A methodology for object identification and tracking in construction based on spatial modeling and image matching techniques

Understanding the motion characteristics of on-site objects is desirable for the analysis of construction work zones, especially in problems related to safety and productivity studies. This article presents a methodology for rapid object identification and tracking. The proposed methodology contains algorithms for spatial modeling and image matching. A high-frame-rate range sensor was utilized for spatial data acquisition. The experimental results indicated that an occupancy grid spatial modeling algorithm could quickly build a suitable work zone model from the acquired data. The results also showed that an image matching algorithm is able to find the most similar object from a model database and from spatial models obtained from previous scans. It is then possible to use the matched information to successfully identify and track objects.

Re-engineering project planning processes : a best practice model for managing project phase integration issues

Competitive markets are increasingly driving new initiatives for shorter cycle times resulting in increased overlapping of project phases. This, in turn, necessitates improving the interfaces between the different phases to be overlapped (integrated), thus allowing transfer of processes, information and knowledge from one individual or team to another. This transfer between phases, within and between projects, is one of the basic challenges to the philosophy of project management. To make the process transfer more transparent with minimal loss of momentum and project knowledge, this paper draws upon Total Quality Management (TQM) and Business Process Re-engineering (BPR) philosophies to develop a Best Practice Model for managing project phase integration. The paper presents the rationale behind the model development and outlines its two key parts; (1) Strategic Framework and (2) Implementation Plan. Key components of both the Strategic Framework and the Implementation Plan are presented and discussed.

Business service management : understanding business models

One of the prominent topics in Business Service Management is business models for (new) services. Business models are useful for service management and engineering as they provide a broader and more holistic perspective on services. Business models are particularly relevant for service innovation as this requires paying attention to the business models that make new services viable and business model innovation can drive the innovation of new and established services. Before we can have a look at business models for services, we first need to understand what business models are. This is not straight-forward as business models are still not well comprehended and the knowledge about business models is fragmented over different disciplines, such as information systems, strategy, innovation, and entrepreneurship. This whitepaper, ‘Understanding business models,’ introduces readers to business models. This whitepaper contributes to enhancing the understanding of business models, in particular the conceptualisation of business models by discussing and integrating business model definitions, frameworks and archetypes from different disciplines. After reading this whitepaper, the reader will have a well-developed understanding about what business models are and how the concept is sometimes interpreted and used in different ways. It will help the reader in assessing their own understanding of business models and that and of others. This will contribute to a better and more beneficial use of business models, an increase in shared understanding, and making it easier to work with business model techniques and tools.

Machine learning techniques to improve software quality

A significant proportion of the cost of software development is due to software testing and maintenance. This is in part the result of the inevitable imperfections due to human error, lack of quality during the design and coding of software, and the increasing need to reduce faults to improve customer satisfaction in a competitive marketplace. Given the cost and importance of removing errors improvements in fault detection and removal can be of significant benefit. The earlier in the development process faults can be found, the less it costs to correct them and the less likely other faults are to develop. This research aims to make the testing process more efficient and effective by identifying those software modules most likely to contain faults, allowing testing efforts to be carefully targeted. This is done with the use of machine learning algorithms which use examples of fault prone and not fault prone modules to develop predictive models of quality. In order to learn the numerical mapping between module and classification, a module is represented in terms of software metrics. A difficulty in this sort of problem is sourcing software engineering data of adequate quality. In this work, data is obtained from two sources, the NASA Metrics Data Program, and the open source Eclipse project. Feature selection before learning is applied, and in this area a number of different feature selection methods are applied to find which work best. Two machine learning algorithms are applied to the data - Naive Bayes and the Support Vector Machine - and predictive results are compared to those of previous efforts and found to be superior on selected data sets and comparable on others. In addition, a new classification method is proposed, Rank Sum, in which a ranking abstraction is laid over bin densities for each class, and a classification is determined based on the sum of ranks over features. A novel extension of this method is also described based on an observed polarising of points by class when rank sum is applied to training data to convert it into 2D rank sum space. SVM is applied to this transformed data to produce models the parameters of which can be set according to trade-off curves to obtain a particular performance trade-off.

Knowledge-based urban development of multimedia super corridor, Malaysia : an overview

In recent years, with the impact of the global knowledge economy, a more comprehensive urban development approach, so called 'knowledge-based urban development', has gained significant popularity. This paper discusses the critical connections among knowledge-based urban development strategies, knowledge-intensive industries and information and communication technology infrastructures. In particular, the research focuses on investigating the application of the knowledge-based urban development concept by discussing one of the South East Asia's large scale knowledge-based urban development manifestations of Malaysia's Multimedia Super Corridor. The paper scrutinises Malaysia's experience in the development and evolution of the Multimedia Super Corridor from the angle of knowledge-based urban development policy implementation, infrastructural implications, and actors involved in its development and management. This paper provides a number of lessons learned from the Multimedia Super Corridor on the orchestration of knowledge-based development that is a necessity for cities seeking successful knowledge city and knowledge economy transformations.

