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kuberan_swe

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Posts posted by kuberan_swe

  1. Posted · Report reply

    Hey ppl you have missed out w3schools.comw3schools.com is the first site that comes to my mind to read about any web development technologies ranging from client-side scripting, server-side scripting, database. HTML/CSS, javascript, AJAX etc...Also w3schools.com is hosted on the Xisto.com server (please correct me if i am wrong)

  2. Posted · Report reply

    I have been developing PHP websites and do the PHP editing on a text editor and i have a really bad time when it comes to debugging my PHP code. When i use include_once('x.php') i should also get syntax suggestions when i use the methods that are available in the x.php file. I also want debugging support. People suggest a good PHP edito rfor me.

  3. Posted · Report reply

    int array[] = {1,2,3,4,5};

     

    a[4] is same as 4[a] because, of the below mentioned reason.

     

    Arrays are also pointers which has a constant address.

     

    int array[]

     

    is same as

     

    int const *ptr; //means that ptr is an integer pointer whose address is constant

     

    So to access the ith integer pointed by the pointer ptr,

     

    *(ptr + i) is used.

     

    while using array array is internally translated by the compiler as *(array + i).

    So even if you use the syntax i[array] it is again internally translated as *(i + array).

     

    By the commutative property of pointer addition, *(array + i) is same as *(i + array).

    So both the syntax array and i[array] are equivalent. :)

  4. Posted · Report reply

    If u wanted to know what are the phones that can read PDFs out of the box , they are very few phones. Whereas if u want to know what are the phones CAPABLE of reading PDFs, almost any JAVA enabled colour display phones can read PDFs with lots of third party J2ME applications available. But reading PDFs even on a very high end PDA phones is a big pain because of the small screen.

  5. Posted · Report reply

    I recently did a detailed research on the file systems of the windows mobile.The windows mobile platform was initially called the Pocket PC platform and later called the Windows Mobile platform from version Windows Mobile 5 onwards.Till Pocket PC there was no default permanent storage for the windows mobile devices. So whenever you do a cold boot of your device, all your data will vanish away and so you have to be careful not to drain all your battery in your Pocket PC powered mobile device. This shows that the windows mobile till Pocket PC 2003 had only a RAM memory and did not have any Flash ROM for storage. This decision was made by Microsoft keeping in mind that the mobile devices will be always required to be ON like the mobile phones we use today. So in this case the RAM(Random Access Memory) itself is used for both the Storage Space(user files and data - equivalent to Hard Disk in todays computer) and Program space(The memory that is used by the programs - equivalent to RAM in today's computer). Then later Microsoft decided to have a permanent flash file ROM storage for storing the Operating System and for Storage Space and so from Windows Mobile 5 onwards by default all mobile device had some ROM storage also. And from Windows Mobile 5 onwards the RAM can never be shared and used for storage space. So application developers who used files to store and read data have to address this issue. When there are frequent access to the file from the application, the same application runs slower in the Windows Mobile 5 or later than the Pocket PC 2003 device because in Pocket PC the files are stored in the storage space which is the RAM and in windows Mobile the files are stored in the Flash ROM memory. Since RAM devices are faster than Flash ROM, the application runs almost 10-20 times faster in windows mobile 5 or later than the Pocket PC 2003 which is an older version :-).There were two major reasons for switch from RAM to ROM for storage space1. RAM is a volatile memory and will get cleared when the power is OFF2. power consumption for RAM is dependent on the RAM size while the consumption by Flash ROM is independent of the memory size.point 2 means that for eg :- power for 64 MB RAM is twice that of 32 MB RAM whereas the power for 32 MB FLash ROM and 64 MB Flash ROM is the same.So application developers take this point when you choose a platform for your windows mobile solution.

  6. Posted · Report reply

    Internet is mobile is not 100% secure. Your IP address is accesible from other mobile phones connected to the Operator for internet. Do not think that other mobile phones cannot harm you if they have access to your machine via IP. All Operators allow their mobile phones to be connected to the Personal Computers or laptops and use the mobile phone as the modem. So in this case your IP address can be accesed from the computer and intrusion tools can be used. So it all depends on the security that your Operator provides. Today most of the mobile phones are assigned class D IP address whose subnet masks would be 255.255.255.255 so that you are not exposed to other mobile terminals that are connected to the internet using your operator's service.

