Design and development of automatic visual inspection system for PCB manufacturing

Inspection of solder joints has been a critical process in the electronic manufacturing industry to reduce manufacturing cost, improve yield, and ensure product quality and reliability. This paper proposes two inspection modules for an automatic solder joint classification system. The “front-end” inspection system includes illumination normalisation, localisation and segmentation. The “back-end” inspection involves the classification of solder joints using the Log Gabor filter and classifier fusion. Five different levels of solder quality with respect to the amount of solder paste have been defined. The Log Gabor filter has been demonstrated to achieve high recognition rates and is resistant to misalignment. This proposed system does not need any special illumination system, and the images are acquired by an ordinary digital camera. This system could contribute to the development of automated non-contact, non-destructive and low cost solder joint quality inspection systems.

The design and development of an undergraduate signal processing laboratory

The School of Electrical and Electronic Systems Engineering of Queensland University of Technology (like many other universities around the world) has recognised the importance of complementing the teaching of signal processing with computer based experiments. A laboratory has been developed to provide a "hands-on" approach to the teaching of signal processing techniques. The motivation for the development of this laboratory was the cliche "What I hear I remember but what I do I understand." The laboratory has been named as the "Signal Computing and Real-time DSP Laboratory" and provides practical training to approximately 150 final year undergraduate students each year. The paper describes the novel features of the laboratory, techniques used in the laboratory based teaching, interesting aspects of the experiments that have been developed and student evaluation of the teaching techniques

The design and development of music eScape: A new iPhone mood management music App

The physical, emotional, educational and social developmental challenges of adolescence can be associated with high levels of emotional vulnerability. Thus, the development of effective emotion-regulation strategies is crucial during this time period. Young people commonly use music to identify, express and regulate their emotions. Modern mobile technology provides an engaging, easily accessible means of assisting young people through music. A systematic contextual review identified 20 iPhone applications addressing emotions through music and two independent raters, using the Mobile App Rating Scale (MARS), evaluated the quality of the apps. Their characteristics, key features and overall quality will be presented. Three participatory design workshops (N=13, 6 males, 7 females; age 15-25) were conducted to explore young people’s use of music to enhance wellbeing. Young people were also asked to trial existing mood and music apps and to conceptualise their ultimate mood targeting music application. A thematic analysis of the participatory design workshops content identified the following music affect-regulation strategies: relationship building, modifying cognitions, modifying emotions, and immersing in emotions. The application of the key learnings from the mobile app review and participatory design workshops and the design and development of the music eScape app were presented and implications for future research was discussed.

Using Participatory Design (PD) as youth engagement strategy to inform design and development of evidence-based wellbeing e-tools

The paper presents a participatory design research framework as a primary method for structuring youth engagement, participation and contribution to the design, development and usability evaluation of three evidencebased e-tools for wellbeing, which include smart phone mobile apps as well as e-health websites. The three projects are part of a series of six e-tools part of Safe and Supportive program under Young and Well CRC. The participatory design method, developed by Zelenko (2012) for application in design of online health promoting technologies, was further piloted in partnership with Inspire USA for specific application within the CRC, deploying a combination of creative design workshops and speculative design activities in developing e-tool prototypes with young people. This paper presents the resulting participatory research framework as it was implemented across the e-tool projects to facilitate active youth participation in co-designing the e-tools and ensuring the final designs are relevant to young people and deliver health messages in engaging ways. The principles of Participatory Design (PD) that inform the new framework include a high degree of participant agency in creative decisionmaking and a commitment to the process of co-designing, with young people working alongside designers and developers. The paper will showcase how the PD framework was applied across three projects to increase young people’s contribution to final design outcome.

