IMPORTANT QUESTIONS ABOUT FILTER DESIGNING?

QUESTION: DEFINE FIR AND IIR FILTERS?
A filter whose point samples are infinite in number is called IIR filter,whereas FIR filter is a sort of filter for which input samples point are finite in number.Furthermore,FIR and IIR filters have different useful and beneficial features required for different purposes.The output of the system obtained when input applied is unit impulse signal is called impulse response of the system.If the system has characteristics of linearity and time invariance,this impulse response will describe completely that system.One should remember the point that impulse response cannot describe the non LTI system.

FIR filter block diagram

IIR FILTER CONFIGUARATION

QUESTION:FEATURES OF FIR AND IIR FILTERS?

We should remember that FIR filter is bounded input bounded output stable system whereas IIR can be stable or not .FIR filter has special feature of phase linearity and IIR filter does not have such linearity feature.One interesting point about IIR filter is it small order means IIR filter possess hardware with least number of components or hardware.FIR filter implementation is easy and interesting but this feature is not as simple in IIR filters.One drawback of FIR fitler is that such filters require more memoy for storage and calculation purposes.

QUESTION: WHAT DOES A DIGITAL FILTER DO?

The functionality of digital filter is the elimination of the noise and extraction of desired signals from various other filters. A digital filter is a basic component of the digital signal processing.
QUESTION: WHAT WILL BE OUR INITIAL STEP IF WE ARE TO DESIGN DIGITAL FILTER?

If we are to design digital filter then firstly we will design normal analog filter then this analog filter is converted to the required digital filter by using various useful techniques.

QUESTION: ENLIST SOME PARAMETERS ABOUT ANALOG FILTERS?

CUTT -OFF FREQUENCY:The cut-off frequency differentiates between stopband and passband

PASSBAND: Attenuation is 0 and it allows a specific band to pass.

STOPBADN: Attenuation is infinite and it stops a specific band

QUESTION: NAME DIFFERENT TYPES OF ANALOG FILTERS?
high pass filter

low pass filter

band rejected filter

band pass filter

all pass filter


Computational complexity
A general desire in any design is that the number of operations (additions and multiplications) needed to compute the filter response is as low as possible. In certain applications, this desire is a strict requirement, for example due to limited computational resources, limited power resources, or limited time. The last limitation is typical in real-time applications.

There are several ways in which a filter can have different computational complexity. For example, the order of a filter is more or less proportional to the number of operations. This means that by choosing a low order filter, the computation time can be reduced.

For discrete filters the computational complexity is more or less proportional to the number of filter coefficients. If the filter has many coefficients, for example in the case of multidimensional signals such as tomography data, it may be relevant to reduce the number of coefficients by removing those which are sufficiently close to zero. In multirate filters, the number of coefficients by taking advantage of its bandwidth limits, where the input signal is downsampled (e.g. to its critical frequency), and upsampled after filtering.

Another issue related to computational complexity is separability, that is, if and how a filter can be written as a convolution of two or more simpler filters. In particular, this issue is of importance for multidimensional filters, e.g., 2D filter which are used in image processing. In this case, a significant reduction in computational complexity can be obtained if the filter can be separated as the convolution of one 1D filter in the horizontal direction and one 1D filter in the vertical direction. A result of the filter design process may, e.g., be to approximate some desired filter as a separable filter or as a sum of separable filters.

The uncertainty principle

As stated in the uncertainty principle, the product of the width of the frequency function and the width of the impulse response cannot be smaller than a specific constant. This implies that if a specific frequency function is requested, corresponding to a specific frequency width, the minimum width of the filter in the signal domain is set. Vice versa, if the maximum width of the response is given, this determines the smallest possible width in the frequency. This is a typical example of contradictory requirements where the filter design process may try to find a useful compromise.Courtesy of wikipedia...