Digital Signal and Image Processing Using MATLAB
The most important theoretical aspects of image and signal processing (ISP) for both deterministic and random signals are covered in this guide to using MATLAB® . The discussion is also supported by exercises and computer simulations relating to real applications such as speech processing and fetal-heart–rhythm tracking, and more than 200 programs and functions for numerical experiments are provided with commentary.
The goal of SPID is to provide the user with tools capable to simulate, preprocess, process and classify in vivo and ex vivo MRS signals. These tools are embedded in a matlab graphical user interface (GUI). (Pre)processing and classification methods can also be automatically run in a row using the matlab command line
With the advent of multimedia, digital signal processing (DSP) of sound has emerged from the shadow of bandwidth-limited speech processing. Today, the main appli- cations of audio DSP are high quality audio coding and the digital generation and manipulation of music signals. They share common research topics including percep- tual measurement techniques and analysis/synthesis methods. Smaller but nonetheless very important topics are hearing aids using signal processing technology and hardware architectures for digital signal processing of audio. In all these areas the last decade has seen a significant amount of application oriented research.
Top module name : SHIFTER (File name : SHIFTER.v)
2. Input pins: SHIFT [3:0], IN [15:0], SIGN, RIGHT.
3. Output pins: OUT [15:0].
4. Input signals generated from test pattern are latched in one cycle and are
synchronized at clock rising edge.
5. The SHIFT signal describes the shift number. The shift range is 0 to 15.
6. When the signal RIGHT is high, it shifts input data to right. On the other hand, it
shifts input data to left.
7. When the signal SIGN is high, the input data is a signed number and it shifts with
sign extension. However, the input data is an unsigned number if the signal SIGN
is low.
8. You can only use following gates in Table I and need to include the delay
information (Tplh, Tphl) in your design.
Top module name : SHIFTER (File name : SHIFTER.v)
2. Input pins: SHIFT [3:0], IN [15:0], SIGN, RIGHT.
3. Output pins: OUT [15:0].
4. Input signals generated from test pattern are latched in one cycle and are
synchronized at clock rising edge.
5. The SHIFT signal describes the shift number. The shift range is 0 to 15.
6. When the signal RIGHT is high, it shifts input data to right. On the other hand, it
shifts input data to left.
7. When the signal SIGN is high, the input data is a signed number and it shifts with
sign extension. However, the input data is an unsigned number if the signal SIGN
is low.
8. You can only use following gates in Table I and need to include the delay
information (Tplh, Tphl) in your design.
Many problems in statistical pattern recognition begin with the preprocessing of multidimensional signals, such as images of faces or spectrograms of speech.
Process a binary data stream using a communication system that
consists of a baseband modulator, channel, and demodulator.
Compute the system s bit error rate (BER). Also, display
the transmitted and received signals in a scatter plot.
ECE345, Visual-to-Audio Electronic Travel Aid
Code for TM320C54x (v2a.asm) download
This project involves the design and implementation of a audio synthesis device that converts moving images into audio signals. The system is built on a TM320C54x DSP with interface to an IMAQ camera module via the serial port on a PC. Brief description: A LabVIEW VI acquires an image from the IMAQ camera module. It quantizes the image into a 5x5, 3-bit image, and sends the data to the TM320C54x DSP via a serial port. The TM320C54x DSP constructs a 64-tap FIR by combining a series of 64-tap head related transfer functions (HRTF) according to the incoming data, and then filters an input audio signal with this FIR filter, in effect creating a correspondence between the filtered signal and the original image.
A MATLAB GUI platform for realizing the radiation pattern of narrowband beamformer with random array geometry. User can specify the array geometry, directions of incoming signals, noise power, and the type of beamformer. Useful for gaining insight about collaborative beamforming in sensor networks and random arrays. You need both randomarray.fig and randomarray.m to work.
