A new approach to image enhancement for the visually impaired

This works aims at enhancing images by using the colour appearance model CIECAM02 for the visually impaired to view digital displays to complement the existing image processing approaches with a reference to normal visions. Specifically, by studying the images perceived by low-vision users, the colour ranges of these perceived views are compared with those viewed by normal vision and then characterized and represented using CIECAM02 correlates, which include lightness, colourfulness, and hue. For low-vision users, the extents of these attributes are therefore obtained. Subsequently, for any input image, these CIECAM02 attributes are subsequently enhanced through histogram equalizer technique within their respective ranges for low-vision users. In comparison with the approach of RGB histogram equalizer, the preliminary result has shown that the proposed method appears to be better to enhance the contents depicted in an image. The evaluation experiment was carried out using an array of low-vision simulator glasses to be worn by a group of subjects with normal vision. The next stage of the work remains to invite real low-vision users to evaluate the proposed work.

What learners want from educational spaces? A framework for assessing impact of architectural decisions in virtual worlds

In this paper we provide a revised presentation of our investigation of how architectural digital design elements of virtual worlds affect learning experiences. The paper provides an initial reflection on learners’ requirements for 3D virtual worlds. Emphasis is given on determining a typology of learning requirements affecting the design of 3D Virtual Learning Environments (VLE). In particular, the research study focused on 3D virtual educational facilities and their impact on learning experience in comparison to real life in-class experiences, by introducing optimum 3D virtual world features in spaces, and turning them into learning places. Emphasis is given on how a range of learning objectives affect design efforts in virtual worlds intended for supporting learning activities. Examples of how virtual worlds may transform learning experiences include information retention, participation and enjoyment. The paper considers design elements that have a causal effect to such learning objectives and considers what design recommendations could be used to enhance the student’s overall learning experience in 3D VLEs. The paper investigates the impact of architectural design guidelines in relation to several features including space shape, size dimensions and height, interior lighting and open walls, colours, textures, floor, wall and ceiling design, architecture style, window design, seating arrangements, and building entrance

The role of architectural design in virtual worlds for educational purposes

This paper discusses the investigation of how architectural digital design elements of virtual worlds affect learning experiences. In particular, the research study focused on 3D virtual educational facilities and their impact on learning experience in comparison to real life in-class experiences. Emphasis is given on how a range of learning objectives affect design efforts in virtual worlds intended for supporting learning activities. Examples of how virtual worlds may transform learning experiences include information retention, participation and enjoyment. The paper considers design elements that have a causal effect to such learning objectives and considers what design recommendations could be used to enhance the student’s overall learning experience in 3D VLEs

Using Optical Head-Mounted Devices (OHMD) for provision of feedback in education

This paper discusses the investigation of using Optical Head-Mounted Devices (OHMD) for provision of feedback in education. In particular it discusses an investigation in the use of Google Glass in real time training and mentoring. First the papers discusses an application created for the device for provision of feedback on student presentation. Next the paper presents, the research conducted with an experiment involving ninety-two participants testing the application in a real life scenario

Assessing the role of optical head-mounted displays in education: an investigation of Google Glass in creating learning portfolios and providing feedback

Technology Enhanced Learning is a field that has seen impressive developments over the past few years. Educators have experimented with the use of web technologies, introduced innovative e-learning approaches, extended the role of virtual learning environments and introduced learning analytics. The authors’ research aims to investigate how ubiquitous computing and augmented reality can further support students in a range of learning activities. In particular, this paper discusses a research study in the role of Optical Head-Mounted Displays (OHMD) in education. Emphasis is given on how the technology can enhance learning through the provision of additional support via augmented reality. The paper describes how OHMD, and more specifically Google Glass has been used by students in a Higher Education Institution as part of their assessment. The research aim is twofold as it considers (i) the role of OHMD in supporting students during the creation of learning portfolios which can be used for formative and summative assessment, and (ii) the impact OHMD technology has in providing alternative ways of feedback. The scope of the research is to assess the suitability of the technology, the benefits that can be introduced in educational contexts as well as the perceived value of the technology from the learners’ point of view. The first part of the study described in the paper describes how learners have used OHMD to construct a portfolio of learning evidence through cooperative evaluation of their work. In particular the study involved students using Google Glass to take snaps of their work, while recording a video diary of their contribution towards group coursework. Users reflected on the experience in terms of ease of use, simplicity and usefulness. They also evaluated the effectiveness of using OHMD during specific tasks including reading, writing and browsing. The second part of the study is focused on providing feedback by using OHMD to attach vignettes on pictures of presentations. The technology is used for commenting on presentation content and delivery, while it is investigated as an alternative for providing feedback on practical activities. The paper also provides a detailed discussion of preliminary findings from the pilot with 92 participants studying at first and final years of a University degree

