OpenLLM-France/wikimedia huggingface dataset:
# Movie Making Manual/Cinematography/Cameras and Formats/XL H1

The Canon XL H1 Records to HDV, the high-defition version of the popular DV codec used in many cameras, including the H1's predecessors the Canon XL 1s and XL 2. HDV is a very well engineered codec and allows for optimal HD compression to tape. Although high quality, the HDV compression codec is in fact compressing the video and disregarding some of the pixels that the XL H1's CCDs are picking up. However, the XLH1 does have an exciting option that none of the other prosumer HD cameras have: a pre-compression HD SDI output. This allows for the exact same signal that is being sent to the HDV codec for compression to be outputted to an HD SDI device through a BNC cable on the side of the camera. Though this feature is designed for use in studio environments where the HD SDI signal can be sent to a video deck such as a DVCproHD or HDCAM deck for full-resolution, uncompressed recording it can also be sent to a computer via an HD SDI video card such as those offered by AJA and Blackmagic and then recorded completely digitally onto a hard drive for editing or higher quality compression.
In a studio the HD SDI output and Genlock input would be highly useful. However, for movie making and general recording the HDV tape option is very high quality and much more compact that working with a large video deck or computer system in the field.

## History of the camera

This camera is fourth in the line of XL series cameras from Canon, featuring the body style and many features and technologies of the XL 1, XL 1s, and XL 2 cameras.

## Links

http://www.canondv.com>
# Movie Making Manual/Cinematography/Green Screen

This Module is part of the Cinematography section of the Movie Making Manual
The process of creating a "green screen" shot has two main parts: shooting and post production.

# Shooting Green-Screen
## General tips

* Light the green background evenly and brightly but don't overexpose it
** Your best bet to get the correct exposure is to run a few tests in a methodological fashion.
** If you can't afford to run tests then light the green screen at about 1 stop over key (i.e. light your greenscreen to reflect twice as much light as your foreground elements and then set your camera's exposure to comfortably capture the foreground elements).
* Try to avoid using filters on the lens. Diffusion can make the key very hard to pull. (And make sure the lens is clean!)
* Avoid throwing shadows on the green screen
* Try to limit the amount of reflected green light bouncing from the screen onto the actors (this can be achieved by careful lighting; by moving the foreground elements as far away from the green screen as possible or by simply covering unused sections of the green screen in black)
* Avoid using the same colour in the actor's clothes as in the green screen.
* One way to check that your exposure is about correct is to place a bit of white card under the key light and then view the scene through a green filter. The white card should "disappear" against the green screen.

## Really difficult things to key cleanly

Try to avoid these things if at all possible:
* Hair, especially if it's moving (e.g. having an actor's/actress' long hair blowing in the wind against a green screen will be a nightmare to work with)

## Does it have to be a green screen?

No, you can use any colour screen you wish. Any compositing software worth its place in this world will be able to chroma-key any colour. Blue and Green are often chosen because the human skin contains very little blue or green pigment. Green screens are often used for shoots outside as sunlight can register on a camera as "blue" causing problems when chroma keying.

## Shooting on video

* Set the camera to a gain of 0db (this gives the best signal to noise ratio and the widest dynamic range)
* Use a video camera with the least chroma subsampling (i.e. 4:4:4 is best). It is possible to get minimally acceptable results from DV (4:1:1) or HDV (4:2:0) but your life will be easier (and the resulting key considerably better) if you use a higher quality capture medium. Since keying relies on color information that is disregarded as part of any color space reduction from 4:4:4, all lossy compression formats will result in decreased edge detail.

## Shooting on film

* Choose a stock with the finest grain posible

# Post-production: pulling a key
## Tools

* Serious Magic has a program called Ultra that uses vector keying, rather than chroma keying.
* Adobe Premiere Pro
* Adobe After Effects
* Autodesk Combustion
* Final Cut Pro/Express (Apple)
* FXhome CompositeLab (Lite and pro) and VisionLab software.
* Wax Invoker

# Glossary

* "Pulling a chroma key" = The process of using computer software to make a particular colour transparent
# Movie Making Manual/Cinematography/HD Links

http://www.hdforindies.com/
# LMIs in Control/Click here to continue/Observer synthesis/Full-order Hinf state Observer

In this section, we design full order H-$\infty$ state observer.

