The parameterless self-organizing map algorithm

The parameterless self-organizing map (PLSOM) is a new neural network algorithm based on the self-organizing map (SOM). It eliminates the need for a learning rate and annealing schemes for learning rate and neighborhood size. We discuss the relative performance of the PLSOM and the SOM and demonstrate some tasks in which the SOM fails but the PLSOM performs satisfactory. Finally we discuss some example applications of the PLSOM and present a proof of ordering under certain limited conditions.

Understanding the crystallography of the eutectoid microstructure in a Zn-Al alloy using the edge-to-edge matching model

The orientation relationship (OR) between the beta(Zn) phase and the alpha(Al) phase and the corresponding habit planes in a Zn-Al eutectoid alloy were accurately determined using convergent beam Kikuchi line diffraction patterns. In addition to the previously reported OR. [11 (2) over bar0](beta)parallel to[110](alpha), (0002)(beta)parallel to ((1) over bar 11)alpha, two new ORs were observed. They are: [11 (2) over bar0](beta)parallel to [110], ((1) over bar 101)(beta) 0.82 degrees from (002)(alpha) and [(1) over bar 100](beta)parallel to[112](alpha), (0002)(beta) 4.5 degrees from (111)(alpha). These ORs can be explained and understood using the recently developed edge-to-edge matching model. (c) 2006 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

A bang-bang PLL employing dynamic gain control for low jitter and fast lock times

Bang-bang phase detector based PLLs are simple to design, suffer no systematic phase error, and can run at the highest speed a process can make a working flip-flop. For these reasons designers are employing them in the design of very high speed Clock Data Recovery (CDR) architectures. The major drawback of this class of PLL is the inherent jitter due to quantized phase and frequency corrections. Reducing loop gain can proportionally improve jitter performance, but also reduces locking time and pull-in range. This paper presents a novel PLL design that dynamically scales its gain in order to achieve fast lock times while improving fitter performance in lock. Under certain circumstances the design also demonstrates improved capture range. This paper also analyses the behaviour of a bang-bang type PLL when far from lock, and demonstrates that the pull-in range is proportional to the square root of the PLL loop gain.

Imitating the neighbours: vocal dialect matching in a mimic-model system

Vocal mimicry provides a unique system for investigating song learning and cultural evolution in birds. Male lyrebirds produce complex vocal displays that include extensive and accurate mimicry of many other bird species. We recorded and analysed the songs of the Albert's lyrebird (Menura alberti) and its most commonly imitated model species, the satin bowerbird (Ptilonorhynchus violaceus), at six sites in southeast Queensland, Australia. We show that each population of lyrebirds faithfully reproduces the song of the local population of bowerbirds. Within a population, lyrebirds show less variation in song structure than the available variation in the songs of the models. These results provide the first quantitative evidence for dialect matching in the songs of two species that have no direct ecological relationship.

Fast-tracking middle schooling reform: A model for the integrated curriculum

To participate effectively in the post-industrial information societies and knowledge/service economies of the 21st century, individuals must be better-informed, have greater thinking and problem-solving abilities, be self-motivated; have a capacity for cooperative interaction; possess varied and specialised skills; and be more resourceful and adaptable than ever before. This paper reports on one outcome from a national project funded by the Ministerial Council on Education, Employment Training and Youth Affairs, which investigated what practices, processes, strategies and structures best promote lifelong learning and the development of lifelong learners in the middle years of schooling. The investigation linked lifelong learning with middle schooling because there were indications that middle schooling reform practices also lead to the development of lifelong learning attributes, which is regarded as a desirable outcome of schooling in Australia. While this larger project provides depth around these questions, this paper specifically reports on the development of a three-phase model that can guide the sequence in which schools undertaking middle schooling reform attend to particular core component changes. The model is developed from the extensive analysis of 25 innovative schools around the nation, and provides a unique insight into the desirable sequences and time spent achieving reforms, along with typical pitfalls that lead to a regression in the reform process. Importantly, the model confirms that schooling reform takes much more time than planners typically expect or allocate, and there are predictable and identifiable inhibitors to achieving it.

Edge-to-edge matching - The fundamentals

The basis of the present authors' edge-to-edge matching model for understanding the crystallography of partially coherent precipitates is the minimization of the energy of the interface between the two phases. For relatively simple crystal structures, this energy minimization occurs when close-packed, or relatively close-packed, rows of atoms match across the interface. Hence, the fundamental principle behind edge-to-edge matching is that the directions in each phase that correspond to the edges of the planes that meet in the interface should be close-packed, or relatively close-packed, rows of atoms. A few of the recently reported examples of what is termed edge-to-edge matching appear to ignore this fundamental principle. By comparing theoretical predictions with available experimental data, this article will explore the validity of this critical atom-row coincidence condition, in situations where the two phases have simple crystal Structures and in those where the precipitate has a more complex structure.

Investigating the performance analysis of EASI algorithm and EKENS algorithm in nonlinear model

This paper investigates the performance of EASI algorithm and the proposed EKENS algorithm for linear and nonlinear mixtures. The proposed EKENS algorithm is based on the modified equivariant algorithm and kernel density estimation. Theory and characteristic of both the algorithms are discussed for blind source separation model. The separation structure of nonlinear mixtures is based on a nonlinear stage followed by a linear stage. Simulations with artificial and natural data demonstrate the feasibility and good performance of the proposed EKENS algorithm.

Machine learning for matching astronomy catalogues

An emerging issue in the field of astronomy is the integration, management and utilization of databases from around the world to facilitate scientific discovery. In this paper, we investigate application of the machine learning techniques of support vector machines and neural networks to the problem of amalgamating catalogues of galaxies as objects from two disparate data sources: radio and optical. Formulating this as a classification problem presents several challenges, including dealing with a highly unbalanced data set. Unlike the conventional approach to the problem (which is based on a likelihood ratio) machine learning does not require density estimation and is shown here to provide a significant improvement in performance. We also report some experiments that explore the importance of the radio and optical data features for the matching problem.