The application of KAZE features to the classification echocardiogram videos

In the computer vision field, both approaches of SIFT and SURF are prevalent in the extraction of scale-invariant points and have demonstrated a number of advantages. However, when they are applied to medical images with relevant low contrast between target structures and surrounding regions, these approaches lack the ability to distinguish salient features. Therefore, this research proposes a different approach by extracting feature points using the emerging method of KAZE. As such, to categorise a collection of video images of echocardiograms, KAZE feature points, coupled with three popular representation methods, are addressed in this paper, which includes the bag of words (BOW), sparse coding, and Fisher vector (FV). In comparison with the SIFT features represented using Sparse coding approach that gives 72% overall performance on the classification of eight viewpoints, KAZE feature integrated with either BOW, sparse coding or FV improves the performance significantly with the accuracy being 81.09%, 78.85% and 80.8% respectively. When it comes to distinguish only three primary view locations, 97.44% accuracy can be achieved when employing the approach of KAZE whereas 90% accuracy is realised while applying SIFT features

A fused deep learning architecture for viewpoint classification of echocardiography

This study extends the state of the art of deep learning convolutional neural network (CNN) to the classification of video images of echocardiography, aiming at assisting clinicians in diagnosis of heart diseases. Specifically, the architecture of neural networks is established by embracing hand-crafted features within a data-driven learning framework, incorporating both spatial and temporal information sustained by the video images of the moving heart and giving rise to two strands of two-dimensional convolutional neural network (CNN). In particular, the acceleration measurement along the time direction at each point is calculated using dense optical flow technique to represent temporal motion information. Subsequently, the fusion of both networks is conducted via linear integrations of the vectors of class scores obtained from each of the two networks. As a result, this architecture maintains the best classification results for eight viewpoint categories of echo videos with 92.1% accuracy rate whereas 89.5% is achieved using only single spatial CNN network. When concerning only three primary locations, 98% of accuracy rate is realised. In addition, comparisons with a number of well-known hand-engineered approaches are also performed, including 2D KAZE, 2D KAZE with Optical Flow, 3D KAZA, Optical Flow, 2D SIFT and 3D SIFT, which delivers accuracy rate of 89.4%, 84.3%, 87.9%, 79.4%, 83.8% and 73.8% respectively

A Firefly-inspired method for protein structure prediction in lattice models

We introduce a Firefly-inspired algorithmic approach for protein structure prediction over two different lattice models in three-dimensional space. In particular, we consider three-dimensional cubic and three-dimensional face-centred-cubic (FCC) lattices. The underlying energy models are the Hydrophobic-Polar (H-P) model, the Miyazawa–Jernigan (M-J) model and a related matrix model. The implementation of our approach is tested on ten H-P benchmark problems of a length of 48 and ten M-J benchmark problems of a length ranging from 48 until 61. The key complexity parameter we investigate is the total number of objective function valuations required to achieve the optimum energy values for the H-P model or competitive results in comparison to published values for the M-J model. For H-P instances and cubic lattices, where data for comparison are available, we obtain an average speed-up over eight instances of 2.1, leaving out two extreme values (otherwise, 8.8). For six M-J instances, data for comparison are available for cubic lattices and runs with a population size of 100, where, a priori, the minimum free energy is a termination criterion. The average speed-up over four instances is 1.2 (leaving out two extreme values, otherwise 1.1), which is achieved for a population size of only eight instances. The present study is a test case with initial results for ad hoc parameter settings, with the aim of justifying future research on larger instances within lattice model settings, eventually leading to the ultimate goal of implementations for off-lattice models

Genetic local search for multicast routing with pre-processing by logarithmic simulated annealing

Over the past few years, several local search algorithms have been proposed for various problems related to multicast routing in the off-line mode. We describe a population-based search algorithm for cost minimisation of multicast routing. The algorithm utilises the partially mixed crossover operation (PMX) under the elitist model: for each element of the current population, the local search is based upon the results of a landscape analysis that is executed only once in a pre-processing step; the best solution found so far is always part of the population. The aim of the landscape analysis is to estimate the depth of the deepest local minima in the landscape generated by the routing tasks and the objective function. The analysis employs simulated annealing with a logarithmic cooling schedule (logarithmic simulated annealing—LSA). The local search then performs alternating sequences of descending and ascending steps for each individual of the population, where the length of a sequence with uniform direction is controlled by the estimated value of the maximum depth of local minima. We present results from computational experiments on three different routing tasks, and we provide experimental evidence that our genetic local search procedure that combines LSA and PMX performs better than algorithms using either LSA or PMX only