File Name: introduction to digital signal processing and filter design .zip
Signal such as sound, heartbeat, heat, earthquake, current, and more are information that get transmitted from one place to another. Systems process those signals to modify or transform them.
- Introduction To Signal Processing Pdf
- Introduction To Digital Signal Processing and Filter Design By B. A. Shenoi
- Digital signal processing
Introduction To Signal Processing Pdf
Signal such as sound, heartbeat, heat, earthquake, current, and more are information that get transmitted from one place to another. Systems process those signals to modify or transform them. Signal is fed into the system as time dependent input stream x t which results in output y t which is the response of the system. The mathematical modeling of signals and systems help in the design and development of electronic devices.
Example - Sinusoidal wave:. Consider a simple signal i. It can be represented in mathematical term as:. Angular frequency is the rotational rate. The ordinary frequency is the number of oscillations cycle in each second of time. If f is the number of oscillations in T then. System is a device or combination of devices such as filters, amplifiers, which can operate on signals and produces corresponding response. Input to a system is called as excitation and output from it is called as response.
System can be linear or nonlinear, time variant or invariant, static or dynamic, causal depends on previous or past inputs or non-causal, invertible or non-invertible, and stable or unstable. For more info on LTI signal, visit Appendix section. A filter is a device or a process that removes some unwanted components or features from a signal that helps to retrieve the desired information from the signal.
Filters are often used in electronic systems to emphasize signals in certain frequency ranges and reject signals in other frequency ranges. Such a filter has a gain which is dependent on signal frequency. Application - FM Tuning:. When you want to listen to your FM at By doing this, it allows to pass that Having only one reactive component capacitor , it becomes the first order or one pole see Appendix: P oles and zeros for details passive filter.
It is a passive filter because it consumes the energy of the signal. The active filters on the other hand can amplify the signal and therefore they require external power.
By plotting the circuit output voltage against different values of input signal frequency, a frequency response curve or Bode plot can be obtained see Fig. The impedance of the capacitor is given by:. If the frequency is very low such as almost zero i. So, output voltage is almost same as the input voltage i. However, at higher frequency, Z gets very small and the voltage drops at resistor R. So, the output voltage is almost zero. Hence the name low pass filter i. The capacitors impedance equals the resistors at cut off frequency f c , and the voltage is split between them.
Each component can have This pass band zone also represents the Bandwidth of the filter. Any signal frequencies above this point cut-off point are generally said to be in the filters Stop bandzone and they will be greatly attenuated.
Here, transfer function H jw i s the gain with the complex components. The magnitude and phase of H jw are given by:. Where, is the filter time constant.
Case 3 happens at cut-off frequency f c. The transfer function of this filter is given by:. The magnitude of the transfer function has a maximum value at a specific frequency w 0 between 0 and infinity, and falls off on either side of that frequency see Fig.
The filter with this general shape is known as a band-pass filter because it passes signals falling within a relatively narrow band of frequencies and attenuates signals outside of that band.
The range of frequencies passed by a filter i. The passband limits are usually assumed to be the frequencies f l and f h where the gain has dropped by 3 decibels i. Such filter response will have a peak value at center frequency f c which is equal to the geometric mean of f l and f h i.
Thus, a high-Q tuned circuit in a radio receiver would be more difficult to tune, but would have more selectivity as it filters out signals from other stations that lie nearby on the spectrum. A digital filter is a system that performs mathematical operations on a sampled, discrete-time signal to reduce or enhance certain aspects of that signal in contrast to analog electronic filter operating on a continuous-time analog signals.
There are two categories of digital filter: the recursive filter and the non-recursive filter. Many digital filters are based on the fast Fourier transform , a mathematical algorithm that quickly extracts the frequency spectrum of a signal, allowing the spectrum to be manipulated before converting the modified spectrum back into a time-series signal with an inverse FFT operation.
These filters give O n log n computational costs whereas conventional digital filters tend to be O n 2. A recursive filter re-uses one or more of its outputs as an input. This feedback typically results in an unending impulse response characterized by either exponentially growing, decaying, or sinusoidal signal output components. It has a finite number of coefficients in the impulse response h[n] see Appendix: Convolution for details. Kalman Filter or FIR. The impulse response or response to any finite length input of FIR is of finite duration, because it settles to zero in finite time.
