MentorDSPAn interactive illustration of signal processingSIGgen  DSPpro  NUMpro  DSPbase MATHstat  IMAGEsee  COMpro 
New:

MentorDSP version 1.0


Available Documentation


Main Features


DSPbaseThe item “RealTimeSound” is a good introduction to the MentorDSP philosophy. At a press of this item in the lab practical list, the screen is organized as follows. The upper screen is the timedomain representation of the sound. The sound waveform is captured in realtime from the computer microphone and appears as a series of samples (512 by default). The waveform corresponds to a human voice (male pronouncing vowels). Situated on the bottom is the realtime spectrogram of the voice. The Fourier Transform is used to compute for each set of 512 samples the frequencydomain components of the input waveform. The frequency is the Y axis while the X axis represents the time. The yellowwhite spots around 200 and 4000 Hz correspond to high energy contents while the greenblue colors corresponds to very low energy. This kind of tool has a very impressive impact on students when illustrating the concept of Fourier Transform.


SigGenMentorDSP can generate basic signals, modulated signals, but also user's defined equation thanks to a power set of routines. The example shown above uses time variables, pi constant and SIN function. Around 50 basic functions as well as advanced functions enable to create complex signals. Noise can also be added to the signal.


DspProIn the screen dedicated to FiniteImpulseResponse (FIR) filter design, the user selects the type of filter (low pass, high pass, band pass, notch), the cutoff frequency and filter order. The coefficients are then automatically computed. The result on a chirp signal may be observed (variation of the amplitude of the Y[n] signal depending on frequency). The low frequency contents are not attenuated, while the frequencies above 1000 Hz are reduced. Similar features are offered to InfiniteImpulseReponse (IIR) filters. The user selects the type of filter (low pass, high pass, band pass, notch). The cutoff frequency and filter parameter numbers may be changed. The synthesis method, based on biquad transform inspired from analog filters, is fully detailed and illustrated.


NumProIn MentorDSP, a specific lab practical has been focused on the format used in digital signals. Not only floating and fixed point formats are important basic concepts of DSP, but also specific functions such as rounding or saturation. The screen enables the following features:


COMProAn important topic covered by MentorDSP concerns modulations. Most modulations are illustrated by coupling time and frequency domain representations and give access to the key parameters such as the modulation amplitude and signal frequencies. The instant spectrum shows the components of the modulated signal according to the theory. The spectrogram of the frequency modulation shows possible discontinuities due to numerical data modulation. The modulation/demodulation implemented in COMpro concern AM, FM, QPSK, GMSK, OFDM Download a paper published at Telecom09 Agadir, Morroco, March 2009 on modulations


MATHstatCorrelation has been the focus of particular efforts to give the teacher ways to illustrate the core idea and stepbystep illustration of the convolution and correlation theory. MentorDSP proposes to compute the convolution between two very simple signals, i.e simple pulses X[n] and Y[n]. The idea is to display the instant value of the convolution and to observe the construction of the result. Once understood on simple cases such as constant probability density functions, convolution may be applied to complex density functions.


IMAGEseeIn the field of Image Processing, the primary objective of the MentorDSP software is to help the student to understand the link between pixel and RGB values, discover the HSV ( Hue , Saturation, Value) coding, illustrate the correspondence in the palette. A set of basic image transforms is proposed to visualize the image inversion and Boolean operation applied to pixels. Addition of two images, substration and complex arithmetic operators iare also available. The image compression is addressed through the Discrete Cosine Transform (DCT), and Invert DCT. The DCT is first applied to very simple images to understand the link between image pixels and frequency. The student may also visualize the impact of quantization resolution and image compression factor on the resulting image quality. Correlation, filtering, may also be applied on images.

Contact the author at : Etienne Sicard etienne.sicard "at" insatoulouse.fr