Contrast sensitivity changes due to glaucoma and normal aging: low-spatial-frequency losses in both magnocellular and parvocellular pathways

PURPOSE: To explore the effects of glaucoma and aging on low-spatial-frequency contrast sensitivity by using tests designed to assess performance of either the magnocellular (M) or parvocellular (P) visual pathways. METHODS: Contrast sensitivity was measured for spatial frequencies of 0.25 to 2 cyc/deg by using a published steady- and pulsed-pedestal approach. Sixteen patients with glaucoma and 16 approximately age-matched control subjects participated. Patients with glaucoma were tested foveally and at two midperipheral locations: (1) an area of early visual field loss, and (2) an area of normal visual field. Control subjects were assessed in matched locations. An additional group of 12 younger control subjects (aged 20-35 years) were also tested. RESULTS: Older control subjects demonstrated reduced sensitivity relative to the younger group for the steady (presumed M)- and pulsed (presumed P)-pedestal conditions. Sensitivity was reduced foveally and in the midperiphery across the spatial frequency range. In the area of early visual field loss, the glaucoma group demonstrated further sensitivity reduction relative to older control subjects across the spatial frequency range for both the steady- and pulsed-pedestal tasks. Sensitivity was also reduced in the midperipheral location of "normal" visual field for the pulsed condition. CONCLUSIONS: Normal aging results in a reduction of contrast sensitivity for the low-spatial-frequency-sensitive components of both the M and P pathways. Glaucoma results in a further reduction of sensitivity that is not selective for M or P function. The low-spatial-frequency-sensitive channels of both pathways, which are presumably mediated by cells with larger receptive fields, are approximately equivalently impaired in early glaucoma.

Low-complexity estimation of CFO and frequency independent I/Q mismatch for OFDM systems

CFO and I/Q mismatch could cause significant performance degradation to OFDM systems. Their estimation and compensation are generally difficult as they are entangled in the received signal. In this paper, we propose some low-complexity estimation and compensation schemes in the receiver, which are robust to various CFO and I/Q mismatch values although the performance is slightly degraded for very small CFO. These schemes consist of three steps: forming a cosine estimator free of I/Q mismatch interference, estimating I/Q mismatch using the estimated cosine value, and forming a sine estimator using samples after I/Q mismatch compensation. These estimators are based on the perception that an estimate of cosine serves much better as the basis for I/Q mismatch estimation than the estimate of CFO derived from the cosine function. Simulation results show that the proposed schemes can improve system performance significantly, and they are robust to CFO and I/Q mismatch.

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.

On feasibility of regional frequency-based emergency control plans

Decentralized and regional load-frequency control of power systems operating in normal and near-normal conditions has been well studied; and several analysis/synthesis approaches have been developed during the last few decades. However in contingency and off-normal conditions, the existing emergency control plans, such as under-frequency load shedding, are usually applied in a centralized structure using a different analysis model. This paper discusses the feasibility of using frequency-based emergency control schemes based on tie-line measurements and local information available within a control area. The conventional load-frequency control model is generalized by considering the dynamics of emergency control/protection schemes and an analytic approach to analyze the regional frequency response under normal and emergency conditions is presented.

Teacher leadership : interns crossing to the domain of higher professional learning with mentors?

Over the last two decades, the notion of teacher leadership has emerged as a key concept in both the teaching and leadership literature. While researchers have not reached consensus regarding a definition, there has been some agreement that teacher leadership can operate at both a formal and informal level in schools and that it includes leadership of an instructional, organisational and professional development nature (York-Barr & Duke, 2004). Teacher leadership is a construct that tends not to be applied to pre-service teachers as interns, but is more often connected with the professional role of mentors who collaborate with them as they make the transition to being a beginning teacher. We argue that teacher leadership should be recognised as a professional and career goal during this formative learning phase and that interns should be expected to overtly demonstrate signs, albeit early ones, of leadership in instruction and other professional areas of development. The aim of this paper is to explore the extent to which teacher education interns at one university in Queensland reported on activities that may be deemed to be ‘teacher leadership.’ The research approach used in this study was an examination of 145 reflective reports written in 2008 by final Bachelor of Education (primary) pre-service teachers. These reports recorded the pre-service teachers’ perceptions of their professional learning with a school-based mentor in response to four outcomes of internship that were scaffolded by their mentor or initiated by them. These outcomes formed the bases of our research questions into the professional learning of the interns and included, ‘increased knowledge and capacity to teach within the total world of work as a teacher;’ ‘to work autonomously and interdependently’; to make ‘growth in critical reflectivity’, and the ‘ability to initiate professional development with the mentoring process’. Using the approaches of the constant comparative method of Strauss and Corbin (1998) key categories of experiences emerged. These categories were then identified as belonging to main meta-category labelled as ‘teacher leadership.’ Our research findings revealed that five dimensions of teacher leadership – effective practice in schools; school curriculum work; professional development of colleagues; parent and community involvement; and contributions to the profession – were evident in the written reports by interns. Not surprisingly, the mentor/intern relationship was the main vehicle for enabling the intern to learn about teaching and leadership. The paper concludes with some key implications for developers of preservice education programmes regarding the need for teacher leadership to be part of the discourse of these programmes.