Search Results

Search found 80 results on 4 pages for 'theta'.

Page 4/4 | < Previous Page | 1 2 3 4 

  • Estimating the boundary of arbitrarily distributed data

    - by Dave
    I have two dimensional discrete spatial data. I would like to make an approximation of the spatial boundaries of this data so that I can produce a plot with another dataset on top of it. Ideally, this would be an ordered set of (x,y) points that matplotlib can plot with the plt.Polygon() patch. My initial attempt is very inelegant: I place a fine grid over the data, and where data is found in a cell, a square matplotlib patch is created of that cell. The resolution of the boundary thus depends on the sampling frequency of the grid. Here is an example, where the grey region are the cells containing data, black where no data exists. OK, problem solved - why am I still here? Well.... I'd like a more "elegant" solution, or at least one that is faster (ie. I don't want to get on with "real" work, I'd like to have some fun with this!). The best way I can think of is a ray-tracing approach - eg: from xmin to xmax, at y=ymin, check if data boundary crossed in intervals dx y=ymin+dy, do 1 do 1-2, but now sample in y An alternative is defining a centre, and sampling in r-theta space - ie radial spokes in dtheta increments. Both would produce a set of (x,y) points, but then how do I order/link neighbouring points them to create the boundary? A nearest neighbour approach is not appropriate as, for example (to borrow from Geography), an isthmus (think of Panama connecting N&S America) could then close off and isolate regions. This also might not deal very well with the holes seen in the data, which I would like to represent as a different plt.Polygon. The solution perhaps comes from solving an area maximisation problem. For a set of points defining the data limits, what is the maximum contiguous area contained within those points To form the enclosed area, what are the neighbouring points for the nth point? How will the holes be treated in this scheme - is this erring into topology now? Apologies, much of this is me thinking out loud. I'd be grateful for some hints, suggestions or solutions. I suspect this is an oft-studied problem with many solution techniques, but I'm looking for something simple to code and quick to run... I guess everyone is, really! Cheers, David

    Read the article

  • How to store generated eigen faces for future face recognition?

