Fiber laser processing of amorphous rare earth NeFeB magnetic materials

Since the exchange coupling theory was proposed by Kneller and Hawig in 1991 there has been a significant effort within the magnetic materials community to enhance the performance of rare earth magnets by utilising nano-composite meta-materials. Inclusions of magnetically soft iron smaller than approximately 10 nm in diameter are exchange coupled to a surrounding magnetically hard Nd2Fe14B matrix and provide an enhanced saturisation magnetisation without reducing coercivity. For such a fine nanostructure to be produced, close control over the thermal history of the material is needed. A processing route which provides this is laser annealing from an amorphous alloy precursor. In the current work, relationships between laser parameters, thermal histories of laser processed amorphous stoichiometric NdFeB ribbons and the magnetic properties of the resulting nanocrystalline products have been determined with a view to applying the process to thick film nanocomposite magnet production.

Enticing general engineering undergrads into signal processing with Photo-Editing and image search

The University of Cambridge is unusual in that its Department of Engineering is a single department which covers virtually all branches of engineering under one roof. In their first two years of study, our undergrads study the full breadth of engineering topics and then have to choose a specialization area for the final two years of study. Here we describe part of a course, given towards the end of their second year, which is designed to entice these students to specialize in signal processing and information engineering topics for years 3 and 4. The course is based around a photo editor and an image search application, and it requires no prior knowledge of the z-transform or of 2-dimensional signal processing. It does assume some knowledge of 1-D convolution and basic Fourier methods and some prior exposure to Matlab. The subject of this paper, the photo editor, is written in standard Matlab m-files which are fully visible to the students and help them to see how specific algorithms are implemented in detail. © 2011 IEEE.

BarraCUDA - a fast short read sequence aligner using graphics processing units.

BACKGROUND: With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy efficient alternative to traditional high-performance computing (HPC) clusters. In this article, we describe the implementation of BarraCUDA, a GPGPU sequence alignment software that is based on BWA, to accelerate the alignment of sequencing reads generated by these instruments to a reference DNA sequence. FINDINGS: Using the NVIDIA Compute Unified Device Architecture (CUDA) software development environment, we ported the most computational-intensive alignment component of BWA to GPU to take advantage of the massive parallelism. As a result, BarraCUDA offers a magnitude of performance boost in alignment throughput when compared to a CPU core while delivering the same level of alignment fidelity. The software is also capable of supporting multiple CUDA devices in parallel to further accelerate the alignment throughput. CONCLUSIONS: BarraCUDA is designed to take advantage of the parallelism of GPU to accelerate the alignment of millions of sequencing reads generated by NGS instruments. By doing this, we could, at least in part streamline the current bioinformatics pipeline such that the wider scientific community could benefit from the sequencing technology.BarraCUDA is currently available from http://seqbarracuda.sf.net.

Initializing Vision Based Trackers Using Semantic Texton Forests

Vision based tracking can provide the spatial location of project related entities such as equipment, workers, and materials in a large-scale congested construction site. It tracks entities in a video stream by inferring their motion. To initiate the process, it is required to determine the pixel areas of the entities to be tracked in the following consecutive video frames. For the purpose of fully automating the process, this paper presents an automated way of initializing trackers using Semantic Texton Forests (STFs) method. STFs method performs simultaneously the segmentation of the image and the classification of the segments based on the low-level semantic information and the context information. In this paper, STFs method is tested in the case of wheel loaders recognition. In the experiments, wheel loaders are further divided into several parts such as wheels and body parts to help learn the context information. The results show 79% accuracy of recognizing the pixel areas of the wheel loader. These results signify that STFs method has the potential to automate the initialization process of vision based tracking.