The Discrete wavelet transform has great capability to analyse the temporal and spectral properties of non stationary signal like ECG. In this paper, we have developed and evaluated a robust algorithm using multiresolution analysis based on the discrete wavelet transform (DWT) for twelve-lead electrocardiogram (ECG) temporal feature extraction. In the first step, ECG was denoised considerably by employing kernel density estimation on subband coefficients then QRS complexes were detected. Further, by selecting appropriate coefficients and applying wave segmentation strategy P and T wave peaks were detected. Finally, the determination of P and T wave onsets and ends was performed. The novelty of this approach lies in detection of different morphologies in ECG wave with few decision rules. We have evaluated the algorithm on normal and abnormal beats from various manually annotated databases from physiobank having different sampling frequencies. The QRS detector obtained a sensitivity of 99.5% and a positive predictivity of 98.9% over the first lead of the MIT-BIH Arrhythmia Database.

Nowadays use of distributed systems such as internet and cloud computing is growing dramatically. Coordinator existence in these systems is crucial due to processes coordinating and consistency requirement as well. However the growth makes their election algorithm even more complicated. Too many algorithms are proposed in this area but the two most well known one are Bully and Ring. In this paper we propose a fault tolerant coordinator election algorithm in typical bidirectional ring topology which is twice as fast as Ring algorithm although far fewer messages are passing due to election. Fault tolerance technique is applied which leads the waiting time for the election reaching to zero.

Nowadays, there are several models of computer systems finding their ways into various offices, houses, organizations as well as remote locations. Any slight malfunction of the computer system’s components could lead to loss of vital data and information. One of the sources of computer system malfunction is overheating of the electronic components. A common method of cooling a computer system is the use of cooling fan(s). Therefore, it is essential to have an appropriate control mechanism for the operation of computer system’s cooling fan in order to save energy, and prevent overheating. Failure to adopt a well designed and efficient performance controller could lead to the malfunction of a computer system. Presently, most controllers in computer systems are pulse width modulation based. That is, they make use of pulses in form of digits, 0 and 1. It was observed that inherent noise is still prevalent in the operation of computer system. Also, eventual breakdown of components is common. A new approach is therefore investigated through the use of fuzzy logic to serve as a base or platform to build an intelligent controller using a set of well defined rules to guide its operational performance. Mamdani-type fuzzy inference system and Sugeno-type fuzzy inference system were used with two input sets each and a single output function each. Simulation was carried out in MATLAB R2007a platform and operational performances of the two approaches were compared. Simulated results of the performances of the Mamdani-type fuzzy inference system based controller and the Sugeno-type fuzzy inference system based controller are presented accordingly.

In this paper, an all-optical regenerator based, photonic packet switch architecture, which consists of the fiber loop for the storage of the contending packets, is considered. In the loop buffer, the available buffer space may not be fully utilized due to the limited re-circulation count of the data placed on buffer. This limit can be counteracted by placing a pool of regenerators inside the buffer. As optical regenerators are costly devices, hence they should be placed optimally in the buffer. The simulations results are presented by consider Prioritized and non – prioritized traffic. It is shown in the results that regeneration of data is essential if prioritized traffic has to be considered.

Land Surface Temperature is a key parameter in energy budget models, estimating soil moisture, forest detection and forecasting, monitoring the state of the crops and many other applications. In this work, a method has been developed to retrieve land surface temperature using the National Oceanic and Atmospheric Administration (NOAA) of USA, Advanced Very High Resolution Radiometer (AVHRR) satellite images. The land surface temperature has been determined taking AVHRR images from February to December in a year. Seasonal variations have also been studied. The results have been presented in the false color composition (fcc) map for each month. Studying the resulting maps it is found that there is a marked spatial and seasonal variations in the surface temperature among different regions of the country. We could not compare our result with the measured data as, till now, no measured data of surface temperature is available for our country. But it is observed that our result is well in conformity with the observed air temperature of the country. This method can thus be used for long term monitoring of surface parameters for the country. The long term study of surface temperature can be very much important for sustainable land resources development and to understand the effect of possible environmental changes.

Exponential accumulation of biological data requires computer scientists and bioinformaticians to improve the efficiency of computer algorithms and databases. The recent advancement of computational tools has boosted the processing capacity of enormous volume of genetic data. This research applied a computational approach to analyze the tandem repeat patterns in Nlrc4 gene. Because the protein product of Nlrc4 gene is important in detecting pathogen and triggering subsequent immune responses, the results of this genetic analysis is essential for the understanding of the genetic characteristics of Nlrc4. The study on the distribution of tandem repeats may provide insights for drug design catered for the Nlrc4-implicated diseases.

Internal combustion (IC) engines are optimized to meet exhaust emission requirements with the best fuel economy. Closed loop combustion control is a key technology that is used to optimize the engine combustion process to achieve this goal. In order to conduct research in the area of closed loop combustion control, a control oriented cycle-to-cycle engine model, containing engine combustion information for each individual engine cycle as a function of engine crank angle, is a necessity. This research aims to design a new methodology to fix the fuel ratio in internal combustion (IC) engine. Baseline method is a linear methodology which can be used for highly nonlinear system’s (e.g., IC engine). To optimize this method, new linear part sliding mode method (NLPSM) is used. This online optimizer can adjust the optimal coefficient to have the best performance.

The histopathological diagnosis of breast diseases requires highly trained and experienced experts, and often strains pathologists’ cognitive capabilities. Accurate and timely diagnosis of breast diseases is essential for the appropriate management of the patients.
The paper presents a knowledge base system that uses a combination of rule-based and case-based techniques to achieve the diagnosis. Rule-based systems handle problems with well-defined knowledge bases this limits the flexibility of such system. Case-based reasoning has been adopted to overcome this inherent weakness of rule-based systems by incorporating previous cases in the generation of new cases to improve the performance of the system. The result of this research shows that the system is capable of assisting pathologists in making accurate, consistent and timely diagnoses. The system also aid in eliminating errors of omission that have been viewed as a prominent cause of medical errors. In conclusion this paper investigated the histological features used in the diagnosis of breast diseases and proposed an integrated knowledge base system based on the features.