Effective pattern discovery for text mining

Many data mining techniques have been proposed for mining useful patterns in text documents. However, how to effectively use and update discovered patterns is still an open research issue, especially in the domain of text mining. Since most existing text mining methods adopted term-based approaches, they all suffer from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern (or phrase) based approaches should perform better than the term-based ones, but many experiments did not support this hypothesis. This paper presents an innovative technique, effective pattern discovery which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information. Substantial experiments on RCV1 data collection and TREC topics demonstrate that the proposed solution achieves encouraging performance.

Students' perceptions of the relevance of mathematics in engineering

In this paper, we report on the findings of an exploratory study into the experience of students as they learn first year engineering mathematics. Here we define engineering as the application of mathematics and sciences to the building and design of projects for the use of society (Kirschenman and Brenner 2010)d. Qualitative and quantitative data on students' views of the relevance of their mathematics study to their engineering studies and future careers in engineering was collected. The students described using a range of mathematics techniques (mathematics skills developed, mathematics concepts applied to engineering and skills developed relevant for engineering) for various usages (as a subject of study, a tool for other subjects or a tool for real world problems). We found a number of themes relating to the design of mathematics engineering curriculum emerged from the data. These included the relevance of mathematics within different engineering majors, the relevance of mathematics to future studies, the relevance of learning mathematical rigour, and the effectiveness of problem solving tasks in conveying the relevance of mathematics more effectively than other forms of assessment. We make recommendations for the design of engineering mathematics curriculum based on our findings.

Discussions on conceptual knowledge and the use of blogs

In both Australia and Norway and through a number of Technology projects conducted since 2007, the authors – together and with other collaborators - have attempted to create positive learning environments supported by Web 2.0 communication tools. Through protected public sites and the oz-Teachernet [http://www.otn.edu.au], we have consistently chosen to use blogs to support the social construction of knowledge, that is, to allow students the opportunity to discuss, share and collaborate on their classroom activities and engagement with Technology artefacts and processes. Through comparisons with findings from a small-scale project in Norway and a large-scale project in Australia, this paper will argue for the potential of discussion through blogs but recommend that the purposeful use of scientific language in student communication will not occur without teacher intervention and scaffolding.

Monitoring mesenchymal stem cell cultures using image processing and pattern recognition techniques

Stem cells have attracted tremendous interest in recent times due to their promise in providing innovative new treatments for a great range of currently debilitating diseases. This is due to their potential ability to regenerate and repair damaged tissue, and hence restore lost body function, in a manner beyond the body's usual healing process. Bone marrow-derived mesenchymal stem cells or bone marrow stromal cells are one type of adult stem cells that are of particular interest. Since they are derived from a living human adult donor, they do not have the ethical issues associated with the use of human embryonic stem cells. They are also able to be taken from a patient or other donors with relative ease and then grown readily in the laboratory for clinical application. Despite the attractive properties of bone marrow stromal cells, there is presently no quick and easy way to determine the quality of a sample of such cells. Presently, a sample must be grown for weeks and subject to various time-consuming assays, under the direction of an expert cell biologist, to determine whether it will be useful. Hence there is a great need for innovative new ways to assess the quality of cell cultures for research and potential clinical application. The research presented in this thesis investigates the use of computerised image processing and pattern recognition techniques to provide a quicker and simpler method for the quality assessment of bone marrow stromal cell cultures. In particular, aim of this work is to find out whether it is possible, through the use of image processing and pattern recognition techniques, to predict the growth potential of a culture of human bone marrow stromal cells at early stages, before it is readily apparent to a human observer. With the above aim in mind, a computerised system was developed to classify the quality of bone marrow stromal cell cultures based on phase contrast microscopy images. Our system was trained and tested on mixed images of both healthy and unhealthy bone marrow stromal cell samples taken from three different patients. This system, when presented with 44 previously unseen bone marrow stromal cell culture images, outperformed human experts in the ability to correctly classify healthy and unhealthy cultures. The system correctly classified the health status of an image 88% of the time compared to an average of 72% of the time for human experts. Extensive training and testing of the system on a set of 139 normal sized images and 567 smaller image tiles showed an average performance of 86% and 85% correct classifications, respectively. The contributions of this thesis include demonstrating the applicability and potential of computerised image processing and pattern recognition techniques to the task of quality assessment of bone marrow stromal cell cultures. As part of this system, an image normalisation method has been suggested and a new segmentation algorithm has been developed for locating cell regions of irregularly shaped cells in phase contrast images. Importantly, we have validated the efficacy of both the normalisation and segmentation method, by demonstrating that both methods quantitatively improve the classification performance of subsequent pattern recognition algorithms, in discriminating between cell cultures of differing health status. We have shown that the quality of a cell culture of bone marrow stromal cells may be assessed without the need to either segment individual cells or to use time-lapse imaging. Finally, we have proposed a set of features, that when extracted from the cell regions of segmented input images, can be used to train current state of the art pattern recognition systems to predict the quality of bone marrow stromal cell cultures earlier and more consistently than human experts.