## The System

Given a state-space representation of a linear system
    ${\begin{aligned}\ {\dot {x}}(t)=Ax(t)+B_{1}u(t)+B_{2}w(t),x(0)=x_{0}\\\ y(t)=C_{1}x(t)+D_{1}u(t)+D_{2}w(t)\\\ z(t)=C_{2}x(t)\\\end{aligned}}$
* $x\in \mathbb {R} ^{n},y\in \mathbb {R} ^{l},zin\mathbb {R} ^{m}$ are the state vector, measured output vector and output vectors of interest.
* $w\in \mathbb {R} ^{p},u\in \mathbb {R} ^{r},$ are the disturbance vector and control vector respectively.

## The Data

$A,B_{1},B_{2},C_{1},C_{2},D_{1},D_{2}$ are system matrices

## Definition

For the system , a full order state observer of the form of equation (1) is introduced and the estimate of interested output is given by .
    ${\begin{aligned}\ {\dot {\hat {x}}}=(A+LC_{1}){\hat {x}}-Ly+(B_{1}+LD_{1})u\\\end{aligned}}$         (1)
The estimate of interested output is
    ${\begin{aligned}{\hat {z}}(t)=C_{2}{\hat {x}}(t)\\\end{aligned}}$         (2)
Given the system and a positive scalar $\gamma$ , L is found such that
    ${\begin{aligned}\|G_{{\tilde {z}}w}(s)\|_{\infty }=\gamma \\\end{aligned}}$         (3)

## LMI Condition

The $H_{\infty }$ state observers problem has a solution if and only if there exists a symmetric positive definite matrix $P$ and a matrix $W$ satisfying the below LMI
    ${\begin{aligned}{\begin{bmatrix}A^{T}P+C_{1}^{T}W^{T}+PA+WC_{1}&PB_{2}+WD_{2}&C_{2}^{T}\\(PB_{2}+WD_{2})^{T}&-\gamma I&0\\C_{2}&0&-\gamma I\end{bmatrix}}\\\end{aligned}}$         (4)
When such a pair of matrics is found, the solution is
    ${\begin{aligned}L=P^{-1}W\\\end{aligned}}$         (5)

## Implementation

This implementation requires Yalmip and Mosek.
* https://github.com/ShenoyVaradaraya/LMI--master/blob/main/hinf_obs.m

## Conclusion

Thus, an $H_{\infty }$ state observer is designed such that the output vectors of interest are accurately estimated.

## External Links

* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
* LMIs in Control Systems: Analysis, Design and Applications - by Guang-Ren Duan and Hai-Hua Yu, CRC Press, Taylor & Francis Group, 2013
# LMIs in Control/Click here to continue/Observer synthesis/Hurwitz Detectability

LMIs in Control/Click here to continue/Observer synthesis/Hurwitz Detectability

## Hurwitz Detectability

Hurwitz detectability is a dual concept of Hurwitz stabilizability and is defined as the matrix pair $(A,C)$, is said to be Hurwitz detectable if there exists a real matrix $L$ such that $A+LC$ is Hurwitz stable.

## The System

    ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+Bu(t),\\y(t)&=Cx(t)+Du(t)\\x(0)&=x_{0}\\\end{aligned}}$
where $x(t)\in \mathbb {R} ^{n}$, $y(t)\in \mathbb {R} ^{m}$, $u(t)\in \mathbb {R} ^{q}$, at any $t\in \mathbb {R}$.

## The Data

* The matrices $A,B,C,D$ are system matrices of appropriate dimensions and are known.

## The Optimization Problem

There exist a symmetric positive definite matrix $P$ and a matrix $W$ satisfying $A^{T}P+PA+W^{T}C+C^{T}W<0$
There exists a symmetric positive definite matrix $P$ satisfying
$N_{c}^{T}(A^{T}P+PA)N_{c}<0$
with $N_{c}$ being the right orthogonal complement of $C$.
There exists a symmetric positive definite matrix $P$ such that
$A^{T}P+PA<\gamma C^{T}C$
for some scalar $\gamma >0$

## The LMI:

Matrix pair $(A,C)$, is Hurwitz detectable if and only if following LMI holds
* $A^{T}P+PA+W^{T}C+C^{T}W<0.$
* $N_{c}^{T}(A^{T}P+PA)N_{c}<0$
* $A^{T}P+PA-\gamma C^{T}C<0$

## Conclusion:

Thus by proving the above conditions we prove that the matrix pair $(A,C)$ is Hurwitz Detectable.

