Browsing by Author "Tan, Guan-Zheng"

Now showing 1 - 6 of 6
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    Analysis of MS Power Saving Scheme to BS with Finite Buffer in IEEE 802.16e networks
    (IEEE, 2011) Musau, Felix; Kipruto, Cheruiyot W.; Mushi, Joseph Cosmas; Tan, Guan-Zheng
    This paper analyzes effects of power saving of mobile station (MS) to Base Station with finite buffer in IEEE802.16e class type I network. IEEE802.16e standard accept MS to switch to sleep-mode to minimize power when MS processing load is reduced. However, when packets destined to MS appear into BS buffer, they should be stored until end of MS sleep window. Although the sleep-mode designed in effort to conserve environment but it risks loss of packets once BS buffer overloaded with accumulated packets. This paper designs numerical analytic model to measure risk of packet drop. Through asymptotic analysis the effect of packets destined to sleeping MS into BS finite buffer is measured and analysed.
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    Genetic Algorithm-Enhanced Retrieval Process for Multimedia Data
    (International Journal of Advancements in Computing Technology, 2011-04) Musau, Felix; Mushi, Joseph Cosmas; Tan, Guan-Zheng; Kipruto, Cheruiyot W.
    The explosive growth of digital media content from various domains has given rise to the need for efficient techniques for retrieval of relevant information is getting more and more attention, especially in the large-scale Multimedia Digital Database (MDD) applications. It is for this reason that there has been an increased interest in the query reformulation for use in Multimedia Information Retrieval (MIR) using a combination of various techniques. In this paper, we propose a retrieval method that is formalized as an optimized similarity search process that combines Singular Value Decomposition (SVD), Query Quality Refinement (QQR) using Histogram Equalization and Genetic Algorithm (GA) enhancement. Experimental results show that the approach significantly narrows the search process by retrieving similar images satisfying the needs of the user.
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    Modeling M-SaaS Delivery Model for Threshold-based Credit Recharging Using Mbanking
    (IEEE, 2011) Musau, Felix; Mushi, Joseph Cosmas; Tan, Guan-Zheng; Kipruto, Cheruiyot W.
    The emerging of cloud computing has brought about new hope for efficient adoption and use of computer based information system. Software as a Service (SaaS) allows online customers to use applications on the Internet on a payas- you-go basis without investing in new infrastructure or software license. However, current SaaS delivery model bring challenge to online customers in community with poor financial services like online bank-transaction or credit cards. This study proposes Mobile-SaaS delivery model to facilitate online charging and credit-recharging mechanisms for SaaS application. The model use mobile-phone monetary services (M-banking) to support online charging, and adopts threshold-based credit recharge technique to support credit recharging.
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    Performance Analysis of Recharging Scheme of M-SaaS through M-banking
    (Journal of Convergence Information Technology (JICT)., 2011-07) Musau, Felix; Mushi, Joseph Cosmas; Tan, Guan-Zheng; Kipruto, Cheruiyot W.
    Mobile SaaS (M-SaaS) is SaaS delivery model designed to use mobile network infrastructure for reserving and recharging credits of SaaS usage in pay-as-you-go manner for areas with poor financial services. The model integrates M-banking monetary feature into delivery process of SaaS so that potential customers in areas with poor access to financial services, such as online banking and credit cards, participate in SaaS delivery in real-time payment manner. In this paper, we present numerical analysis of M-SaaS recharging process, which together with M-SaaS credit-reservation scheme make M-SaaS model. The analysis shows that utilization and throughput of components involved in the scheme are healthy to avoid interruption of their involvement in other mobile network activities. However, load distribution at equilibrium leads to great imbalance, which needs further study for usefulness of M-SaaS model to revel.
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    Query quality refinement in singular value decomposition to improve genetic algorithms for multimedia data retrieval
    (Springer-Verlag 2011-Multimedia Systems, 2011-04-03) Musau, Felix; Mushi, Joseph Cosmas; Cheruiyot, Wilson; Tan, Guan-Zheng
    With the development of internet and availability of multimedia data capturing devices, the size of Multimedia Digital Database (MDD) collection is increasing rapidly. The complex data presented by such systems do not have the total ordering property presented by the traditional data handled by Database Management Systems (DBMSs). The quality of the search experience in such systems is also normally a big challenge since users from various domains require efficient data searching, browsing and retrieval tools. This has triggered an important research topic in Multimedia information retrieval concerning efficient and effective image similarity search. Modern search algorithms are fast and effective on a wide range of problems, but on MDD with a large number of parameters and observations, manipulations of large matrices, storage and retrieval of large amounts of information may render an otherwise useful method slow or inoperable. The focus of this work is the application of image enhancement technique, using histogram equalization, to the images retrieved using singular value decomposition (SVD). SVD is a linear algebra technique used for discovering correlations within data. The approach, herein referred to as query quality refinement (QQR) technique, improves the image similarity search result, and when incorporated with genetic algorithms further optimizes the search. These beneficial applications can be extended to other different types of multimedia data in various areas such as the P2P and WiMAX networks.
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    Using Genetic Algorithms to Optimize Web Caching in MultimediaIntegrated e-Learning Content
    (International Journal of Digital Content Technology and its Applications, 2011-08) Musau, Felix; Mushi, Joseph Cosmas; Tan, Guan-Zheng; Kipruto, Cheruiyot W.
    The evolution of e-learning as a new information technology built around the use of internet has originated new possibilities to develop pedagogical methodologies that provide the necessary knowledge and skills in the higher education environment. This has in effect given rise to a large number of learners who desire to access the educational resources provided by the new technology. But for many institutions of higher learning, the users often experiences poor performance when they try to access contents or download e-learning files. The main reasons for such are often performance problems concerning network infrastructure and the fact that many people tend to access the same piece of information repetitively. This paper uses web caching scheme and genetic algorithm optimization to effectively lessen this service bottleneck, diminish the user access latency and reduce the network traffic.