitude`

— Distance between eye levelsEye amplitude is the distance in V between the mean value of two eye levels.

`Eye Height`

— Statistical minimum distance between eye levelsEye height is the distance between μ – 3σ of the upper eye level and μ + 3σ of the lower eye level. μ is the mean of the eye level and σ is the standard deviation.

`Vertical Opening`

— Distance between BER threshold pointsThe vertical opening is the distance between the two points
that correspond to the BER threshold. For example, for a BER threshold of
10^{–12}, these points correspond to the 7σ distance
from each eye level.

`Eye SNR`

— Signal-to-noise ratioThe eye SNR is the ratio of the eye level difference to the difference of the vertical standard deviations corresponding to each eye level:

$$\text{SNR}=\frac{{L}_{1}-{L}_{0}}{{\sigma}_{1}-{\sigma}_{0}}\text{\hspace{0.17em}},$$

where *L*_{1} and
*L*_{0} represent the means of the
upper and lower eye levels and σ_{1} and
σ_{0} represent their standard deviations.

`Q Factor`

— Quality factorThe Q factor is calculated using the same formula as the Eye SNR. However, the standard deviations of the vertical histograms are replaced with those computed with the dual-Dirac analysis.

`Crossing Levels`

— Amplitude levels for eye crossingsThe crossing levels are the amplitude levels at which the eye crossings occur.

`Crossing Times`

— Times for which crossings occurThe crossing times are the times at which the crossings occur. The times are computed as the mean values of the horizontal (jitter) histograms.

`Eye Delay`

— Mean time between eye crossingsEye delay is the midpoint between the two crossing times.

`Eye Width`

— Statistical minimum time between eye crossingsEye width is the horizontal distance between μ + 3σ of the left crossing time and μ – 3σ of the right crossing time. μ is the mean of the jitter histogram and σ is the standard deviation.

`Horizontal Opening`

— Time between BER threshold pointsThe horizontal opening is the distance between the two points
that correspond to the BER threshold. For example, for a
10^{–12} BER, these two points correspond to the 7σ
distance from each crossing time.

`Rise Time`

— Time to transition from low to highRise time is the mean time between the low and high thresholds defined in the eye diagram. The default thresholds are 10% and 90% of the eye amplitude.

`Fall Time`

— Time to transition from high to lowFall time is the mean time between the high and low thresholds defined in the eye diagram. The default thresholds are 10% and 90% of the eye amplitude.

`Deterministic Jitter`

— Deterministic deviation from ideal signal timingThe deterministic jitter (DJ) is the distance between the two peaks of the dual-Dirac histograms. The probability density function (PDF) of DJ is composed of two delta functions.

`Random Jitter`

— Random deviation from ideal signal timingThe random jitter (RJ) is the Gaussian unbounded jitter
component. The random component of jitter is modeled as a zero-mean Gaussian
random variable with a specified standard-deviation, *σ*. The
random jitter is computed as:

$$\text{RJ}=({Q}_{L}+{Q}_{R})\sigma \text{\hspace{0.17em}},$$

where

$$Q=\sqrt{2}{\mathrm{erfc}}^{-1}\left(2\frac{BER}{\rho}\right)\text{\hspace{0.17em}}.$$

BER is the specified BER threshold. *ρ* is the amplitude of
the left and right Dirac function, which is determined from the bin counts of
the jitter histograms.

`Total Jitter`

— Deviation from ideal signal timingTotal jitter (TJ) is the sum of the deterministic and random jitter, such that TJ = DJ + RJ.

The total jitter PDF is the convolution of the DJ PDF and the RJ PDF.

`RMS Jitter`

— Standard deviation of jitterRMS jitter is the standard deviation of the jitter calculated in the horizontal (jitter) histogram at the decision boundary.

`Peak-to-Peak Jitter`

— Distance between extreme data points of histogramPeak-to-peak jitter is the maximum horizontal distance between the left and right nonzero values in the horizontal histogram of each crossing time.

You can programmatically configure the scope properties with callbacks or within scripts by using a scope configuration object as describe in Control Scope Blocks Programmatically (Simulink).

[1] Stephens, Ransom. "Jitter Analysis: The Dual-Dirac Model, RJ/DJ, and Q-Scale." Agilent Technologies Whitepaper. December 2004.

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

Usage notes and limitations:

Supports MEX code generation by treating the calls to the object as extrinsic. Does not support code generation for standalone applications.

See System Objects in MATLAB Code Generation (MATLAB Coder).

아래 MATLAB 명령에 해당하는 링크를 클릭하셨습니다.

이 명령을 MATLAB 명령 창에 입력해 실행하십시오. 웹 브라우저에서는 MATLAB 명령을 지원하지 않습니다.

Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

Select web siteYou can also select a web site from the following list:

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

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

- 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)