Regionally distributed architecture for dynamic e-learning environment (RDADeLE)

e-Learning is becoming an influential role as an economic method and a flexible mode of study in the institutions of higher education today which has a presence in an increasing number of college and university courses. e-Learning as system of systems is a dynamic and scalable environment. Within this environment, e-learning is still searching for a permanent, comfortable and serviceable position that is to be controlled, managed, flexible, accessible and continually up-to-date with the wider university structure. As most academic and business institutions and training centres around the world have adopted the e-learning concept and technology in order to create, deliver and manage their learning materials through the web, it has become the focus of investigation. However, management, monitoring and collaboration between these institutions and centres are limited. Existing technologies such as grid, web services and agents are promising better results. In this research a new architecture has been developed and adopted to make the e-learning environment more dynamic and scalable by dividing it into regional data grids which are managed and monitored by agents. Multi-agent technology has been applied to integrate each regional data grid with others in order to produce an architecture which is more scalable, reliable, and efficient. The result we refer to as Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE). Our RDADeLE architecture is an agent-based grid environment which is composed of components such as learners, staff, nodes, regional grids, grid services and Learning Objects (LOs). These components are built and organised as a multi-agent system (MAS) using the Java Agent Development (JADE) platform. The main role of the agents in our architecture is to control and monitor grid components in order to build an adaptable, extensible, and flexible grid-based e-learning system. Two techniques have been developed and adopted in the architecture to build LOs' information and grid services. The first technique is the XML-based Registries Technique (XRT). In this technique LOs' information is built using XML registries to be discovered by the learners. The registries are written in Dublin Core Metadata Initiative (DCMI) format. The second technique is the Registered-based Services Technique (RST). In this technique the services are grid services which are built using agents. The services are registered with the Directory Facilitator (DF) of a JADE platform in order to be discovered by all other components. All components of the RDADeLE system, including grid service, are built as a multi-agent system (MAS). Each regional grid in the first technique has only its own registry, whereas in the second technique the grid services of all regional grids have to be registered with the DF. We have evaluated the RDADeLE system guided by both techniques by building a simulation of the prototype. The prototype has a main interface which consists of the name of the system (RDADeLE) and a specification table which includes Number of Regional Grids, Number of Nodes, Maximum Number of Learners connected to each node, and Number of Grid Services to be filled by the administrator of the RDADeLE system in order to create the prototype. Using the RST technique shows that the RDADeLE system can be built with more regional grids with less memory consumption. Moreover, using the RST technique shows that more grid services can be registered in the RDADeLE system with a lower average search time and the search performance is increased compared with the XRT technique. Finally, using one or both techniques, the XRT or the RST, in the prototype does not affect the reliability of the RDADeLE system.Royal Commission for Jubail and Yanbu - Directorate General For Jubail Project Kingdom of Saudi Arabi

The influence of granulation methods and particle size on pharmaceutical granules flowability using ring shear tester and particle image velocimetry

Good powder flowability is required in many industrial applications, particularly drug manufacturing. Pharmaceutical production processes strongly rely on powder flowability because the flowability of the powder ingredients a affects the quality and integrity of the final product (Prescott and Barnum, 2000). Many characteristics of particles and their surrounding environment are influenced by powder flowability (Antony, 2007), including: particle size, shape, particle size distribution, moisture content and chemical composition (Bodhmage, 2006). In spite of the extensive amount of research reported so far, powder flow behaviour still remains difficult to predict accurately (Ganesan et al., 2008). To enhance the flowability of powder industries use the granulation method to improve the flow behaviour of powder. The main aim of this project is to study the flowability improvement of the granules prepared by wet and melt granulation in pharmaceutical industries, in addition to the flowability enhancement using different binding ratios, particle size ranges and storage geometries. In this research, two granulation methods were applied (i.e. melt and wet) to examine the improvements of physiomechanical properties of the granules. Different variables were considered for the prepared granules: granulation method, binding ratios (5%-15%-25% w/w), particle size ranges (very fine 45-106µm, moderately coarse 250-355µm and coarse 600-710µm), and different hopper geometries. Granular flowability was measured qualitatively using different conventional methods such as, angle of repose, bulk and tapped density, Carr’s index and Hausner Ratio. Schulze (2002) developed a fully automated ring shear cell RST-XS to measure the powder flow quantitatively using different loads which is used in this study. The output results were used to measure granules flowability, cohesivity and wall friction. These parameters are important for designing the silo and hopper geometries with proper internal hopper angles and outlet dimensions to produce uniform flow of powders. Silo design is performed for achieving better flowability for two geometries (i.e. conical and wedge-shaped hopper) which were designed using the data obtained from the ring shear test results. A new non-invasive and advanced flow dynamic method called digital particle image velocimetry (DPIV) was used to understand the velocity distribution of the granular flow inside a typical case of silo with internal angles of 45° and 70°. The starch granules prepared by wet granulation showed better flowability than that prepared using melt granulation. This trend is in agreement with the outcomes of applying shear cell test stated earlier. Additionally, particle size has a significant influence on the flowability of granules, relatively stronger than the binding ratios. Also, PEG granules prepared using melt granulation required a relatively small internal hopper angle compared to starch granules prepared using wet granulation. In addition, starch granules moderately coarse and coarse were able to produce a symmetric flow trend within the flow champers when compared with the PEG granules. New insights are provided on the flow behaviour of granules in terms of different granulation methods, single-particle characteristics and geometrical conditions. The integrated approach adopted here viz., designing the flow geometries based on using classical shear cells and DPIV provides a holistic and better pathway for designing powder flow geometries

