Numerous users are reporting that the computer does not wake up from sleep; hangs and doesn't respond after updating to Windows 8.1. The only way out is the laptop has to be rebooted. We've found multiple solutions to this problem and will give you five possible solutions which will hopefully get rid of this sloppy problem.
Microsoft documentation has worried a lot of Windows 8 users because it says that its not possible to upgrade from Windows 8.1 Preview to Windows 8.1 RTM. But wallop the worries now because we will tell you the other way around to safely upgrade to Windows 8.1 RTM from Windows 8.1 Preview without loosing your files, apps and data.
Lets get started, follow these steps:
1) Open the "Windows_8.1_EN-US_x64.ISO" or any other Windows 8.1 RTM ISO with any famous ISO explorer like Daemon Tools, PowerISO, Ultra ISO
2) Browse through the folder \sources\
3) Open "cvversion.ini" file using Notepad or any other text editor
Nirvana of Windows 8 is compelling most of the Windows OS users to have a spin for Windows 8.1 but many are worried about loosing data, files and most importantly the apps installed. Though Microsoft documentation says that you cannot upgrade to "Windows 8.1” without loosing your apps as the update only gives “Keep personal files only” option but there is a way around.
In principle upgrading Windows 8 to 8.1 without loosing app data, requires that the installation's language must match your system's default UI language and a specific Windows 8 edition can only be upgraded to its equivalent or higher edition.
An average computer programmer gets paid with around $71,380 annually and reports suggest that the rate is elevating with a ballooning speed because of the augmenting needs of software in the industry. A deft command on a single famous programming language can bulk your pockets quickly. Speaking of programming languages, C++ is one of the top paid skills in the corporate industry. So if you are on a lookout for a one stop place to know some of the best ways of learning C++ at home, in a short time; you have landed the to the right page. We are going to give some of the top ways through which you can get on the right track and start racking up bucks. So let’s get started straightaway with the most efficient way to learn the florid skill.
Who doesn't use photo editing tools? Almost everyone of us has to tweak the not-so-good camera results or lightning. Photo Editing software are handy when it comes to little fixes like dealing with red eyes, brightness and fine tuning photos to make them look better. Photoshop still rocks the market but for an average users, you don't need that expensive and fancy photo editor as there are a plethora of other convenient, free photo editing tools that can help you do the same. We've reviewed the market for some of the top image-editing programs and decided to put them all head-to-head.
An opaque data type is a type whose implementation is hidden from the user. The only way to use opaque data type is via an abstract pointer interface exposed in the API. A famous example is the FILE data type in the C standard I/O library.
The advantage of opaque data type is that user does not need to worry about the implementation details and data members of the type. All that is needed by the user, are the interface functions to manipulate the type. Even if the internal details of the data type are changed, the user does not need to change the code using it. It is also useful in case when different vendors of a library have different implementation of the same data type, but they appear same to the user.
Many times you would have come across a problem, in which some data has to be saved in a file repeatedly, say in a loop or something. If the data is different for each iteration, it would be desirable to create a new file for each different data. Let us take a case, for instance we are iterating over a video file, and we want to write each frame data in different text files like frame0.txt, frame1.txt, frame2.txt…. and so on. The file name would change in each iteration, so how can we change the name of file serially, without involving any user effort.
The purpose of CUDA applications is to speed up computation on GPUs. Coding on CUDA naturally involves timing the application to measure the speedup over CPU counterpart.
To profile a CUDA application, you can either use tools such as NVIDIA NSight and Visual Profiler or you can use timing functions; in this article we will use the later approach. CUDA runtime API calls and kernel launches can be timed accurately using CUDA events available in the toolkit. For stuff other than device code, we use the host timers. The following example demonstrated how CUDA runtime calls can be timed using events.
If you are used to the programming GPU applications using CUDA runtime API, and have a clear concept of CUDA architecture, porting the application to OpenCL might be a little bit confusing but not difficult.
To exactly demonstrate the difference between CUDA runtime and OpenCL, a downloadable example of vector addition is attached at the end of the tutorial.
