An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.

Determining propeller induced erosion alongside quay walls in harbours using Artificial Neural Networks

In shallow waters, such as those found close to berth structures, the wash from a manoeuvring ship’s propeller can cause erosion of the seabed. This erosion can be increased if the wash intersects a berth structure. A number of researchers have undertaken model studies and used regression analysis to develop predictive relationships for the scouring action. This paper presents an experimental investigation with Artificial Neural Networks (ANN’s), used to analyse the results. The purpose of using ANN’s was to examine the prediction accuracy of the Networks in comparison with previous regression analysis methods. ANN’s were found to provide a more accurate method of predicting propeller wash scour than the equations presented by previous investigators.

Lorentz TEM investigation of the switching behavior in artificial spin ice building blocks

Frustration – the inability to simultaneously satisfy all interactions – occurs in a wide range of systems including neural networks, water ice and magnetic systems. An example of the latter is the so called spin-ice in pyrochlore materials [1] which have attracted a lot of interest not least due to the emergence of magnetic monopole defects when the ‘ice rules’ governing the local ordering breaks down [2]. However it is not possible to directly measure the frustrated property – the direction of the magnetic moments – in such spin ice systems with current experimental techniques. This problem can be solved by instead studying artificial spin-ice systems where the molecular magnetic moments are replaced by nanoscale ferromagnetic islands [3-8]. Two different arrangements of the ferromagnetic islands have been shown to exhibit spin ice behaviour: a square lattice maintaining four moments at each vertex [3,8] and the Kagome lattice which has only three moments per vertex but equivalent interactions between them [4-7]. Magnetic monopole defects have been observed in both types of lattices [7-8]. One of the challenges when studying these artificial spin-ice systems is that it is difficult to arrive at the fully demagnetised ground-state [6-8].
Here we present a study of the switching behaviour of building blocks of the Kagome lattice influenced by the termination of the lattice. Ferromagnetic islands of nominal size 1000 nm by 100 nm were fabricated in five island blocks using electron-beam lithography and lift-off techniques of evaporated 18 nm Permalloy (Ni80Fe20) films. Each block consists of a central island with four arms terminated by a different number and placement of ‘injection pads’, see Figure 1. The islands are single domain and magnetised along their long axis. The structures were grown on a 50 nm thick electron transparent silicon nitride membrane to allow TEM observation, which was back-coated with a 5 nm film of Au to prevent charge build-up during the TEM experiments.
To study the switching behaviour the sample was subjected to a magnetic field strong enough to magnetise all the blocks in one direction, see Figure 1. Each block obeys the Kagome lattice ‘ice-rules’ of “2-in, 1-out” or “1-in, 2-out” in this fully magnetised state. Fresnel mode Lorentz TEM images of the sample were then recorded as a magnetic field of increasing magnitude was applied in the opposite direction. While the Fresnel mode is normally used to image magnetic domain structures [9] for these types of samples it is possible to deduce the direction of the magnetisation from the Lorentz contrast [5]. All images were recorded at the same over-focus judged to give good Lorentz contrast.
The magnetisation was found to switch at different magnitudes of the applied field for nominally identical blocks. However, trends could still be identified: all the blocks with any injection pads, regardless of placement and number, switched the direction of the magnetisation of their central island at significantly smaller magnitudes of the applied magnetic field than the blocks without injection pads. It can therefore be concluded that the addition of an injection pad lowers the energy barrier to switching the connected island, acting as a nucleation site for monopole defects. In these five island blocks the defects immediately propagate through to the other side, but in a larger lattice the monopoles could potentially become trapped at a vertex and observed [10].
References

[1] M J Harris et al, Phys Rev Lett 79 (1997) p.2554.
[2] C Castelnovo, R Moessner and S L Sondhi, Nature 451 (2008) p. 42.
[3] R F Wang et al, Nature 439 (2006) 303.
[4] M Tanaka et al, Phys Rev B 73 (2006) 052411.
[5] Y Qi, T Brintlinger and J Cumings, Phys Rev B 77 (2008) 094418.
[6] E Mengotti et al, Phys Rev B 78 (2008) 144402.
[7] S Ladak et al, Nature Phys 6 (2010) 359.
[8] C Phatak et al, Phys Rev B 83 (2011) 174431.
[9] J N Chapman, J Phys D 17 (1984) 623.
[10] The authors gratefully acknowledge funding from the EPSRC under grant number EP/D063329/1.

A sparse representation based fast detection method for surface defect detection of bottle caps

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.

