Earticle

RUP is based on UP and OOP methodology in order to develop an application whose scope spans to enterprise level but it is labeled as a immature and disorganize development as it involves rework, provided any shortcomings are found in the accomplished phases. Moreover it is observed that risk identification and resolution is not properly handled in RUP as its analysis has no roots towards mathematical framework and no statistical grounds for correctness and reliability. The RUCM, Rational Unified Cleanroom Model- The proposed model, comprises of best practices and principles both of CRM and RUP. Mathematical framework, Mathematical modeling, quantification, statistical test, certification process are the implemented best practices which ultimately leads towards the complete customer satisfaction and a defect rate of zero percentage.

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods.

As the information age matures, biometric identification technology will be at the heart of computer interaction with humans and the biosphere in which they reside. Automated biometric systems for human identification measure a “signature” of the human body, compare the resulting characteristic to a database, and render an application dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are typically distinctive, although time varying, such as fingerprints, hand geometry, face, voice, lip movement, gait, and iris patterns. Multi-biometric systems, which consolidate information from multiple biometric sources, are gaining popularity because they are able to overcome limitations such as non-universality, noisy sensor data, large intra-user variations and susceptibility to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the con-cept issues and the applications strategies of multi-biometric systems.

Disabled students in higher education are faced with a lot of difficulties accessing learning resources when e-learning systems are inaccessible. When instructional designers and developers of e-learning systems overlook the needs of disabled students, this leads to exclusion in what is termed disability divide. This paper reviews some disabilities encountered in higher education and assistive technologies used in accessing e-learning environments and presents disabled students’ recommendations on designing inclusive e-learning systems, obtained during the user evaluation of a disability-aware e-learning software. It is hoped that these recommendations would be adopted by designers and developers of e-learning and web-based systems so that they can meet the needs of disabled students.

Optical character recognition (OCR) refers to a process of generating a character input by optical means, like scanning, for recognition in subsequent stages by which a printed or handwritten text can be converted to a form which a computer can understand and manipulate. A generic character recognition system has different stages like noise removal, skew detection and correction, segmentation, feature extraction and classification. Results of the later stages can affect the performance of the subsequent stages in the OCR process. To make the results of the subsequent stages more accurate, the skew detection and correction and segmentation play an important role. In this paper, we have proposed schemes for skew detection and correction, segmentation of handwritten Kannada document using bounding box technique, Hough transform and contour detection respectively. An average segmentation rate of 91% and 70% for lines and words is obtained respectively.

This paper presents an Enhanced version of the Embedded Zerotree Wavelet (EEZW) dedicated to Phonocardiogram (PCG) compression. This algorithm is constituted of six steps: applying Discrete Wavelet Transform, uniform scalar quantizing, creating dominant and subordinate lists, generating preliminary symbol stream and finally using arithmetic coding. The compression algorithm has been tested and evaluated by its application on several databases. The adapted 1-D algorithm shows better performance than the original EZW, the modified Ouafi’s EZW (MEZW) and the popular MP3 audio-specific format, regarding to the well-known trade-off rate/distortion. Also a low computational complexity of our method is obtained. It is due to the only one time subordinate pass calculated one time after the achievement of all dominant passes.

Due to the motion of gas-bubbles and the particles back-mixing, the particles flow pattern through the plug flow fluid bed dryer deviates from the ideal plug flow and is considered as a dispersed plug flow. In this study, the residence time distribution (RTD) characteristics, flow pattern, and dispersion coefficient of rough rice in a plug flow fluid bed dryer under various experimental conditions were investigated. The effects of solid feed rate (46, 96, and 135 g/min), weir height (5 and 10 cm), and inlet solid moisture content (10 and 30% d.b) on RTD were studied using the pulse input of tracer (dye-coated rough rice). The flow of particles in the dryer is a dispersed plug flow with large deviation from ideal plug flow. The values of dispersion coefficient range from 2.60×10−4 to 4.50×10−4 m2/s over the investigated conditions. The flow in the dryer approaches plug flow with increase in the solid feed rate, decrease in the weir height, and increase in the inlet solid moisture content.

