In this paper, ARIMA(1,1,1) model and Artificial Neural Network (ANN) models like Multi Layer Perceptron (MLP), Functional-link Artificial Neural Network (FLANN) and Legendre Polynomial Equation ( LPE) were used to predict the time series data. MLP, FLANN and LPE gave very accurate results for complex time series model. All the Artificial Neural Network model results matched closely with the ARIMA(1,1,1) model with minimum Absolute Average Percentage Error(AAPE). Comparing the different ANN models for time series analysis, it was found that FLANN gives better prediction results as compared to ARIMA model with less Absolute Average Percentage Error (AAPE) for the measured rainfall data.

Although planning techniques achieved a significant progress during recent years, solving many planning problem still difficult even for modern planners. In this paper, we will adopt landmark concept to hybrid planning setting - a method that combines reasoning about procedural knowledge and causalities. Landmarks are a well-known concept in the realm of classical planning. Recently, they have been adapted to hierarchical approaches. Such landmarks can be extracted in a pre-processing step from a declarative hierarchical planning domain and problem description. It was shown how this technique allows for a considerable reduction of the search space by eliminating futile plan development options before the actual planning. Therefore, we will present a new approach to integrate landmark pre-processing technique in the context of hierarchical planning with landmark technique in the classical planning. This integration allows to incorporate the ability of using extracted landmark tasks from hierarchical domain knowledge in the form of HTN and using landmark literals from classical planning. To this end, we will construct a transformation technique to transform the hybrid planning domain into a classical domain model. The methodologies in this paper have been implemented successfully, and we will present some experimental results that give evidence for the consid-erable performance increase gained through planning system.

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

A problem of upgrading to the Next Generation Wireless Network (NGWN) is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, I propose a new genetic algorithm based on a combination of two populations to solve multi-objective optimization infrastructure upgrade problem in NGWN. Network topology model has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. My objective function is the costs of connection from sources to concentrators such as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. I generate two populations satisfies constraints and combine its to build solutions and evaluate the performance of my algorithm with data randomly generated. The experimental results show that this approach is appropriate and effective Finally, I have applied this algorithm to planning of upgrade infrastructure in telecommunication networks in Haiphong city.

Wireless Body Area Network has the ability to collect and send data on body measurement to the server through PDA or other device. Nodes (sensors) collect vital signs from the body or environmental factor and check them. In IEEE 802.15.6 routing is discussed as a part of the link layer where multihop is not fully considered. Improving network performance, reducing energy consumption, thus extending the network lifetime is the main challenge in BANs. Several studies mention that multihop for BANs helps for achieving network performance, reducing energy consumption and extending network lifetime. One work presents the Adaptive multihop tree-based Routing (AMR) protocol that is extensively evaluated in a real testbed deployment. They use fuzzy logic to combine all metrics they use. Another limitation is that they have used Prim's algorithm which is not a realistic approach. So in this work we have improved their multihop tree-based Routing (AMR) protocol using Kruskal's algorithm instead of Prim's algorithm. The time complexity of Kruskal's algorithm is way less than prims's algorithm. We have used network simulator 3 (NS3) to simulate and found that our algorithm is better than AMR if many of nodes.

Owing to its outstanding performance and rich features, Smart phones have been the rapid development over the past decade, more and more people love to use its mobile Application to process the day-to-day affairs instead of PC, including the normal call and Short Messaging Service, Personal Information Management, send and receive e-mail, browse Web, multimedia Applications and online shopping. In April 2011, the Taipei people began to use the free smart phone Apps “Good Travel in Taipei” to check the real-time traffic information of Taipei City Department of Transportation and get the best route plans according to your location, the Apps software brings together road, bus, subway, bike, high-speed rail, airport, parking and other traffic information, can be easily, simple and fast delivery to the public. The papers will introduce the case of “Good Travel in Taipei” firstly, then Zhengzhou is as an example in China to illustrate the Application of integrated traffic information service system for public travel based on smart phones, we hope it can provide a reference for the future construction of the similar mobile App of traffic information service system in the other cities for public travel.

