Uncoordinated coexisting IEEE 802.15.4 networks for machine to machine communications

IEEE 802.15.4 standard has been proposed for low power wireless personal area networks. It can be used as an important component in machine to machine (M2M) networks for data collection, monitoring and controlling functions. With an increasing number of machine devices enabled by M2M technology and equipped with 802.15.4 radios, it is likely that multiple 802.15.4 networks may be deployed closely, for example, to collect data for smart metering at residential or enterprise areas. In such scenarios, supporting reliable communications for monitoring and controlling applications is a big challenge. The problem becomes more severe due to the potential hidden terminals when the operations of multiple 802.15.4 networks are uncoordinated. In this paper, we investigate this problem from three typical scenarios and propose an analytic model to reveal how performance of coexisting 802.15.4 networks may be affected by uncoordinated operations under these scenarios. Simulations will be used to validate the analytic model. It is observed that uncoordinated operations may lead to a significant degradation of system performance in M2M applications. With the proposed analytic model, we also investigate the performance limits of the 802.15.4 networks, and the conditions under which coordinated operations may be required to support M2M applications. © 2012 Springer Science + Business Media, LLC.

Distribute machine learning

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

Reliability monitoring, feedback and improvement systems:their application in the machine tool industry

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

The direction of motion of machine tool controls

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

Single-phase active rectifier selection for fault tolerant machine

A fault tolerant, 5-phase PM generator has been developed for use on the low pressure (LP) shaft of an aircraft gas turbine engine. The machine operates at variable speed and therefore has a variable voltage, variable frequency electrical output (VVVF). The generator is to be used to provide a 350V DC bus for distribution throughout the aircraft, and a study has been carried out that identifies the most suitable AC-DC converter topology for this machine in terms of losses, electrical component ratings, filtering requirements and circuit complexity.

Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Background - The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. Results - We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides. Conclusion - As a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.

Towards novel compact laser sources for non-invasive diagnostics and treatment

An important field of application of lasers is biomedical optics. Here, they offer great utility for diagnosis, therapy and surgery. For the development of novel methods of laser-based biomedical diagnostics careful study of light propagation in biological tissues is necessary to enhance our understanding of the optical measurements undertaken, increase research and development capacity and the diagnostic reliability of optical technologies. Ultimately, fulfilling these requirements will increase uptake in clinical applications of laser based diagnostics and therapeutics. To address these challenges informative biomarkers relevant to the biological and physiological function or disease state of the organism must be selected. These indicators are the results of the analysis of tissues and cells, such as blood. For non-invasive diagnostics peripheral blood, cells and tissue can potentially provide comprehensive information on the condition of the human organism. A detailed study of the light scattering and absorption characteristics can quickly detect physiological and morphological changes in the cells due to thermal, chemical, antibiotic treatments, etc [1-5]. The selection of a laser source to study the structure of biological particles also benefits from the fact that gross pathological changes are not induced and diagnostics make effective use of the monochromatic directional coherence properties of laser radiation.

Wireless machine-to-machine networks 2013

Editorial

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.

AllerTOP v.2 - a server for in silico prediction of allergens

Allergy is an overreaction by the immune system to a previously encountered, ordinarily harmless substance - typically proteins - resulting in skin rash, swelling of mucous membranes, sneezing or wheezing, or other abnormal conditions. The use of modified proteins is increasingly widespread: their presence in food, commercial products, such as washing powder, and medical therapeutics and diagnostics, makes predicting and identifying potential allergens a crucial societal issue. The prediction of allergens has been explored widely using bioinformatics, with many tools being developed in the last decade; many of these are freely available online. Here, we report a set of novel models for allergen prediction utilizing amino acid E-descriptors, auto- and cross-covariance transformation, and several machine learning methods for classification, including logistic regression (LR), decision tree (DT), naïve Bayes (NB), random forest (RF), multilayer perceptron (MLP) and k nearest neighbours (kNN). The best performing method was kNN with 85.3% accuracy at 5-fold cross-validation. The resulting model has been implemented in a revised version of the AllerTOP server (http://www.ddg-pharmfac.net/AllerTOP). © Springer-Verlag 2014.