    - by user3237134
    My code works in the following manner: 1.First, it obtains several images from the training set 2.After loading these images, we find the normalized faces,mean face and perform several calculation. 3.Next, we ask for the name of an image we want to recognize 4.We then project the input image into the eigenspace, and based on the difference from the eigenfaces we make a decision. 5.Depending on eigen weight vector for each input image we make clusters using kmeans command. Source code i tried: clear all close all clc % number of images on your training set. M=1200; %Chosen std and mean. %It can be any number that it is close to the std and mean of most of the images. um=60; ustd=32; %read and show images(bmp); S=[]; %img matrix for i=1:M str=strcat(int2str(i),'.jpg'); %concatenates two strings that form the name of the image eval('img=imread(str);'); [irow icol d]=size(img); % get the number of rows (N1) and columns (N2) temp=reshape(permute(img,[2,1,3]),[irow*icol,d]); %creates a (N1*N2)x1 matrix S=[S temp]; %X is a N1*N2xM matrix after finishing the sequence %this is our S end %Here we change the mean and std of all images. We normalize all images. %This is done to reduce the error due to lighting conditions. for i=1:size(S,2) temp=double(S(:,i)); m=mean(temp); st=std(temp); S(:,i)=(temp-m)*ustd/st+um; end %show normalized images for i=1:M str=strcat(int2str(i),'.jpg'); img=reshape(S(:,i),icol,irow); img=img'; end %mean image; m=mean(S,2); %obtains the mean of each row instead of each column tmimg=uint8(m); %converts to unsigned 8-bit integer. Values range from 0 to 255 img=reshape(tmimg,icol,irow); %takes the N1*N2x1 vector and creates a N2xN1 matrix img=img'; %creates a N1xN2 matrix by transposing the image. % Change image for manipulation dbx=[]; % A matrix for i=1:M temp=double(S(:,i)); dbx=[dbx temp]; end %Covariance matrix C=A'A, L=AA' A=dbx'; L=A*A'; % vv are the eigenvector for L % dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx'; [vv dd]=eig(L); % Sort and eliminate those whose eigenvalue is zero v=[]; d=[]; for i=1:size(vv,2) if(dd(i,i)>1e-4) v=[v vv(:,i)]; d=[d dd(i,i)]; end end %sort, will return an ascending sequence [B index]=sort(d); ind=zeros(size(index)); dtemp=zeros(size(index)); vtemp=zeros(size(v)); len=length(index); for i=1:len dtemp(i)=B(len+1-i); ind(i)=len+1-index(i); vtemp(:,ind(i))=v(:,i); end d=dtemp; v=vtemp; %Normalization of eigenvectors for i=1:size(v,2) %access each column kk=v(:,i); temp=sqrt(sum(kk.^2)); v(:,i)=v(:,i)./temp; end %Eigenvectors of C matrix u=[]; for i=1:size(v,2) temp=sqrt(d(i)); u=[u (dbx*v(:,i))./temp]; end %Normalization of eigenvectors for i=1:size(u,2) kk=u(:,i); temp=sqrt(sum(kk.^2)); u(:,i)=u(:,i)./temp; end % show eigenfaces; for i=1:size(u,2) img=reshape(u(:,i),icol,irow); img=img'; img=histeq(img,255); end % Find the weight of each face in the training set. omega = []; for h=1:size(dbx,2) WW=[]; for i=1:size(u,2) t = u(:,i)'; WeightOfImage = dot(t,dbx(:,h)'); WW = [WW; WeightOfImage]; end omega = [omega WW]; end % Acquire new image % Note: the input image must have a bmp or jpg extension. % It should have the same size as the ones in your training set. % It should be placed on your desktop ed_min=[]; srcFiles = dir('G:\newdatabase\*.jpg'); % the folder in which ur images exists for b = 1 : length(srcFiles) filename = strcat('G:\newdatabase\',srcFiles(b).name); Imgdata = imread(filename); InputImage=Imgdata; InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]); temp=InImage; me=mean(temp); st=std(temp); temp=(temp-me)*ustd/st+um; NormImage = temp; Difference = temp-m; p = []; aa=size(u,2); for i = 1:aa pare = dot(NormImage,u(:,i)); p = [p; pare]; end InImWeight = []; for i=1:size(u,2) t = u(:,i)'; WeightOfInputImage = dot(t,Difference'); InImWeight = [InImWeight; WeightOfInputImage]; end noe=numel(InImWeight); % Find Euclidean distance e=[]; for i=1:size(omega,2) q = omega(:,i); DiffWeight = InImWeight-q; mag = norm(DiffWeight); e = [e mag]; end ed_min=[ed_min MinimumValue]; theta=6.0e+03; %disp(e) z(b,:)=InImWeight; end IDX = kmeans(z,5); clustercount=accumarray(IDX, ones(size(IDX))); disp(clustercount); QUESTIONS: 1.It is working fine for M=50(i.e Training set contains 50 images) but not for M=1200(i.e Training set contains 1200 images).It is not showing any error.There is no output.I waited for 10 min still there is no output. I think it is going infinite loop.What is the problem?Where i was wrong? 2.Instead of running the training set everytime how eigen faces generated are stored so that stored eigen faces are used for future face recoginition for a new input image.So it reduces wastage of time.

