After introducing floating point numbers and sharing a function to convert a floating point number to its binary representation in the first two posts of this series, I would like to provide a function that converts a binary string to a floating point number. In addition. I will convert between different types of binary representations and discuss their merits.
In the last post on floating point numbers, I presented a brief overview of floating point numbers, introduced several Matlab functions that provide information about floats (realmin, realmax, and eps), and explored the workings of eps. In this post, I would like to introduce a function that I wrote in Matlab to convert a floating point number to its binary representation and use that function to explain the floating-point representations of ten different numbers.
Floating point numbers are utilized in most calculations performed in Matlab and other programming languages. Often misunderstood, floating-point arithmetic can cause many confounding problems in addition, subtraction, multiplication, division, comparison, and other types of calculations. In this series of posts, I would like to describe the basics of floating point numbers that conform to IEEE Standard 754 , introduce several Matlab functions that provide information about floating point numbers, provide a pair of functions that convert between the decimal and binary floating point representations, present some examples of how to view floating point numbers in different formats, and demonstrate how to handle some common problems with their arithmetic. In this post, I will give a brief overview of floating point numbers, introduce several Matlab functions that handle floats, and delve into detail of one of these functions named eps.
We just launched our new Forums page on Matlab Geeks. Because of the large quantity of questions that we receive through email and on our posts, we thought that it would be more productive for you (and easier for us) if these questions saw more public exposure. If you have a question about Matlab, please post it on our forums. Additionally, if you know the answer to a question that someone has posted, please answer it. We hope to build a community of Matlab users on this site which will be mutually beneficial for all of those involved. To participate in the forums, just click this link or the “Forums” link under the header.
In the last post, many moons ago, I introduced the 2-D FFT and discussed the magnitude and phase components of the spatial Fourier domain. In the next few posts, I would like to describe a concrete application of the 2-D FFT, namely blurring. Blurring and deblurring are useful image processing techniques that can be used in a variety of fields. I will explain what blur is mathematically and how it is performed artificially. In future posts, I’ll go into more depth about what happens in the spatial domain, different types of blur, and some current deblurring technology.