  8. Posted · Report reply

    It depends on the phone model you use. Normal medium-end phones provide firmware updates which can be updated using their PC suite applications like Nokia PC suite for Nokia, ActiveSync for Windows Mobile phones.But very few high end PDA phones have the option of installing your own OS. There are also some distributions of embedded linux specially for Mobile phones like HP iPaq and HTC touch mobile phones.

  10. Posted · Report reply

    Hello, Everyone.
    I've been wanting to start up my own business for years and finally decided to take the plunge. I filed a resignation letter to the current company I'm connected with. However, the more I research, the more overwhelmed I get! I begin to see all the risks and I start finding myself backing out of my plan.

    I have always known that starting a business is risky. However, now more than ever, I find myself obsessing over the risks and getting overwhelmed by all the things I need to consider (e.g., should i sell online?, apparel or accessories?, etc). I have always wanted to open online store but given all the competition around me, I feel as though I'd do better selling something less risky if I go online. And, soon, my mind starts to spin out of control!

    I know in my heart I want my own business but I am just terrified. Financially, it's a huge risk. Has anyone else gone through this emotional rollercoaster? If so, can you please share how you overcame your fear? And, has anyone ever backed out of a plan and NOT regretted it? I am trying to figure out how to tell when it's right to back out versus move forward (though, like I said, in my heart I know I want this).

    And, does anyone have advice on how I can tackle all the questions surrounding the type of business to have, whether to go online or get a physical store, etc? Thanks so much, everyone!


    Hey its always very thrilling in life to take risks. When you want to do such things. You got to have a daring attitude to take any risks. you must also be ready to face the negative consequences if any. We Must also realize the real worth of taking the risk and also realize the big leap we would get if we overcome and execute this risk. In your case resignation of your existing job is an extra risk in addition to your risk of doing a new business.

  12. Posted · Report reply

    The future OS will be completely abstract for the end-users. Even today to do some advanced tasks, we require some technical knowldedge to troubleshoot. The future OS must completely encapsulate all the complexities and provide an easy interface to the user. The end-user must completely be unaware what a real computer OS is. It must use very advanced Graphical User Interface and 3D interfaces with very advanced user experience.

  15. Posted · Report reply

    Wow thatz an amazing bandwidth. But still this is only for communication between supercomputers. But definitely this bandwidth would nt ve been reached due to the power of the super computer. It must be due to the power of the transmission channel or transmission medium. So to achieve this bandwidth in the public internetwork or Internet(WWW) it is prerequisite to replace all the current transmission channels with the new technology. This must be a comercially unviable or might take a very long time to become true.

  19. Posted · Report reply

    Introduction

    The first step towards an understanding of why the study and knowledge of algorithms are so important is to define exactly what we mean by an algorithm. According to the popular algorithms textbook Introduction to Algorithms (Second Edition by Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein), "an algorithm is any well-defined computational procedure that takes some value, or set of values, as input and produces some value, or set of values as output." In other words, algorithms are like road maps for accomplishing a given, well-defined task. So, a chunk of code that calculates the terms of the Fibonacci sequence is an implementation of a particular algorithm. Even a simple function for adding two numbers is an algorithm in a sense, albeit a simple one.

     

    Some algorithms, like those that compute the Fibonacci sequences, are intuitive and may be innately embedded into our logical thinking and problem solving skills. However, for most of us, complex algorithms are best studied so we can use them as building blocks for more efficient logical problem solving in the future. In fact, you may be surprised to learn just how many complex algorithms people use every day when they check their e-mail or listen to music on their computers. This article will introduce some basic ideas related to the analysis of algorithms, and then put these into practice with a few examples illustrating why it is important to know about algorithms.

     

    Runtime Analysis

    One of the most important aspects of an algorithm is how fast it is. It is often easy to come up with an algorithm to solve a problem, but if the algorithm is too slow, it's back to the drawing board. Since the exact speed of an algorithm depends on where the algorithm is run, as well as the exact details of its implementation, computer scientists typically talk about the runtime relative to the size of the input. For example, if the input consists of N integers, an algorithm might have a runtime proportional to N2, represented as O(N2). This means that if you were to run an implementation of the algorithm on your computer with an input of size N, it would take C*N2 seconds, where C is some constant that doesn't change with the size of the input.