This paper describes a method for semantic analysis of natural language queries for Natural Language Interface to Database (NLIDB) using domain ontology. Implementation of NLIDB for serious applications like railway inquiry, airway inquiry, corporate or government call centers requires higher precision. This can be achieved by increasing role of language knowledge and domain knowledge at semantic level. Also design of semantic analyzer should be such that it can easily be ported for other domains as well. In this paper a design of semantic analyzer for railway inquiry domain is reported. Intermediate result of the system is evaluated for a corpus of natural language queries collected from casual users who were not involved in the system design.

Control of robotic manipulator is very important in field of robotic, because robotic manipulators are multi-input multi-output (MIMO), nonlinear and most of dynamic parameters are uncertainty. Today, robot manipulators used in unknown and unstructured environment which caused to provides sophisticated systems, therefore strong mathematical tools used in new control methodologies to design adaptive nonlinear robust controller with acceptable performance (e.g., minimum error, good trajectory, disturbance rejection). One of the best nonlinear robust controller which can be used in uncertainty nonlinear systems, are sliding mode controller but pure sliding mode controller has some disadvantages therefore this research focuses on the design fuzzy sliding mode controller. One of the most important challenging in pure sliding mode controller and sliding mode fuzzy controller is sliding surface slope. This paper focuses on adjusting the sliding surface slope in sliding mode fuzzy controller to have the best performance and reduce the limitation.

To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI) based techniques such as fuzzy logic whereas on the other hand adaptive mechanism ensures adjusting of the controller parameters. A self-organized fuzzy logic controller (SOFLC) is intelligent in nature and adapts its performance to meet the figure of merit. This paper presents an optimized SOFLC for pH control using performance correction table. The fuzzy adaptation mechanism basically involves a penalty for the output membership functions if the controller performance is poor. The evolutionary genetic algorithm (GA) is used for optimization of input-output scaling factors of the conventional fuzzy logic controller (FLC) as well as elements of the fuzzy performance correction table. The resulting optimized SOFLC is compared with optimized FLC for servo and regulatory control. Comparison indicate superior performance of SOFLC over FLC in terms of much reduced integral of squared error (ISE), maximum overshoot and undershoot, and increased speed of response.

Advances in FPGA technology have dramatically increased the use of FPGAs for computer vision applications. Availability of on-chip processor (like PowerPC) made it possible to design embedded systems using FPGAs for video processing applications. The objective of this research is to evaluate the performance of different memory components available on FPGA boards for embedded/platform-based implementations of image/video processing applications. The clustering based change detection algorithm for Ubiquitous Multimedia Environment is selected for evaluating the effect of different memory components (DDR/BRAM) on performance of the system in terms of frame rate (frames per second).

Target tracking is very important field of research as it has wider applications in defense as well as civilian applications. Kalman filter is generally used for such applications. When the process and measurements are non linear extensions of Kalman filters like Extended Kalman Filter, Unscented Kalman Filters are widely used. UKF can give estimations up to second order characteristics of random process. The target is maneuvering and switching among different models like constant velocity (CV), constant acceleration (CA) or constant turn (CT), Interactive Multiple Models (IMM) are employed. Implementation of IMM filters for any application is difficult because of initialization of Kalman filter i,e, tuning of filter has to be performed before applying to real time situations. It demands prior estimations of Noise covariance matrices which are left for engineering intuitions. This paper presents the nonlinear state estimation using IMM and tuning of the filter is done using bio-inspired algorithms like PSO GA and Hybrid GA-PSO.

Refer to this research, a gradient descent optimization methodology for position fuzzy- model based computed torque controller (GDFCTC) is proposed for highly nonlinear continuum robot manipulator. The main problem of the pure computed torque controller (CTC) was equivalent problem in uncertain systems. The simulation results exhibit that the CTC works well in certain system. To eliminate the continuum robot manipulator system’s dynamic; Mamdani fuzzy inference system is design and applied to CTC. This methodology is based on applied fuzzy logic in equivalent nonlinear dynamic part to estimate unknown parameters. This relatively controller is more plausible to implement in an actual real-time when compared to other techniques of nonlinear controller methodology of continuum arms. Based on the gradient descent optimization method, the PD-gain updating factor has been developed in certain and partly uncertain continuum robots. The new techniques proposed and methodologies adopted in this paper supported by MATLAB/SIMULINK results represent a significant contribution to the field of design an optimized nonlinear computed torque controller for continuum robots.