## Implementation

Find the MATLAB implementation at this link below Hurwitz detectability

## Related LMIs

Links to other closely-related LMIs LMI for Hurwitz stability LMI for Schur stability Schur Detectability

## External Links

A list of references documenting and validating the LMI.
* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.

## Return to Main Page:

* https://en.wikibooks.org/wiki/LMIs_in_Control
# LMIs in Control/Click here to continue/LMIs in system and stability Theory/Transient State Bound for Non-Autonomous LTI Systems
## The System

For a continuous-time LTI system with a state-space representation of:
${\dot {x}}=Ax+Bu$
where $A\in \mathbb {R} ^{nxn}$, $B\in \mathbb {R} ^{nxm}$ and x(0) = x$_0$,
the transient bound can be evaluated with the following LMI.

## The Data

$A\in \mathbb {R} ^{nxn}$, $B\in \mathbb {R} ^{nxm}$ and x(0) = x$_0$.

## The LMI:

The Euclidean norm of the state satisfies:
$\lVert x(T)\rVert _{2}^{2}\leq \gamma ^{2}(\lVert x_{0}\rVert _{2}^{2}+\lVert u\rVert _{2T}^{2}),\forall T\in \mathbb {R} _{\geq 0}$
if there exists $P\in \mathbb {S} ^{n}$ and $\gamma \in \mathbb {R} _{>0}$, where P > 0, such that:
* $P-\gamma 1\leq 0,$
* ${\begin{bmatrix}P&1\\*&\gamma 1\end{bmatrix}}\geq 0,$
* ${\begin{bmatrix}PA+A^{T}P&PB\\*&-\gamma 1\end{bmatrix}}\leq 0.$
if x$_0$ = 0 and u is a unit-energy input ($\lVert u\rVert _{2T}\leq 1,\forall T\in \mathbb {R} _{\geq 0}$), then the above LMIs ensure that $\lVert x(T)\rVert _{2}\leq \gamma ,\forall T\in \mathbb {R} _{\geq 0}$

## Conclusion

By using this LMI the transient state bound can be analyzed for a given non-autonomous LTI system.

## Implementation

The LMI given above can be implemented and solved using a tool such as YALMIP, along with an LMI solver such as MOSEK.

## Related LMIs

* Output Energy Bound for Autonomous LTI Systems

## External Links

* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
# LMIs in Control/Click here to continue/LMIs in system and stability Theory/mu-Analysis

Mu Synthesis. The technique of $\mu$ synthesis extends the methods of $H\infty$ synthesis to design a robust controller for an uncertain plant. You can perform $\mu$ synthesis on plants with parameter uncertainty, dynamic uncertainty, or both using the "musyn" command in MATLAB. $\mu$ analysis is an extremely powerful multivariable technique which has been applied to many problems in the almost every industry including Aerospace, process industry etc.

## The System:

Consider the continuous-time generalized LTI plant with minimal states-space realization
    ${\begin{aligned}{\dot {x}}&=Ax+Bu\\y&=Cx+Du\\\end{aligned}}$
where it is assumed that $D$ is Invertible.

## The Data

The matrices needed as inputs are only, $A$ and $D$.

## The LMI: $\mu$- Analysis

The inequality ${\overline {\sigma }}(DAD^{-1})<\gamma$ holds if and only if there exist $X\in \mathbb {S} ^{n}$ and $\gamma \in \mathbb {R} _{>0}$, where $X>0$, satisfying:
       ${\begin{aligned}A^{T}XA-\gamma ^{2}X<0\end{aligned}}$

## Conclusion:

The inequality ${\overline {\sigma }}(DAD^{-1})<\gamma$ holds for $D=X^{1/2},$ where X satisfies the above Inequality.

