Curbing paper wastage using flavoured feedback

In November 2009 the researcher embarked on a project aimed at reducing the amount of paper used by Queensland University of Technology (QUT) staff in their daily workplace activities. The key goal was to communicate to staff that excessive printing has a tangible and negative effect on their workplace and local environment. The research objective was to better understand what motivates staff towards more ecologically sustainable printing practises, whilst meeting their job’s demands. The current study is built on previous research that found that one interface does not address the needs of all users when creating persuasive Human Computer Interaction (HCI) interventions targeting resource consumption. In response, the current study created and trialled software that communicates individual paper consumption in precise metrics. Based on preliminary research data different metric sets have been defined to address the different motivations and beliefs of user archetypes using descriptive and injunctive normative information.

Fear and danger in nocturnal urban environments

At the centre of this research is an ethnographic study that saw the researcher embedded within the fabric of inner city life to better understand what characteristics of user activity and interaction could be enhanced by technology. The initial research indicated that the experience of traversing the city after dark unified an otherwise divergent user group through a shared concern for personal safety. Managing this fear and danger represented an important user need. We found that mobile social networking systems are not only integral for bringing people together, they can help in the process of users safely dispersing as well. We conclude, however, that at a time when the average iPhone staggers under the weight of a plethora of apps that do everything from acting as a carpenter’s level to a pregnancy predictor, we consider the potential for the functionality of a personal safety device to be embodied within a stand alone artifact.

Women are people too : the problem of designing for gender

Cultural theory breaks with Modern analysis by rejecting traditional notions of race, gender, class and sexuality. In doing so, alternative frameworks such as Post-Feminism emerge which are useful for thinking about culture, technology and what our interactions with it mean. From a Post-Feminist perspective it can be seen how in our multi-cultural, post-industrial, digitized world, there is space to move beyond traditional ways of dividing up society such as ‘male’ and ‘female’. We are then free to re-construct our identity in light of a rich diversity of individually relevant experiences. Therefore, in order to get a better understanding of the highly nuanced cultural interactions that characterize our use of technology, this paper argues against using the inherently stereotyped lens of gender and allowing a new set of user needs to emerge.

Householder experiences with resource monitoring technology in sustainable homes

The use of feedback technologies, in the form of products such as Smart Meters, is increasingly seen as the means by which 'consumers' can be made aware of their patterns of resource consumption, and to then use this enhanced awareness to change their behaviour to reduce the environmental impacts of their consumption. These technologies tend to be single-resource focused (e.g. on electricity consumption only) and their functionality defined by persons other than end-users (e.g. electricity utilities). This paper presents initial findings of end-users' experiences with a multi-resource feedback technology, within the context of sustainable housing. It proposes that an understanding of user context, supply chain management and market diffusion issues are important design considerations that contribute to technology 'success'.

Effect of pedestrian movement on MIMO-OFDM channel capacity in an indoor environment

The rapid growth of mobile telephone use, satellite services, and now the wireless Internet and WLANs are generating tremendous changes in telecommunication and networking. As indoor wireless communications become more prevalent, modeling indoor radio wave propagation in populated environments is a topic of significant interest. Wireless MIMO communication exploits phenomena such as multipath propagation to increase data throughput and range, or reduce bit error rates, rather than attempting to eliminate effects of multipath propagation as traditional SISO communication systems seek to do. The MIMO approach can yield significant gains for both link and network capacities, with no additional transmitting power or bandwidth consumption when compared to conventional single-array diversity methods. When MIMO and OFDM systems are combined and deployed in a suitable rich scattering environment such as indoors, a significant capacity gain can be observed due to the assurance of multipath propagation. Channel variations can occur as a result of movement of personnel, industrial machinery, vehicles and other equipment moving within the indoor environment. The time-varying effects on the propagation channel in populated indoor environments depend on the different pedestrian traffic conditions and the particular type of environment considered. A systematic measurement campaign to study pedestrian movement effects in indoor MIMO-OFDM channels has not yet been fully undertaken. Measuring channel variations caused by the relative positioning of pedestrians is essential in the study of indoor MIMO-OFDM broadband wireless networks. Theoretically, due to high multipath scattering, an increase in MIMO-OFDM channel capacity is expected when pedestrians are present. However, measurements indicate that some reductions in channel capacity could be observed as the number of pedestrians approaches 10 due to a reduction in multipath conditions as more human bodies absorb the wireless signals. This dissertation presents a systematic characterization of the effects of pedestrians in indoor MIMO-OFDM channels. Measurement results, using the MIMO-OFDM channel sounder developed at the CSIRO ICT Centre, have been validated by a customized Geometric Optics-based ray tracing simulation. Based on measured and simulated MIMO-OFDM channel capacity and MIMO-OFDM capacity dynamic range, an improved deterministic model for MIMO-OFDM channels in indoor populated environments is presented. The model can be used for the design and analysis of future WLAN to be deployed in indoor environments. The results obtained show that, in both Fixed SNR and Fixed Tx for deterministic condition, the channel capacity dynamic range rose with the number of pedestrians as well as with the number of antenna combinations. In random scenarios with 10 pedestrians, an increment in channel capacity of up to 0.89 bits/sec/Hz in Fixed SNR and up to 1.52 bits/sec/Hz in Fixed Tx has been recorded compared to the one pedestrian scenario. In addition, from the results a maximum increase in average channel capacity of 49% has been measured while 4 antenna elements are used, compared with 2 antenna elements. The highest measured average capacity, 11.75 bits/sec/Hz, corresponds to the 4x4 array with 10 pedestrians moving randomly. Moreover, Additionally, the spread between the highest and lowest value of the the dynamic range is larger for Fixed Tx, predicted 5.5 bits/sec/Hz and measured 1.5 bits/sec/Hz, in comparison with Fixed SNR criteria, predicted 1.5 bits/sec/Hz and measured 0.7 bits/sec/Hz. This has been confirmed by both measurements and simulations ranging from 1 to 5, 7 and 10 pedestrians.

