An effective Enterprise Architecture Implementation Methodology

Enterprise Architecture (EA) is a holistic strategy that is commonly used to improve the alignment of enterprise’s business and Information Technology. Enterprise Architecture Implementation Methodology (EAIM) prepares a set of methods and practices for developing, managing, and maintaining an EA implementation project. There is ineffectiveness in existing EAIMs due to complexities emerging from EAIM practices, models, factors, and strategy. Consequently, EA projects may encounter lack of support in the following areas: requirements analysis, governance and evaluation, guideline for implementation, and continual improvement of EA implementation. The aim of this research is to develop an effective EAIM to support EA implementation. To fulfill this objective, the first step is to identify effective EA implementation practices and the factors that impact the effectiveness of EAIM. In this regard, a Systematic Literature Review (SLR) was conducted in order to identify the effective practices and factors of EAIM. Secondly, the proposed EAIM is developed based on the foundations and information extracted from the SLR and semi-structured interviews with EA practitioners. Finally, the proposed EAIM is evaluated by means of case study as the research methodology. The target audience for this research is twofold: (1) researchers who would extend the effective EA implementation and continue this research topic with further analysis and exploration; (2) practitioners who would like to employ an effective and lightweight EAIM for an EA project.     

Topologies and performance of intelligent algorithms: a comprehensive review

Recently, optimization makes an important role in our day-to-day life. Evolutionary and population-based optimization algorithms are widely employed in several of engineering areas. The design of an optimization algorithm is a challenging endeavor caused of physical phenomena in order to obtain appropriate local and global search operators. Generally, local operators are fast. In contrast, global operators are used to find best solution in the search space; therefore they are slower compare to the local ones. The best review-knowledge of papers show that there are many optimization algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and etc in the engineering as a powerful tools. However, there is not a comprehensive review for theirs topologies and performance; therefore, the main goal of this paper is filling of this scientific gap. Moreover, several aspects of optimization heuristic designs and analysis are discussed in this paper. As a result, detailed explanation, comparison, and discussion on AI are achieved. Furthermore, some future research fields on AI are well summarized.     

Semi-supervised and Unsupervised Privacy-Preserving Distributed Transfer Learning Approach in HAR Systems

Wireless Personal Communications - One of the challenges faced by machine learning in human activity recognition systems is the different distributions of the training and test samples. Transfer...     

Exciton recycling via InP quantum dot funnels for luminescent solar concentrators

Luminescent solar concentrators (LSC) absorb large-area solar radiation and guide down-converted emission to solar cells for electricity production. Quantum dots (QDs) have been widely engineered at device and quantum dot levels for LSCs. Here, we demonstrate cascaded energy transfer and exciton recycling at nanoassembly level for LSCs. The graded structure composed of different sized toxic-heavy-metal-free InP/ZnS core/shell QDs incorporated on copper doped InP QDs, facilitating exciton routing toward narrow band gap QDs at a high nonradiative energy transfer efficiency of 66%. At the final stage of non-radiative energy transfer, the photogenerated holes make ultrafast electronic transitions to copper-induced mid-gap states for radiative recombination in the near-infrared. The exciton recycling facilitates a photoluminescence quantum yield increase of 34% and 61% in comparison with semi-graded and ungraded energy profiles, respectively. Thanks to the suppressed reabsorption and enhanced photoluminescence quantum yield, the graded LSC achieved an optical quantum efficiency of 22.2%. Hence, engineering at nanoassembly level combined with nonradiative energy transfer and exciton funneling offer promise for efficient solar energy harvesting.     

Comparison of practical methods for an efficient FPGA implementation of STAP

This study investigated and compared the practical methods used for the efficient Field- Programmable Gate Array (FPGA) implementation of space-time adaptive processing (STAP). The most important part of calculating the STAP weights is the QR decomposition (QRD), which can be implemented using the modified Gram-Schmidt (MGS) algorithm. The results show that the method that uses QRD with less computational burden leads to a more effective implementation. Its structure was parameterised with the vector size to create a trade-off between the hardware and performance factors. For this purpose, QRD-MGS algorithm was first modified to increase the speed, and then the STAP weight vector was calculated. The implementation results show that decreasing the vector size decreases the resource utilisation, computational burden and the consumption power. While the computation time increases slightly, the updated rate of the STAP weights is maintained. For example, the STAP weights in a system with 6 antenna arrays, 10 received pulses and 200 range samples computed in 262 µs using a vector size of 17 on the Arria10 FPGA that has a maximum of 155 µs correlates to the QRD-MGS algorithm and 107 µs correlates to the other parts. Therefore, QRD-MGS algorithm is the most important component of the calculation of the STAP weight vector, and its simplification leads to efficient implementation.