     

    However, the execution time of many complex algorithms can vary due to factors other than the size of the input. For example, a sorting algorithm may run much faster when given a set of integers that are already sorted than it would when given the same set of integers in a random order. As a result, you often hear people talk about the worst-case runtime, or the average-case runtime. The worst-case runtime is how long it would take for the algorithm to run if it were given the most insidious of all possible inputs. The average-case runtime is the average of how long it would take the algorithm to run if it were given all possible inputs. Of the two, the worst-case is often easier to reason about, and therefore is more frequently used as a benchmark for a given algorithm. The process of determining the worst-case and average-case runtimes for a given algorithm can be tricky, since it is usually impossible to run an algorithm on all possible inputs. There are many good online resources that can help you in estimating these values.

     

    Sorting

    Sorting provides a good example of an algorithm that is very frequently used by computer scientists. The simplest way to sort a group of items is to start by removing the smallest item from the group, and put it first. Then remove the next smallest, and put it next and so on. Unfortunately, this algorithm is O(N2), meaning that the amount of time it takes is proportional to the number of items squared. If you had to sort a billion things, this algorithm would take around 1018 operations. To put this in perspective, a desktop PC can do a little bit over 109 operations per second, and would take years to finish sorting a billion things this way.

     

    Luckily, there are a number of better algorithms (quicksort, heapsort and mergesort, for example) that have been devised over the years, many of which have a runtime of O(N * Log(N)). This brings the number of operations required to sort a billion items down to a reasonable number that even a cheap desktop could perform. Instead of a billion squared operations (1018) these algorithms require only about 10 billion operations (1010), a factor of 100 million faster.

     

    Shortest Path

    Algorithms for finding the shortest path from one point to another have been researched for years. Applications abound, but lets keep things simple by saying we want to find the shortest path from point A to point B in a city with just a few streets and intersections. There are quite a few different algorithms that have been developed to solve such problems, all with different benefits and drawbacks. Before we delve into them though, lets consider how long a naive algorithm - one that tries every conceivable option - would take to run. If the algorithm considered every possible path from A to B (that didn't go in circles), it would not finish in our lifetimes, even if A and B were both in a small town. The runtime of this algorithm is exponential in the size of the input, meaning that it is O(CN) for some C. Even for small values of C, CN becomes astronomical when N gets even moderately large.

     

    One of the fastest algorithms for solving this problem has a runtime of O(E*V*Log(V)), where E is the number of road segments, and V is the number of intersections. To put this in perspective, the algorithm would take about 2 seconds to find the shortest path in a city with 10,000 intersections, and 20,000 road segments (there are usually about 2 road segments per intersection). The algorithm, known as Djikstra's Algorithm, is fairly complex, and requires the use of a data structure known as a priority queue. In some applications, however, even this runtime is too slow (consider finding the shortest path from New York City to San Francisco - there are millions of intersections in the US), and programmers try to do better by using what are known as heuristics. A heuristic is an approximation of something that is relevant to the problem, and is often computed by an algorithm of its own. In the shortest path problem, for example, it is useful to know approximately how far a point is from the destination. Knowing this allows for the development of faster algorithms (such as A*, an algorithm that can sometimes run significantly faster than Djikstra's algorithm) and so programmers come up with heuristics to approximate this value. Doing so does not always improve the runtime of the algorithm in the worst case, but it does make the algorithm faster in most real-world applications.

     

    Approximate algorithms

    Sometimes, however, even the most advanced algorithm, with the most advanced heuristics, on the fastest computers is too slow. In this case, sacrifices must be made that relate to the correctness of the result. Rather than trying to get the shortest path, a programmer might be satisfied to find a path that is at most 10% longer than the shortest path.