Designing for participation in local social ridesharing networks : grass roots prototyping of IT systems

This abstract explores the possibility of a grass roots approach to engaging people in community change initiatives by designing simple interactive exploratory prototypes for use by communities over time that support shared action. The prototype is gradually evolved in response to community use, fragments of data gathered through the prototype, and participant feedback with the goal of building participation in community change initiatives. A case study of a system to support ridesharing is discussed. The approach is compared and contrasted to a traditional IT systems procurement approach.

Iterative design within a local community communication fabric

Our research considers the problem designing support for local community communications. We present a description of a suburban community communication fabric as revealed through observations of long-term use of a networked community noticeboard and the introduction of tailored email digest to registered noticeboard users. The paper contributes an understanding of how iterative situated design in a user community can help us to design for participation in the use of technologies that can support growth of a community communication fabric. The different roles of the situated display and email digest are discussed.

Medication warnings about driving : risk perceptions among French and Australian Communities

Increased crash risk is associated with sedative medications and researchers and health-professionals have called for improvements to medication warnings about driving. The tiered warning system in France since 2005 indicates risk level, uses a color-coded pictogram, and advises the user to seek the advice of a doctor before driving. In Queensland, Australia, the mandatory warning on medications that may cause drowsiness advises the user not to drive or operate machinery if they self-assess that they are affected, and calls attention to possible increased impairment when combined with alcohol. Objectives The reported aims of the study were to establish and compare risk perceptions associated with the Queensland and French warnings among medication users. It was conducted to complement the work of DRUID in reviewing the effectiveness of existing campaigns and practice guidelines. Methods Medication users in France and Queensland were surveyed using warnings about driving from both contexts to compare risk perceptions associated with each label. Both samples were assessed for perceptions of the warning that carried the strongest message of risk. The Queensland study also included perceptions of the likelihood of crash and level of impairment associated with the warning. Results Findings from the French study (N = 75) indicate that when all labels were compared, the majority of respondents perceived the French Level-3 label as the strongest warning about risk concerning driving. Respondents in Queensland had significantly stronger perceptions of potential impairment to driving ability, z = -13.26, p <.000 (n = 325), and potential chance of having a crash, z = -11.87, p < .000 (n = 322), after taking a medication that displayed the strongest French warning, compared with the strongest Queensland warning. Conclusions Evidence suggests that warnings about driving displayed on medications can influence risk perceptions associated with use of medication. Further analyses will determine whether risk perceptions influence compliance with the warnings.

Design from the everyday: continuously evolving, embedded exploratory prototypes

One of the major challenges in the design of social technologies is the evaluation of their qualities of use and how they are appropriated over time. While the field of HCI abounds in short-term exploratory design and studies of use, relatively little attention has focused on the continuous development of prototypes longitudinally and studies of their emergent use. We ground the exploration and analysis of use in the everyday world, embracing contingency and open-ended use, through the use of a continuously-available exploratory prototype. Through examining use longitudinally, clearer insight can be gained of realistic, non-novelty usage and appropriation into everyday use. This paper sketches out a framework for design that puts a premium on immediate use and evolving the design in response to use and user feedback. While such design practices with continuously developing systems are common in the design of social technologies, they are little documented. We describe our approach and reflect upon its key characteristics, based on our experiences from two case studies. We also present five major patterns of long-term usage which we found useful for design.

