Das All und dein Lächeln :)

Es viernes de traducción de música alemán. Hoy escuché una de esas canciones cursis en donde recuerdas al amorcino con el convives día a día y te trae tantas sonrisas. No tengo mucho que decir hoy, fue un buen día con muchas sonrisas. Anywho without further a due.

Lass mich nie mehr loss... Wie New York ohne Sinatra Wie Wien ohne den Prater Wie ein Herzschlag ohne Blut Wie Lindenberg ohne Hut Wie ne Eiszeit ohne Schnee England ohne Tee So als ob bei Steve McQueen die ganze Coolheit fehlt Jeder Boxer braucht ne Linke, Kiss braucht viermal Schminke Tonic braucht Gin. Wie wär ein Leben ohne Sinn? Wie ein leeres Paket Wie ein Rad das sich nicht dreht So als ob anstatt nem Sturm nur ein leichter Wind weht So bin ich ohne dich Du hältst mich mir fehlt nichts Lass mich nie mehr los Lass mich lass mich nie mehr los Lass mich nie mehr los Lass mich lass mich nie mehr los Wie das All ohne Planeten Astronauten ohne Raketen Paul Newman ohne Clou Old Shatterhand ohne Winnetou Wie ein Dieb der nicht stiehlt Wie ein Wort das nicht zählt So als ob dem Alphabet ein Buchstabe fehlt So bin ich ohne dich Du hältst mich mir fehlt nichts Lass mich nie mehr los Lass mich lass mich nie mehr los Ich lass dich nie mehr los Lass mich lass mich nie mehr los Lass mich nie mehr los Lass mich lass mich nie mehr los Lass mich nie mehr los Lass mich lass mich nie mehr los So bin ich ohne dich So bin ich ohne dich So bin ich ohne dich (wie ein Herzschlag ohne Blut) So bin ich ohne dich (lass mich nie mehr los!) Lass mich nie mehr los

Ya no me dejes ir Como Nueva York sin Sinatra. Como Austria sin Prater Como un latido de corazón sin sangre. Como un Glaciar sin nieve. Inglaterra sin su Té. Como si SteveMcQueen perdiera toda su actitud cool. Todo boxeador requiere de un brazo izquierdo. Kiss requiere de mucho maquillaje. La quina requiere de la Ginebra. ¿Cómo sería una vida sin sentido? Como un paquete vacio. Como una rueda que no gira. Como si en vez de la tempestad se tuviera tan sólo un viento suave. Así soy yo sin ti Tu me abrazas y no me falta nada. Ya no me dejes ir Ya no me dejes ir Ya no me dejes ir. Ya no me dejes ir. Como sería el universo sin planetas. Astronautas sin cohetes. el Viejo Shatterhand sin Winnetou Como un ratero que no roba, como una palabra que no cuenta, como si al alfabeto una letra le faltara. Así soy yo sin ti. Tu me abrazas y no me falta nada. Ya no me dejes ir Ya no me dejes ir Yo ya no te dejaré ir Ya no me dejes ir Ya no me dejes ir Ya no me dejes ir Ya no me dejes ir Ya no me dejes ir Asi soy yo sin ti Asi soy yo sin ti Asi soy yo sin tu (Como un latido de corazon sin sangre) Asi soy yo sin ti (Ya no me dejes ir!) Ya no me dejes ir

Aquí está la rolita:


Y aquí hay algunas notas acerca de algunas cosillas que menciona la canción:
Prater es un parquecito muy famoso y caracterísitico de Austria
SteveMcQueen era un actor al que le llamaron el Rey del Cool->King of Cool
El Viejo Shatterhand era un personaje ficticio de las novelas del viejo Oeste, era un alemán que tenía de mejor amigo a un indígena llamado Wnnentou

Otros Posts relacionados:
Traducción: Wir Trafen uns in einem Garten-2raumwohnung
Traducción: Nur ein Wort-Wir sind Helden

The hate and love relationships on social networks...