     

    In fact, there are quite a few important problems for which the best-known algorithm that produces an optimal answer is insufficiently slow for most purposes. The most famous group of these problems is called NP, which stands for non-deterministic polynomial (don't worry about what that means). When a problem is said to be NP-complete or NP-hard, it mean no one knows a good way to solve them optimally. Furthermore, if someone did figure out an efficient algorithm for one NP-hard problem, that algorithm would be applicable to all NP-hard problems.

     

    A good example of an NP-hard problem is the famous traveling salesman problem. A salesman wants to visit N cities, and he knows how long it takes to get from each city to each other city. The question is "how fast can he visit all of the cities?" Since the fastest known algorithm for solving this problem is too slow - and many believe this will always be true - programmers look for sufficiently fast algorithms that give good, but not optimal solutions.

     

    Random Algorithms

    Yet another approach to some problems is to randomize an algorithm is some way. While doing so does not improve the algorithm in the worst case, it often makes very good algorithms in the average case. Quicksort is a good example of an algorithm where randomization is often used. In the worst case, quicksort sorts a group of items in O(N2), where N is the number of items. If randomization is incorporated into the algorithm, however, the chances of the worst case actually occurring become diminishingly small, and on average, quicksort has a runtime of O(N*Log(N)). Other algorithms guarantee a runtime of O(N*Log(N)), even in the worst case, but they are slower in the average case. Even though both algorithms have a runtime proportional to N*Log(N), quicksort has a smaller constant factor - that is it requires C*N*Log(N) operations, while other algorithms require more like 2*C*N*Log(N) operations.

     

    Another algorithm that uses random numbers finds the median of a group of numbers with an average runtime of O(N). This is a significant improvement over sorting the numbers and taking the middle one, which takes O(N*Log(N)). Furthermore, while deterministic (non-random) algorithms exist for finding the median with a runtime of O(N), the random algorithm is attractively simple, and often faster than the deterministic algorithms.

     

    The basic idea of the median algorithm is to pick one of the numbers in the group at random, and count how many of the numbers in the group are less than it. Lets say there are N numbers, and K of them are less than or equal to the number we picked at random. If K is less than half of N, then we know that the median is the (N/2-K) th number that is greater than the random number we picked, so we discard the K numbers less than or equal to the random number. Now, we want to find the (N/2-K) th smallest number, instead of the median. The algorithm is the same though, and we simply pick another number at random, and repeat the above steps.

     

    Compression

    Another class of algorithm deals with situations such as data compression. This type of algorithm does not have an expected output (like a sorting algorithm), but instead tries to optimize some other criteria. In the case of data compression, the algorithm (LZW, for instance) tries to make the data use as few bytes as possible, in such a way that it can be decompressed to its original form. In some cases, this type of algorithm will use the same techniques as other algorithms, resulting in output that is good, but potentially sub-optimal. JPG and MP3 compression, for example, both compress data in a way that makes the final result somewhat lower quality than the original, but they create much smaller files. MP3 compression does not retain every feature of the original song file, but it attempts to maintain enough of the details to capture most of the quality, while at the same time ensuring the significantly reduced file size that we all know and love. The JPG image file format follows the same principle, but the details are significantly different since the goal is image rather than audio compression.

     

    The Importance of Knowing Algorithms

    As a computer scientist, it is important to understand all of these types of algorithms so that one can use them properly. If you are working on an important piece of software, you will likely need to be able to estimate how fast it is going to run. Such an estimate will be less accurate without an understanding of runtime analysis. Furthermore, you need to understand the details of the algorithms involved so that you'll be able to predict if there are special cases in which the software won't work quickly, or if it will produce unacceptable results.

     

    Of course, there are often times when you'll run across a problem that has not been previously studied. In these cases, you have to come up with a new algorithm, or apply an old algorithm in a new way. The more you know about algorithms in this case, the better your chances are of finding a good way to solve the problem. In many cases, a new problem can be reduced to an old problem without too much effort, but you will need to have a fundamental understanding of the old problem in order to do this.