Data driven and practice-based evidence : design and development of efficient and effective clinical decision support system

Decision-making is such an integral aspect in health care routine that the ability to make the right decisions at crucial moments can lead to patient health improvements. Evidence-based practice, the paradigm used to make those informed decisions, relies on the use of current best evidence from systematic research such as randomized controlled trials. Limitations of the outcomes from randomized controlled trials (RCT), such as “quantity” and “quality” of evidence generated, has lowered healthcare professionals’ confidence in using EBP. An alternate paradigm of Practice-Based Evidence has evolved with the key being evidence drawn from practice settings. Through the use of health information technology, electronic health records (EHR) capture relevant clinical practice “evidence”. A data-driven approach is proposed to capitalize on the benefits of EHR. The issues of data privacy, security and integrity are diminished by an information accountability concept. Data warehouse architecture completes the data-driven approach by integrating health data from multi-source systems, unique within the healthcare environment.

Design and Development of an In-Vehicle Human Machine Interface to Improve Fuel Efficiency & Safety

Road transport plays a significant role in various industries and mobility services around the globe and has a vital impact on our daily lives. However it also has serious impacts on both public health and the environment. In-vehicle feedback systems are a relatively new approach to encouraging driver behaviour change for improving fuel efficiency and safety in automotive environments. While many studies claim that the adoption of eco-driving practices, such as eco-driving training programs and in-vehicle feedback to drivers, has the potential to improve fuel efficiency, limited research has integrated safety and eco-driving. Therefore, this research seeks to use human factors related theories and practices to inform the design and evaluation of an in-vehicle Human Machine Interface (HMI) providing real-time driver feedback with the aim of improving both fuel efficiency and safety.

Smart Grid Test Bed Design and Implementation

Given there is currently a migration trend from traditional electrical supervisory control and data acquisition (SCADA) systems towards a smart grid based approach to critical infrastructure management. This project provides an evaluation of existing and proposed implementations for both traditional electrical SCADA and smart grid based architectures, and proposals a set of reference requirements which test bed implementations should implement. A high-level design for smart grid test beds is proposed and initial implementation performed, based on the proposed design, using open source and freely available software tools. The project examines the move towards smart grid based critical infrastructure management and illustrates the increased security requirements. The implemented test bed provides a basic framework for testing network requirements in a smart grid environment, as well as a platform for further research and development. Particularly to develop, implement and test network security related disturbances such as intrusion detection and network forensics. The project undertaken proposes and develops an architecture of the emulation of some smart grid functionality. The Common Open Research Emulator (CORE) platform was used to emulate the communication network of the smart grid. Specifically CORE was used to virtualise and emulate the TCP/IP networking stack. This is intended to be used for further evaluation and analysis, for example the analysis of application protocol messages, etc. As a proof of concept, software libraries were designed, developed and documented to enable and support the design and development of further smart grid emulated components, such as reclosers, switches, smart meters, etc. As part of the testing and evaluation a Modbus based smart meter emulator was developed to provide basic functionality of a smart meter. Further code was developed to send Modbus request messages to the emulated smart meter and receive Modbus responses from it. Although the functionality of the emulated components were limited, it does provide a starting point for further research and development. The design is extensible to enable the design and implementation of additional SCADA protocols. The project also defines an evaluation criteria for the evaluation of the implemented test bed, and experiments are designed to evaluate the test bed according to the defined criteria. The results of the experiments are collated and presented, and conclusions drawn from the results to facilitate discussion on the test bed implementation. The discussion undertaken also present possible future work.

Guidelines on building design and operation to minimize air quality impacts from major roads in transit oriented development

Poor air quality has a huge detrimental effect, both economic and on the quality of life, in Australia. Transit oriented design (TOD), which aims to minimise urban sprawl and lower dependency on vehicles, leads to an increasing number of buildings close to transport corridors. This project aims at providing guidelines that are appropriate to include within City Plan to inform future planning along road corridors, and provide recommendations on when mitigation measures should be utilised.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Creativity, design and entrepreneurship : management education and development for innovation

Creativity, design and entrepreneurship, have been recognized as important contributors to a firm’s innovation and to the nation’s economic growth. Creativity and design play important roles in the fuzzy front end of a firm’s innovation process and also in corporate venturing processes, but the relationship between creativity, design and entrepreneurship to a large extent has not explicitly been examined. This exploratory conceptual paper briefly reviews the separate bodies of research on creativity, design and entrepreneurship, identifying similarities and differences in constructs and applications and identifying implications for business and for management education.