Service enhanced manufacturing in the building and construction product system

This report examines the involvement of manufacturers in value-adding through service-enhancement of product offerings. This focus has been prompted by:  emphasis in the knowledge-economy literature on the increasing role played by services in economic growth; and  recent analysis which suggests that the most dynamic sector of many economies is an integrated manufacturing-services sector (see Part One of this report). The report initially describes the emergence of an integrated manufacturing-services sector in the context of increasingly knowledge-based economic systems. Part Two reports on the results of a survey of manufacturers in the building and construction product system, investigating their involvement in service provision. Parts Three and Four present two case studies of exemplary manufacturers involved in adding value to their manufacturing operations through services offered on building and construction projects. The report examines manufacturers of materials, products, equipment and machinery used on building and construction projects. The two case study sections of the report, in part, focus on a major project undertaken by each of the manufacturers. This project element of activity is focussed on (as opposed to wholesale or retail supply), because this area of activity involves a broader array of service-enhancement mechanisms and more complex bundling of products and services.

Road crash proneness prediction using data mining

Developing safe and sustainable road systems is a common goal in all countries. Applications to assist with road asset management and crash minimization are sought universally. This paper presents a data mining methodology using decision trees for modeling the crash proneness of road segments using available road and crash attributes. The models quantify the concept of crash proneness and demonstrate that road segments with only a few crashes have more in common with non-crash roads than roads with higher crash counts. This paper also examines ways of dealing with highly unbalanced data sets encountered in the study.

Modeling object identification and tracking errors on image-based safety assessment of earthmoving operations

Object identification and tracking have become critical for automated on-site construction safety assessment. The primary objective of this paper is to present the development of a testbed to analyze the impact of object identification and tracking errors caused by data collection devices and algorithms used for safety assessment. The testbed models workspaces for earthmoving operations and simulates safety-related violations, including speed limit violations, access violations to dangerous areas, and close proximity violations between heavy machinery. Three different cases were analyzed based on actual earthmoving operations conducted at a limestone quarry. Using the testbed, the impacts of device and algorithm errors were investigated for safety planning purposes.

Adaptive GPS duty cycling and radio ranging for energy-efficient localization

This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application specific positioning criteria.

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

User profiling based on folksonomy information in Web 2.0 for personalized recommender systems

Information overload has become a serious issue for web users. Personalisation can provide effective solutions to overcome this problem. Recommender systems are one popular personalisation tool to help users deal with this issue. As the base of personalisation, the accuracy and efficiency of web user profiling affects the performances of recommender systems and other personalisation systems greatly. In Web 2.0, the emerging user information provides new possible solutions to profile users. Folksonomy or tag information is a kind of typical Web 2.0 information. Folksonomy implies the users‘ topic interests and opinion information. It becomes another source of important user information to profile users and to make recommendations. However, since tags are arbitrary words given by users, folksonomy contains a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise makes it difficult to profile users accurately or to make quality recommendations. This thesis investigates the distinctive features and multiple relationships of folksonomy and explores novel approaches to solve the tag quality problem and profile users accurately. Harvesting the wisdom of crowds and experts, three new user profiling approaches are proposed: folksonomy based user profiling approach, taxonomy based user profiling approach, hybrid user profiling approach based on folksonomy and taxonomy. The proposed user profiling approaches are applied to recommender systems to improve their performances. Based on the generated user profiles, the user and item based collaborative filtering approaches, combined with the content filtering methods, are proposed to make recommendations. The proposed new user profiling and recommendation approaches have been evaluated through extensive experiments. The effectiveness evaluation experiments were conducted on two real world datasets collected from Amazon.com and CiteULike websites. The experimental results demonstrate that the proposed user profiling and recommendation approaches outperform those related state-of-the-art approaches. In addition, this thesis proposes a parallel, scalable user profiling implementation approach based on advanced cloud computing techniques such as Hadoop, MapReduce and Cascading. The scalability evaluation experiments were conducted on a large scaled dataset collected from Del.icio.us website. This thesis contributes to effectively use the wisdom of crowds and expert to help users solve information overload issues through providing more accurate, effective and efficient user profiling and recommendation approaches. It also contributes to better usages of taxonomy information given by experts and folksonomy information contributed by users in Web 2.0.