     

    As an example of this, lets consider what a switch does on the Internet. A switch has N cables plugged into it, and receives packets of data coming in from the cables. The switch has to first analyze the packets, and then send them back out on the correct cables. A switch, like a computer, is run by a clock with discrete steps - the packets are send out at discrete intervals, rather than continuously. In a fast switch, we want to send out as many packets as possible during each interval so they don't stack up and get dropped. The goal of the algorithm we want to develop is to send out as many packets as possible during each interval, and also to send them out so that the ones that arrived earlier get sent out earlier. In this case it turns out that an algorithm for a problem that is known as "stable matching" is directly applicable to our problem, though at first glance this relationship seems unlikely. Only through pre-existing algorithmic knowledge and understanding can such a relationship be discovered.

     

    More Real-world Examples

    Other examples of real-world problems with solutions requiring advanced algorithms abound. Almost everything that you do with a computer relies in some way on an algorithm that someone has worked very hard to figure out. Even the simplest application on a modern computer would not be possible without algorithms being utilized behind the scenes to manage memory and load data from the hard drive.

     

    There are dozens of applications of complicated algorithms, but I'm going to discuss two problems that require the same skills as some past TopCoder problems. The first is known as the maximum flow problem, and the second is related to dynamic programming, a technique that often solves seemingly impossible problems in blazing speed.

     

    Maximum Flow

    The maximum flow problem has to do with determining the best way to get some sort of stuff from one place to another, through a network of some sort. In more concrete terms, the problem first arose in relation to the rail networks of the Soviet Union, during the 1950's. The US wanted to know how quickly the Soviet Union could get supplies through its rail network to its satellite states in Eastern Europe.

     

    In addition, the US wanted to know which rails it could destroy most easily to cut off the satellite states from the rest of the Soviet Union. It turned out that these two problems were closely related, and that solving the max flow problem also solves the min cut problem of figuring out the cheapest way to cut off the Soviet Union from its satellites.

     

    The first efficient algorithm for finding the maximum flow was conceived by two Computer Scientists, named Ford and Fulkerson. The algorithm was subsequently named the Ford-Fulkerson algorithm, and is one of the more famous algorithms in computer science. In the last 50 years, a number of improvements have been made to the Ford-Fulkerson algorithm to make it faster, some of which are dauntingly complex.

     

    Since the problem was first posed, many additional applications have been discovered. The algorithm has obvious relevance to the Internet, where getting as much data as possible from one point to another is important. It also comes up in many business settings, and is an important part of operations research. For example, if you have N employees and N jobs that need to be done, but not every employee can do every job, the max flow algorithm will tell you how to assign your N employees to jobs in such a way that every job gets done, provided that's possible. Graduation, from SRM 200, is a good example of a TopCoder problem that lends itself to a solution using max flow.

     

    Sequence comparison

    Many coders go their entire careers without ever having to implement an algorithm that uses dynamic programming. However, dynamic programming pops up in a number of important algorithms. One algorithm that most programmers have probably used, even though they may not have known it, finds differences between two sequences. More specifically, it calculates the minimum number of insertions, deletions, and edits required to transform sequence A into sequence B.

     

    For example, lets consider two sequences of letters, "AABAA" and "AAAB". To transform the first sequence into the second, the simplest thing to do is delete the B in the middle, and change the final A into a B. This algorithm has many applications, including some DNA problems and plagiarism detection. However, the form in which many programmers use it is when comparing two versions of the same source code file. If the elements of the sequence are lines in the file, then this algorithm can tell a programmer which lines of code were removed, which ones were inserted, and which ones were modified to get from one version to the next.

     

    Without dynamic programming, we would have to consider a - you guessed it - exponential number of transformations to get from one sequence to the other. As it is, however, dynamic programming makes for an algorithm with a runtime of only O(N*M), where N and M are the numbers of elements in the two sequences.

     

    Conclusion

    The different algorithms that people study are as varied as the problems that they solve. However, chances are good that the problem you are trying to solve is similar to another problem in some respects. By developing a good understanding of a large range of algorithms, you will be able to choose the right one for a problem and apply it properly. Furthermore, solving problems like those found in TopCoder's competitions will help you to hone your skills in this respect. Many of the problems, though they may not seem realistic, require the same set of algorithmic knowledge that comes up every day in the real world.


    Notice from rvalkass:

    You're hosted - you should know the rules. Don't copy without QUOTE tags.


