Improved quality block-based low bit rate video coding.

The aim of this research is to develop algorithms for enhancing the subjective quality and coding efficiency of standard block-based video coders. In the past few years, numerous video coding standards based on motion-compensated block-transform structure have been established where block-based motion estimation is used for reducing the correlation between consecutive images and block transform is used for coding the resulting motion-compensated residual images. Due to the use of predictive differential coding and variable length coding techniques, the output data rate exhibits extreme fluctuations. A rate control algorithm is devised for achieving a stable output data rate. This rate control algorithm, which is essentially a bit-rate estimation algorithm, is then employed in a bit-allocation algorithm for improving the visual quality of the coded images, based on some prior knowledge of the images. Block-based hybrid coders achieve high compression ratio mainly due to the employment of a motion estimation and compensation stage in the coding process. The conventional bit-allocation strategy for these coders simply assigns the bits required by the motion vectors and the rest to the residual image. However, at very low bit-rates, this bit-allocation strategy is inadequate as the motion vector bits takes up a considerable portion of the total bit-rate. A rate-constrained selection algorithm is presented where an analysis-by-synthesis approach is used for choosing the best motion vectors in term of resulting bit rate and image quality. This selection algorithm is then implemented for mode selection. A simple algorithm based on the above-mentioned bit-rate estimation algorithm is developed for the latter to reduce the computational complexity. For very low bit-rate applications, it is well-known that block-based coders suffer from blocking artifacts. A coding mode is presented for reducing these annoying artifacts by coding a down-sampled version of the residual image with a smaller quantisation step size. Its applications for adaptive source/channel coding and for coding fast changing sequences are examined

The use of stories as a means of teaching moral development in two Singapore secondary schools.

Presently, the Singapore’s Ministry of Education requires the teaching of a set of values in its “Civics & Moral Education” 2007 syllabus (“CME”) for all students in the primary (7 years old to 12 years old), secondary (13 years old to 16 years old) and junior college (17 years old to 18 years old) academic levels. Although there are presently no prescribed or standardized methods in teaching these values, it was inferred from the findings collected from the two schools in this research study that the didactic approach to teach such values to the students is commonly employed by most teachers. This qualitative research study explored the use of stories to teach the CME and moral development in schools as an alternative to the present didactic approach. This research study was carried out on a group of 18 secondary 2 students (14 years old) at two government-funded schools located within a 5-km radius and in one of Singapore’s ubiquitous public housing estates. The aim of this research is to introduce and use stories to teach values as prescribed in the CME in these schools. In so doing it was important to select the appropriate qualitative methods to achieve this and three methods were selected. These involved principally the use of in-depth interviewing methods together with focus-group discussion and non-participant observation methods to collect, understand and present the data of the rich, diverse and detailed responses, reactions and interpretations of the students when and after listening to a set of six stories at two different periods with a three-month interval in between them. The next was to use Kohlberg’s and Biggs and Collis’ taxonomies to assess and evaluate for students’ learning outcomes and whether there has been any apparent or initial evidence of moral or character development. The importance of this research study is that from the positive findings, discussions were carried out and recommendations made to contribute to these schools for their consideration on the use of stories for their teaching of values as prescribed in the CME

Statistical selection algorithm for peer-to-peer system

Over the years, the distributed database has been developed so fast that there's a need to develop an effective selection algorithm for it. Loo et. al. (2002) has proposed a statistical selection algorithm with the same objective and run in multicast / broadcast environment that has been proved that it is the best among others in terms of the number of messages needed to complete the searching process. However, this algorithm has a high probability of failure. A few improvements have been done to this original algorithm. This improved algorithm is developed based on the simulation of the real multicast environment. Modifications have been added in the improved algorithm to ensure that the unique pivot that never been used before is selected every time, and to solve problem that involve rank for certain key value that occur in more than one participant. Four performance measures have been conducted for the purpose of performance analysis between original and improved algorithm. These measures include probability of failure, number of messages needed, number of rounds needed and execution time. As a result, the probability of failure for the newly improved algorithm is 3.2% while the original algorithm is 19.2% without much overhead in increasing the number of messages and number of rounds needed