    Read the article

  • Vectorization of matlab code for faster execution

    - by user3237134
    My code works in the following manner: 1.First, it obtains several images from the training set 2.After loading these images, we find the normalized faces,mean face and perform several calculation. 3.Next, we ask for the name of an image we want to recognize 4.We then project the input image into the eigenspace, and based on the difference from the eigenfaces we make a decision. 5.Depending on eigen weight vector for each input image we make clusters using kmeans command. Source code i tried: clear all close all clc % number of images on your training set. M=1200; %Chosen std and mean. %It can be any number that it is close to the std and mean of most of the images. um=60; ustd=32; %read and show images(bmp); S=[]; %img matrix for i=1:M str=strcat(int2str(i),'.jpg'); %concatenates two strings that form the name of the image eval('img=imread(str);'); [irow icol d]=size(img); % get the number of rows (N1) and columns (N2) temp=reshape(permute(img,[2,1,3]),[irow*icol,d]); %creates a (N1*N2)x1 matrix S=[S temp]; %X is a N1*N2xM matrix after finishing the sequence %this is our S end %Here we change the mean and std of all images. We normalize all images. %This is done to reduce the error due to lighting conditions. for i=1:size(S,2) temp=double(S(:,i)); m=mean(temp); st=std(temp); S(:,i)=(temp-m)*ustd/st+um; end %show normalized images for i=1:M str=strcat(int2str(i),'.jpg'); img=reshape(S(:,i),icol,irow); img=img'; end %mean image; m=mean(S,2); %obtains the mean of each row instead of each column tmimg=uint8(m); %converts to unsigned 8-bit integer. Values range from 0 to 255 img=reshape(tmimg,icol,irow); %takes the N1*N2x1 vector and creates a N2xN1 matrix img=img'; %creates a N1xN2 matrix by transposing the image. % Change image for manipulation dbx=[]; % A matrix for i=1:M temp=double(S(:,i)); dbx=[dbx temp]; end %Covariance matrix C=A'A, L=AA' A=dbx'; L=A*A'; % vv are the eigenvector for L % dd are the eigenvalue for both L=dbx'*dbx and C=dbx*dbx'; [vv dd]=eig(L); % Sort and eliminate those whose eigenvalue is zero v=[]; d=[]; for i=1:size(vv,2) if(dd(i,i)>1e-4) v=[v vv(:,i)]; d=[d dd(i,i)]; end end %sort, will return an ascending sequence [B index]=sort(d); ind=zeros(size(index)); dtemp=zeros(size(index)); vtemp=zeros(size(v)); len=length(index); for i=1:len dtemp(i)=B(len+1-i); ind(i)=len+1-index(i); vtemp(:,ind(i))=v(:,i); end d=dtemp; v=vtemp; %Normalization of eigenvectors for i=1:size(v,2) %access each column kk=v(:,i); temp=sqrt(sum(kk.^2)); v(:,i)=v(:,i)./temp; end %Eigenvectors of C matrix u=[]; for i=1:size(v,2) temp=sqrt(d(i)); u=[u (dbx*v(:,i))./temp]; end %Normalization of eigenvectors for i=1:size(u,2) kk=u(:,i); temp=sqrt(sum(kk.^2)); u(:,i)=u(:,i)./temp; end % show eigenfaces; for i=1:size(u,2) img=reshape(u(:,i),icol,irow); img=img'; img=histeq(img,255); end % Find the weight of each face in the training set. omega = []; for h=1:size(dbx,2) WW=[]; for i=1:size(u,2) t = u(:,i)'; WeightOfImage = dot(t,dbx(:,h)'); WW = [WW; WeightOfImage]; end omega = [omega WW]; end % Acquire new image % Note: the input image must have a bmp or jpg extension. % It should have the same size as the ones in your training set. % It should be placed on your desktop ed_min=[]; srcFiles = dir('G:\newdatabase\*.jpg'); % the folder in which ur images exists for b = 1 : length(srcFiles) filename = strcat('G:\newdatabase\',srcFiles(b).name); Imgdata = imread(filename); InputImage=Imgdata; InImage=reshape(permute((double(InputImage)),[2,1,3]),[irow*icol,1]); temp=InImage; me=mean(temp); st=std(temp); temp=(temp-me)*ustd/st+um; NormImage = temp; Difference = temp-m; p = []; aa=size(u,2); for i = 1:aa pare = dot(NormImage,u(:,i)); p = [p; pare]; end InImWeight = []; for i=1:size(u,2) t = u(:,i)'; WeightOfInputImage = dot(t,Difference'); InImWeight = [InImWeight; WeightOfInputImage]; end noe=numel(InImWeight); % Find Euclidean distance e=[]; for i=1:size(omega,2) q = omega(:,i); DiffWeight = InImWeight-q; mag = norm(DiffWeight); e = [e mag]; end ed_min=[ed_min MinimumValue]; theta=6.0e+03; %disp(e) z(b,:)=InImWeight; end IDX = kmeans(z,5); clustercount=accumarray(IDX, ones(size(IDX))); disp(clustercount); Running time for 50 images:Elapsed time is 103.947573 seconds. QUESTIONS: 1.It is working fine for M=50(i.e Training set contains 50 images) but not for M=1200(i.e Training set contains 1200 images).It is not showing any error.There is no output.I waited for 10 min still there is no output. I think it is going infinite loop.What is the problem?Where i was wrong?

