Signal Processing Research Paper Example | Topics and Well Written Essays - 1000 words

Signal Processing - Research Paper manakinOne of these digital signal processing techniques is accommodative clicking. Adaptive Filters Haykin (2006) defines an adaptative filter as a outline which is self-designing and reliant on a recursive algorithm for its operation. This feature enables an adaptive to satisfactorily transact in an environment where there is scarce or no knowledge of the applicable statistics. Diniz & Netto (2002) observe that an adaptive filter is used when either the fixed specifications be not known, or these specifications cannot be met by filters which atomic number 18 time-invariant. Adaptive filters characteristics depend on the input signal and such filters are time-varying because their parameters continu totallyy change so as to satisfy a performance requirement. The two main groups of adaptive filters are linear and nonlinear. According to Stearns & Widrow (1985), linear adaptive filters calculate an approximation of the desired response by uti lizing a linear permutation of the available group of observables that are applied to the filters input. Nonlinear adaptive filters are those that depend on the input signal and their parameters change continually. Also, adaptive filters can be classified ad as supervised and unsupervised adaptive filters. Supervised adaptive filters apply the presence of a instruction series that gives different proceedss of a desired ouput for a particular input signal. The response that is desired is compared against the accepted output due to the input signal, and the wrongful conduct signal that results is used in adjusting the filters liberate parameters. Unsupervised adaptive filters perform alterations of their free parameters without the requirement for a desired response. Such filters are designed with a group of rules that enable it to calculate the input-output mapping with particular desirable properties (Sayed, 2003). Adaptive Filtering System variety Drumright (1998) establishes 4 major types of adaptive filtering physiques. These include adaptive noise cancellation, adaptive inverse system, adaptive system identification and adaptive linear prediction. Algorithm implementation in all these systems, but the configuration is different. They all have the same general characteristics which include an input signal x(n), a desired result d(n), an output signal y(n), an adaptive transfer function w(n) and an error signal e(n). e(n)=d(n)-y(n) The adaptive system identification determines a discrete approximation of the transfer function for an incomprehensible analog or digital system. A same input x(n) is applied to both the unknown system and the adaptive filter and the outputs are compared. The y(n) of the adaptive filter is subtracted from that of the unknown resulting in an error signal e(n) which is used to manipulate the filter coefficients of the adaptive system. In the adaptive noise cancellation configuration, an input x(n) and a noise source N1(n) a re compared with a desired signal d(n) which comprises of a signal s(n) corrupted by another noise N0(n). The adaptive filter coefficients adapt to cause the error signal to be a noiseless version of the signal s(n). The adaptive linear prediction configuration performs two operations linear prediction and noise cancellation. Finally, the adaptive inverse system models the inverse of the unknown system u(n), an aspect which is useful in adaptive equalization (Drumright, 1998). Conclusion Just as discussed above, the untarnished applications of adaptive filt