This is in contrast to infinite impulse response IIR filters, which may have internal feedback and may continue to respond indefinitely usually decaying response. Digital IIR filters can be based on well-known solutions for analog filters such as the Chebyshev filter, Butterworth filter , and elliptic filter , inheriting the characteristics of those solutions. Nonrecursive Filter - FIR. Consider a 3-term moving average filter with filter coefficients given by. Consider a finite length input sequence as shown in Fig.
The transfer function in terms of z-transform becomes. Stability: The system is stable as all of the poles of the system function lie inside the unit circle.
Case - Filtering Windowed Noise Sequence:. The action of moving average filter has resulted in the smoother output than the input see Fig. Output y[n]:. Consider a difference equation for recursive filter - IIR filter. The transfer function can be written as. The zeros and poles are shown in Fig. Why high order filters? The filter design assumes an ideal filter that has unity gain in the passband and zero gain in the stop band.
In order to process the signal through computer, the continuous signals are sampled at regular intervals T s called sampling period resulting in:. We need to realize that sample spacing needs to be small enough relative to the frequency such that when plotted by connecting the dots linear interpolation , the waveform picture is not too distorted.
A reasonable plot can be created with about 10 samples per period, i. Sampling Theorem establishes a sufficient condition for a sample rate that permits a discrete digital sequence of samples to capture all the information from a continuous-time analog signal of finite bandwidth. Nyquist—Shannon sampling theorem: A sufficient sample-rate is therefore anything larger than 2 times B B is the Bandwidth samples per second of the signal.
Equivalently, for a given sample rate f s , perfect reconstruction is guaranteed possible for a bandwidth. Sinusoidal waves can be used as simple building blocks to describe and approximate any periodic waveform i. Any periodic signal can be decomposed or expanded into the sum of a possibly infinite set of simple oscillating sine or cosine functions.
Fourier series makes use of the orthogonality relationships of the sine and cosine functions. The computation and study of Fourier series is known as harmonic analysis and is extremely useful as a way to break up an arbitrary periodic function into a set of simple terms that can be plugged in, solved individually, and then recombined to obtain the solution to the original problem or an approximation to it to whatever accuracy is desired or practical.
Where T 0 is the fundamental time period and k is the harmonic number. The analysis equation that determines a k from x t is given by:.
The special case, DC component i. In trigonometric form, it becomes:. Where Fourier coefficients a 0 , a n , and b n are defined by the integrals. Comparing this with the general Fourier series synthesis equation,. The magnitude spectrum is shown in Fig Refer to Appendix: Spectrum :. The approximation is not perfect and moreover in the signal with discontinuities like square wave, increasing the approximations there does not do much improvement. We define the error between the approximation and the true signal as:.
There is ringing of ear or the overshoots as the waveform makes discontinuous steps from 0 to 1 and 1 back to 0 known as Gibbs phenomenon. Fourier transform decomposes the signal into its constituent frequencies. It is frequency domain representation of original signal. Request a compute node. Load the matlab module. Now, check the wave Fig. Now, check out the frequency and T. FIR Filter:. Let's plot magnitude Fig 16a and phase angle Fig. Open Matlab.
Introduction To Digital Signal Processing and Filter Design By B. A. Shenoi
Reviews the underlying principles of digital signal processing DSP with little recourse to mathematics. Aims to encourage experimentation to obtain a feel for the processes involved. DSP provides a powerful numeric means of extracting useful information from sensor data applied to a system. Discusses the basic concepts of such processing techniques and introduces some useful algorithms. McFarlane, A. Report bugs here. Please share your general feedback.
Digital signal processing
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There are mainly two types of modulation namely synchronous and asynchronous modulation. Questions and comments. Catalog description: Sampling and data acquisition, design of simple digital. Supplemental materials: none. The inherent flexibility of digital elements permits the utilization of a variety of sophisticated signal processing techniques which had previously been impractical to implement. Moreover, an introduction to physiological systems and non-invasive measurement of data is given. PDF versions of readings will be available on the web site.
In signal processing , a filter is a device or process that removes some unwanted components or features from a signal. Filtering is a class of signal processing , the defining feature of filters being the complete or partial suppression of some aspect of the signal. Most often, this means removing some frequencies or frequency bands. However, filters do not exclusively act in the frequency domain ; especially in the field of image processing many other targets for filtering exist. Correlations can be removed for certain frequency components and not for others without having to act in the frequency domain.
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