Applications of digital signal processing
- 1. Applications of Digital Signal processing By: Rajeev Piyare
- 2. DSP and Its Benefits By a signal we mean any variable that carries or contains some kind of 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 bearing of distant targets
- 3. Application 1: Signal Compression • Signals carry information, and the objective of signal processing is to preserve the information contained in the signal and extract and manipulate it when necessary. • For efficient storage of digital signals, it is often necessary to compress the data into a smaller size requiring significantly fewer number of bits. • Data transmission and storage cost money. The more information being dealt with, the more it costs. • Data compression is the general term for the various algorithms and programs developed to address this problem.
- 4. Signal Compression • A signal coding system consists of an encoder and a decoder. The input to the encoder is the signal x to be compressed, and its output is the compressed bit stream d. The decoder performs the reverse operation. Its input is the compressed bit stream d developed by the encoder, and its output is a reasonable replica of the original input signal of the encoder. The block diagram representation of the signal compression system.
- 5. Signal Compression • The signal compression methods can be classified into two basic groups: lossless and lossy. 1. A lossless technique means that the restored data file is identical to the original for example: executable code, word processing files, tabulated numbers, etc. • In comparison, data files that represent images and other acquired signals do not have to be keep in perfect condition for storage or transmission. 2. All real world measurements inherently contain a certain amount of noise. If the changes made to these signals resemble a small amount of additional noise, no harm is done. Compression techniques that allow this type of degradation are called lossy. This distinction is important because lossy techniques are much more effective at compression than lossless methods. The higher the compression ratio, the more noise is added to the data.
- 6. Techniques for signal compression • There are 5 techniques for signal/data compression: The first three are simple encoding techniques, called: runlength, Huffman, and delta encoding. The last two are elaborate procedures that have established themselves 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.
- 7. Application 2: Dual-Tone Multi-Frequency (DTMF) Signal Detection • Dual-tone Multi-Frequency (DTMF) signaling is the basis for voice communications control and is widely used worldwide in modern telephony to dial numbers and configure switchboards. It is also used in systems such as in voice mail, electronic mail, telephone banking 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. These frequencies were chosen to prevent any harmonics from being incorrectly detected by the receiver as some other DTMF frequency.
- 8. 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:
- 9. Estimating DTMF Tones with the Goertzel Algorithm • The minimum duration of a DTMF signal defined by the ITU standard is 40 ms. Thus, with a sampling rate of 8 kHz, there are at most 0.04 x 8000 = 320 samples available for estimation and detection. The DTMF decoder needs to estimate the frequencies contained in these short signals. • One common approach to this estimation problem is to compute the Discrete-Time Fourier Transform (DFT) samples close to the seven fundamental tones. For a DFT-based solution, it has been shown that using 205 samples in the frequency domain minimizes the error between the original frequencies and the points at which the DFT is estimated. • To minimize the error between the original frequencies and the points at which the DFT is estimated, we truncate the tones, keeping only 205 samples or 25.6 ms for further processing.
- 10. Goertzel Algorithm • At this point we could use the Fast Fourier Transform (FFT) algorithm to calculate the DFT. However, the popularity of the Goertzel algorithm in this context lies in the small number of points at which the DFT is estimated. In this case, the Goertzel algorithm is more efficient than the FFT algorithm.
- 11. Detecting DTMF Tones • The digital tone detection can be achieved by measuring the energy present at the seven frequencies estimated previously . Each symbol can be separated by simply taking the component of maximum energy in the lower and upper frequency groups.
- 12. 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
- 13. Power line Noise
- 14. 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
- 15. Ideal Signal Vs. Signal with Power line Noise
- 16. 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
- 17. Method
- 18. 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