This research paper proposes a recently developed new variant of Particle Swarm Optimization (PSO) called Accelerated Particle Swarm Optimization (APSO) in speech enhancement application. Accelerated Particle Swarm Optimization technique is developed by Xin she Yang in 2010. APSO is simpler to implement and it has faster convergence when compared to the standard PSO (SPSO) algorithm. Hence as an alternative to SPSO based speech enhancement algorithm, APSO is introduced to speech enhancement in the present paper. The present study aims to analyze the performance of APSO and to compare it with existing standard PSO algorithm, in the context of dual channel speech enhancement. Objective evaluation of the proposed method is carried out by using three objective measures of speech quality SNR, Improved SNR, PESQ and one objective measure of speech intelligibility FAI. The performance of the algorithm is studied under babble and factory noise environments. Simulation result proves that APSO based speech enhancement algorithm is superior to the standard PSO based algorithm with an improved speech quality and intelligibility measures.

Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired by biological evolution. Nowadays, EAs have proven their ability and effectiveness to solve combinatorial problems. However, these methods require a considerable time of calculation. To overcome this problem, several parallelization strategies have been proposed in the literature. In this paper, we present a new parallel agent-based EC framework for solving numerical optimization problems in order to optimize computation time and solutions quality.

This paper extends writer's previous work and proposes design, modeling and control issues of a simple robot arm design. Mathematical, Simulink models and MATLAB program are developed to return maximum numerical visual and graphical data to select, design, control and analyze arm system. Testing the proposed models and program for different input values, when different control strategies are applied, show the accuracy and applicability of derived models. The proposed are intended for education and research purposes.

The performance of optimization algorithms is problem dependent and as per no free lunch theorem, there exists no such algorithm which can be efficiently applied to every type of problem(s). However, we can modify the algorithm/ technique in a manner such that it is able to deal with a maximum type of problems. In this study we have modified the structure of basic Artificial Bee Colony (ABC), a recently proposed metaheuristic algorithm based on the concept of swarm intelligence to optimize the models of software reliability. The modified variant of ABC is termed as balanced ABC (B-ABC). The simulated results show the efficiency and capability of the variant to solve such type of the problems.

Fuzzy set theory was introduced by L.A. Zadeh in 1965. Immediately, it has many applications in practice and in building databases, one of which is the construction of a fuzzy relational database based on similar relationship. The study of cases of fuzzy relations in different environments will help us understand its applications. In this paper, the rough fuzzy relation on Cartesian product of two universe sets is defined, and then the algebraic properties of them, such as the max, min, and composition of two rough fuzzy relations are examined. Finally, reflexive, α-reflexive, symmetric and transitive rough fuzzy relations on two universe sets are also defined.

In this paper, performance of OFDM digital communication systems have been analyzed with improved ICI cancellation technique. The bit error rate has been regarded as a fundamental information theoretic measure of a communication system. A novel parallel ICI cancellation technique has been proposed for mitigating frequency offset of OFDM digital communication systems. The simulated results of the proposed technique is compared with ICI self cancellation scheme. The simulated results show better performance over ICI self cancellation scheme.

Sliding mode controller (SMC) is a significant nonlinear controller under condition of partly uncertain dynamic parameters of system. This controller is used to control of highly nonlinear systems especially for robot manipulators, because this controller is a robust and stable. Conversely, pure sliding mode controller is used in many applications; it has two important drawbacks namely; chattering phenomenon, and nonlinear equivalent dynamic formulation in uncertain dynamic parameter. The nonlinear equivalent dynamic formulation problem and chattering phenomenon in uncertain system can be solved by using artificial intelligence theorem. However fuzzy logic controller is used to control complicated nonlinear dynamic systems, but it cannot guarantee stability and robustness. In this research parallel fuzzy logic theory is used to compensate the system dynamic uncertainty.

The Technological evolution which occurred in digital systems favored the appearance of new services to be applied in railways. Radio communications played an important role in the management, exploration, and maintenance of railway transports. Due to the vital importance of security, the track-to-train communications system is studied in depth, with significant changes through the integration in its operation of the global system for mobile communication and global positioning systems. This paper has proposed the implementation of a global positioning system (GPS) based train monitoring system that could locate a train at every instant. Here a GPS–GPRS module transmits the location information to a web server. The paper also presents a low-budget system to track the trains inside tunnels. The paper presents a solution implemented at Egyptian National railways, to provide an intelligent train tracking and management system to improve the existing railway transport service.