I had recently the "joy" of reading a very interesting articled by Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. And since it's a lazy sunday afternoon,I thought I might take the time to comment on my readings. The paper is titled "Signed Networks in Social Media", and this paper studies how the interaction between positive and negative relationships affects the structure of online social networks. They considered that the richness of a social network consists of a mixture of both positive and negative interactions that co-exist in one single structure.
It might be difficult to understand at first what a negative or a positive relationship in a social network is, since the vast majority of social networks only allow for positive relationships. Facebook for example, only permits users to designate who their friends are and state that they like the activity, status, picture etc of an individual. A user for example, is not capable of showing his dislike for another person's activity.
The study they carried out was therefore focused on social networks that do allow for positive and negative relationships.They obtained large-scale datasets from social applications where the sign of each link-positive or negative- could be reliably obtained.The social networks that were studied are:

• Epinions: an online rating site that lets people give both positive and negative ratings not only on items but also to other raters. It basically has Web of Trust per User: A network of reviewers whose reviews and ratings the user has consistently found to be valuable. It also has a Block List: list of authors whose reviews the user did not find valuable. “... If you encounter a member whose reviews are consistently offensive, inaccurate, or otherwise low quality, you can add that member to your Block List...”
  

• Slashdot: An online discussion site that allows users to tag other users as "friends" or "foes". The user can observe the different kind of relationships it holds with each different user. So if a person were to add someone as a friend, he/she would view them as a friend->http://slashdot.org/my/friends. While if you add a user as a foe, you view them as a foe. http://slashdot.org/my/foes
  If a user adds you as their friend, you would view them as a fan http://slashdot.org/my/fans
  If a user adds you as your foe, you would view them as a freak. http://slashdot.org/my/freaks
  

• Votes for Wikipedia admin candidate:When a Wikipedia user is considered for a promotion to the status of an admin, the community is able to cast public votes in favor of or against the promotion of this admin candidate. A positive vote is taken as a positive link from the voter to the candidate, and a negative vote as a negative link.
  
  This paper worked with the 3 datasets stated above and used these datasets along with 2 different theories of signed networks to reason about how different patterns of positive and negative links provide evidence of the different kind of relationships that exist across these networks. The 2 different theories of signed network that were used are:
  
  Structural Balance Theory: This was a theory that was proposed by Heider (1958) . Balance theory deals with three kinds of entities. The person (P) whose subjective environment we are concerned with, another person (O); and the object (X), which may be a third person.

Balance theory proposes that with three entities, person-another person-object (POX), three sets of relations exist i.e. between P and O; between P and X and; between O and X
Each of the three relations, P-O, P-X and O-X, can have one of two values. You can either 'like' (+) or 'dislike' (-). With three sets of possible relationships, each taking on one of two values (+/-) eight possible states of affairs exist. This theory states that balance state occurs when all sign multiplication of its sentiment relation charges positive.
For example, take the following diagram:

Here we have P, who we can name Pete that likes O, who we will call Olivia. Pete happens to hate hot peppers and chocolate, X. Balance Theory says, that for the relationship of these 3 individuals to be balanced, Olivia would also need to hate hot peppers and chocolate.
Some of the basic guidlines that Balance Theory follows are:
my friend’s friend is my friend
my friend’s enemy is my enemy
my enemy’s friend is my enemy
my enemy’s enemy is my friend

Now, for this paper they started analysis the social networks mentioned above, by comparing if these networks effectively followed the theorems of Balance theory , in this first stage they only considered undirected relationships between individuals, ie they only considered that Pete likes Olivia, but they did not consider whether or not Olivia likes or dislikes Pete. To carry out the comparative,the frequencies of different types of signed triads were taken into account.
They stated that a certain triad was overrepresented when the following occurred:
p(Ti ) larger than p0 (Ti )
Where P(Ti) represents the fractions of triads Ti. (Ti represents the number of triads of type Ti, for example it could represent the number of 3 person relationships whose edges are all POSITIVE) And P0(Ti) is the apriori probability of Ti based on sign distribution.
They also stated that a certain triad was underrepresented,
if p(Ti )less than po(ti) .
They saw that using status theory they were here able to explain much better these social interactions.
Status theory, claims the following:
Considering nodes A and B, a positive edge from A to B means: “I think B has higher status than I (A) do” A negative edge from A to B means: “I think B has lower status than I (A) do”
Thus, the theory of status predicts that if A links positive to B, then “A regards B as having higher status and" and if B links positive to C then " B regards C as having higher status – so C should regard A as having low status and hence be inclined to link negatively to A