Improvement and performance analysis on statistical selection algorithms

Over the years, the distributed database has been developed so fast that there's a need to develop an effective selection algorithm for it. Loo and Choi has proposed a statistical selection algorithm with the same objective and run in multicast / broadcast environment that has been proved that it is the best among others in terms of the number of messages needed to complete the searching process. However, this algorithm has a high probability of failure. A few improvements have been done to this original algorithm. This new algorithm is developed based on the simulation of the real multicast environment. Three modifications have been added in the new algorithm to solve the problem. Two performance measures have been conducted for the purpose of performance analysis between original and new algorithm

Static range multiple selection algorithm for peer-to-peer system

In this research, a new multiple selection algorithm, which is known as "static range statistical multiple selection algorithm" is proposed. This algorithm is developed based on the statistical knowledge about the uniform distribution nature of the data which has been arranged according to certain order in the file. A global file with n keys is distributed evenly among n peers in the peer-to-peer network. The selection algorithm can performs multiple selections concurrently to find multiple target keys with different predefined target ranks. The algorithm uses a fixed filter approach in which the algorithm is able to make sure that the target key is within certain filter range in each local file. The range is made smaller and smaller as the selection process iterates until all target keys are found. The algorithm is able to reduce the number of messages needed and increases the success rate of all multiple selections in the selection process compared to the previous multiple selection algorithms proposed by Loo in 2005

High-order discontinuous Galerkin method for elastohydrodynamic lubrication line contact problems

In this paper a high-order discontinuous Galerkin method is used to solve steady-state isothermal line contact elastohydrodynamic lubrication problems. This method is found to be stable across a wide range of loads and is shown to permit accurate solutions using just a small number of degrees of freedom provided suitable grids are used. A comparison is made between results obtained using this proposed method and those from a very large finite difference calculation in order to demonstrate excellent accuracy for a typical highly loaded test problem

Statistical fixed range multiple selection algorithm for peer-to-peer system

In this research, a new multiple selection algorithm, which is known as "statistical fixed range multiple selection algorithm" is proposed. This algorithm is developed based on the statistical knowledge about the uniform distribution nature of the data which has been arranged in ascending order in the local file. A global file with n keys is distributed evenly among p peers in the peer-to-peer network. The selection algorithm can performs multiple selections concurrently to find multiple target keys with different predefined target ranks. The algorithm uses a fixed filter range approach that has been defined before the process begin, in which the algorithm is able to make sure that the target key is within the specified filter range in each local file. The range is made smaller and smaller as the selection process iterates until all target keys are found. The algorithm is able to reduce the number of rounds needed and increase the success rate of all multiple selections in the selection process compared to the previous multiple selection algorithms proposed by Loo in 2005

A novel mining system for criminal issues from a video file within cloud computing environment

This paper presents a description of a novel mining system which mines the different occurrences (instances) of the same object from a video file. The framework of the system consists of four steps: segmenting the video file into stable tracks, extracting objects and their features from the tracks, grouping these tracks into clusters based on their residing objects, and finally mining the instances of each object in the shared pool of configurable computing resources within cloud environment for more security. The paper also presents a critique and feedback for the system and proposes an idea to improve its performance

QoS class-based proportional resource allocation for LTE downlink

In LTE multi-service communication system, a trade-off between QoS assurance and fairness is a challenging issue, since the QoS provisioning at the cost of starving users in low service demand classes is not favorable for the operator. In this paper, we adopt the time-domain Knapsack algorithm and fine tune it to provide fair resource allocation while support QoS requirements in LTE downlink scheduling system when the bearers are from different classes of service, having different QoS characteristics. We demonstrate that more efficient performance can be achieved in two aspects of fairness and QoS provisioning in terms of normalized throughput, and packet loss and delay rate, which are evaluated using simulation results