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.     

Evaporation and Evolution of Suspended Solution Droplets at Atmospheric and Reduced Pressures

Thermal history and solute precipitation behavior of suspended solution droplets of sodium chloride (NaCl), magnesium sulphate (MgSO₄), and zirconium hydroxychloride (ZrO(OH)Cl) evaporating at atmospheric and reduced pressures are studied. Experimental measurements on the variation of droplet diameter, solution concentration, and temperature during the evaporation period are presented and discussed. The results of solute precipitation behavior in solution droplets observed under an optical microscope are displayed and discussed. Results indicate that reducing the pressure (∼ 33 kPa) results in a change in the solution droplet evaporation rate, but the thermal histories of a particular solution droplet are similar at the atmospheric and reduced pressures. At atmospheric and reduced pressures used in this study, the d² law for solution droplets is valid at early stages of the evaporation and before the solute precipitation initiates. Drying of MgSO₄ and ZrO(OH)Cl solution droplets results in the formation of spherical particles, whereas drying of spherical NaCl solution droplets results in the formation of cubic particles.     

Effect of Reactor Ambient Pressure on the Morphology of Spray Dried Magnesium Sulphate Powders

The effect of reducing the reactor air pressure on the morphology of spray dried magnesium sulphate powders is investigated, experimentally. A reactor, capable of drying and pyrolyzing solution sprays at low pressures, is designed and manufactured. A vibrating mesh nebulizer is employed to generate the spray. Four different pressures, starting from 60 Torr to the atmospheric pressure, and two different reactor air temperatures of 130°C and 420°C, are considered. In addition, two different concentrations of magnesium sulphate solutions are tested. The results are explained based on the effect of reactor air pressure on the droplet evaporation rate.     

A basic program for calculating subsurface water temperatures using chemical geothermometers-implication to geothermal reservoir estimation

GEOTHERM is a computer program written in BASIC language to estimate geothermal reservoir temperature using the well-known chemical gèothermometers. The empirical equations used in the program were obtained from the literature. Three different chemical geothermometers are included in the program: Na-K, Na-K-Ca, and silica geothermometers; this gives the user the opportunity not only to select the most reliable geothermometer in estimating subsurface temperature, but also to select the type of geothermometer according to available data. A sample input file of geothermal waters obtained from Iceland has been tested, so as to show the applicability and usefulness of the program.     

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.     

An Efficient Medical Image Deep Fusion Model Based on Convolutional Neural Networks

Medical image fusion is considered the best method for obtaining one image with rich details for efficient medical diagnosis and therapy. Deep learning provides a high performance for several medical image analysis applications. This paper proposes a deep learning model for the medical image fusion process. This model depends on Convolutional Neural Network (CNN). The basic idea of the proposed model is to extract features from both CT and MR images. Then, an additional process is executed on the extracted features. After that, the fused feature map is reconstructed to obtain the resulting fused image. Finally, the quality of the resulting fused image is enhanced by various enhancement techniques such as Histogram Matching (HM), Histogram Equalization (HE), fuzzy technique, fuzzy type Π, and Contrast Limited Histogram Equalization (CLAHE). The performance of the proposed fusion-based CNN model is measured by various metrics of the fusion and enhancement quality. Different realistic datasets of different modalities and diseases are tested and implemented. Also, real datasets are tested in the simulation analysis.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.