An indirect bridge inspection method incorporating a wavelet-based damage indicator and pattern recognition

In recent years, there has been a move towards the development of indirect structural health monitoring (SHM)techniques for bridges; the low-cost vibration-based method presented in this paper is such an approach. It consists of the use of a moving vehicle fitted with accelerometers on its axles and incorporates wavelet analysis and statistical pattern recognition. The aim of the approach is to both detect and locate damage in bridges while reducing the need for direct instrumentation of the bridge. In theoretical simulations, a simplified vehicle-bridge interaction model is used to investigate the effectiveness of the approach in detecting damage in a bridge from vehicle accelerations. For this purpose, the accelerations are processed using a continuous wavelet transform as when the axle passes over a damaged section, any discontinuity in the signal would affect the wavelet coefficients. Based on these coefficients, a damage indicator is formulated which can distinguish between different damage levels. However, it is found to be difficult to quantify damage of varying levels when the vehicle’s transverse position is varied between bridge crossings. In a real bridge field experiment, damage was applied artificially to a steel truss bridge to test the effectiveness of the indirect approach in practice; for this purpose a two-axle van was driven across the bridge at constant speed. Both bridge and vehicle acceleration measurements were recorded. The dynamic properties of the test vehicle were identified initially via free vibration tests. It was found that the resulting damage indicators for the bridge and vehicle showed similar patterns, however, it was difficult to distinguish between different artificial damage scenarios.

Urethral atrophy after artificial urinary sphincter placement:Is cuff downsizing effective?

Purpose: We reviewed the outcome of cuff downsizing with an artificial urinary sphincter for treating recurrent incontinence due to urethral atrophy.

Materials and Methods: We analyzed the records of 17 patients in a 7-year period in whom clinical, radiological and urodynamic evidence of urethral atrophy was treated with cuff downsizing. Cuff downsizing was accomplished by removing the existing cuff and replacing it with a 4 cm. cuff within the established false capsule. Incontinence and satisfaction parameters before and after the procedure were assessed by a validated questionnaire.

Results: Mean patient age was 70 years (range 62 to 79). Average time to urethral atrophy was 31 months (range 5 to 96) after primary sphincter implantation. Mean followup after downsizing was 22 months (range 1 to 64). Cuff downsizing caused a mean decrease of 3.9 to 0.5 pads daily. The number of severe leakage episodes decreased from a mean of 5.4 to 2.1 The mean SEAPI (stress leakage, emptying, anatomy, protection, inhibition) score decreased from 8.2 to 2.4. Patient satisfaction increased from 15% to 80% after cuff downsizing. In 1 patient an infected cuff required complete removal of the device.

Conclusions: Patient satisfaction and continence parameters improved after cuff downsizing. We believe that this technique is a simple and effective method of restoring continence after urethral atrophy.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.

The Role and Effectiveness of Public – Private Partnerships (NHS LIFT) in the Development of Enhanced Primary Care Premises and Services:Report for the National Institute for Health Research Service Delivery and Organisation programme

The research project analysed the role and effectiveness of LIFT via a multi-method study which included semi-structured interviews with policy elites and users, as well as case studies and an exploratory analysis of the financial characteristics of three LIFT Companies. While the team felt that it was able to identify key aspects relating to the advantages and drawbacks surrounding LIFT, some aspects relating to the representativeness of the study was adversely affected by a reluctance of PCTs to participate in the case study analysis and commercial confidentiality restrictions. The study was nonetheless able to identify important issues in relation to the funding and procurement of primary care premises and services.

Mixed integer non linear programming and artificial neural network based approach to ancillary services dispatch in competitive electricity markets

Ancillary services represent a good business opportunity that must be considered by market players. This paper presents a new methodology for ancillary services market dispatch. The method considers the bids submitted to the market and includes a market clearing mechanism based on deterministic optimization. An Artificial Neural Network is used for day-ahead prediction of Regulation Down, regulation-up, Spin Reserve and Non-Spin Reserve requirements. Two test cases based on California Independent System Operator data concerning dispatch of Regulation Down, Regulation Up, Spin Reserve and Non-Spin Reserve services are included in this paper to illustrate the application of the proposed method: (1) dispatch considering simple bids; (2) dispatch considering complex bids.

