Stability criteria for unsupervised temporal association networks

A biologically realizable, unsupervised learning rule is described for the online extraction of object features, suitable for solving a range of object recognition tasks. Alterations to the basic learning rule are proposed which allow the rule to better suit the parameters of a given input space. One negative consequence of such modifications is the potential for learning instability. The criteria for such instability are modeled using digital filtering techniques and predicted regions of stability and instability tested. The result is a family of learning rules which can be tailored to the specific environment, improving both convergence times and accuracy over the standard learning rule, while simultaneously insuring learning stability.

An industry/university collaboration to upgrade software engineering knowledge and skills in industry

This paper describes an ongoing collaboration between Boeing Australia Limited and the University of Queensland to develop and deliver an introductory course on software engineering. The aims of the course are to provide a common understanding of the nature of software engineering for all Boeing Australia's engineering staff, and to ensure they understand the practices used throughout the company. The course is designed so that it can be presented to people with varying backgrounds, such as recent software engineering graduates, systems engineers, quality assurance personnel, etc. The paper describes the structure and content of the course, and the evaluation techniques used to collect feedback from the participants and the corresponding results. The immediate feedback on the course indicates that it has been well received by the participants, but also indicates a need for more advanced courses in specific areas. The long-term feedback from participants is less positive, and the long-term feedback from the managers of the course participants indicates a need to expand on the coverage of the Boeing-specific processes and methods. (C) 2004 Elsevier Inc. All rights reserved.

Synthesis, characterization, and electrical conduction of 10 mol% Dy2O3 doped CeO2 ceramics

Well-densified 10 mol% Dy2O3-doped CeO2 (20DDC) ceramics with average grain sizes of similar to 0.12-1.5 mu m were fabricated by pressureless sintering at 950-1550 degrees C using a reactive powder thermally decomposed from a carbonate precursor, which was synthesized via a carbonate coprecipitation method employing nitrates as the starting salts and ammonium carbonate as the precipitant. Electrical conductivity of the ceramics, measured by the dc three-point impedance method, shows a V-shape curve against the average grain size. The sample with the smallest grain size of 0.12 mu m exhibits a high conductivity of similar to 10(-1.74) S/cm at the measurement temperature of 700 degrees C, which is about the same conduction level of the micro-grained 10 mol% Sm2O3- or Gd2O3-doped CeO2, two leading electrolyte materials. (c) 2004 Elsevier Ltd. All rights reserved.

Thermal and electrical currents in nanoscale electronic interferometers

We theoretically study thermal transport in an electronic interferometer comprising a parallel circuit of two quantum dots, each of which has a tunable single electronic state which are connected to two leads at different temperature. As a result of quantum interference, the heat current through one of the dots is in the opposite direction to the temperature gradient. An excess heat current flows through the other dot. Although locally, heat flows from cold to hot, globally the second law of thermodynamics is not violated because the entropy current associated with heat transfer through the whole device is still positive. The temperature gradient also induces a circulating electrical current, which makes the interferometer magnetically polarized.

Summarizing level-two topological relations in large spatial datasets

Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.

Making the link between engineering management and undergraduate research

This paper describes and analyses an innovative engineering management course that applies a project management framework in the context of a feasibility study for a prospective research project. The aim is to have students learn aspects of management that will be relevant from the outset of their professional career while simultaneously having immediate value in helping them to manage a research project and capstone design project in their senior year. An integral part of this innovation was the development of a web-based project management tool. While the main objectives of the new course design were achieved, a number of important lessons were learned that would guide the further development and continuous improvement of this course. The most critical of these is the need to achieve the optimum balance in the mind of the students between doing the project and critically analyzing the processes used to accomplish the work.

The role of software tools in influencing creative problem solving in engineering design and education

Creativity is increasingly recognised as an essential component of engineering design. This paper describes an exploratory study into the nature and importance of creativity in engineering design problem solving in relation to the possible impact of software design tools. The first stage of the study involved an empirical investigation in the form of a case study of the use of standard CAD tool sets and the development of a systems engineering software support tool. It was found that there were several ways in which CAD influenced the creative process, including enhancing visualisation and communication, premature fixation, circumscribed thinking and bounded ideation. The tool development experience uncovered the difficulty in supporting creative processes from the developer's perspective. The issues were the necessity of making assumptions, achieving a balance between structure and flexibility, and the pitfalls of satisfying user wants and needs. The second part of the study involved the development of a model of the creative problem solving process in engineering design. This provided a possible explanation for why purpose designed engineering software tools might encourage an analytical problem solving approach and discourage a more creative approach.

Using the EM Algorithm to Train Neural Networks: Misconceptions and a New Algorithm for Multiclass Classification

The expectation-maximization (EM) algorithm has been of considerable interest in recent years as the basis for various algorithms in application areas of neural networks such as pattern recognition. However, there exists some misconceptions concerning its application to neural networks. In this paper, we clarify these misconceptions and consider how the EM algorithm can be adopted to train multilayer perceptron (MLP) and mixture of experts (ME) networks in applications to multiclass classification. We identify some situations where the application of the EM algorithm to train MLP networks may be of limited value and discuss some ways of handling the difficulties. For ME networks, it is reported in the literature that networks trained by the EM algorithm using iteratively reweighted least squares (IRLS) algorithm in the inner loop of the M-step, often performed poorly in multiclass classification. However, we found that the convergence of the IRLS algorithm is stable and that the log likelihood is monotonic increasing when a learning rate smaller than one is adopted. Also, we propose the use of an expectation-conditional maximization (ECM) algorithm to train ME networks. Its performance is demonstrated to be superior to the IRLS algorithm on some simulated and real data sets.