Karmic Koala was released into the wild last Thursday, so naturally I was going to upgrade my desktop installations at home. In this past I had been doing fresh installs whenever there was a new release, since there might be some issues with the new Ext4 file system, I decided to upgrade my current 9.04 installation in place and perform a fresh installation when the Ext4 file system stabilizes.
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In a heterogeneous computing environment (e.g. Linux and Windows), it is necessary to use Samba to share files between the Linux systems and Windows systems. Due to the security enhancements in Windows XP SP2 and above however, the administrative share access (e.g. \\{computer name}\c$ is disabled by default.
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Prime number theorem dictates the asymptotic behavior of prime number distributions. In layman terms, the distance between prime numbers increases at a logarithmic pace. This gives the familiar logarithm figure.
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About two years ago, I blogged about why hybrids were not saving us any energy by taking into consideration the amount of energy needed to produce the vast battery packs and the inherent low efficiencies in battery technologies.
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I was writing an ASP.Net 2.0 web application using Windows authentication (since I needed to capture users’ credentials). The web application saves data into a MS SQL 2005 database using a SQL server account. While developing this application, I ran into a very strange issue.
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I am running Ubuntu 9.04 64bit. One thing I noticed is that the integrated debugger is behaving quite flaky for the included Monodevelop 2.0 package. After some research, it turned out that a lot of people are having similar issues.
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Even though multi-core processors are almost ubiquitous nowadays, applications are slow to catch up. Of course, one could always re-write the applications in order to take the full advantages in a multi-core environment. But it is not an easy undertaking.
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It appeared that Google Finance was down for at least half an hour (from before 9:30 EST till after 10:00 EST) today. I captured this screen-shot during the outage:
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In my previous three articles (1,2,3) I discussed how to use Canny edge detection and Hough transform to identify blur images. Here I will show some results from the algorithm discussed before.
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I will continue where I left off in my previous post. After performing Hough transform, and extracted the longest sections of lines for each corresponding Hough line detected, we will need to calculate the gradients of the image pixels luminance around the line sections.
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In my previous post, I briefly discussed the rationale behind automated blur detection in digital imagery and did an overview of an algorithm that could be used to detect blur images. Here I will show some implementation details along with some C++ code snippets.
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It is often necessary to identify and classify images based on their clarities. For instance, it is desirable for an automated process to locate blurred images within a large digitized image library and then automatically sharpen the blurred images via inverse filtering or blind deconvolution. In the following series of articles, I will discuss a practical method in detecting blur images using Hough Transform.
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