Abstract-The stability of power production in photovoltaics (PV) power plants is an important issue for large-scale gridconnected systems. This is because it affects the control and operation of the electrical grid. An efficient forecasting model is proposed in this paper to predict the next-day solar photovoltaic power using the Levenberg-Marquardt (LM) and Bayesian Regularization (BR) algorithms and real-time weather data. The correlations between the global solar irradiance, temperature, solar photovoltaic power, and the time of the year were studied to extract the knowledge from the available historical data for the purpose of developing a real-time prediction system. The solar PV generated power data were extracted from the power plant installed on-top of the faculty of engineering building at Applied Science Private University (ASU), Amman, Jordan and weather data with real-time records were measured by ASU weather station at the same university campus. Huge amounts of training, validation, and testing experiments were carried out on the available records to optimize the Neural Networks (NN) configurations and compare the performance of the LM and BR algorithms with different sets and combinations of weather data. Promising results were obtained with an excellent realtime overall performance for next-day forecasting with a Root Mean Square Error (RMSE) value of 0.0706 using the Bayesian regularization algorithm with 28 hidden layers and all weather inputs. The Levenberg-Marquardt algorithm provided a 0.0753 RMSE using 23 hidden layers for the same set of learning inputs. This research shows that the Bayesian regularization algorithm outperforms the reported real-time prediction systems for the PV power production.

Background: Cloud Computing has already started to revolutionize of storing and accessing data. Although cloud computing is on its way to becoming a huge success, there are some challenges arise while managing the cloud. This indeed reveals many new knowledge areas, skills and consequently new challenges that need to be overcome so that software project managers can cope and make use of the new available cloud services. This research aims to identify the challenges faced by project managers in cloud computing and highlight the knowledge areas and skills needed to meet these challenges. Method: This paper presents its findings through three stages. First, the pre-survey questionnaire to validate skills and knowledge areas and it will be selected and eventually adopted for the main survey. Second, interviews with experts in the field to discuss challenges identified in the literature review also it will be adopted with the pre-survey to build the main survey. Third, the main survey to identify the critical skills and knowledge areas needed for managing cloud projects. Results: The study drew recommendations so that possible systems and tools can be developed and integrated to overcome some of these challenges. Which gives it the importance of providing guidance for the managers in the field to improve the performance of leading project successfully. Conclusion: The study leads to gain the understanding of the attributes of a competent cloud manager and it determines knowledge areas and skills to help them effectively in overcoming the challenges faced in software and cloud projects.

No abstract

Cloud computing is one of the promising approaches that add a lot of revolutionary contribution to power many real life applications. More power is gained by the ability of such approach to handle different programming languages that were previously handled by one standalone platform (normal computer) only. There are many different solar activities that originate from the Sun and may extremely impact daily life. The existence of these activities is highly associated to different solar features that can be modelled using the huge number of available solar images. There are many different agencies that analyse these images to visualize these activities and features. In this paper, a new cloud-based service that allows efficient and remote access for the users and specialists in the Solar imaging field is proposed without overwhelming the users with any software installation, maintenance, and not requiring them to upgrade to new releases of this service. It is aimed to provide such a service in real time using cloud computing capabilities while saving the cost of procuring resources.

No abstract

This paper describes the design and the evaluation for a simulation model for the dual curvature ultrasound phased array. The main goal of this simulation is to enhance the usage of dual curvature ultrasound phased array in ultrasound therapy with three dimensional phased arrays. In our simulation, focusing the total acoustic power within a specific phase can be controlled dynamically for each individual element. Moreover, a 45 steering angle in x-z plan was achieved. A software based simulation using MATLAB was performed to verify focusing and steering capability of the proposed array for three different types of arrays. The results showed a safety improvement for both of focusing and steering angle for the dual curvature phased array compared with planner and spherical types. Furthermore, the phased arrays have three different shapes; the planner phased array which was used as reference, spherical, and dual curvature shapes. Based on the conducted experiments, the results showed that the spherical and dual curvature shapes have a better focusing and steering angle generally compared with the reference phased array. As well as, the dual curvature phased array has additional capability of focusing outside the geometric volume, without any grating lobe effects.

Abstract-A confocal microscope provides a sequence of images of the various corneal layers and structures at different depths from which medical clinicians can extract clinical information on the state of health of the patient's cornea. Preprocessing the confocal corneal images to make them suitable for analysis is very challenging due the nature of these images and the amount of the noise present in them. This paper presents an efficient preprocessing approach for confocal corneal images consisting of three main steps including enhancement, binarisation and refinement. Improved visualisation, cell counts and measurements of cell properties have been achieved through this system and an interactive graphical user interface has been developed.

Abstract-The visual extraction of cellular, nuclear and tissue components from medical images is very vital in the diagnosis routine of different health related abnormalities and diseases. The objective of this work is to modify and efficiently combine different image processing methods supported by cascaded artificial neural networks in an automated system to perform segmentation analysis of medical microscopy images to extract nuclei located in either simple or complex clusters. The proposed system is applied on a publicly available data sets of microscopy nuclei cells. A GUI is designed and presented in this work to ease the analysis and screening of these images. The proposed system shows promising performance and reduced computational time cost. It is hoped that thus system and the corresponding GUI will construct platform base for several biomedical studies in the field of cellular imaging where further complex investigations and modelling of microscopy images could take place.