Aquisição por Visão Artificial e Medição Automática de Micro Dureza Vickers

O ensaio de dureza, e mais concretamente o ensaio de micro dureza Vickers, é no universo dos ensaios mecânicos um dos mais utilizados quer seja na indústria, no ensino ou na investigação e desenvolvimento de produto no âmbito das ciências dos materiais. Na grande maioria dos casos, a utilização deste ensaio tem como principal aplicação a caracterização ou controlo da qualidade de fabrico de materiais metálicos. Sendo um ensaio de relativa simplicidade de execução, rapidez e com resultados comparáveis e relacionáveis a outras grandezas físicas das propriedades dos materiais. Contudo, e tratando-se de um método de ensaio cuja intervenção humana é importante, na medição da indentação gerada por penetração mecânica através de um sistema ótico, não deixa de exibir algumas debilidades que daí advêm, como sendo o treino dos técnicos e respetivas acuidades visuais, fenómenos de fadiga visual que afetam os resultados ao longo de um turno de trabalho; ora estes fenómenos afetam a repetibilidade e reprodutibilidade dos resultados obtidos no ensaio. O CINFU possui um micro durómetro Vickers, cuja realização dos ensaios depende de um técnico treinado para a execução do mesmo, apresentando todas as debilidades já mencionadas e que o tornou elegível para o estudo e aplicação de uma solução alternativa. Assim, esta dissertação apresenta o desenvolvimento de uma solução alternativa ao método ótico convencional na medição de micro dureza Vickers. Utilizando programação em LabVIEW da National Instruments, juntamente com as ferramentas de visão computacional (NI Vision), o programa começa por solicitar ao técnico a seleção da câmara para aquisição da imagem digital acoplada ao micro durómetro, seleção do método de ensaio (Força de ensaio); posteriormente o programa efetua o tratamento da imagem (aplicação de filtros para eliminação do ruído de fundo da imagem original), segue-se, por indicação do operador, a zona de interesse (ROI) e por sua vez são identificadas automaticamente os vértices da calote e respetivas distâncias das diagonais geradas concluindo, após aceitação das mesmas, com o respetivo cálculo de micro dureza resultante. Para validação dos resultados foram utilizados blocos-padrão de dureza certificada (CRM), cujos resultados foram satisfatórios, tendo-se obtido um elevado nível de exatidão nas medições efetuadas. Por fim, desenvolveu-se uma folha de cálculo em Excel com a determinação da incerteza associada às medições de micro dureza Vickers. Foram então comparados os resultados nas duas metodologias possíveis, pelo método ótico convencional e pela utilização das ferramentas de visão computacional, tendo-se obtido bons resultados com a solução proposta.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Artificial Cavities and Nest Site Selection by Puerto Rican Parrots: a Multiscale Assessment

We examined nest site selection by Puerto Rican Parrots, a secondary cavity nester, at several spatial scales using the nest entrance as the central focal point relative to 20 habitat and spatial variables. The Puerto Rican Parrot is unique in that, since 2001, all known nesting in the wild has occurred in artificial cavities, which also provided us with an opportunity to evaluate nest site selection without confounding effects of the actual nest cavity characteristics. Because of the data limitations imposed by the small population size of this critically endangered endemic species, we employed a distribution-free statistical simulation approach to assess site selection relative to characteristics of used and unused nesting sites. Nest sites selected by Puerto Rican Parrots were characterized by greater horizontal and vertical visibility from the nest entrance, greater density of mature sierra palms, and a more westerly and leeward orientation of nest entrances than unused sites. Our results suggest that nest site selection in this species is an adaptive response to predation pressure, to which the parrots respond by selecting nest sites offering advantages in predator detection and avoidance at all stages of the nesting cycle. We conclude that identifying and replicating the “nest gestalt” of successful nesting sites may facilitate conservation efforts for this and other endangered avian species.

Artificial keys for botanical identification using a multilayer perceptron neural network (MLP)

In this paper, practical generation of identification keys for biological taxa using a multilayer perceptron neural network is described. Unlike conventional expert systems, this method does not require an expert for key generation, but is merely based on recordings of observed character states. Like a human taxonomist, its judgement is based on experience, and it is therefore capable of generalized identification of taxa. An initial study involving identification of three species of Iris with greater than 90% confidence is presented here. In addition, the horticulturally significant genus Lithops (Aizoaceae/Mesembryanthemaceae), popular with enthusiasts of succulent plants, is used as a more practical example, because of the difficulty of generation of a conventional key to species, and the existence of a relatively recent monograph. It is demonstrated that such an Artificial Neural Network Key (ANNKEY) can identify more than half (52.9%) of the species in this genus, after training with representative data, even though data for one character is completely missing.