The following table presents their findings:



ti referes to the selected triad. count refers to the number of times that particular triad was encountered in the network. P (+): prob. that closing red edge is positive;SG: surprise of edge initiator giving a positive edge (This is, surprise that Pete would give a positive link to Olivia);Sr: surprise of edge destination receiving a positive edge (surprise that Olivia would get a positive link);Bg:consistency of balance theory with generative surprise;
Br: consistency of balance with receptive surprise;
Sg: consistency of status with generative surprise; Sr: consistency of status with receptive surprise.

They also analyzed, mutual back and forth interactions,they noticed here that the principles of balance are more pronounced than they are in the larger portions of the networks where signed linking (and hence evaluation of others) takes place asymmetrically. They noticed that balance-based effects seem to be at work in the portions of the networks where directed edges point in both directions, reinforcing mutual relationships.

They also noticed that positive ties are more likely to be clumped together,while negative ties tend to act more like bridges between islands of positive ties.

Another interesting observation that they made, was that in their real data, an edge that was more embedded tended to be increasingly positive. These findings are consistent with the social-capital theory that states that embedded edges are more “on display" and thus tend to be positive.
The following picture shows these findings. Rnd denotes random signs created from the network, and real denotes the true signs that this network holds.


















Conclusion:
This paper presents a new perspective on how to reason social media sites by interpreting it as interactions between positive and negative relationships.
They also provide good insight with valid results, on to what theory depending on the network, is more fitted on explaining the nature of the network, i.e whether the network is directed or undirected.
I only have one doubt about the paper: They said that "...balance-based effects seems to be at work in the portions of the networks where directed edges point in both directions, reinforcing mutual relationship..", we could take these portions of the network as the community. Other papers, have only been interested in studying the community aspect of social networks, and feel that when in large scale these communities blend into the entire network, true human behavior is lost. Therefore we could question, if their status theory is modeling human behavior accurately.

None the less very interesting article, would highly recommend :)

Das bekommt mir nicht! == Eso no me sienta muy bien...

Tengo miles de pendientes, sin embargo quería hacerme un pequeño momento para poner una canción que acabo de conocer a traves de un servicio del cual me enamoré...y sí no es ningún "adult service". El servicio del cual hablo es blip.fm, es una combinación de twitter con youtube y te crea playlists, todo mundo es un DJ con sus comentarios graciosos a las rolas que les laten...
En fin, atraves de ese servicio conocí esta canción, no sé exactamente por qué pero me fascinó. Es una canción alemana y le hago tributo traduciendola al español!
Aquí esta la trAducción de la rola de 2raumwohnung -> translation to spanish
Translation to spanish!
Por cierto, de nuevo doy gracias a mis clases del cele que me permiten hacer esto.
Comentarios de la traducción son bienvenidos, tuve algunas dudas en varias partes de la canción, tonz apreciaría cualquier comentario al respecto...
Enjoy!