## Implementation

* https://github.com/Ricky-10/coding107/blob/master/Mu%20Analysis - $\mu$-Analysis

## External links

* -MATLAB $\mu$-synthesis Implementation
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
# LMIs in Control/Click here to continue/Observer synthesis/Discrete Time Detectability

Discrete-Time Detectability
A discrete time system operates on a discrete time signal input and produces a discrete time signal output. They are used in digital signal processing, such as digital filters for images or sound. The class of discrete time systems that are both linear and time invariant, known as discrete time LTI systems.
Discrete-Time LTI systems can be made detectable using observer gain L, which can be found using LMI optimization, such that the close loop system is detectable.

## The System

Discrete-Time LTI System with state space realization $(A_{d},B_{d},C_{d},D_{d})$ ${\begin{aligned}&A_{d}\in {\bf {{R^{n*n}},}}&B_{d}\in {\bf {{R^{n*m}},}}&C_{d}\in {\bf {{R^{p*n}},}}&D_{d}\in {\bf {{R^{p*m}}\;}}\\\end{aligned}}$

## The Data

The matrices: System $(A_{d},B_{d},C_{d},D_{d}),P,W$.

## The Optimization Problem

The following feasibility problem should be optimized:
Maximize P while obeying the LMI constraints. Then L is found.

## The LMI:

Discrete-Time Detectability
The LMI formulation
${\begin{aligned}P\in {S^{n}};W\in {R^{m*n}}\;\\&P>0\\{\begin{bmatrix}P&A_{d}^{T}P+C_{d}^{T}W\\*&P\end{bmatrix}}&>0,\\L=P^{-1}W\end{aligned}}$

## Conclusion:

The system is detectabe iff there exits a $P$, such that $P>0$. The matrix $A_{d}+LC_{d}$ is Schur with $L=P^{-1}W$

## Implementation

A link to CodeOcean or other online implementation of the LMI MATLAB Code

## Related LMIs

- Continuous time Detectability

## External Links

A list of references documenting and validating the LMI.
* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/Click here to continue/LMIs in system and stability Theory/alpha-Region via the Dilation Lemma
## Definition

Consider $A\in \mathbb {R} ^{n\times n}$. The matrix $A$ is ${\mathcal {D}}$-stable if and only if there exists $P\in \mathbb {S} ^{n}$, where $P>0$, such that
$[\lambda _{kl}P+\phi _{kl}AP+\phi _{lk}PA^{T}]_{1<k,l<m}<0$,
or equivalent
$\Lambda \otimes P+\Phi \otimes (AP)+\Phi ^{T}\otimes (PA^{T})<0$,
where $\otimes$ is the Kroenecker product,
The eigenvalues of a ${\mathcal {D}}$-stable matrix lie within the LMI region ${\mathcal {D}}$, which is defined as
${\mathcal {D}}=\{z\in \mathbb {C} :f_{D}(z)<0\}$, where
$f_{D}(z):=\Lambda +z\Phi +{\bar {z}}\Phi ^{T}=\{\lambda _{kl}+\phi _{kl}z+\phi _{lk}{\bar {z}}\}_{1\leq k,l\leq m}$,
$\Lambda \in \mathbb {S} ^{m}$, $\Phi \in \mathbb {R} ^{m\times m}$, and ${\bar {z}}$ is the complex conjugate of $z$.

## α-Region Stability via the Dilation Lemma

Consider $A\in \mathbb {R} ^{n\times n}$ and $\alpha \in \mathbb {R} _{>0}$. The matrix $A$ satisfies $\lambda (A)\subset {\mathcal {H}}(\alpha )$, where ${\mathcal {H}}(\alpha ):=\{\lambda \in \mathbb {C} :Re(\lambda )<-\alpha \}$ if and only if there exist $X\in \mathbb {S} ^{n}$ and $\epsilon \in \mathbb {R} _{>0}$, where $X>0$, such that
$AX+XA^{T}+2\alpha X<0$.
Equivalently, the matrix $A$ satisfies $\lambda (A)\subset {\mathcal {H}}(\alpha )$ if and only if there exist $X\in \mathbb {S} ^{n}$, $\epsilon \in \mathbb {R} _{>0}$, and $F\in \mathbb {R} ^{n\times n}$, where $X>0$, such that
${\begin{bmatrix}0&-X&X\\*&0&0\\*&*&-{\frac {1}{2}}\alpha ^{-1}X\end{bmatrix}}+He{\begin{Bmatrix}{\begin{bmatrix}A\\1\\0\end{bmatrix}}F{\begin{bmatrix}1&-\epsilon 1&\epsilon 1\end{bmatrix}}\end{Bmatrix}}<0$.
Moreover, for every $X$ that satisfies
$AX+XA^{T}+2\alpha X<0$
$X$ and $F=-\epsilon ^{-1}(A-\epsilon ^{-1}1)^{-1}X$ are solutions to
${\begin{bmatrix}0&-X&X\\*&0&0\\*&*&-{\frac {1}{2}}\alpha ^{-1}X\end{bmatrix}}+He{\begin{Bmatrix}{\begin{bmatrix}A\\1\\0\end{bmatrix}}F{\begin{bmatrix}1&-\epsilon 1&\epsilon 1\end{bmatrix}}\end{Bmatrix}}<0$.

