How to install OpenCV in Ubuntu 14.04

     Today, I am going to discuss installation of one of the very crucial and important software tool OpenCV {Open Computer Vision}. Installation of OpenCV on Windows is very easy as compared to Linux and that too on Ubuntu 14.04.

How to Install OpenCV in Fedora 20

     Today, I am going to discuss installation of one of the very crucial and important software tool OpenCV {Open Computer Vision}. Installation of OpenCV on Windows is very easy as compared to Linux and that too on Fedora 20.

     Lets start the discussion. First of all, do remember that Fedora 20 supports 2.4.* version of OpenCV. So it may happen that when you try to install OpenCV on your Machine you may get latest version of OpenCV.

Date-Time Server and Client program in JAVA

Server-Client Visualization

Networking is very powerful area which allows different machines to get connected. Human life became very easy through the networking concepts. Lets start the discussion of the same with understanding basics of the Networking & How to do it in JAVA.

Networking allows communication between 2 or more computers. The diagram shown here is just for example to get an idea of how networking is done. Only one Server & 8 Clients are shown in the diagram. Clients makes requests to Server & Server serves those requests.

Bouncing Ball Program in JAVA using Multithreading and Applet

Bouncing Balls

          In the last post we learned what is & how to use Multithreading in JAVA. The next ideal task to learn is how to use multithreading for Animation or how to use Multithreading in Applets. Today we will discuss program of Bouncing Balls in Java by using Multithreading and Applet.  I am going have a button to start the Balls from one point in the Applet window. We need Applet class, AWT class to perform this particular task.

Multithreading in JAVA using Runnable Interface

                       After learning How to perform Multithreading in Java, the next step is to learn the same by using Runnable Interface. As explained in earlier post (Multithreading in Java) a class needs to extend Thread Class to achieve Multithreading. But when we want to do the same by using Runnable Interface, we must understand that Runnable is a interface not a class.

Multithreading in JAVA

                     JAVA is a very powerful language which supports many features. Multi-threading & Graphics programming are only few of them. Today, I am going to discuss a very simple program to explain both these concepts. Also I will share code with you on this post as well as on my GitHub account. Lets start the discussion with Multi-threading. Multi-treading allows programmer to use computer's resources in a very efficient manner. A Multi-threaded program is one which contains 2 or more parts that can run concurrently. Each part of such program is called a Thread & it defined the separate path for execution. Now a days all modern Operating Systems supports Multitasking. Multi-tasking is basically divided into two types: Process Based &  Thread Based.

How To Install CUDA on Fedora 20

          Today, I am going to discuss about the installation of CUDA 6.5 Toolkit & Drivers on Fedora 20. From last few days a number of query regarding this problems are being asked. And Yes, Installation of CUDA Toolkit on Fedora 20 (Heisenbug) release is really a tough and challenging task. There are number of problems encountered during the installation of CUDA Toolkit.

Vector Arithmetic Operations in CUDA

After learning how to perform addition of two numbers, the next step is to learn how to perform addition and subtraction of two Vectors. The only difference between these two programs is the memory required to store the two Vectors. Also the kernel functions to perform the task.

Addition of two numbers in CUDA: A Simple Approach

Addition is the very Basic & one of the arithmetic operation. To perform it in C language is also a very easy and simple task. In this post, we will convert the C language code into a CUDA code. The steps to remember for writing a CUDA code for any program are as follows:

Two Dimensional (2D) Image Convolution in CUDA by Shared & Constant Memory: An Optimized way

          After learning the concept of two dimension (2D) Convolution and its implementation in C language; the next step is to learn to optimize it. As Convolution is one of the most Compute Intensive task in Image Processing, it is always better to save time required for it. So, today I am going to share a technique to optimize the Convolution process by using CUDA. Here we will use Shared Memory and Constant Memory resources available in CUDA to get fastest implementation of Convolution.

Two Dimensional (2D) Image Convolution : A Basic Approch

Image Convolution is a very basic operation in the field of Image Processing. It is required in many algorithms in Image Processing. Also it is very compute intensive task as it involves operation with pixels.

What is CUDA?

CUDA (Compute Unified Device Architecture) is invented by NVIDIA Corporation Pvt. Ltd. First release of CUDA was in the year mid 2007.
CUDA is a Parallel Computing Platform one of its first kind which enables General Purpose Computing (widely known as GPGPU) in a very efficient and easy way. CUDA enables user to exploit the computing power of Graphics Processing Unit (GPU) present in underlying hardware. Before the inception of CUDA, one had to use DirectX or OpenGL

One Dimensional (1D) Image Convolution in CUDA by using TILES

          Tiled algorithms are a special case in CUDA as we can Optimize the algorithm implementation, by using this strategy. It is very useful when we want to achieve maximum usage of available GPU hardware, present in the system. It has several advantages over naive CUDA implementations such as improved Memory bandwidth, reduced memory read/write operations,etc. Tiled implementation uses Shared memory available in GPU hardware which is much faster as compared to Global Memory in GPU. In any naive CUDA implementation only Global memory is used for all read and write operations. So, if these memory (read/write) operations are huge in number then the more time is wasted only in transferring the data which results in low/poor performance.

One Dimensional (1D) Image Convolution in CUDA

          First let me tell you that if you are reading this page then you are already looking for some advance stuff in today's technology as both CUDA & Image Processing are highly demanding as well as advanced technologies. On this blog we will be mainly focusing on use of CUDA(Compute Unified Device Architecture) technology to improve Image Processing Algorithms.The improvement is mainly with respective to Time & Space required to Image Processing Algorithm. You may refer to concern links provided to get more information about both the fields.