Wir Trafen uns in einem Garten Wir Trafen Uns In Einem Garten, Wahrscheinlich Unter Einem Baum. oder War´s In Einem Flugzeug, - Wohl Kaum - Wohl Kaum. es War Einfach Alles Anders, Viel Zu Gut Für Den Moment, wir Waren Ziemlich Durcheinander Und Haben Uns Dabei Getrennt. komm´ Doch Mal Auf Ein Stück Kucken, Später Geh´n Wir In Den Zoo. und Dann Lassen Wir Uns Suchen - Übers Radio. ich Weiß Nicht Ob Du Mich Verstehst Oder Ob Du Denkst Ich Spinn´, weil Ich Immer Wenn Du Nicht Da Bist Ganz Schrecklich Einsam Bin. dann Denk Ich Mal An Was Anderes Als Immer Nur An Dich denn Das Viele "an Dich Denken" Bekommt Mir Nicht. am Nächsten Tag Bin Ich So Müde, Ich Pass Gar Nicht Auf. und Meine Freunde Sagen Ich Seh Fertig Aus. es Hat Seit Tagen Nicht Geregnet, Es Hat Seit Wochen Nicht Geschneit. der Himmel Ist So Klar - Und Die Straßen Sind Breit. ist Das Leben Wie Ein Spielfilm Oder Geht´s Um Irgendwas? wir Haben Jede Menge Zeit Und Du Sagst :"na Ich Weiß Nicht - Stimmt Das?" fahr Doch Mit Mir Nach Italien, Wir Verstehen Zwar Kein Wort aber Lieber Mal Gar Nichts Verstehen Als Nur Bei Uns Im Ort dann Denk Ich Mal An Was Anderes ... (... Und Meine Freunde Sagen: "man Siehst Du Fertig Aus") alle Fenster Haben Gardinen, Ich Geh Alleine Durch Die Stadt. ich Frag Mich Ob Mich Jemand Liebt, Der Meine Telefonnummer Hat? warum Immer Alle Fernsehen? Das Macht Doch Dick! ich Stell Mit Vor Ich Wär´ Ein Fuchs In Einem Zeichentrick

Nos topamos en un jardín. Nos encontramos en un jardín, talvez fue más bien debajo de un árbol. O fue en un avión-no no Todo fue simplemente diferente, demasiado bueno para el momento, aunque estábamos un poco confundidos y desde allí nos separamos. :( Andale ven a tomarte un pedacito de pastel, después podemos ir al zológico, y dejamos que nos búsquen --por el radio. Yo no sé si me entiendas, o pienses que estoy loca, Porque siempre que estás lejos, me siento horriblemente sola. Entonces en vez de estar todo el tiempo pensando en tí, pienso en otra cosa, porque estar pensando mucho en ti, no me sienta muy bien. Al día siguiente estoy muy cansada, no puedo poner atención a casi nada. Y mis amigos me dicen que me veo acabada! Desde hace un par de días no ha llovido, desde hace un par de semanas no ha nevado, el cielo está tan frío y las calles están tan amplías, la vida es como una película, o acaso es para otra cosa? Tenemos ambos un gran cacho de tiempo libre, y tu dices: ah neta? es cierto esto?. Andale viaja conmigo a Italia, no vamos a entener nadita, pero es mejor no entender nadita a estar en nuestra casa todo el tiempo. Entonces en vez de estar todo el tiempo pensando en tí, pienso en otra cosa ...(..Y mis amigos me dicen que me veo acabada!) Todas las ventanas tienen cortinas, camino solitaria por la ciudad. Me preguntó a mi misma, si me ama ese individuo que tiene mi número telefónico. Y por qué todos ven la Tele? Te hace verte gordo, no??! Me imagino como si yo fuera un zorro dentro de una animación.

Aquí está la rolita:

Rose Mary, she likes berries, believes in Fairies and is in love with Harry

We will explain here, how to carry out the Harris Corner Detector Algorithm in Matlab.
We will divide the task in 2 parts, one part will calculate the corner points of the image, and the other part, will draw small squares around those corner points with the purpose of having a way of displying them to the user.
The part of detecting the corner points of the image is the following:

%we create a function named harris that receives an rgb image
%and the desired k value. We will return a matrix named A that
%has information about whether or not a certain pixel represents
%a corner.
function [A] = harris(rgb, k)


%If we received an RGB image we convert it to Gray Scale.
%We do this, because if we were to work with an RGB image it
%would be necessary to work with 3 channels, the red, green and blue channel, %instead of just one, which is possible with the gray scale image .

if( size(rgb,3) >= 3 )
img = double( rgb2gray(rgb) );
end

%Before , we continue , it is important to recall that in the Harris Corner Detector, %we obtained a weighted sum of squared differences between an area (uv) and %another area that was obtained by shifting the original area by (x,y), and thus we %had the following form:

%w(u,v) represented a weighted sum. It is important to note that I(u + x,v + y) %can be approximated by a Taylor expansion.

