This is our ELEC 301 Project for the Fall 2004 semester. We implemented a uniform linear array of six microphones. We then sampled the data and analyzed it in LabVIEW in order to listen in one direction. We also explored listening for a particular frequency and its direction.
A discussion of the project results and conclusion. Using the second laptop, we positioned a 1000 Hz signal at about 40 degrees and a 1600Hz signal at about -25 degrees relative to the axis perpendicular to our array. Both signals were on simultaneously and at equal volume. As you can see from the spectrum of the output of our program, changing the dial to tune to the array to different directions results in the expected behavior.
Linear algebra, vector space methods, and functional analysis are a powerful setting for many topics in engineering, science (including social sciences), and business. This collection starts with the simple idea of a matrix times a vector and develops tools and interpretations for many signal processing and system analysis and design methods.
Subject:
Business, Mathematics and Statistics, Science and Technology, Social Sciences
Java Digital Signal Processing (J-DSP) is an object-oriented visual programming tool that enables users to establish and run online signal processing simulations, and visualize Internet based interactive demos. It has been used in laboratories involving b
We found that our identification system was able to tolerate an extremely high level of noise. For an SNR above 5 we were able to get the heart rate within two beats per minute one-hundred percent of the time and when it was above three we were able to identify the signal types with one-hundred percent accuracy.
We will now look at aliasing and its effect on the sampled signal. As you know, aliasing exists whenever signal frequencies greater than Fs/2 are sampled using a sampling frequency of Fs. To eliminate aliasing, most sound cards and DSP boards have some sort of built-in analog anti-aliasing filter that removes all input signals greater than a certain frequency prior to sampling. It is important to remember that anti-aliasing filters must do the filtering prior to sampling ? otherwise, the high-frequency signals would have already aliased to lower frequencies by the sampling process.
NI LabVIEW DSP is a block diagram-based DSP development platform that allows the user to quickly set up complex DSP algorithms. The true power of LabVIEW lies in its ability to interface with external DSP devices and/or internal sound cards that are installed on the PC. The developed algorithms are downloaded to the DSP board, which then runs the algorithm in a real-time environment. In this lab, we will only scratch the surface regarding LabVIEW DSP's capabilities. For starters, we will look at how LabVIEW DSP interfaces with the A/D and D/A operations of the DSP board. Specifically, we will simply connect the A/D converter to the D/A converter so that the DSP system plays back audio signal sent to it.
The purpose of this lab is to examine FIR filter design/implementation using LabVIEW Digital Filter Design Toolkit and LabVIEW DSP Module. In this lab, it is assumed that the student is already familiar with the basic operation of LabVIEW.
The purpose of this lab is to examine IIR filter design/implementation using LabVIEW DSP. Particular attention is drawn to the comparison between theoretical filter characteristics and actual filter performance. In this lab, it is assumed that the student is already familiar with the basic operation of LabVIEW DSP.
The purpose of this lab is to examine IIR filter design/implementation using LabVIEW DSP. Particular attention is drawn to the comparison between theoretical filter characteristics and actual filter performance. In this lab, it is assumed that the student is already familiar with the basic operation of LabVIEW DSP.
The purpose of this lab is to familiarize students with the DSP development workstation in the signal processing lab by examining sampling, analysis, and reconstruction of continuous-time signals. Specifically, we will first look at sampling/reconstruction of continuous-time signals. We will then examine time- and frequency-domain displays. Finally, we will examine the importance of sampling frequency and its effects on aliasing.
This course is a demonstration of using the Connexions project to host foreign language content in the context of a DSP lab course. It borrows material from Doug Jones' ECE320 course at UIUC and provides translations of some of the material in the Japanese, Chinese and Thai languages.
Development of real-time digital signal processing (DSP) systems using a DSP microprocessor; several structured laboratory exercises, such as sampling and digital filtering, followed by an extensive DSP project of the student's choice.
Our results illustrate that redundant dictionaries can reveal the innate structure of signals - sharp lines and gradual changes in color can represent themselves as artifacts in a compressed image. The conciseness of the representations depends on both the dictionary chosen and the nature of the signal, the compression rates will vary depending on their similarities. In real world situations, ie those in which we could gather an idea of the kind of signal we were trying to represent, it would be possible to choose a more "fitting" basis. This project was very effective at helping us to better understand the basics of signal processing in different domains. Unfortunately, a comparison of the images generated with a non-redundant basis, as opposed to those with an overcomplete basis, suggests that compression schemes that use a single basis are superior to the new, multiple basis schemes (with the exception of the dirac basis). It is important to understand, however, that this is probably due to the very nature of a greedy algorithm (particularly their propensity to paint themselves into corners), and, as our research suggests, is one of the major unsolved problems facing the field of image compression over redundant dictionaries. Also, it should be noted that, while the images generated look different, they have very similar levels of error when calculated strictly mathematically (ie power of the resultant over power of the original signal).
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