Presently, linear parameter varying (LPV) systems are broadly used in a wide range of applications such as in aerospace, energy, health, mechatronics, process control, computational systems, etc. Essentially, an LPV system is a linear system whose parameters are functions of a scheduling signal. It can be described by state-space or input/output models, in continuous or discrete-time. The incre...

In many of the studies concerning Accelerated life testing (ALT), the log linear function between life and stress which is just a simple re-parameterization of the original parameter of the life distribution is used to obtain the estimates of original parameters but from the statistical point of view, it is preferable to work with the original parameters instead of developing inferences for the...

Wireless communication systems operating over time-varying fading channels require adaptive signal processing to equalize the channel variations at the receiver. In wireless applications, the received signal is typically affected by frequency-selective fading and channel equalization is required to mitigate the resulting inter symbol interference (ISI). In this paper an adaptive model has been ...

This paper aims at optimizing the parameters involved in stress analysis of perforated plates, in order to achieve the least amount of stress around the square-shaped holes located in a finite isotropic plate using metaheuristic optimization algorithms. Metaheuristics may be classified into three main classes: evolutionary, physics-based, and swarm intelligence algorithms. This research uses Ge...

The accuracy of target position detection in IR seeker depends on the accuracy of tracking error signal (TES) extraction from seeker Field of View (FOV). The type of reticle inside the seeker determines the output modulation signal that carries the TES. In this paper, the stationary wagon wheel reticle is used, which makes the type of the output signal as FM modulation in the linear region of F...

A simple, yet powerful, learning method is presented by combining the famed kernel trick and the least-mean-square (LMS) algorithm, called the KLMS. General properties of the KLMS algorithm are demonstrated regarding its well-posedness in very high dimensional spaces using Tikhonov regularization theory. An experiment is studied to support our conclusion that the KLMS algorithm can be readily u...