  21. Posted · Report reply

    Every computer newbie (almost) try to learn C/C++ as their first programming language...So it's very important to learn these languages very well,as it lays a foundation for you to develop programming skills and aids you in studying still more new programming languages in the future...If you have a sound knowledge of C/C++ you can study anyother programming language....C and C++ should be studied from the perspective of programming.You should not simply study about the language...You should spend time in implementing many concepts....Concepts are very important while studying a Programming language....Suppose you study about constructors and destructors in C++,first you should know why it is used...then what disadvantage it overcomes....Why did they think of a new concept like this and then how it is implemented in C++....After that you should also study if there is any disadvantage of that....WhenEver you study about a concept you relate that with a real world example...For example References are available in C++...It can be summed up in word as Pseudo-name....You can imagine as a nickname to a person....You call your friend as Vignesh....Let his nickname be Fatso....Whenever you refer Fatso it refers to vignesh.....Whenever Fatso gets hurt vignesh also gets hurt...References are used instead of pointers......So berfore moving to references you should have known the disadvantage of pointers....So building strong cocepts and fundas are very important....Ask Why for every concept...Like why initialisation when there is assignment....When you start to think like this your thought process gets streamlined....The next step is that Programming language is to program...It's not enough that you should study these concepts theoratically...You should also spend time on implementing these concepts....The best environment to study C/c++ is Linux....All Linux Distro's comes with GCC(GNU Compiler Collection)....It's the best compiler....There are many books on How to use GCC...Just Google GCC e-books....Have great fun in learning C/C++

  22. Posted · Report reply

    Data Structures and Algorithms are considered to be one of the important areas in computer science.They both are really important to all who want to pursue a degree in ComputerScience.They both co-exist together.So it's necessary for the student community to study these subjects very well.....The saddest part is that the teachers who handles this subject in many colleges are itself not well versed in this subject.I am a student of those sort...Don't judge your knowledge in that subject by the marks which you get in the semester...There are lot of resources available in the net for studying DataStructures and Algorithms.....There are many online video lectures by very great researchers at MIT...You can download those videos from http://forums.xisto.com/no_longer_exists/ ..... All the videos available there can be viewed only online...To download those videos you need to download HIDownload Download Manager...Just google it ...You can download the evaluation version...

    You can also download videos of another famous author Sartaj Sahni..... http://www.cise.ufl.edu/~sahni/cop3530/lectures.htm ....These videos are also downloadable with the HIDownload Manager....

    It's important yo have a very strong mathematics funda to study algorithms....You have to be thoriugh with Relations,Functions,Set Theory and should have done a course on Discrete Structures....Besides studying this subject theoratically you have to be good at Programming...It's very important to have very good programming skills and a sharp apptitude...

    There are many websites which conduct online algorithm competition...If you think you are good at Programming try competiting st http://forums.xisto.com/no_longer_exists/ should be either good at JAVA or should have known STL in C++....You can also code in VB...It's really to solve these problems here...If you could solve these problems,it means you are good at Data Structures and Algorithms....

  23. Posted · Report reply

    sony have been putting software ( called xcp ) on some of their audio cds.if you play these cds on your pc it automaticallyinstalls software on your pc.
    this software uses "rootkit" to hide the file from the user.

    here is a list of cds with the xcp software.

    the software limits listeners' ability to copy the music onto their computers, and locks copied files so they cannot be freely distributed over the Internet.
    it also also secretly communicates with sony over the internet when listeners play the discs.
    hackers must have looked at this and redesigned the xcp code to communicate other secret data to the hacker from the users pc , like bank details etc.
    it looks as though sony and first 4 internet ( the software writers ) might be facing a big law suit over this one.

    is there any motorola virus?

  25. Posted · Report reply

    Wup
    i just finished sweeping my pc with spysweeper, cause a spyware totally infected my pc, the damm thing disabled my wallaper, i could only change a color, plus damaged norton, change my home page, and installed a spysherrif program that was supposed to removed the spyware, of course you need to buy it, plus installed a thing that every3 minutes show me a message in the minitray(righ down corner), like if it was from windows, that tells me that my pc is infected.

    SpySweeper apparently removed all the thing, but i still cant change my wallpaper, someone please hellp me, what can i do?

    please

    cya

    dont worry frnd i will help u!!
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