Applications of Digital Signal processing

By: Rajeev Piyare

DSP and Its Benefits

By a signal we mean any variable that carries or contains some kindof information that can be conveyed, displayed or manipulated.

Examples of signals of particular interest are:

- speech, is encountered in telephony, radio, and everyday life

- biomedical signals, (heart signals, brain signals)

- Sound and music, as reproduced by the compact disc player

- Video and image,

- Radar signals, which are used to determine the range and bearingof distant targets

Application 1: Signal Compression

• Signals carry information, and the objective of signal processing is topreserve the information contained in the signal and extract andmanipulate it when necessary.

• For efficient storage of digital signals, it is often necessary to compressthe data into a smaller size requiring significantly fewer number of bits.

• Data transmission and storage cost money. The more information beingdealt with, the more it costs.

• Data compression is the general term for the various algorithms andprograms developed to address this problem.

Signal Compression

• A signal coding system consists of an encoder and a decoder. Theinput to the encoder is the signal x to be compressed, and itsoutput is the compressed bit stream d. The decoder performs thereverse operation. Its input is the compressed bit stream ddeveloped by the encoder, and its output is a reasonable replicaof the original input signal of the encoder.

The block diagram representation of the signal compression system.

Signal Compression

• The signal compression methods can be classified into two basicgroups: lossless and lossy.

1. A lossless technique means that the restored data file is identical tothe original for example: executable code, word processing files,tabulated numbers, etc.

• In comparison, data files that represent images and other acquiredsignals do not have to be keep in perfect condition for storage ortransmission.

2. All real world measurements inherently contain a certain amount ofnoise. If the changes made to these signals resemble a small amount ofadditional noise, no harm is done. Compression techniques that allowthis type of degradation are called lossy. This distinction is importantbecause lossy techniques are much more effective at compression thanlossless methods. The higher the compression ratio, the more noise isadded to the data.

Techniques for signal compression

• There are 5 techniques for signal/data compression: The first three aresimple encoding techniques, called: runlength, Huffman, and deltaencoding. The last two are elaborate procedures that have establishedthemselves as industry standards: LZW(Lempel–Ziv–Welch) and JPEG(Joint Photographic Experts Group).

• Images transmitted over the world wide web are an excellent example of why data compression is important.

Application 2: Dual-Tone Multi-Frequency (DTMF) Signal Detection

• Dual-tone Multi-Frequency (DTMF) signaling is the basis for voicecommunications control and is widely used worldwide in moderntelephony to dial numbers and configure switchboards. It is alsoused in systems such as in voice mail, electronic mail, telephonebanking and ATM machines.

Generating DTMF Tones

• A DTMF signal consists of the sum of two sinusoids - or tones -with frequencies taken from two mutually exclusive groups. Thesefrequencies were chosen to prevent any harmonics from beingincorrectly detected by the receiver as some other DTMFfrequency.

Generating DTMF Tones

• Each pair of tones contains one frequency of the low group (697 Hz, 770 Hz, 852 Hz, 941 Hz) and one frequency of the high group (1209 Hz, 1336 Hz, 1477Hz) and represents a unique symbol. The frequencies allocated to the push-buttons of the telephone pad are shown below:

Estimating DTMF Tones with the GoertzelAlgorithm

• The minimum duration of a DTMF signal defined by the ITU standard is40 ms. Thus, with a sampling rate of 8 kHz, there are at most 0.04 x8000 = 320 samples available for estimation and detection. The DTMFdecoder needs to estimate the frequencies contained in these shortsignals.

• One common approach to this estimation problem is to compute theDiscrete-Time Fourier Transform (DFT) samples close to the sevenfundamental tones. For a DFT-based solution, it has been shown thatusing 205 samples in the frequency domain minimizes the errorbetween the original frequencies and the points at which the DFT isestimated.

• To minimize the error between the original frequencies and the pointsat which the DFT is estimated, we truncate the tones, keeping only 205samples or 25.6 ms for further processing.

Goertzel Algorithm

• At this point we could use the Fast Fourier Transform (FFT) algorithm tocalculate the DFT. However, the popularity of the Goertzel algorithm inthis context lies in the small number of points at which the DFT isestimated. In this case, the Goertzel algorithm is more efficient than the

FFT algorithm.

Detecting DTMF Tones

• The digital tone detection can be achieved by measuring theenergy present at the seven frequencies estimated previously .Each symbol can be separated by simply taking the component ofmaximum energy in the lower and upper frequency groups.

Application 3: Biomedical

The Problem

• most biomedical signals are weak signals

• environment is contaminated with many other signals

• “other” signals: interferences, artifacts, “noise”

• sources of “noise”

– from environment

– from measurement equipment (instrumentation)

– physiological

Solution:

• signal processing techniques (filters) to remove the various interferences

Power line Noise

ECG Analysis

• The ECG is nothing but the recording of the heart’s electrical activity. The deviations in the normal electrical patterns indicate various cardiac disorders.

Schematic representation of normal ECG

Ideal Signal Vs. Signal with Power line Noise

Pan-Tompkins algorithm

• The QRS detection provides the fundamentals for almost all automated ECG analysis algorithms.

• Pan & Tompkins (1985) proposed a real-time QRS detection algorithm based on analysis of the slope, amplitude, and width of the QRS complexes of typical cardiac signal

Steps in implementation of Pan-Tompkins Algorithm

Method

DSP is everywhere!

• Audiological equipment

– Hearing aids

– Otoacoustic systems

– Audiometers

– Aural rehabilitation programs

– ABRs

• Telecommunications

– Cellular phones

– Voice over Internet

• Audio

– CD, DVD, DAT players

– MP3 players

• Biomedical monitoring equipment

• Digital Television