Employing a recently introduced framework, within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases, a generic, variable step-size adaptive filter has been presented. Variable Step-Size (VSS) Normalized least mean square (VSSNLMS) and VSS Affine Projection Algorithms (VSSAPA) are particular examples of adaptive algorithms covered by this generic variable step-size adaptive filter. In this paper, the new VSS Block Normalized least mean square (VSSBNLMS) adaptive filter algorithm is introduced, based on the generic VSS adaptive filter. The proposed algorithm shows the higher convergence rate and lower steady-state mean square error compared to the ordinary BNLMS algorithm.

To develop a system for specific purpose, it needs to estimate its parameters (parameterization). It can be used in different fields like engineering, industry etc. In this work, authors used adaptive algorithm to model a system that is applicable in industry for control. This adaptive model is non-linear where its estimation is based on Kernel based least-mean square (LMS) algorithm. The Kernel used as Polynomial and Gaussian. As the system is nonlinear polynomial Kernel-based algorithm fails to prove its efficacy, though it is of low complexity approach. Gaussian Kernel-based application for nonlinear system control performance better as compared to polynomial Kernel. Further its complexity is reduced and used for faster performance. The result shows its performance in form of MSE, MAE, RMSE for identification and control that is very useful in industrial application.

In this paper a low noise amplifier (LNA) in the frequency interval of [3-5] GHz is designed and optimized. The design process consists of three parts. At first, the stability of the bipolar junction transistor (BJT) is investigated. Simulation results show that it is unconditionally stable in the whole frequency interval. In the second part, the input and output matching networks are constructed in such a way that the desired transducer power gain (GT=10dB) and possible minimum noise figure (Fn) are achieved simultaneously. In order to achieve approximately constant GT and Fn in the desired frequency interval, the input and output matching networks are finally optimized using least mean square error technique. Once the optimization process is converged, variations of GT and Fn in the whole frequency interval are respectively less than 1dB and 0.5 dB.  Besides, the variation of output phase is approximately linear.

This paper introduces a meter of the root-mean-square value of deterministic and stochastic signals of an arbitrary shape that are generated over the set time interval. Such a meter involves only the minimum number of simple arithmetic operations to obtain results, and it ensures a high degree of measurement accuracy. For this purpose, the direct calculation of the signal root-mean-square value is applied while the measurement of the half-period average straightened signal value is carried out by means of the traditional measurement devices. Implementation of this meter requires neither the knowledge of what the signal period is, nor the synchronization with the processed sampling. Simulation is then carried out demonstrating the high efficiency of the proposed measurement algorithm. We analyze the characteristics of the meter operating within a wide frequency range of the measurable signals. The recommendations concerning the hardware implementation of such a meter by means of the field programmable gate arrays are considered. The meter can be used when designing digital high-frequency AC voltmeters and ammeters and it can provide the readings that do not depend upon the signal waveform.