%Where Ix and Iy are the partial derivatives of I.
%Resulting in:

%Since we will carry out an operation that involves partial derivatives we need to %carry out a smoothing, because computation of derivatives generally involves a %stage of scale-space smoothing. For this,we will use the convolution of the gray %image with a Gaussian kernel.
So the next steps that we will carry out are:
a) Calculate the partial derivatives with respect to X and to respect to Y of the image. This will give us the gradients with respect to X and with respect to Y.
b) convolve these gradients with a Gaussian Kernel.
The following is our Gaussian Kernel, which will give us blurring on both directions%

g = 1/16 * [1 2 1; 2 4 2; 1 2 1];

%Here we define the Gradients Operators, we will use the Prewitt Gradient Kernel to obtain the gradients. If we pay more attention to this matrix, we notice that this kernel considers that the orthogonal and diagonal pixel differentials equally

dx = [-1 0 1; -1 0 1; -1 0 1]; %Prewitt Gradient Kernel in X
dy = dx';

% We obtain all the partial derivatives in x and y of the image. These are the %Gradient values.
Ix = conv2(img, dx, 'same');
Iy = conv2(img, dy, 'same');
% We obtain a matrix, that will have the product of Ix*Iy for all Ix and all Iy
Ixy= Ix .* Iy;

% We will now obtain the square value of Ix and of Iy and we will obtain a blurring of Ix square,Iy square and of Ixy.The blurring will be carried out by using the gaussian kernel.We need those square values , since let's recall we had the following:

Which produces the approximation

which can be written in matrix form:

where A is:

%So we calculate the values that are inside this matrix.

Ix2 = conv2(Ix .* Ix, g, 'same');
Iy2 = conv2(Iy .* Iy, g, 'same');
Ixy = conv2(Ixy, g, 'same');


%We now have 3 matrices , Ix2 that hold the values of all the xs in the image, but they are squared and have been convolved with a Gaussian, so all the xto the square values are blurred a bit. It is important to note, that this operation helps to reduce noise. It smooths the image. We have another matrix Iy2 with the ys to the square value and also blurred as well as third matrix Ixy that holds the values of all the x's times their corresponding y. All 3 of these matrices will help us to calculate the Harris corner response that each pixel of the image has.
The Harris Corner response for each pixel will come out of a matrix A, that as we had said before handwas conformed of:

%Where Ix represents the x value of the pixel, and Iy the y value of the pixel. The Harris Corner response %of a pixel will be Mc:

%What our Harris Function will return will be a matrix that will hold the harris corner response for each %one of the pixels that conform the image:

A = (Ix2.*Iy2 - Ixy.^2) - k * (Ix2 + Iy2).^2;

end

We will now write the function that draws a small red square around the detected corner points.

%This function receives the image we wish to detect and draw the corner points of, %it also receives the desired k value to use, and the desired threshold. It is %important to remember that in the Harris corner detector, we consider a corner %to be a corner when the measure of corner response surpasses a certain %threshold. This measure is computed through the determinant and the trace of the % matrix.

function img_h = project1(img, k, threshold)
%We store the original image in img_h, we need to store it, since we will draw the %squares denoting the corners above it.
img_h = img;
%We use the function we had defined above and with it obtain a matrix that holds %all the corner response measures for all the pixels of the image%
M = my_harris(img, k);

%We iterate through the whole matrix
for x = 2 : size( M, 1 )
for y = 2 : size(M, 2)
%If we find a point of the matrix, that has a value above the threshold, then that %point is a corner and we will draw a rectangle on that pixel.
if M(x,y) > threshold
for xpos = x - 1:x+1
img_h(xpos,y-1,1) = 255;
img_h(xpos,y+1,1) = 255;
end

for ypos = y - 1:y+1
img_h(x-1,ypos,1) = 255;
img_h(x+1,ypos,1) = 255;
end
end
end
end

end

We now present a image that was used for this purpose.
The original image is:

And the image with the corners detected is:

The Harris Marris whose not Ferris post...