    Read the article

  • A Look Inside JSR 360 - CLDC 8

    - by Roger Brinkley
    If you didn't notice during JavaOne the Java Micro Edition took a major step forward in its consolidation with Java Standard Edition when JSR 360 was proposed to the JCP community. Over the last couple of years there has been a focus to move Java ME back in line with it's big brother Java SE. We see evidence of this in JCP itself which just recently merged the ME and SE/EE Executive Committees into a single Java Executive Committee. But just before that occurred JSR 360 was proposed and approved for development on October 29. So let's take a look at what changes are now being proposed. In a way JSR 360 is returning back to the original roots of Java ME when it was first introduced. It was indeed a subset of the JDK 4 language, but as Java progressed many of the language changes were not implemented in the Java ME. Back then the tradeoff was still a functionality, footprint trade off but the major market was feature phones. Today the market has changed and CLDC, while it will still target feature phones, will have it primary emphasis on embedded devices like wireless modules, smart meters, health care monitoring and other M2M devices. The major changes will come in three areas: language feature changes, library changes, and consolidating the Generic Connection Framework.  There have been three Java SE versions that have been implemented since JavaME was first developed so the language feature changes can be divided into changes that came in JDK 5 and those in JDK 7, which mostly consist of the project Coin changes. There were no language changes in JDK 6 but the changes from JDK 5 are: Assertions - Assertions enable you to test your assumptions about your program. For example, if you write a method that calculates the speed of a particle, you might assert that the calculated speed is less than the speed of light. In the example code below if the interval isn't between 0 and and 1,00 the an error of "Invalid value?" would be thrown. private void setInterval(int interval) { assert interval > 0 && interval <= 1000 : "Invalid value?" } Generics - Generics add stability to your code by making more of your bugs detectable at compile time. Code that uses generics has many benefits over non-generic code with: Stronger type checks at compile time. Elimination of casts. Enabling programming to implement generic algorithms. Enhanced for Loop - the enhanced for loop allows you to iterate through a collection without having to create an Iterator or without having to calculate beginning and end conditions for a counter variable. The enhanced for loop is the easiest of the new features to immediately incorporate in your code. In this tip you will see how the enhanced for loop replaces more traditional ways of sequentially accessing elements in a collection. void processList(Vector<string> list) { for (String item : list) { ... Autoboxing/Unboxing - This facility eliminates the drudgery of manual conversion between primitive types, such as int and wrapper types, such as Integer.  Hashtable<Integer, string=""> data = new Hashtable<>(); void add(int id, String value) { data.put(id, value); } Enumeration - Prior to JDK 5 enumerations were not typesafe, had no namespace, were brittle because they were compile time constants, and provided no informative print values. JDK 5 added support for enumerated types as a full-fledged class (dubbed an enum type). In addition to solving all the problems mentioned above, it allows you to add arbitrary methods and fields to an enum type, to implement arbitrary interfaces, and more. Enum types provide high-quality implementations of all the Object methods. They are Comparable and Serializable, and the serial form is designed to withstand arbitrary changes in the enum type. enum Season {WINTER, SPRING, SUMMER, FALL}; } private Season season; void setSeason(Season newSeason) { season = newSeason; } Varargs - Varargs eliminates the need for manually boxing up argument lists into an array when invoking methods that accept variable-length argument lists. The three periods after the final parameter's type indicate that the final argument may be passed as an array or as a sequence of arguments. Varargs can be used only in the final argument position. void warning(String format, String... parameters) { .. for(String p : parameters) { ...process(p);... } ... } Static Imports -The static import construct allows unqualified access to static members without inheriting from the type containing the static members. Instead, the program imports the members either individually or en masse. Once the static members have been imported, they may be used without qualification. The static import declaration is analogous to the normal import declaration. Where the normal import declaration imports classes from packages, allowing them to be used without package qualification, the static import declaration imports static members from classes, allowing them to be used without class qualification. import static data.Constants.RATIO; ... double r = Math.cos(RATIO * theta); Annotations - Annotations provide data about a program that is not part of the program itself. They have no direct effect on the operation of the code they annotate. There are a number of uses for annotations including