An energy optimization with improved QOS approach for adaptive cloud resources

In recent times, the utilization of cloud computing VMs is extremely enhanced in our day-to-day life due to the ample utilization of digital applications, network appliances, portable gadgets, and information devices etc. In this cloud computing VMs numerous different schemes can be implemented like multimedia-signal-processing-methods. Thus, efficient performance of these cloud-computing VMs becomes an obligatory constraint, precisely for these multimedia-signal-processing-methods. However, large amount of energy consumption and reduction in efficiency of these cloud-computing VMs are the key issues faced by different cloud computing organizations. Therefore, here, we have introduced a dynamic voltage and frequency scaling (DVFS) based adaptive cloud resource re-configurability (ACRR) technique for cloud computing devices, which efficiently reduces energy consumption, as well as perform operations in very less time. We have demonstrated an efficient resource allocation and utilization technique to optimize by reducing different costs of the model. We have also demonstrated efficient energy optimization techniques by reducing task loads. Our experimental outcomes shows the superiority of our proposed model ACRR in terms of average run time, power consumption and average power required than any other state-of-art techniques

A Typology of Criteria used by Microfinance Institutions to Evaluate Potential Microentrepreneurs

The purpose of this research is to identify and assess microfinance institution (MFI) loan standards and develop a typology of how MFIs evaluate potential microentrepreneurs for lending micro loans. A knowledge gap exists between microentrepreneurs and MFIs such that potential microentrepreneurs do not know or understand how they are evaluated by MFIs, and that differences exist in evaluation criteria among MFIs. Because the relationship between MFIs and microentrepreneurs is a routine one, this research addresses this issue by providing a coarse-grained typology that helps bridge this knowledge gap so that microentrepreneurs can (1) gain micro loans and (2) entrepreneurship can thrive in the emerging markets in which MFIs operate. To address this issue, interview data were collected from MFI representatives from across the globe. These data were supplemented with information from both scholarly and practitioner-oriented articles. From these data, a typology was developed on the foundations of diverse data and information gathered via methodology discussed above. Comprehensively, the proposed typology bridges the gap between MFIs and microentrepreneurs in an effort to provide understanding that can facilitate entrepreneurship and impact economic growth in emerging markets

Investigation on the quality of videoconferencing over the Internet and intranet environments

This study deals with the scope and feasibility of video-conferencing on the Internet and Intranet, for a real-time implementation of a classroom atmosphere linking different universities. I have considered the effects of various factors on video conferencing and different tests have been performed to study the data transfer during the online sessions. Readings of send rate, received rate and CPU load have been considered during these tests and the results have been plotted in the form of graphs. The study also gives conclusions at regular intervals on the tests performed and the limitations on various video confencing sessions. From the statistics collected I have concluded on the hardware requirements for optimized performance of video conferencing over the Internet. The study also states the scope of research to be undertaken in future for much better performance and understanding of different types of protocols. This thesis includes the study of various network-monitoring tools

Bird Chirps Annotation UsingTime-Frequency Domain Analysis

There are around 10,426 bird species around the world. Recognizing the bird species for an untrained person is almost impossible either by watching or listening them. In order to identify the bird species from their sounds, there is a need for an application that can detect the bird species from its sound. Time-frequency domain analysis techniques are used to implement the application. We implemented two time-frequency domain feature extraction methods. In feature extraction, a signature matrix which consist of extracted features is created for bird sound signals. A database of signature matrix is created with bird chirps extracted features. We implemented two feature classification methods. They are auto-correlation feature classification method and reference difference feature classification method. An unknown bird chirp is compared with the database to detect the species name. The main aim of the research is to implement the time-frequency domain feature extraction method, create a signature matrix database, implement two feature classification methods and compare them. At last, bird species were identified in the research and the auto-correlation classification method detects the bird species better than the reference difference classification method