This article presents a new method, which reduces costs and processing time for spatial object motion detection by focusing on the bare-hand motion that mimics computer mouse functions to allow the user to move the mouse pointer in real-time by the motion of his/her hand without any gloves worn, any object carried, or any key hit. In this article, the study of this topic is from the viewpoint of computer vision and image processing. The principals of the difference of the absolute differences (DAD) are investigated. A new method based on the DAD principles, which is conceptually different from all the existing approaches to spatial object motion detection, is developed and applied successfully to the bare-hand motion. The real-time implementation of the bare-hand motion detection demonstrates the accuracy and efficiency of the DAD method.

This paper presents a self-tuning method of fuzzy logic controllers. The consequence part of the fuzzy logic controller is self-tuned through the Q-learning algorithm of reinforcement learning. The off policy temporal difference algorithm is used for tuning which directly approximate the action value function which gives the maximum reward. In this way, the Q-learning algorithm is used for the continuous time environment. The approach considered is having the advantage of fuzzy logic controller in a way that it is robust under the environmental uncertainties and no expert knowledge is required to design the rule base of the fuzzy logic controller.

Software cost estimation is an important phase in software development. It predicts the amount of effort and development time required to build a software system. It is one of the most critical tasks and an accurate estimate provides a strong base to the development procedure. In this paper, the most widely used software cost estimation model, the Constructive Cost Model (COCOMO) is discussed. The model is implemented with the help of artificial neural networks and trained using the perceptron learning algorithm. The COCOMO dataset is used to train and to test the network. The test results from the trained neural network are compared with that of the COCOMO model. The aim of our research is to enhance the estimation accuracy of the COCOMO model by introducing the artificial neural networks to it.

The simulation of wetland landscape spatial-temporal distribution not only can reveal the mechanisms and laws of landscape evolution, but achieve the sustainable land use as well as provide supports for wetland conservation and management. In this report, the inland freshwater wetlands in the Sanjiang Plain of China were selected for wetland landscape changing process simulation studies. Results showed that both visual effects of simulation and prediction were good and the total accuracy co-efficiency of points to points was also significantly high (above 82%), which demonstrated the feasibility and effectiveness of wetland landscape spatial-temporal distribution simulation using Multi-Agent System (MAS). Scales exerted influence on visual effects, simulation accuracies and statistics of landscape index. Scale effects were obvious during simulation process using MAS. It was demonstrated that 60m was the best scale for simulation. It was shown that contagion index lines were exponential distribution while accuracy lines were lognormal distribution with the scale rising, which provided a reference for scale effect assessment and simulation scale selection.

Clustering of huge spatial databases is an important issue which tries to track the densely regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. Clustering approach should be efficient and can detect clusters of arbitrary shapes because spatial objects cannot be simply abstracted as isolated points they have different boundary, size, volume, and location. In this paper we use discrete wave atom transformation technique in clustering to achieve more accurate result .By using multi-resolution transformation like wavelet and wave atom we can effectively identify arbitrary shape clusters at different degrees of accuracy. Experimental results on very large data sets show the efficiency and effectiveness of the proposed wave atom bases clustering approach compared to other recent clustering methods. Experimental result shows that we get more accurate result and denoised output than others.

The task of medical diagnosis, unlike other diagnostic processes is more complex because a lot of vagueness, linguistic uncertainty, subjectivity, measurement imprecision, natural diversity are all prominently present in medical diagnosis. Osteoarthritis (OA) of the knee is a major public health issue causing chronic disability and reduction in quality of life; it is reported to be associated with a significant decline in function and causes a higher rate of disability than any other chronic condition. Osteoarthritis (OA) exacts a cost in terms of pain, limited mobility, and decreased function among a wide range of individuals. With improvement in science and technology, intelligent computing has been used to assist in enhancing qualitative services.

This paper reports the development of a fuzzy-based system to determine the level of severity of knee osteoarthritis, given some input conditions. The system was implemented and simulated using MATLAB Fuzzy Logic Toolbox. The results are entrusting and promising based on the flexibility and case of adaptability.

With the recent surge in acceptance of face recognition systems, more and more work is needed to perfect the existing grey areas. A major concern is the issue of illumination intensities in the images used as probe and images trained in the database. This paper presents the adoption and use of fuzzy histogram equalization in combating illumination variations in face recognition systems. The face recognition algorithm used is Principal Component Analysis, PCA. Histogram equalization was enhanced using some fuzzy rules in order to get an efficient light normalization. The algorithms were implemented and tested exhaustively with and without the application of fuzzy histogram equalization to test our approach. A good and considerable result was achieved.