Job checkpointing is one of the most common utilized techniques for providing fault tolerance in computational grids. The efficiency of checkpointing depends on the choice of the checkpoint interval. Inappropriate checkpointing interval can delay job execution. In this paper, a fault-tolerant job scheduling system based on checkpointing technique is presented and evaluated. When scheduling a job, the system uses both average failure time and failure rate of grid resources combined with resources response time to generate scheduling decisions. The system uses the failure rate of the assigned resources to calculate the checkpoint interval for each job. Extensive simulation experiments are conducted to quantify the performance of the proposed system. Experiments have shown that the proposed system can considerably improve throughput, turnaround time and failure tendency.

Current Floating-point divisor architectures have low frequency, larger area and high latency in nature. With advent of more graphic, scientific and medical applications, floating point dividers have become indispensable and increasingly important. However, most of these modern applications need higher frequency or low latency of operations with minimal area occupancy. In this work, highly optimized pipelined architecture of an IEEE-754 standard double precision floating point divider is designed to achieve high frequency on FPGAs. By using secondary clock to perform mantissa division the overall latency of the divisor is reduced to 30 clock cycles, i.e. 52% less compared to conventional divisors. This design is mapped onto a Virtex-6 FPGA and an operating frequency of 452.69 MHz is achieved. The proposed design also handles all the IEEE specified four rounding modes, overflow, underflow and various exception conditions.

As more organizations and businesses are using computer networks and the Internet, the need for a secure computing environment must also be increased. A secure computing environment must consist of the provisions and policies to prevent and monitor unauthorized access, misuse, modification, and distribution of computing resources. This paper presents a security framework for securing computer networks, to guide organizations, businesses and individuals to prevent information leak or being passed to unauthorized users.

This paper presents a development of a self-tuning fuzzy PID controller to overcome the appearance of nonlinearities and uncertainties in the system. The self-tuning fuzzy PID controller is the combination of a classical PID and fuzzy controller. The controller is designed based on the expert knowledge of the system. Fuzzy logic is used to tune each parameter of PID controller. Appropriate fuzzy rules are designed to tune the parameter Kp, Ki and Kd of the PID controller, the performance of the buck converter has improved significantly compare to conventional PID controller.

This paper develops a novel framework for feature extraction based on a combination of Linear Discriminant Analysis and cross-correlation. Multiple Electrocardiogram (ECG) signals, acquired from the human heart in different states such as in fear, during exercise, etc. are used for simulations. The ECG signals are composed of P, Q, R, S and T waves. They are characterized by several parameters and the important information relies on its HRV (Heart Rate Variability). Human interpretation of such signals requires experience and incorrect readings could result in potentially life threatening and even fatal consequences. Thus a proper interpretation of ECG signals is of paramount importance. This work focuses on designing a machine based classification algorithm for ECG signals. The proposed algorithm filters the ECG signals to reduce the effects of noise. It then uses the Fourier transform to transform the signals into the frequency domain for analysis. The frequency domain signal is then cross correlated with predefined classes of ECG signals, in a manner similar to pattern recognition. The correlated co-efficients generated are then thresholded. Moreover Linear Discriminant Analysis is also applied. Linear Discriminant Analysis makes classes of different multiple ECG signals. LDA makes classes on the basis of mean, global mean, mean subtraction, transpose, covariance, probability and frequencies. And also setting thresholds for the classes. The distributed space area is divided into regions corresponding to each of the classes. Each region associated with a class is defined by its thresholds. So it is useful in distinguishing ECG signals from each other. And pedantic details from LDA (Linear Discriminant Analysis) output graph can be easily taken in account rapidly. The output generated after applying cross-correlation and LDA displays either normal, fear, smoking or exercise ECG signal. As a result, the system can help clinically on large scale by providing reliable and accurate classification in a fast and computationally efficient manner. The doctors can use this system by gaining more efficiency. As very few errors are involved in it, showing accuracy between 90% - 95%.