In many robotic problems it is necessary for the machine to be able to detect the depth and distance of certain objects sometimes to avoid obstacles and other times to retrieve with precision a certain object from the scene.
To accomplish this, the robot is usually equipped with 2 cameras that take pictures of the environment they are in. These 2 cameras commonly hold a distance from each other, a distance similar to the one we present with our eyes, due to it, the pictures from one camera are slightly shifted with respect to the ones taken by the other camera. This shift is usually denominated disparity and is what the computer uses to know whether an object is close by or far away.
One major problem that the machine encounters while carrying out this task, is how does it detect that the red cup in picture 1 is moved , let's say, 4 cm with respect to where the red cup is in picture 2?
These vision tasks require finding corresponding features across 2 or more views.


Therefore the first necessary step is to find the features of a scene. But how do we do this?

What we can start doing is making image patches.Elements to be matched are image patches of a fixed size..The task is therefore to find the best (most similar) patch in the second image, it is clear that the chosen patch should be very distinctive (there should only be one patch in the second picture that looks similar). One good patch is one that presents large variation in the neighborhood of a point in all directions.
For example Take the following 2 images:








A good patch image patch could be:


while a bad one, because it has many matchings is:


We are looking for stable features over changes of view points. One type of features that maintain this type of characteristic are Corners.
The Harris Corner detector provides a mathematical tool for finding them.
With an image patch, we can have the following cases:
a) The patch represents a 'flat' zone.
b)The patch represents an edge .
c) The patch represents a corner .



A Flat region as we can see from the above image, presents no change in all directions, an edge presents no change along the edge direction, and a corner presents significant change in all directions. This means that if we shift the window of where we are gathering the patch image, we should perceive a large change in appearance.
The Harris Corner Detector gives us a way to determine which of the above cases hold.
But how does it do it exactly???!

The Harris Corner Detector utilizes the following expression:
E (u,v)=∑ W(Xi,Yi)[I1(Xi+U,Yi+V)-I0(Xi,Yi)]^2
W(Xi,Yi) is a window function. Which sets:


I0(Xi,Yi) is the intensity that is present in the pixel located in (Xi,Yi) and I1(Xi+U,Yi+V) is the intensity located in the pixel Xi+U,Yi+V it is called the shifted or displaced Intensity.
It is easy to see that to detect corners, we want points where E(u,v) is very large.
Using Taylor's first order approximation and matrix algebra the above expression can be rewritten as:


Where M is a 2x2 matrix computed from image derivatives.


The classification can be carried out by analyzing the eigenvalues of the M matrix.


The measure of the corner response is actually set by:
R=(determinant of M)+k(trace of M)^2
Determinant of M=λ1λ2
Trace of M=λ1+λ2
K is an empirical constant that varies from .04-.06

For corners R tends to be very large.
For edges, R tends to be a very large negative number.
For flat areas R tends to zero.

Your cellphone can tell me what you did last summer

Our cellphones are nowadays an item, which we are very much accustomed to bringing along to every single place we go to. They are also devices that are very similar to little computers with multiple sensors and interaction modalities, they are equipped with GPS, Bluetooth, accelerometers, cameras, microphones,magnetometers,keyboards, and touch-sensitive displays,they also have great computation power and memory,graphics capabilities, and various communications capabilities.
All of these elements aside from providing a novel multi modal user interface experience give the means through which cellphones are a perfect device for tracing human activity. With all of the cellphone's sensors, one can obtain a collection of data, that is related with what a person did through out the day, then by using data-mining algorithms one can infer human relationships and behaviors, this is often refereed to as Reality Mining. The MIT Media Lab gives a far more formal definition of what Reality Mining is: "...R.M defines the collection of machine-sensed environmental data pertaining to human social behavior..."



The problem that is currently being faced is to understand exactly how the joint use of multiple modalities,like for example location and proximity to others, help understand a person’s routines. It is important to point out that many issues actually arise when one wishes to understand patterns in the life of an individual. It is not simple to automatically infer a person's activities as well as efficiently represent them . For example, having a stay home alone Thursday and a Thursday of Beer Hotness with friends at your place define entirely different social situations, yet they could be considered identical from the sole perspective of location. It is thus very important to have detailed descriptions of the activities done by a person for characterizing the users and their habits.