## External Links

* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A downloadable book on LMIs by Ryan James Caverly and James Richard Forbes.
# LMIs in Control/Click here to continue/Observer synthesis/FDI Filter Design For Systems With Sensor Faults

FDI Filter Design For Systems With Sensor Faults: an LMI
Systems with faulty sensors are a very common type of systems. In many cases, redundancy is added in the form of additional sensors, but in certain cases it could be a costly solution. For general linear system models, the LMI in this section can be utilized to design state estimators which can detect and isolate faulty sensor readings in order to mitigate their effects.

## The System

    ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+B_{1}w(t)+B_{2}u(t)\\y(t)&=Cx(t)+v(t)\end{aligned}}$
where $x(t)\in \mathbb {R} ^{n}$ is the state, $u(t)\in \mathbb {R} ^{m}$ is the control input, $w(t)\in \mathbb {R} ^{r}$ is the process noise, $y(t)\in \mathbb {R} ^{p}$ is the output and $v(t)\in \mathbb {R} ^{q}$ is the measurement noise.

## The Data

The state space matrices $(A,B_{1},B_{2},C)$ are required to be known.

## The Optimization LMI

The following LMI is used to design the Fault Detection and Isolation (FDI) filter:
       ${\begin{aligned}&{\text{min}}_{\Phi ,\Theta ,\gamma }\;\gamma ,\\&{\text{subj. to: }}\Phi >0,\\&\quad \quad {\begin{bmatrix}A^{\top }\Phi +\Phi A+C^{\top }\Theta ^{\top }+\Theta C&\Phi B_{1}&\Theta &I\\*&-\gamma I&0&0\\*&*&-\gamma I&0\\*&*&*&-\gamma I\end{bmatrix}}<0\\\end{aligned}}$
Then the filter is $K=\Phi ^{-1}\Theta$.

## Conclusion:

The LMI designed in this section is used to design filters that can work on systems that are prone to sensors getting damaged or faulty.

## Implementation

To solve the feasibility LMI, YALMIP toolbox is required for setting up the feasibility problem, and SeDuMi is required to solve the problem. The following link showcases an example of the feasibility problem:
https://github.com/smhassaan/LMI-Examples/blob/master/FDI_Filter_example.m

## Related LMIs

H-infinity Optimal Filter
H-infinity Optimal Observer

## External Links

A list of references documenting and validating the LMI.
* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/Click here to continue/Observer synthesis/Full-order state Observer

LMIs in Control/Click here to continue/Observer synthesis/Full-order state Observer

## Full-Order State Observer

The problem of constructing a simple full-order state observer directly follows from the result of Hurwitz detectability LMI's, Which essentially is the dual of Hurwitz stabilizability. If a feasible solution to the first LMI for Hurwitz detectability exist then using the results we can back out a full state observer $L$ such that $A+LC$ is Hurwitz stable.

## The System

    ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+Bu(t),\\y(t)&=Cx(t)+Du(t)\\x(0)&=x_{0}\\\end{aligned}}$
where $x(t)\in \mathbb {R} ^{n}$, $y(t)\in \mathbb {R} ^{m}$, $u(t)\in \mathbb {R} ^{q}$, at any $t\in \mathbb {R}$.

## The Data

* The matrices $A,B,C,D$ are system matrices of appropriate dimensions and are known.