Search engine is one of the most important tools for managing the massive amount of distributed web content. Web spamming tries to deceive search engines to rank some pages higher than they deserve. Many methods have been proposed to combat web spamming and to detect spam pages. One basic one is using classification, i.e., learning a classification model for classifying web pages to spam or non-spam. This work tries to select the best feature set for classification of web spam using imperialist competitive algorithm and genetic algorithm. Imperialist competitive algorithm is a novel optimization algorithm that is inspired by socio-political process of imperialism in the real world. Experiments are carried out on WEBSPAM-UK2007 data set, which show feature selection improves classification accuracy, and imperialist competitive algorithm outperforms GA.

This paper presents the design of low noise amplifier (LNA) at 2.45 GHz and integrated at 0.18 µm RF CMOS process technology. This type of LNA at 2.45 GHz is use in the Bluetooth receiver. The proposed method is useful to optimize noise performance and power gain while maintaining good input and output matching. The amplifier is designed to be used as first stage of a receiver for wireless communication. The main aim of designer is to achieve low noise figure with improved gain with the help of CMOS technology by using single stage n-MOS amplifier. The simulation results show a forward gain of 14.0 dB, a noise-figure of 0.5 dB and stability factor is approximate unity, in which the circuit operates at 14.2 mA drain current with supply voltage of 3.5 V and biasing voltage of 1.5 V.

The Indian textile industry has a significant presence in the economy as well as in the international textile economy. In this research Paper we study the socio economic problems faced by power loom workers in Avinashi in Tamilnadu, India, using Induced Fuzzy Cognitive Maps (IFCMs). We have interviewed 50 households in the study area using a linguistic questionnaire. As the problems faced by them at large, involved so much of feelings and uncertainties. We felt it to fit to use fuzzy theory in general and induced fuzzy cognitive maps in particular. For IFCMs is the best suited tool when the data is an unsupervised one.

In robotic applications and research, inverse kinematics is one of the most important problems in terms of robot kinematics and control. Consequently, finding the solution of Inverse Kinematics in now days is considered as one of the most important problems in robot kinematics and control. As the intricacy of robot manipulator increases, obtaining the mathematical, statistical solutions of inverse kinematics are difficult and computationally expensive. For that reason, now soft-computing based highly intelligent based model applications should be adopted to getting appropriate solution for inverse kinematics. In this paper, a novel application of artificial neural network is used for controlling a robotic manipulator. The proposed methods are based on the establishments of the non-linear mapping between Cartesian and joint coordinates using multi layer perceptron and functional link artificial neural network.

It is quite common to have access to geospatial (temporal/spatial) panel data generated by a set of similar data for analyses in a meta-data setup. Within this context, researchers often employ pooling methods to evaluate the efficacy of meta-data analysis. One of the simplest techniques used to combine individual-study results is the fixed-effects model, which assumes that a true-effect is equal for all studies. An alternative, and intuitively-more-appealing method, is the random-effects model. A paper was presented by the first author, and his co-authors addressing the efficient estimation problem, using this method in the aforesaid meta-data setup of the ‘Geospatial Data’ at hand, in Map World Forum meeting in 2007 at Hyderabad; INDIA. The purpose of this paper had been to address the estimation problem of the fixed-effects model and to present a simulation study of an efficient confidence-interval estimation of a mean true-effect using the panel-data and a random-effects model, too in order to establish appropriate ‘confidence interval’ estimation for being readily usable in a decision-makers’ setup. The present paper continues the same perspective, and proposes a much more efficient estimation strategy furthering the gainful use of the ‘Geospatial Panel-Data’ in the Global/Continental/ Regional/National contexts of “Socioeconomic & other Developmental Issues’. The ‘Statistical Efficient Confidence Interval Estimation Theme’ of the paper(s) has a wider ambit than its applicability in the context of ‘Socioeconomic Development’ only. This ‘Statistical Theme’ is, as such, equally gainfully applicable to any area of application in the present world-order at large inasmuch as the “Data-Mapping” in any context, for example, the issues in the topically significant area of “Global Environmental Pollution-Mitigation for Arresting the Critical phenomenon of Global Warming”. Such similar issues are tackle-able more readily, as the impactful advances in the “GIS & GPS” technologies have led to the concept of “Managing Global Village” in terms of ‘Geospatial Meta-Data’. This last fact has been seminal to special zeal-n-motivation to the authors to have worked for this improved paper containing rather a much more efficient strategy of confidence-interval estimation for decision-making team of managers for any impugned area of application.