In this paper, we have proposed a new SEP protocol called as Deterministic-SEP (D-SEP), for electing cluster heads in a distributed fashion in two-, three-, and multi-level hierarchical wireless sensor networks. The significant improvement has been shown using D-SEP in comparison with SEP in terms of network lifetime, energy consumption and data transmission to BS. Our expectations are demonstrated by simulation results. We have introduced the superior characteristic of our protocol and discussed the cluster head selection algorithm by describing the threshold and probability equations. In order to reach the constructive conclusion, two cases of two-level and four cases of three-level heterogeneity have been reported and compared. The results reveal that there is 323% & 207% improvement in the overall lifetime of the network by using D-SEP after comparing two-level (m=0.3, a=1.5) & three-level (m=0.5, m0=0.4, a=1.5, b=3) respectively. The investigations ascertain the stable region and maximized lifetime of the network by using D-SEP over SEP. The development of 17.8 fold in the lifetime of the network is reported by using D-SEP. Moreover the energy depletion slope per round is lower in case of D-SEP over SEP.

This paper presents a new approach for solving optimal control problems for switched systems. We focus on problems in which a pre-specified sequence of active subsystems is given. For such problems, we need to seek both the optimal switching instants and the optimal continuous inputs. A Bezier control points method is applied for solving an optimal control problem which is supervised by a switched dynamic system. Two steps of approximation exist here. First, the time interval is divided into k sub-intervals. Second, the trajectory and control functions are approximatedby Bezier curves in each subinterval. Bezier curves have been considered as piecewise polynomials of degree n, then they will be determined by n+1 control points on any subinterval. The optimal control problem is there by converted into a nonlinear programming problem (NLP), which can be solved by known algorithms. However in this paper the MATLAB optimization routine FMINCON is used for solving resulting NLP.

Training neural networks is a complex task of great importance in the supervised learning field of research. We intend to show the superiority (time performance and quality of solution) of the new metaheuristic bat algorithm (BA) over other more “standard” algorithms in neural network training. In this work we tackle this problem with five algorithms, and try to over a set of results that could hopefully foster future comparisons by using a standard dataset (Proben1: selected benchmark composed of problems arising in the field of Medicine) and presentation of the results. We have selected two gradient descent algorithms: Back propagation and Levenberg-Marquardt, and three population based heuristic: Bat Algorithm, Genetic Algorithm, and Particle Swarm Optimization. Our conclusions clearly establish the advantages of the new metaheuristic bat algorithm over the other algorithms in the context of eLearning.

This paper presents a comparison of the three fuzzy based image segmentation methods namely Fuzzy C-Means (FCM), TYPE-II Fuzzy C-Means (T2FCM), and Intuitionistic Fuzzy C-Means (IFCM) for digital images with varied levels of noise. Apart from qualitative performance, the paper also presents quantitative analysis of these three algorithms using four validity functions-Partition coefficient (V_pc), Partition entropy (V_pe), Fukuyama-Sugeno (V_fs), and Xie-Beni (V_xb) functions and also compared the performance on the basis of their execution time.

Many media forms can be stored easily at present. Photographs, for example, can be easily stored even though most of them have not been edited. This means they will gradually lose their value and become essentially unusable. To make better use of photographs, we tried to make use of information provided by viewers who had seen and commented on them. We felt that analyzing this information would enable us to make maximum use of photographic data. To do this, we defined a ''tag propagation'' model and relationships between photos. We also proposed a system that uses image processing to analyze viewers' handling of photos and how the photos are relevant to each other. We then validated our model by using it.

The development of a fuzzy-logic controller for a class of industrial hydraulic manipulator is described. The main element of the controller is a PI-type fuzzy control technique which utilizes a simple set of membership functions and rules to meet the basic control requirements of such robots. Using the triangle shaped membership function, the position of the servocylinder was successfully controlled. When the system parameter is altered, the control algorithm is shown to be robust and more faster compared to the traditional PID controller. The robustness and tracking ability of the controller were demonstrated through simulations.

This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qualitatively. These algorithms are implemented on synthetic images in case of without noise along with Gaussian and salt and pepper noise for better review and comparison. Based on outputs best algorithm is suggested.

In our daily lives, we enjoy the service of thousands of devices and systems that have made our lives easier and more comfortable. Electric fan is one of the most popular and used systems in developing countries like Bangladesh for its cost effectiveness and low power consumption. In the era of twenty-first century we expect all of our living and working systems will be intelligent when it will provide the service. We have developed a fuzzy inference system that effectively and intelligently controls the rotating speed of an electric fan according to the temperature of environment and its relative humidity. We used experimental data and verified the experimental data with different mathematical procedure to ensure that our result is well enough. We designed a simulation system to test the result but it can be easily implemented on hardware level, since fuzzy logic toolbox provides such facility.