Comparison of tri-polar concentric ring electrodes to disc electrodes for decoding real and imaginary finger movements, A

2019 Spring.Includes bibliographical references.The electroencephalogram (EEG) is broadly used for diagnosis of brain diseases and research of brain activities. Although the EEG provides a good temporal resolution, it suffers from poor spatial resolution due to the blurring effects of volume conduction and signal-to-noise ratio. Many efforts have been devoted to the development of novel methods that can increase the EEG spatial resolution. The surface Laplacian, which is the second derivative of the surface potential, has been applied to EEG to improve the spatial resolution. Tri-polar concentric ring electrodes (TCREs) have been shown to estimate the surface Laplacian automatically with better spatial resolution than conventional disc electrodes. The aim of this research is to study how well the TCREs can be used to acquire EEG signals to decode real and imaginary finger movements. These EEG signals will be then translated into finger movements commands. We also compare the feasibility of discriminating finger movements from one hand using EEG recorded from TCREs and conventional disc electrodes. Furthermore, we evaluated two movement-related features, temporal EEG data and spectral features, in discriminating individual finger from one hand using non-invasive EEG. To do so, movement-related potentials (MRPs) are measured and analyzed from four TCREs and conventional disc electrodes while 13 subjects performed either motor execution or motor imagery of individual finger movements. The tri-polar-EEG (tEEG) and conventional EEG (cEEG) were recorded from electrodes placed according to the 10-20 International Electrode Positioning System over the motor cortex. Our results show that the TCREs achieved higher spatial resolution than conventional disc electrodes. Moreover, the results show that signals from TCREs generated higher decoding accuracy compared to signals from conventional disc electrodes. The average decoding accuracy of five-class classification for all subjects was of 70.04 ± 7.68% when we used temporal EEG data as feature and classified it using Artificial Neural Networks (ANNs) classifier. In addition, the results show that the TCRE EEG (tEEG) provides approximately a four times enhancement in the signal-to-noise ratio (SNR) compared to disc electrode signals. We also evaluated the interdependency level between neighboring electrodes from tri-polar, disc, and disc with Hjorth's Laplacian method in time and frequency domains by calculating the mutual information (MI) and coherence. The MRP signals recorded with the TCRE system have significantly less mutual information (MI) between electrodes than the conventional disc electrode system and disc electrodes with Hjorth's Laplacian method. Also, the results show that the mean coherence between neighboring tri-polar electrodes was found to be significantly smaller than disc electrode and disc electrode with Hjorth's method, especially at higher frequencies. This lower coherence in the high frequency band between neighboring tri polar electrodes suggests that the TCREs may record a more localized neuronal activity. The successful decoding of finger movements can provide extra degrees of freedom to drive brain computer interface (BCI) applications, especially for neurorehabilitation

P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors

Includes bibliographical references.2016 Fall.Brain-computer interfaces (BCIs) allow interactions between human beings and comput- ers without using voluntary muscle. Enormous research effort has been employed in the last few decades to design convenient and user-friendly interfaces. The aim of this study is to provide the people with severe neuromuscular disorders a new augmentative communication technology so that they can express their wishes and communicate with others. The research investigates the capability of Emotiv EPOC+ headset to capture and record one of the BCIs signals called P300 that is used in several applications such as the P300 speller. The P300 speller is a BCI system used to enable severely disabled people to spell words and convey their thoughts without any physical effort. In this thesis, the effects of matrix size, flash duration, and colors were studied. Data are collected from five healthy subjects in their home environments. Different programs are used in this experiment such as OpenViBE platform and MATLAB to pre-process and classify the EEG data. Moreover, the Linear Discriminate Analysis (LDA) classification algorithm is used to classify the data into target and non-target samples

Regionally Distributed Architecture for Dynamic e-Learning Environment (RDADeLE)

Non

COMPUTER AIDED DIAGNOSIS OF VENTRICULAR ARRHYTHMIAS FROM ELECTROCARDIOGRAM LEAD II SIGNALS

In this work, we use computer aided diagnosis (CADx) to extract features from ECG signals and detect different types of cardiac ventricular arrhythmias including Ventricular Tachycardia (VT),Ventricular Fibrillation (VF), Ventricular Couplet (VC), and Ventricular Bigeminy (VB).Our methodology is unique in computing features of lower and higher order statistical parameters from six different data domains: time domain, Fourier domain, and four Wavelet domains (Daubechies, Coiflet, Symlet, and Meyer). These features proved to give superior classification performance, in general, regardless of the type of classifier used as compared with previous studies. However, Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers got better performance than other classifiers tried including KNN and Naïve Bayes classifiers. Our unique features enabled classifiers to perform better in comparison with previous studies: for VT, 100% accuracy while best previous work got 95.8%, for VF, 100% accuracy while best previous work got 97.5%, for VC, 100% sensitivity while best previous work got 71.8%, and for VB, 100% sensitivity while best previous work got 84.6%

