Linear Phase FIR Low Pass Filter Design Based on Firefly Algorithm

In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality

Chaos and Bifurcation Control Using Nonlinear Recursive Controller

Chaos and bifurcation control is achieved by nonlinear controller that is able to mitigate the characteristics of a class of nonlinear systems that are experiencing such phenomenon. In this paper, a backstepping nonlinear recursive controller is presented. Comparison has been made between it and a Pole Placement controller. The study shows the effectiveness of the proposed control under various operating conditions

Modeling nonlinear systems using multiple piecewise linear equations

This paper describes a technique for modeling nonlinear systems using multiple piecewise linear equations. The technique provides a means for linearizing the nonlinear system in such a way as to not limit the large signal behavior of the target system. The nonlinearity in the target system must be able to be represented as a piecewise linear function. A simple third order nonlinear system is used to demonstrate the technique. The behavior of the modeled system is compared to the behavior of the nonlinear system

Robust pole placement using firefly algorithm

In this paper, the new automatic tool that is based on the firefly algorithm whose purpose is optimization of pole location in the control of state feedback has been presented. The aim is satisfying specifications of performance like settling and rise time, steady state as well as overshoot error. Utilization of Firefly algorithm has demonstrated the benefits of controllers based on this kind of time domain over controllers based on the frequency domain like Proportional-Integral Derivative (PID). The presented method is more particular for the multi-input multi-output (MIMO) systems that have substantial state numbers. The simulation results indicated that the proposed method had superior performance in providing solution to the problems that involved stabilization of helicopter under the Rationalized Model of helicopter/ Moreover, it demonstrates the Firefly algorithm effectiveness with regards to, the state observer design and feedback controller and auto-tuning

Modeling marine cargo traffic to identify countries in Africa with greatest risk of invasion by Anopheles stephensi

Anopheles stephensi, an invasive malaria vector native to South Asia and the Arabian Peninsula, was detected in Djibouti's seaport, followed by Ethiopia, Sudan, Somalia, and Nigeria. If An. stephensi introduction is facilitated through seatrade, similar to other invasive mosquitoes, the identification of at-risk countries are needed to increase surveillance and response efforts. Bilateral maritime trade data is used to (1) identify coastal African countries which were highly connected to select An. stephensi endemic countries, (2) develop a prioritization list of countries based on the likelihood of An. stephensi introduction through maritime trade index (LASIMTI), and (3) use network analysis of intracontinental maritime trade to determine likely introduction pathways. Sudan and Djibouti were ranked as the top two countries with LASIMTI in 2011, which were the first two coastal African countries where An. stephensi was detected. With Djibouti and Sudan included as source populations, 2020 data identify Egypt, Kenya, Mauritius, Tanzania, and Morocco as the top countries with LASIMTI. Network analysis highlight South Africa, Mauritius, Ghana, and Togo. These tools can prioritize efforts for An. stephensi surveillance and control in Africa. Surveillance in seaports of identified countries may limit further expansion of An. stephensi by serving as an early warning system

Predictors For TESE Outcomes and Fertility Potentials Among Infertile Adult Men

Background: Spermatogenesis is an essential process for human reproduction.   Gonadotropin-releasing hormone (GnRH), luteinizing hormone (LH), follicle-stimulating hormone (FSH), and testosterone play vital roles in the development and maturation of sperm. Growth hormone (GH) is thought to play a role in the reproductive system of both males and females. Growth Hormone deficiency can lead to reproductive problems. Aim: to assess predictors of fertility potentials and TESE outcomes among adult males. Methods: we enrolled 162 males and assessed FSH, LH, basal GH, clonidine (GH) stimulation test one time and insulin stimulation test in another time. We designed a predictive model to identify the fertility potentials, Fertility Score= 4.442 + (Basal GH*0.074) + (GH_CLON*0.035) - (FSH*0.021) (BMI*0.062)- (Smoker*0.429). The net result of this equation should be approximated to the nearest integer to predict the fertility status where, 1=TESE Negative, 2= TESE Positive, 3=Oligozoospermia, and 4= Fertile control. Results: multivariate analysis showed smoking status, testicular volume, BMI, Serum FSH, basal GH are not predictors for fertility potentials. GH after clonidine and after insulin stimulation GH after clonidine stimulation correlates positively with total motile count. Other semen parameters do not correlate with basal GH or GH after insulin or clonidine stimulation. Receiver Operator Characteristic (ROC) curve analysis is used to detect the cut off levels at which sperm recovery yield change. For the GH assessment only, the basal GH could be applied to predict the SRR in men with azoospermia, AUC=0.672 (95% CI: 0.499 to 0.844). Growth hormone after clonidine (AUC= 0.510) or Insulin stimulation (AUC=0.556) and therefore, cannot differentiate between TESE positive and TESE negative cases. Conclusion: Basal, post clonidine GH levels, has BMI and smoking are predictive factors for fertility potentials, our model have high sensitivity in predicting fertility potentials among positive TESE males. Basal GH can significantly predict TESE negative males

A New Bivariate Class of Life Distributions

Abstract Some concepts of multivariate aging for exchangeable random variables have been considered i