Kernel Spectral Matched Filter for Hyperspectral Imagery

In this paper a kernel-based nonlinear spectral matched filter is introduced for target detection in hyperspectral imagery, which is implemented by using the ideas in kernel-based learning theory. A spectral matched filter is defined in a feature space of high dimensionality, which is implicitly generated by a nonlinear mapping associated with a kernel function. A kernel version of the matched filter is derived by expressing the spectral matched filter in terms of the vector dot products form and replacing each dot product with a kernel function using the so called kernel trick property of the Mercer kernels. The proposed kernel spectral matched filter is equivalent to a nonlinear matched filter in the original input space, which is capable of generating nonlinear decision boundaries. The kernel version of the linear spectral matched filter is implemented and simulation results on hyperspectral imagery show that the kernel spectral matched filter outperforms the conventional linear matched filter.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Handwriting style classification

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier.Received: 6 September 2002, Accepted: 19 September 2002, Published online: 6 June 2003     

A brain–computer interface for the Dasher alternative text entry system

To investigate whether the alternative text entry system, Dasher, is useful to physically and intellectually disabled students when controlled by a brain–computer interface (BCI) a new software tool was developed to allow subjects to type words onto a computer screen via Dasher using their thoughts. A case study approach was adopted. Subjects were selected based on their suitability for the experiment, and the potential benefit to them of this system, by their head teacher. Subjects entered literacy level-matched phrases onto a computer using QWERTY keyboard, Dasher-mouse and Dasher-BCI. A researcher recorded qualitative and quantitative data, including characters entered per minute and their system preferences. Informed written consent was given for seven subjects to participate (aged 14–19 years, five male, with a range of physical and intellectual disabilities). After a short training period, all subjects had some degree of control over the Dasher-BCI system. With regard to typing speed, Dasher-BCI performed relatively poorly (3.9 ± 1.5 characters per minute), and QWERTY keyboard performed the best (31.9 ± 21.9 characters per minute). Dasher-BCI was the most preferred method. Areas of weakness in Dasher and the BCI hardware were highlighted and suggestions for improvement given. BCI-based text entry is not yet ready to compete with more established methods for students with combined cognitive and physical disabilities. Although underpowered, this study suggests that for people whose predominant disability is physical (cerebral palsy), BCI technology shows great potential as a viable text entry alternative. Suggestions for further research are discussed.     

Exploring creative thinking in graphically mediated synchronous dialogues

This paper reports on an aspect of the EC funded Argunaut project which researched and developed awareness tools for moderators of online dialogues. In this study we report on an investigation into the nature of creative thinking in online dialogues and whether or not this creative thinking can be coded for and recognized automatically such that moderators can be alerted when creative thinking is occurring or when it has not occurred after a period of time. We outline a dialogic theory of creativity, as the emergence of new perspectives from the interplay of voices, and the testing of this theory using a range of methods including a coding scheme which combined coding for creative thinking with more established codes for critical thinking, artificial intelligence pattern-matching techniques to see if our codes could be read automatically from maps and ‘key event recall’ interviews to explore the experience of participants. Our findings are that: (1) the emergence of new perspectives in a graphical dialogue map can be recognized by our coding scheme supported by a machine pattern-matching algorithm in a way that can be used to provide awareness indicators for moderators; (2) that the trigger events leading to the emergence of new perspectives in the online dialogues studied were most commonly disagreements and (3) the spatial representation of messages in a graphically mediated synchronous dialogue environment such as Digalo may offer more affordance for creativity than the much more common scrolling text chat environments. All these findings support the usefulness of our new account of creativity in online dialogues based on dialogic theory and demonstrate that this account can be operationalised through machine coding in a way that can be turned into alerts for moderators.     

Inactivation of pathogenic Klebsiella pneumoniae by CuO/TiO2 nanofibers: A multifunctional nanomaterial via one-step electrospinning

The fabrication and characterization of one-dimensional CuO/TiO₂ nanofibers with high photocatalytic and antibacterial activities are presented. The CuO/TiO₂ nanofibers were prepared by electrospinning of colloid composed of titanium isopropoxide, poly(vinylpyrroliodine) (PVP) and copper nanoparticles and calcination at 700 °C in air for 1 h. The antibacterial activity was tested using Klebsiella pneumoniae as model organism by calculation of the minimum inhibitory concentration (MIC). The obtained CuO/TiO₂ nanofibers showed prominent photocatalytic activity under visible light to degrade reactive black5 and reactive orange16 dyes in aqueous solutions and effectively catalyze K. pneumoniae inactivation. The decomposition process of the cell wall and cell membrane was directly observed by TEM analysis after the exposure of the K. pneumoniae to the nanofibers. Interestingly, the introduced photocatalyst can be reused with the same photocatalytic activity. Overall, the combination of CuO and TiO₂ can be synergistic and resulted in CuO/TiO₂ composite nanofibers having superior photocatalytic and antimicrobial potential to impede K. pneumoniae growth which causes bacterium to die ultimately.