Investigation of the reliability of satellite imagery for generating shallow water depth information for navigational purposes, Red Sea, Saudi Arabia

The Red Sea is considered to be one of the most dangerous seas for navigation. The Saudi territorial waters along the Red Sea are characterised by coral reefs, which cause difficulties for local navigation, especially for coast guard vessels. The responsibilities of the Saudi Border guard necessitate safe navigation through out the entire area including the area of coral reefs; in this thesis a new approach and methods of study have been developed to generate navigational charts from photographic images. Two study areas investigated: Rabigh 150km north of Jeddah and Kishran 200 km south of Jeddah. IKONOS multispectral satellite images acquired specifically for this study with a spatial resolution of 4 meters. The images were processed and geo referenced against control points taken during field measurements. The field work was carried out during the period of acquisition with dGPS being the positioning system (with an accuracy of +-5cm), and depths being measured using HTIOO portable digital echo sounder, purchased specifically for this purpose. After calibration of the satellite images and converting the images from digital numbers to radiance and then finally to reflectance, and after collecting the field depths for both study areas linear regression technique have been employed to build suitable models for extracting the estimated depths, this methodology and process led to successful results with an accuracy reached 97%. The same process employed under the most unfavourable conditions, where the humidity and temperature and water vapour reach the maximum, produced results with an accuracy of 65% are not recommended. Application of the study results were carried out for navigational purposes, by converting the images from raster format to a map containing all the necessary information which is normally available in navigational charts such as depths, contour lines (labeled with depth figures), compasses, scales ... etc. This process was carried out using Geographical Information System (GIS) techniques. As a conclusion the results achieved in this study prove that satellite imagery could play an important part in updating navigational charts by deriving the bathymetry of shallow water areas, especially in areas where the traditional survey could not be undertaken due to the spread of coral reefs, islands and underwater obstacles. - Blue band was found to be the best for penetration and extracting depth data - Timing of acquisition was found to be very important factor for achiving the best accuracy (97.3%)EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Investigation of the reliability of satellite imagery for generating shallow water depth information for navigational purposes, Red Sea, Saudi Arabia

The Red Sea is considered to be one of the most dangerous seas for navigation. The Saudi territorial waters along the Red Sea are characterised by coral reefs, which cause difficulties for local navigation, especially for coast guard vessels. The responsibilities of the Saudi Border guard necessitate safe navigation through out the entire area including the area of coral reefs; in this thesis a new approach and methods of study have been developed to generate navigational charts from photographic images. Two study areas investigated: Rabigh 150km north of Jeddah and Kishran 200 km south of Jeddah. IKONOS multispectral satellite images acquired specifically for this study with a spatial resolution of 4 meters. The images were processed and geo referenced against control points taken during field measurements. The field work was carried out during the period of acquisition with dGPS being the positioning system (with an accuracy of +-5cm), and depths being measured using HTIOO portable digital echo sounder, purchased specifically for this purpose. After calibration of the satellite images and converting the images from digital numbers to radiance and then finally to reflectance, and after collecting the field depths for both study areas linear regression technique have been employed to build suitable models for extracting the estimated depths, this methodology and process led to successful results with an accuracy reached 97%. The same process employed under the most unfavourable conditions, where the humidity and temperature and water vapour reach the maximum, produced results with an accuracy of 65% are not recommended. Application of the study results were carried out for navigational purposes, by converting the images from raster format to a map containing all the necessary information which is normally available in navigational charts such as depths, contour lines (labeled with depth figures), compasses, scales ... etc. This process was carried out using Geographical Information System (GIS) techniques. As a conclusion the results achieved in this study prove that satellite imagery could play an important part in updating navigational charts by deriving the bathymetry of shallow water areas, especially in areas where the traditional survey could not be undertaken due to the spread of coral reefs, islands and underwater obstacles. - Blue band was found to be the best for penetration and extracting depth data - Timing of acquisition was found to be very important factor for achiving the best accuracy (97.3%)EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Potential of Camel Dung as Promising Organic Manure in Saudi Arabia

Energy consumption in Saudi Arabia (KSA) is growing rapidly due to the high living standards, population growth and urbanization rates. KSA has planned for the development and use of biomass renewable energy sources, which are available abundantly, carbon-neutral and climate mitigation source. The main purpose of this review article is to analyze and discuss the challenges and opportunities of Camel Manure (CM) as an effective management of the livestock waste, and its potential development in the management of biomass and energy technology (BETM). The trends in the camel population growth projected at approximately 1.6 million camels in the Arab Peninsula and approximately 53% of the total in KSA with untapped manure falling along with the Municipal Solid Waste (MSW) making them ideal for energy extractions and applications in remote and rural areas. Research is needed to assess camel manure resources (supply and demand), the technology scope for economic, energy capacity and application evaluation to fill energy potential and challenges applications for KSA.Keywords: Camel Manure; Biomass; Bioenergy; Manure Technology; KSA