# Tag Archives: signal processing

## Back to the classes

Last week I got an enjoyable novelty in my mailbox: the acceptance letter of Masters application. I applied to Postgraduate Electrical Engineering Program at UFRJ (Federal University of Rio de Janeiro) in Electronics/Signal Processing area.

I am very glad to be accepted for many reasons:

• First of all, I am thirsty of breathing the academical air. I stopped breathing it since I interrupted the masters I was taking at UFPE (Federal University of Pernambuco – also in Electrical Engineering Program in Signal Processing area. I am sick and tired of only reading papers. I do want to write them too!
• UFRJ’s Electrical Engineering Program owns a fantastic reputation in the academia and industry as well. It is on top of Brazilian Ministry of Education ranking.
• The diversity and quality of the faculty members is awesome: most of them with a huge amount of publications, an intense research activity and some really active members of IEEE.
• Besides all of this, (at least here in Brazil) Masters is a necessary step to the Doctorate studies.

I am really motivated to get started, but I need to wait until march.

## Mashing up the MATLAB Central Blogs

Although there are many sites, blogs and other resources about MATLAB on the web, one is worth be the frequently accessed by users: MATLAB Central, an official exchange area for users community. Inside, there are 4 blogs I like:

• Loren on the Art of MATLAB: Loren Shure works on design of the MATLAB language at The MathWorks. She writes here about once a week on MATLAB programming and related topics.
• Doug’s Pick of the Week: Doug is an Application Engineer at The MathWorks. A MATLAB user since 1994, he gets paid to live, eat, and breathe MATLAB! Each week, he highlights a submission from the File Exchange that he finds useful or interesting.
• Steve on Image Processing: Steve Eddins manages the Image & Geospatial development team at The MathWorks and coauthored Digital Image Processing Using MATLAB. He writes here about image processing concepts, algorithm implementations, and MATLAB.
• Inside the MATLAB Desktop: The MATLAB Desktop team, comprised of eight developers, builds the main user interface for MATLAB, including the Command Window, the Editor, and the Current Directory browser.

Using pipes again, I decided to join the four feeds (what also includes some podcasts) into one:

http://pipes.yahoo.com/dilsonlira/matlab_central_blogs

To get this feed into your mobile phone, I also created a WidSets widget:

## IEEE Communications Digest Mashup

I believe that some facilities brought by so called Web 2.0 should not be ignored. Who usually reads news in different sources has reasons enough to become a mashup user. My favorite mashup tool is still a beta release but works fine so far: Yahoo! Pipes – there I can mix different feed sources into one or exactly the opposite: split one source into many other subfeeds. More sophisticated ideas (like geocode a feed into a map, filter and even translate the feeds) can also be implemented. Everything is done graphically, in a very intuitive way, connecting blocks and pipes – that’s why the name.

Certainly this short description of Pipes can be useful, but it’s not the main idea of this post: one of the first pipes I’ve created is a IEEE Communications Digest (for those who are not familiarized with, IEEE means Institute of Electrical and Electronics Engineers and is the biggest technical professional organization in the world). This mashup is a short compilation of some IEEE publications feeds in Communications area. It aggregates:

If you are interested, check it out: http://pipes.yahoo.com/dilsonlira/ieee_communications_digest. To have access to the texts pointed by the feeds, an IEEE member login is required. Otherwise, only the feed titles can be read.

## A Nyquist–Shannon Sampling Theorem misunderstanding

The Nyquist-Shannon Sampling Theorem estabilishes that a sampling process of a continuous-time $x(t)$ signal is perfectally reversible if the sampling frequency is at least the Nyquist rate (the double of the sampled signal bandwidth). However, I’ve been realizing some really experienced engineers have a lightly distorced interpretation of the theorem: to believe that the quality of a reconstructed signal increases with the sampling frequency and the frequencies much higher than Nyquist rate are needed to perform a satisfatory reconstruction.

A strongly possible reason to this erroneous understanding is the thought that the signal reconstruction is made by a linear interpolation of the samples. From that perspective, a higher rate sampling really makes the reconstructed signal closer to its original shape. But, the point is that the signal is not simply reconstructed by an interpolation. Instead, the process is compose by two stages:

• A train of impulses is generated, which one multiplicated by it respective sample value;

$x_i(t)=\sum\limits_{n=-\infty}^{\infty}x[n]\delta(t-nT)$

• Then, the resulting signal passes into a low-pass filter to discard all frequencies above the original signal bandwidth.

It is important to know that in practical terms, the reconstruction is not perfect because the theorem is only valid for bandlimited signals – requiring the signal to be prefiltered what makes a distortion on it and the low-pass filter is unrealizable because it’s response is not causal. So, although the mathematical behavior of the theorem is not achived phisically, the constraint is not the sampling rate, since the Nyquist rate be respected.