Improved Orientation Sampling for Indexing Diffraction Patterns of Polycrystalline Materials

Orientation mapping is a widely used technique for revealing the microstructure of a polycrystalline sample. The crystalline orientation at each point in the sample is determined by analysis of the diffraction pattern, a process known as pattern indexing. A recent development in pattern indexing is the use of a brute-force approach, whereby diffraction patterns are simulated for a large number of crystalline orientations, and compared against the experimentally observed diffraction pattern in order to determine the most likely orientation. Whilst this method can robust identify orientations in the presence of noise, it has very high computational requirements. In this article, the computational burden is reduced by developing a method for nearly-optimal sampling of orientations. By using the quaternion representation of orientations, it is shown that the optimal sampling problem is equivalent to that of optimally distributing points on a four-dimensional sphere. In doing so, the number of orientation samples needed to achieve a indexing desired accuracy is significantly reduced. Orientation sets at a range of sizes are generated in this way for all Laue groups, and are made available online for easy use.Comment: 11 pages, 7 figure

Compressive Sensing for Spread Spectrum Receivers

With the advent of ubiquitous computing there are two design parameters of wireless communication devices that become very important power: efficiency and production cost. Compressive sensing enables the receiver in such devices to sample below the Shannon-Nyquist sampling rate, which may lead to a decrease in the two design parameters. This paper investigates the use of Compressive Sensing (CS) in a general Code Division Multiple Access (CDMA) receiver. We show that when using spread spectrum codes in the signal domain, the CS measurement matrix may be simplified. This measurement scheme, named Compressive Spread Spectrum (CSS), allows for a simple, effective receiver design. Furthermore, we numerically evaluate the proposed receiver in terms of bit error rate under different signal to noise ratio conditions and compare it with other receiver structures. These numerical experiments show that though the bit error rate performance is degraded by the subsampling in the CS-enabled receivers, this may be remedied by including quantization in the receiver model. We also study the computational complexity of the proposed receiver design under different sparsity and measurement ratios. Our work shows that it is possible to subsample a CDMA signal using CSS and that in one example the CSS receiver outperforms the classical receiver.Comment: 11 pages, 11 figures, 1 table, accepted for publication in IEEE Transactions on Wireless Communication

Autoencoding beyond pixels using a learned similarity metric

We present an autoencoder that leverages learned representations to better measure similarities in data space. By combining a variational autoencoder with a generative adversarial network we can use learned feature representations in the GAN discriminator as basis for the VAE reconstruction objective. Thereby, we replace element-wise errors with feature-wise errors to better capture the data distribution while offering invariance towards e.g. translation. We apply our method to images of faces and show that it outperforms VAEs with element-wise similarity measures in terms of visual fidelity. Moreover, we show that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic

Identifying the key process factors affecting project performance

Purpose A construction project traditionally involves a variety of participants. Owners, consultants, and contractors all have diverse opinions and interests, but they all seek to ensure project success. Success is habitually measured as performance output regarding cost, time, and quality. Despite previous research mapping the success and failure factors, construction managers seem to have difficulty in attaining success. To provide clearer guidance on how to fulfill success criteria, the purpose of this paper is to identify the underlying factors that affect performance and thus project success in construction processes. Design/methodology/approach A questionnaire survey based on a literature review provided 25 key process factors divided into five key categories. Based on the responses from commonly involved construction parties, the factors were ranked and tested for significant differences between the parties. Findings The top five most important process factors were found to relate to the sharing of knowledge and communication. Moreover, testing the ranking for significant differences between owners, consultants, and contractors revealed five differences. The differences related to the interpretation and importance of trust, shared objectives, project coordination, and alternative forms of coordination. Originality/value All respondents identify improved knowledge sharing and communication as the key to improved cost, time, and quality performance and are therefore the areas where construction managers need to focus their resources. Thus, improved experience sharing and communication will increase the likelihood of project success, through improving competences, commitment, and coordination. </jats:sec

SE-ENRICHMENT OF CARROT AND ONION VIA FOLIAR APPLICATION

The aim of this work was to study the selenium accumulation in carrot and onion plants using foliar application by sodium selenite and sodium selenate. Furthermore, we aimed at identifying the Se species biosynthesised by onion and carrot plants. The results were used to prepare for production of 77Se enriched plants for an ongoing human absorption study