## The Optimization Problem

The full-order state observer problem essential is finding a positive definite $P$ such that the following LMI conclusions hold.

## The LMI:

1) The full-order state observer problem has a solution if and only if there exist a symmetric positive definite Matrix $P$ and a matrix $W$ that satisfy
* $A^{T}P+PA+W^{T}C+C^{T}W<0.$
Then the observer can be obtained as $L=P^{-1}W$ 2) The full-state state observer can be found if and only if there is a symmetric positive definite Matrix $P$ that satisfies the below Matrix inequality
* $A^{T}P+PA-C^{T}C<0$
In this case the observer can be reconstructed as $L=-{\frac {1}{2}}P^{-1}C^{T}$. It can be seen that the second relation can be directly obtained by substituting $W=-{\frac {1}{2}}C^{T}$ in the first condition.

## Conclusion:

Hence, both the above LMI's result in a full-order observer $L$ such that $A+LC$ is Hurwitz stable.

## External Links

A list of references documenting and validating the LMI.
* LMIs in Control Systems Analysis, Design and Applications - Duan and Yu
* LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
* LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
* LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
# LMIs in Control/Click here to continue/Observer synthesis

Observer synthesis
* Continuous Time
** FDI Filter Design For Systems With Sensor Faults
** Hurwitz Detectability
** Full-order state Observer
** Full-order Hinf state Observer
** Reduced-Order State Observer
** Switched Systems
* Discrete-Time
** Discrete-Time Detectability
** Schur Detectability
# Leonese

The Asturian-leonese language is the endonym term used to refer to all vernacular Romance dialects of the Astur-Leonese linguistic group in the Spanish province of Asturias, León and Zamora and includes the Mirandese language of Miranda do Douro in Portugal.
* Alphabet
* Phonology
* Personal Pronouns
* Vocabulary/Months
* Vocabulary/Seasons
* Vocabulary/Days of the Week
* Vocabulary/The colors
* Vocabulary/The body
* Verbs
* Vocabulary/Family
* Numbers 1-10
# Leonese/Alphabet
#### The alphabet

Alphabet :
| Letter | Name | Phoneme |
| A, a | a | /a/ |
| B, b | be | /b/ |
| C, c | ce | /θ/, /k/ |
| D, d | de | /d/ |
| E, e | e | /e/ |
| F, f | efe | /f/ |
| G, g | gue | /g/ |
| H, h | hache | - |
| I, i | i | /i/ |
| L, l | ele | /l/ |
| Ll, ll | elle | /ʎ/ |
| M, m | eme | /m/ |
| N, n | ene | /n/ |
| Ñ, ñ | eñe | /ɲ/ |
| O, o | o | /o/ |
| P, p | pe | /p/ |
| Q, q | cu | /k/ |
| R, r | erre | /r/, /ɾ/ |
| S, s | ese | /s/ |
| T, t | te | /t/ |
| U, u | u | /u/ |
| V, v | uve | /b/ |
| X, x | xe | /ʃ/ [ɕ] |
| Y, y | ye, y griega | /ʝ/, /i/ |
| Z, z | zeta, zeda, ceda | /θ/ |
# PHP Programming/html output

There are a few different way you can display HTML using PHP. Generally, you will use the echo command to output something. This will be seen by a web browser, and then it will format it.

## Breaking PHP for Output

In addition to using functions such as echo and print, you can also end your script, and anything beyond the end of the script will be output as normal HTML to the browser. You can also restart your script whenever you want after you've closed the PHP tag. Confused? It's actually pretty simple.
Let's say you had a for loop to count up to five and output it.
While I would tend to use templates for larger pages that output a lot, we'll get to that later. Remember how all your PHP scripts start with <?php and end with ?>? Those don't have to be the very start and end of your file. In fact, PHP handles ending and restarting scripting just like if everything between the ?> and <?php tags were inside of an echo statement.
Thus, you could do something like this:
This is actually a very common method of outputting variables in a script, especially if there is a lot of HTML surrounding the variables. As I said before, I personally rarely ever do this, as in my opinion, using echo for smaller scripts keeps your code cleaner (and I would use templates for larger ones). However, we want to cover most of the language here, so this is another method you could use.