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The Self-organizing map (SOM) has been extensively applied to image analysis, data clustering, dimension reduction, and so forth. The conventional SOM find the winner neuron and update the weights of winner and its neighborhood regardless of distance from input. In this study, we propose a modified SOM which calculate the distance from input data and find the nearest neuron among neighborhood of winner neuron (BMU). It also calculates the winning frequency of each neuron. We apply modified SOM to various input data set and investigate the performance of both SOM using three standard measurements. We conclude that modified SOM reaches to all input data in better way compare to conventional SOM. The modified SOM preserves the input topology in much better way compare to conventional SOM. The modified SOM self organize in better way than the conventional SOM in every corner of the input data.

An improved analytical two dimensional (2-D) model for AlGaN/GaN modulation doped field effect transistor (MODFET) has been developed. The model is based on the solution of 2-D Poisson’s equation. The model includes the spontaneous and piezoelectric polarization effects. The effects of field dependent mobility, velocity saturation and parasitic resistances are included in the current voltage characteristics of the developed two dimensional electron gas (2-DEG) model. The small-signal microwave parameters have been evaluated to determine the output characteristics, device transconductance and cut-off frequency for 50 nm gate length. The peak transconductance of 165mS/mm and a cut-off frequency of 120 GHz have been obtained. The results so obtained are in close agreement with experimental data, thereby proving the validity of the model.

The Self-organizing map (SOM) has been extensively applied to data clustering, image analysis, dimension reduction, and so forth. The conventional SOM does not calculate the winning frequency of each neuron. In this study, we propose a modified SOM which calculate the winning frequency of each neuron. We investigate the behavior of modified SOM in detail. The learning performance is evaluated using the three measurements. We apply modified SOM to various input data set and confirm that modified SOM obtain a more effective map reflecting the distribution state of the input data.

In wireless communication, broadcasting is one of the most suitable solutions of information dissemination. It is very attractive for limited resource handheld mobile devices in asymmetric communications. Access time and Tuning time are two criteria to evaluate the performance of air indexing technique. Indexing can reduce tuning time of the mobile devices by switching mobile device into doze mode while waiting for desired of data to arrive and active mode while desired of data has presented. Some air indexing techniques can save limited battery power while incurring only limited overhead on access time. In this paper, we discuss and analyze energy efficient air indexing techniques for single and multi-level wireless channel. The basic indexing techniques i.e. B+ tree, the hashing and the signature schemes of data broadcasting are compared between Single and Multi-level channel.

The strength of Self Organizing Map (SOM) learning algorithm completely depends on the weights adjustments done in its network. Prior to the weight adjustments done, important step is to initialize the values to the weight. The choice of these initial values for weight vectors affects the performance of SOM training when applied to clustering. This paper proposes a different approach for initializing SOM. This approach depends on Frequency Sensitive Competitive Learning (FSCL) algorithm to pre-process the weights in order to improve the results obtained from trained input patterns in terms of better neuron utilization and less quantization and topographic error. Two datasets are used to analyze the performance of SOM algorithm. First dataset is evenly distributed 2D Gaussian data and second dataset is taken from the well reputed Engineering Educational Organization. Applying existing approaches of weight initializations, results obtained with first dataset showed that decreasing learning rate to a specific value gives better performance further but with second dataset results did not improve on decreasing learning rate. But with this new approach, results showed significant improvement as compared to the existing approaches of weight initialization.

The evaluation of software is important for enhancing the modification and improvement in a software development process. There are many factors to evaluate a software process. One of the factors is the Quality of software, which cannot be calculated with ease; as Quality of software is dependent on other factors. Software Usability is one of the significant aspects on which quality of software depends. A number of software usability models have been proposed by a number of researchers, each model considers a set of factors. In real world, we are facing many obstacles in implementing any of these proposed usability models as there is a lack in its precise definition and the concept of globally accepted usability. This paper aims to define the term ‘usability’ using a detailed taxonomy which includes all the aspects of usability and is globally accepted. Generalized Usability Model (GUM) with taxonomy has been proposed in this paper. This paper also shows how to determine the usability of a software application using a fuzzy based system which has been implemented using multistage fuzzy logic toolbox.