The big impact that reality mining has on us, is that it is able to create models of individual as well as group behavior from the recollected data, this could enable smart personal assistants, as well as monitoring of personal and community health.

References:
http://reality.media.mit.edu/
http://docs.google.com/fileview?id=0B4gV5GYvQzR1NjA2YmVkODMtZGMwYi00MjBjLTg0NDgtNDYzYzU5YjI0N2Zm&hl=en
http://docs.google.com/fileview?id=0B4gV5GYvQzR1YjVkZmM0N2QtYzM3ZC00NGYzLTg0OTMtYTA2ZTFjNGZhOWIz&hl=en

http://docs.google.com/fileview?id=0B4gV5GYvQzR1NTliNmYxM2MtNzM0Mi00NTViLWE2OGEtYzgzNjEzNzc3NWY1&hl=en

http://docs.google.com/fileview?id=0B4gV5GYvQzR1Y2I1MjA1ZjAtMjNhYy00MmFhLWFlZjQtMDBkMGM2YTk3NWMy&hl=en

Creando tus propios programas de multimedia en la tablet del n900

Ya tengo por fin mi nuevo telefono!!
Muchas gracias a mi querida universidad, la cual para una de las materias que estoy llevando consiguió una donación de nokia y nos regalaron a todos los alumnos un n900!
Las cosas sí que han sido ahora divertidas =)

Anyway,hoy hablaremos acerca de como utilizar gstreamer dentro del celular para poder hacer lindos programitas que puedan involucrar musica o incluso video! =)
Para empezar es importante entender lo que es Gstreamer y como es que nos ayuda a hacer applicaciones multimedia.
Gstreamer es un framework de multimedia que te ayuda a crear, editar y tocar multimedia al construir unos "pipelines" (como líneas de ensamblaje) que poseen elementos de multimedia. Simplemente se crea un pipeline, la cual posee muchos elementos que entre sí permiten que la musica se pueda tocar o que un video se pueda ver. Funciona de un modo muy similar a como son las líneas de ensamblaje en Linux/BSD/UNIX.

Con Gstreamer se atan a los elementos entre sí, y cada elemento lleva acabo algo en particular. Para demostrar esto, en una terminal escribe lo siguiente:

foo@bar:~$ gst-launch-0.10 filesrc location=jeans-ilusion_primer_amor.mp3 ! decodebin ! audioconvert ! alsasink

Cuando la línea anterior se corre, se escucha de pronto la grandiosa melodía de "...ni lo amigos saben que es lo que me paaasa,(...)con la ilusión del primer amor desesperado te amoooo..." ...ah good times ñ_ñ.
El comando gst-launch-0.10 se puede utilizar para correr pipelines de GStreamer y cada elemento esta ligado entre sí mediante el símbolo !. Puedes pensar que el ! es similar al pipe | que usas en la línea de comandos normalmente.
Ahora bien, como se pueden dar cuenta tenemos una serie de diversos elementos dentro de la línea de ensamblaje, estos son:
* filesrc – Este elemento permite cargar archivos que esten dentro del disco duro. Al ladito de este elemento se debe poner la dirección y el archivo que se quiere cargar
* decodebin – Necesitamos algo para poder descifrar al archivo que se acaba de cargar. Este elemento detecta el tipo de archivo con el cual deseamos trabajar y construye un elemento que lo decifrará.
* audioconvert – El tipo de información que posee u archivo de sonido y el tipo de información que necesitamos salga de las bocinas son diferentes, así que usamos a este elemento para hacer un buen mapeo entre lo que se tiene en el archivo de sonido y lo que se escuchará.
* alsasink – Este elemento escupe todo el audio a tu tarjeta de sonido usando ALSA.

