If you’ve taken any digital signal processing classes at a college or university, you’ve probably been exposed to MATLAB. However, if you want to do your own work, you might think about Linux and one of the many scientific computing applications available for it.
[David Duarte] recently published a three-part tutorial on using Octave to do scientific audio processing. The first part covers basic reading, writing, and playing of audio files. Part two covers synthesis of signals, plotting, and some basic transformations. Modulation is the topic of the third part. If you prefer your tutorials on video, you can check out the video below.
We’ve talked about MATLAB before in the context of message cracking. Then again, some of the best signal processing is done by humans. If you don’t like Octave, you might try Scilab, another Linux package that is similar. There’s also Freemat, Sage, and Spyder. Of course, you can also run MATLAB under Linux.
If you do any electronics work–especially digital signal processing–you probably know that any signal can be decomposed into a bunch of sine waves. Conversely, you can generate any signal by adding up a bunch of sine waves. For example, consider a square wave. A square wave of frequency F can be made with a sine wave of frequency F along with all of its odd harmonics (that is, 3F, 5F, 7F, etc.). Of course, to get a perfect square wave, you need an infinite number of odd harmonics, but in practice only a few will do the job.
Like a lot of abstract concepts, it is easy to understand the basic premise and you could look up any of the mathematical algorithms that can take a signal and perform a Fourier transform on it. But can you visualize why the transform works the way it does? If you can’t (or even if you can), you should check out [Mehmet’s] MATLAB visualization of harmonic circles. If you don’t have MATLAB yourself, you can always check out the video (see below).
Continue reading “Visualizing the Fourier Transform”
Hackers everywhere are having a lot of fun with SDR – as is obvious from the amount of related posts here on Hackaday. And why not, the hardware is cheap and easily available. There are all kinds of software tools you can use to dig in and explore, such as SDR# , Audacity, HDSDR and so on. [illias] has been following SDR projects for a while, which piqued his interest enough for him to start playing with it. He didn’t have any real project in mind so he focused on studying the methodology and the tools available for analyzing 433MHz RF transmission. He describes the process of using MATLAB to recover the transmissions being received by the SDR
He started off by studying the existing tools available to uncover the details of the protocol. The test rig uses an Arduino UNO with the rc-switch library to transmit via a common and inexpensive 433MHz module. SDR# is used to record the transmissions and Audacity allows [illias] to visualize the resulting .wav files. But the really interesting part is where he documents the signal analysis using MATLAB.
He used the RTL-SDR package in conjunction with the Communications System Toolbox to perform spectrum analysis, noise filtering and envelope extraction. MATLAB may not be the easiest to work with, nor the cheapest, but its powerful features and the fact that it can easily read data coming from the SDR makes it an interesting tool. For the full skinny on what this SDR thing is all about, check out Why you should care about Software Defined Radio.
Whether you are trying to drop some fat or build some muscle, it’s important to track progress. It’s easy enough to track your weight, but weight doesn’t tell the whole story. You might be burning fat but also building muscle, which can make it appear as though you aren’t losing weight at all. A more useful number is body fat percentage. Students from Cornell have developed their own version of an electrical body fat analyzer to help track body fat percentage.
Fat free body mass contains mostly water, whereas fat contains very little water. This means that if you were to pass an electrical current through a body, the overall bioelectrical impedance will vary depending on how much fat or water there is. This isn’t a perfect system, but it can give a rough approximation in a relatively easy way.
The students’ system places an electrode on one hand and another on the opposite foot. This provides the longest electrical path possible in the human body to allow for the most accurate measurement possible. An ATMega1284P is used to generate a 50kHz square wave signal. This signal is opto-isolated for user safety. Another stage of the circuit then uses this source signal to generate a 10ua current source at 50kHz. This is passed through a human body and fed back to the microcontroller for analysis.
The voltage reading is sent to a MATLAB script via serial. The user must also enter in their weight and age. The MATLAB script uses these numbers combined with the voltage reading to estimate the body fat percentage. In order to calibrate the system, the students measured the body fat of 12 of their peers using body fat calipers. They admit that their sample size is too small. All of the sample subjects are about 21 years old and have a similar body fat percentage. This means that their system is currently very accurate for people in this range, but likely less accurate for anyone else. Continue reading “DIY Electrical Body Fat Analyzer”
Hallo iedereen! All the way from the Netherlands comes this fairly unique CNC milling machine built by a handful of Mechanical Engineering students over at the Delft University of Technology. These guys only had one week to build the mill in order to fulfill a requirement of their Mechtronics class. Unfortunately, directly after showing the machine worked, it had to be disassembled.
If the frame looks a little toy-ish, it’s because it is. This particular system is called Fischertechnik and the main support beams are similar to that of aluminum extrusion (ex 80/20, Misumi) except that it is made from nylon. Notice the extremely long cutting bit and comparatively abnormal large Z axis travel capability. What this system lacks in rigidity is made up by being able to carve a very 3D shape with steep sides without the machine hitting the work piece. The loss of rigidity was totally acceptable since the team was only planning on cutting foam and the project’s purpose was to learn mechanics and automation.
Continue reading “Fischertechnik CNC Machine Looks Innocent Whilst Cutting Your Face”
Learning how magnets and magnetic fields work is one thing, but actually being able to measure and see a magnetic field is another thing entirely! [Stanley’s] latest project uses a magnetometer attached to a robotic arm with 3 degrees of freedom to measure magnetic fields.
Using servos and aluminium mounting hardware purchased from eBay, [Stanley] build a simple robot arm. He then hooked an HMC5883L magnetometer to the robotic arm. [Stanley] used an Atmega32u4 and the LUFA USB library to interface with this sensor since it has a high data rate. For those of you unfamiliar with LUFA, it is a Lightweight USB Framework for AVRs (formerly known as MyUSB). The results were plotted in MATLAB (Octave is free MATLAB alternative), a very powerful mathematical based scripting language. The plots almost perfectly match the field patterns learned in introductory classes on magnetism. Be sure to watching the robot arm take the measurements in the video after the break, it is very cool!
[Stanley] has graciously provided both the AVR code and the MATLAB script for his project at the end of his write-up. It would be very cool to see what other sensors could be used in this fashion! What other natural phenomena would be interesting to map in three dimensions?
Continue reading “Measuring Magnetic Fields with a Robotic Arm”
[Jeremy Blum], [Jason Wright], and [Sam Sinensky] combined forces for twenty-four hours to automate how the entertainment and lighting works at their hackerspace. They commandeered the whiteboard and used an already present webcam as part of their project. You can see the black tokens which can be moved around the blue tape outline to actuate the controls.
MATLAB is fed an image from the webcam which monitors the space. Frames are received once every second and parsed for changes in the tokens. There are small black squares which either skip to the next track of music or affect pause/play. Simply move them off of their designated spot and the image processing does the rest. This goes for the volume slider as well. We think the huge token for the lights is to ensure that the camera can sense a change in a darkened room.
If image processing isn’t your thing you can still control your audio entertainment with a frickin’ laser.
Continue reading “24-hour hackathon project adds object-based automation to hackerspace”