IT as enabler of sustainable farming:an empirical analysis of farmers' adoption decision of precision agriculture technology

Precision agriculture (PA) describes a suite of IT based tools which allow farmers to electronically monitor soil and crop conditions and analyze treatment options. This study tests a model explaining the difficulties of PA technology adoption. The model draws on theories of technology acceptance and diffusion of innovation and is validated using survey data from farms in Canada. Findings highlight the importance of compatibility among PA technology components and the crucial role of farmers' expertise. The model provides the theoretical and empirical basis for developing policies and initiatives to support PA technology adoption.

The characterisation and emulation of rotary mechanical machines

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT

A study of stray load losses in core clamp plates of salient pole synchronous machines

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The use of electronic techniques in the construction of machines which can learn

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Storage capacity of ultrametric committee machines

The problem of computing the storage capacity of a feed-forward network, with L hidden layers, N inputs, and K units in the first hidden layer, is analyzed using techniques from statistical mechanics. We found that the storage capacity strongly depends on the network architecture αc ∼ (log K)1-1/2L and that the number of units K limits the number of possible hidden layers L through the relationship 2L - 1 < 2log K. © 2014 IOP Publishing Ltd.

Learning higher-level features with convolutional restricted Boltzmann machines for sentiment analysis

In recent years, learning word vector representations has attracted much interest in Natural Language Processing. Word representations or embeddings learned using unsupervised methods help addressing the problem of traditional bag-of-word approaches which fail to capture contextual semantics. In this paper we go beyond the vector representations at the word level and propose a novel framework that learns higher-level feature representations of n-grams, phrases and sentences using a deep neural network built from stacked Convolutional Restricted Boltzmann Machines (CRBMs). These representations have been shown to map syntactically and semantically related n-grams to closeby locations in the hidden feature space. We have experimented to additionally incorporate these higher-level features into supervised classifier training for two sentiment analysis tasks: subjectivity classification and sentiment classification. Our results have demonstrated the success of our proposed framework with 4% improvement in accuracy observed for subjectivity classification and improved the results achieved for sentiment classification over models trained without our higher level features.

Precision emulsification for droplet and capsule production

Emulsions and microcapsules are typical structures in various dispersion formulations for pharmaceutical, food, personal and house care applications. Precise control over size and size distribution of emulsion droplets and microcapsules are important for effective use and delivery of active components and better product quality. Many emulsification technologies have been developed to meet different formulation and processing requirements. Among them, membrane and microfluidic emulsification as emerging technologies have the feature of being able to precisely manufacture droplets in a drop-by-drop manner to give subscribed sizes and size distributions with lower energy consumption. This paper reviews fundamental sciences and engineering aspects of emulsification, membrane and microfluidic emulsification technologies and their use for precision manufacture of emulsions for intensified processing. Generic application examples are given for single and double emulsions and microcapsules with different structure features. © 2013 The Society of Powder Technology Japan. Published by Elsevier B.V.

Responsive fixture design using dynamic product inspection and monitoring technologies for the precision machining of large-scale aerospace parts

When machining a large-scale aerospace part, the part is normally located and clamped firmly until a set of features are machined. When the part is released, its size and shape may deform beyond the tolerance limits due to stress release. This paper presents the design of a new fixing method and flexible fixtures that would automatically respond to workpiece deformation during machining. Deformation is inspected and monitored on-line, and part location and orientation can be adjusted timely to ensure follow-up operations are carried out under low stress and with respect to the related datum defined in the design models.

Comparison of the measurement performance of high precision multi-axis metal cutting machine tools

High precision manufacturers continuously seek out disruptive technologies to improve the quality, cost, and delivery of their products. With the advancement of machine tool and measurement technology many companies are ready to capitalise on the opportunity of on-machine measurement (OMM). Coupled with business case, manufacturing engineers are now questioning whether OMM can soon eliminate the need for post-process inspection systems. Metrologists will however argue that the machining environment is too hostile and that there are numerous process variables which need consideration before traceable measurement on-the-machine can be achieved. In this paper we test the measurement capability of five new multi-axis machine tools enabled as OMM systems via on-machine probing. All systems are tested under various operating conditions in order to better understand the effects of potentially significant variables. This investigation has found that key process variables such as machine tool warm-up and tool-change cycles can have an effect on machine tool measurement repeatability. New data presented here is important to many manufacturers whom are considering utilising their high precision multi-axis machine tools for both the creation and verification of their products.

Measurement and compensation of pivot point positional error on five-axis machines

Measuring and compensating the pivot points of five-axis machine tools is always challenging and very time consuming. This paper presents a newly developed approach for automatic measurement and compensation of pivot point positional errors on five-axis machine tools. Machine rotary axis errors are measured using a circular test. This method has been tested on five-axis machine tools with swivel table configuration. Results show that up to 99% of the positional errors of the rotary axis can be compensated by using this approach.

It's about bits of machines & stuff:why girls don't 'do' engineering

This paper draws upon part of the findings of an ethnographic study in which two seventeen year old girls were employed to interview their peer about engineering as a study and career choice. It argues that whilst girls do view engineering as being generally masculine in nature, other factors such as a lack of female role models and an emphasis on physics and maths act as barriers to young women entering the discipline. The paper concludes by noting that engineering has much to offer young women, the problem is, they simply don't know this is the case! Copyright © 2013 Jane Andrews & Robin Clark.

Long-range and high-precision correlation optical time-domain reflectometry utilizing an all-fiber chaotic source

We propose a long range, high precision optical time domain reflectometry (OTDR) based on an all-fiber supercontinuum source. The source simply consists of a CW pump laser with moderate power and a section of fiber, which has a zero dispersion wavelength near the laser's central wavelength. Spectrum and time domain properties of the source are investigated, showing that the source has great capability in nonlinear optics, such as correlation OTDR due to its ultra-wide-band chaotic behavior, and mm-scale spatial resolution is demonstrated. Then we analyze the key factors limiting the operational range of such an OTDR, e. g., integral Rayleigh backscattering and the fiber loss, which degrades the optical signal to noise ratio at the receiver side, and then the guideline for counter-act such signal fading is discussed. Finally, we experimentally demonstrate a correlation OTDR with 100km sensing range and 8.2cm spatial resolution (1.2 million resolved points), as a verification of theoretical analysis.

Permanent matching of coupled optical bottle resonators with better than 0.16 GHz precision

The fabrication precision is one of the most critical challenges to the creation of practical photonic circuits composed of coupled high Q-factor microresonators. While very accurate transient tuning of microresonators based on local heating has been reported, the record precision of permanent resonance positioning achieved by post-processing is still within 1 and 5 GHz. Here we demonstrate two coupled bottle microresonators fabricated at the fiber surface with resonances that are matched with a better than 0.16 GHz precision. This corresponds to a better than 0.17 Å precision in the effective fiber radius variation. The achieved fabrication precision is only limited by the resolution of our optical spectrum analyzer and can be potentially improved by an order of magnitude.

It’s about bits of machines & stuff:why girls don’t ‘do’ engineering

This paper draws upon part of the findings of an ethnographic study in which two seventeen year old girls were employed to interview their peer about engineering as a study and career choice. It argues that whilst girls do view engineering as being generally masculine in nature, other factors such as a lack of female role models and an emphasis on physics and maths act as barriers to young women entering the discipline. The paper concludes by noting that engineering has much to offer young women, the problem is, they simply don’t know this is the case!

Support vector machines to detect physiological patterns for EEG and EMG-based human-computer interaction:a review

Support Vector Machines (SVMs) are widely used classifiers for detecting physiological patterns in Human-Computer Interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the application of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables, and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.