Me parece que ya es claro que Gstreamer trabaja como un línea de ensamblaje, cada elemento le da como entrada al siguiente elemento su salida.
Ya que nos es claro que pex con Gstreamer, haremos ahora un pequeño código en C para probarlo.
Haremos un pequeño hola_mundo.c para Gstreamer. Desde la línea de comandos diremos que queremos que se toque y deberíamos posteriormente escuchar la cancioncita =)
El código, que usaremos es el siguiente:

#include gst/gst.h
#include stdbool.h

static GMainLoop *loop;

static gboolean bus_call (GstBus *bus,GstMessage *msg, gpointer user_data)
{
switch (GST_MESSAGE_TYPE (msg))
{
case GST_MESSAGE_EOS:
{
g_message ("End-of-stream");
g_main_loop_quit (loop);
break;
}
case GST_MESSAGE_ERROR:
{
gchar *debug;
GError *err;
gst_message_parse_error (msg, &err, &debug);
g_free (debug);
g_error ("%s", err->message);
g_error_free (err);
g_main_loop_quit (loop);
break;
}
default:
break;
}

return true;
}

void play_uri (gchar *uri)
{
GstElement *pipeline;
loop = g_main_loop_new (NULL, FALSE);
pipeline = gst_element_factory_make ("playbin", "player");
if (uri)
{

g_object_set (G_OBJECT (pipeline), "uri", uri, NULL);
}

{
GstBus *bus;

bus = gst_pipeline_get_bus (GST_PIPELINE (pipeline));
gst_bus_add_watch (bus, bus_call, NULL);
gst_object_unref (bus);
}

gst_element_set_state (GST_ELEMENT (pipeline), GST_STATE_PLAYING);

g_main_loop_run (loop);

gst_element_set_state (GST_ELEMENT (pipeline), GST_STATE_NULL);
gst_object_unref (GST_OBJECT (pipeline));
}

int main (int argc,char *argv[])
{
gst_init (&argc, &argv);

play_uri (argv[1]);

return 0;
}

hmmm...creo que no tengo mucho tiempo de explicar el código...
Pero para compilarlo y probarlo, en la consola escriban:

gcc -Wall -g gstreame.c `pkg-config --cflags gtk+-2.0 gstreamer-0.10 gmodule-2.0` -o gstreame `pkg-config --libs gtk+-2.0 gstreamer-0.10` `pkg-config --libs gmodule-2.0`

Ahora bien, si lo quieren probar en su tablet de n900, lo que deben hacer es abrir su scratchbox, utilizar el target u objetivo de ARMEL, ( se pueden cambiar a ese objetivo escribiendo desde scratchbox: sb-conf se FREMANTLE_X86) y desde allí compilar el archivo, una vez que se tenga compilado, mediante scp pasaremos el binario a nuestro celular y ese binario será lo que correremos.
Para pasarlo a nuestra tableta de n900, es necesario primero saber el ip de nuestro celular, para hacerlo nos vamos al menu de applicaciones y hay un ícono que dice: More, ó Más, ó Mehr, dependiendo del idioma en que lo tengan configurado, dentro de ese ícono existe una ventanita negra, llamada X terminal, lo abrimos y tendremos la consola del celular! Allí simplemente escribimos ifconfig y apuntamos la dirección ip que se nos muestra.
Ahora,desde nuestra PC en scratchbox en el target de ARMEL escribimos:

scp gstreame user@192.168.0.12:

en donde gstreame es el binario que queremos pasar a nuestra tableta y 192.168.0.12 es la dirección IP de nuestro celular.
Para correrlo en la tableta n900, desde la consola X escribimos:

./gstreame "file://$PWD/test.wav"

En donde test.wav es el archivo que se escuchará. Es necesario escribirlo de esta forma debido a que el programa recibe una URL.
Y con esto, ya hemos ehcho un pequeño programa que toca musica desde nuestro n900!

Los dejo con un videito casero malo que hice,(non-ameteur porn) que muestra mi nuevo smart phone tocando una estación alemana, oh sí! el programa como recibe URI puede tocar estaciones de radio!! =)
Felices Hackeos!

Otros Posts relacionados:
Programa para cargar Fotos a tu celular
Hola mundo en celular

Refrencias:
http://wiki.forum.nokia.com/index.php/GStreamer#
http://bluwiki.com/go/GStreamer/C_Hello_World
http://www.jonobacon.org/2006/08/28/getting-started-with-gstreamer-with-python/