information for the compiler, compiler-time and deployment-time processing, and run-time processing. They can be applied to a program's declarations of classes, fields, methods, and other program elements. @Deprecated public void clear(); The language changes from JDK 7 are little more familiar as they are mostly the changes from Project Coin: String in switch - Hey it only took us 18 years but the String class can be used in the expression of a switch statement. Fortunately for us it won't take that long for JavaME to adopt it. switch (arg) { case "-data": ... case "-out": ... Binary integral literals and underscores in numeric literals - Largely for readability, the integral types (byte, short, int, and long) can also be expressed using the binary number system. and any number of underscore characters (_) can appear anywhere between digits in a numerical literal. byte flags = 0b01001111; long mask = 0xfff0_ff08_4fff_0fffl; Multi-catch and more precise rethrow - A single catch block can handle more than one type of exception. In addition, the compiler performs more precise analysis of rethrown exceptions than earlier releases of Java SE. This enables you to specify more specific exception types in the throws clause of a method declaration. catch (IOException | InterruptedException ex) { logger.log(ex); throw ex; } Type Inference for Generic Instance Creation - Otherwise known as the diamond operator, the type arguments required to invoke the constructor of a generic class can be replaced with an empty set of type parameters (<>) as long as the compiler can infer the type arguments from the context.  map = new Hashtable<>(); Try-with-resource statement - The try-with-resources statement is a try statement that declares one or more resources. A resource is an object that must be closed after the program is finished with it. The try-with-resources statement ensures that each resource is closed at the end of the statement.  try (DataInputStream is = new DataInputStream(...)) { return is.readDouble(); } Simplified varargs method invocation - The Java compiler generates a warning at the declaration site of a varargs method or constructor with a non-reifiable varargs formal parameter. Java SE 7 introduced a compiler option -Xlint:varargs and the annotations @SafeVarargs and @SuppressWarnings({"unchecked", "varargs"}) to supress these warnings. On the library side there are new features that will be added to satisfy the language requirements above and some to improve the currently available set of APIs.  The library changes include: Collections update - New Collection, List, Set and Map, Iterable and Iteratator as well as implementations including Hashtable and Vector. Most of the work is too support generics String - New StringBuilder and CharSequence as well as a Stirng formatter. The javac compiler  now uses the the StringBuilder instead of String Buffer. Since StringBuilder is synchronized there is a performance increase which has necessitated the wahat String constructor works. Comparable interface - The comparable interface works with Collections, making it easier to reuse. Try with resources - Closeable and AutoCloseable Annotations - While support for Annotations is provided it will only be a compile time support. SuppressWarnings, Deprecated, Override NIO - There is a subset of NIO Buffer that have been in use on the of the graphics packages and needs to be pulled in and also support for NIO File IO subset. Platform extensibility via Service Providers (ServiceLoader) - ServiceLoader interface dos late bindings of interface to existing implementations. It helpe to package an interface and behavior of the implementation at a later point in time.Provider classes must have a zero-argument constructor so that they can be instantiated during loading. They are located and instantiated on demand and are identified via a provider-configuration file in the METAINF/services resource directory. This is a mechansim from Java SE. import com.XYZ.ServiceA; ServiceLoader<ServiceA> sl1= new ServiceLoader(ServiceA.class); Resources: META-INF/services/com.XYZ.ServiceA: ServiceAProvider1 ServiceAProvider2 ServiceAProvider3 META-INF/services/ServiceB: ServiceBProvider1 ServiceBProvider2 From JSR - I would rather use this list I think The Generic Connection Framework (GCF) was previously specified in a number of different JSRs including CLDC, MIDP, CDC 1.2, and JSR 197. JSR 360 represents a rare opportunity to consolidated and reintegrate parts that were duplicated in other specifications into a single specification, upgrade the APIs as well provide new functionality. The proposal is to specify a combined GCF specification that can be used with Java ME or Java SE and be backwards compatible with previous implementations. Because of size limitations as well as the complexity of the some features like InvokeDynamic and Unicode 6 will not be included. Additionally, any language or library changes in JDK 8 will be not be included. On the upside, with all the changes being made, backwards compatibility will still be maintained. JSR 360 is a major step forward for Java ME in terms of platform modernization, language alignment, and embedded support. If you're interested in following the progress of this JSR see the JSR's java.net project for details of the email lists, discussions groups.