The concept of threads, blocks, and kernels is the same, one of the major differences, however, is how the kernel is launched and the number of API calls required to do so. OpenCL is more or less same as the CUDA driver API, but in this article, we will show how a CUDA runtime equivalent OpenCL program can be written. Following are the terminologies and API calls used in a CUDA runtime application, along with their OpenCL counterparts.
I'm sure you have come across C/C++ projects that contain thousands of lines of code, split into many source and header files. Not 5 or 10, we are talking about hundreds or even thousands of files. One of the productivity hits in a software project is the time required to compile these source files. Depending on the amount of code, the time may range from seconds to hours. While some cunning developers may take advantage of this (see the comic below), others may find it frustrating to wait for the compilation to finish.
Many CUDA beginners learn how to write, test and profile CUDA kernels, but most of the times they use randomly generated input data. When it comes to actual real world problem, they are confused how to acquire the input data and process it on the GPU.
In this tutorial, I will show you how to acquire input images on host using OpenCV, then pass that input to CUDA kernel for processing. For this specific tutorial, I will write a basic CUDA function to convert the input color image to gray image. I assume that user has CUDA Toolkit and OpenCV installed in his system. Here’s a good tutorial on setting up OpenCV on your machine with Visual Studio.
We start by writing a CUDA kernel for converting an input BGR image to a gray scale image. Your CUDA kernel will look something like this:
OpenCV is a vast Image processing and computer vision library that offers ease and flexibility for programmers. A good thing about the library is that it's neatly divided into different modules based on functionality ranging from basic image processing to advanced computer vision algorithms. As of current version (2.4.6), there are a total of 21 modules in the package.
While the modular approach is good to keep the dependencies minimal, some programmers also find it frustrating to link each of the required module separately with the project. When shipping an end-user-application built with OpenCV, all the libraries needed have to be shipped and some developers are not satisfied with this clutter of shared libraries. This is where OpenCV world module comes in.
This is a step by step guide on running a test program that performs simple color conversion via OpenCV GPU module using Microsoft Visual Studio. You can download the sample source file at the end of this tutorial.
Create a new Microsoft Visual C++ project. Add the source file (available at the end of the tutorial) and add it to the project. Currently, the code will not compile, because the paths to OpenCV header and library files are unknown to the compiler. Let's first set environment variables for OpenCV folders in our system for ease of use
Setting Environment Variables
To create environment variables, go to System Properties. In the Advanced tab, click on Environment Variables. Click on New in the User Variables section. Set Variable Name to CV_INC. In the Variable Value box, paste the path of the OpenCV header files directory ( in our case it's D:\opencv\opencv_build\include). Similarly create environment variable for lib and bin folders as:
This tutorial will guide you through how to build and use gpu module of OpenCV version 2.4.6 with Microsoft Visual Studio. I have attached a sample source file for Microsoft Visual C++ that simply performs color conversion on GPU using OpenCV. You can download the source file at the end of this post.
OpenCV's GPU module is a set of classes and functions to utilize computational capabilities of NVIDIA's CUDA capable GPUs. To do so we can either use the pre-built binaries shipped with OpenCV, or we can compile it from scratch. In this tutorial, we will use the latter approach. To compile OpenCV with GPU support, we need A C++ compiler (Microsoft Visual C++, MinGW etc), CUDA toolkit and CMake. For today's tutorial we're using Microsoft Visual Studio 2010, CUDA Toolkit 5.0, and CMake 2.8.11 and Windows 7 as platform.
Probably the most useful, sizzling and much anticipated update by Android has been released by Google and it is now available at the Store. Yes! We are talking about Android Device Manager, having a plethora of new slants and most interesting of them all is the lost phone tracking feature. If you have unfortunately lost your phone and looking to trail it, You can track the current phone location by logging in with your Gmail account anywhere. Yes ! I mean anywhere, on PC, laptop or some other Android phone. Android Device Manager takes you to what looks like a Google Maps interface and it will show you exactly where your phone is going philanderer and junket.