    Read the article

  • Optimizing a 3D World Javascript Animation

    - by johnny
    Hi! I've recently come up with the idea to create a tag cloud like animation shaped like the earth. I've extracted the coastline coordinates from ngdc.noaa.gov and wrote a little script that displayed it in my browser. Now as you can imagine, the whole coastline consists of about 48919 points, which my script would individually render (each coordinate being represented by one span). Obviously no browser is capable of rendering this fluently - but it would be nice if I could render as much as let's say 200 spans (twice as much as now) on my old p4 2.8 Ghz (as a representative benchmark). Are there any javascript optimizations I could use in order to speed up the display of those spans? One 'coordinate': <div id="world_pixels"> <span id="wp_0" style="position:fixed; top:0px; left:0px; z-index:1; font-size:20px; cursor:pointer;cursor:hand;" onmouseover="magnify_world_pixel('wp_0');" onmouseout="shrink_world_pixel('wp_0');" onClick="set_askcue_bar('', 'new york')">new york</span> </div> The script: $(document).ready(function(){ world_pixels = $("#world_pixels span"); world_pixels.spin(); setInterval("world_pixels.spin()",1500); }); z = new Array(); $.fn.spin = function () { for(i=0; i<this.length; i++) { /*actual screen coordinates: x/y/z --> left/font-size/top 300/13/0 300/6/300 | / |/ 0/13/300 ----|---- 600/13/300 /| / | 300/20/300 300/13/600 */ /*scale font size*/ var resize_x = 1; /*scale width*/ var resize_y = 2.5; /*scale height*/ var resize_z = 2.5; var from_left = 300; var from_top = 20; /*actual math coordinates: 1 -1 | / |/ 1 ----|---- -1 /| / | 1 -1 */ //var get_element = document.getElementById(); //var font_size = parseInt(this.style.fontSize); var font_size = parseInt($(this[i]).css("font-size")); var left = parseInt($(this[i]).css("left")); if (coast_line_array[i][1]) { } else { var top = parseInt($(this[i]).css("top")); z[i] = from_top + (top - (300 * resize_z)) / (300 * resize_z); //global beacause it's used in other functions later on var top_new = from_top + Math.round(Math.cos(coast_line_array[i][2]/90*Math.PI) * (300 * resize_z) + (300 * resize_z)); $(this[i]).css("top", top_new); coast_line_array[i][3] = 1; } var x = resize_x * (font_size - 13) / 7; var y = from_left + (left- (300 * resize_y)) / (300 * resize_y); if (y >= 0) { this[i].phi = Math.acos(x/(Math.sqrt(x^2 + y^2))); } else { this[i].phi = 2*Math.PI - Math.acos(x/(Math.sqrt(x^2 + y^2))); i } this[i].theta = Math.acos(z[i]/Math.sqrt(x^2 + y^2 + z[i]^2)); var font_size_new = resize_x * Math.round(Math.sin(coast_line_array[i][4]/90*Math.PI) * Math.cos(coast_line_array[i][0]/180*Math.PI) * 7 + 13); var left_new = from_left + Math.round(Math.sin(coast_line_array[i][5]/90*Math.PI) * Math.sin(coast_line_array[i][0]/180*Math.PI) * (300 * resize_y) + (300 * resize_y)); //coast_line_array[i][6] = coast_line_array[i][7]+1; if ((coast_line_array[i][0] + 1) > 180) { coast_line_array[i][0] = -180; } else { coast_line_array[i][0] = coast_line_array[i][0] + 0.25; } $(this[i]).css("font-size", font_size_new); $(this[i]).css("left", left_new); } } resize_x = 1; function magnify_world_pixel(element) { $("#"+element).animate({ fontSize: resize_x*30+"px" }, { duration: 1000 }); } function shrink_world_pixel(element) { $("#"+element).animate({ fontSize: resize_x*6+"px" }, { duration: 1000 }); } I'd appreciate any suggestions to optimize my script, maybe there is even a totally different approach on how to go about this. The whole .js file which stores the array for all the coordinates is available on my page, the file is about 2.9 mb, so you might consider pulling the .zip for local testing: metaroulette.com/files/31218.zip metaroulette.com/files/31218.js P.S. the php I use to create the spans: <?php //$arbitrary_characters = array('a','b','c','ddsfsdfsdf','e','f','g','h','isdfsdffd','j','k','l','mfdgcvbcvbs','n','o','p','q','r','s','t','uasdfsdf','v','w','x','y','z','0','1','2','3','4','5','6','7','8','9',); $arbitrary_characters = array('cat','table','cool','deloitte','askcue','what','more','less','adjective','nice','clinton','mars','jupiter','testversion','beta','hilarious','lolcatz','funny','obama','president','nice','what','misplaced','category','people','religion','global','skyscraper','new york','dubai','helsinki','volcano','iceland','peter','telephone','internet', 'dialer', 'cord', 'movie', 'party', 'chris', 'guitar', 'bentley', 'ford', 'ferrari', 'etc', 'de facto'); for ($i=0; $i<96; $i++) { $arb_digits = rand (0,45); $arbitrary_character = $arbitrary_characters[$arb_digits]; //$arbitrary_character = "."; echo "<span id=\"wp_$i\" style=\"position:fixed; top:0px; left:0px; z-index:1; font-size:20px; cursor:pointer;cursor:hand;\" onmouseover=\"magnify_world_pixel('wp_$i');\" onmouseout=\"shrink_world_pixel('wp_$i');\" onClick=\"set_askcue_bar('', '$arbitrary_character')\">$arbitrary_character</span>\n"; } ?>

    Read the article

< Previous Page | 1 2 3 4