Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays

This work analyzes the performance of the implementable detectors for multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system condi- tions, including antenna correlation and array configuration. Time-domain channel model has been used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order and antenna arrays configurations. A bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high performance combined with high capacity systems and manageable computational complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.Comment: 26 pgs, 16 figures and 5 table

Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

Multi-Objective Particle Swarm Optimisation Methods

Copyright © 2004 University of ExeterThis study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with `distance' based gbest selection techniques. Theoretical justification for both of these approaches is discussed, in terms of the two types of search that these methods promote, and the potential problem of particle clumping in MOPSO is described. The popular pbest selection methods in the literature are also compared, and the ffect of the recently introduced turbulence term is viewed in terms of the additional search it promotes, across all parameter combinations. In light of the discussion, new avenues of MOPSO research are highlighted.Department of Computer Science, University of Exete

Risk Response Selection in Construction Projects

Risk and its management  is  important  for the success of the project, the  risk management, which encompassed of planning, identification, analysis, and response has an important phase, which is risk response  and it should not be undermined, as its  success going to  the projects  the capability  to overcome the  uncertainty and  thus an effective  tool in project risk management, risk response used the collective information in the analysis stage and in order  to take decision how to improve the possibility to complete the project within time, cost and performance. This stage work on preparing the response to the main risks and appoint the people who are responsible for each response.  When it's needed risk response may be started in quantitative analysis stage and the repetition may be possible between the analysis and risk response stage. The aim of this research is to provide a methodology to make the plane for unexpected events and control uncertain situations and identify the reason for risk response failure and to respond to risk successfully by using the optimization method to select the best strategy. The methodology of this research divided into four parts, the first part main object is to find the projects whose risk response is failed, the second part includes the reasons for risk response Failure, the third part includes   finding   the most important risks generated from risk response that leads to increasing the cost of construction projects, the fourth part of the management system is selecting the optimal risk response strategy. An optimization model was used to select the optimal strategy to treat the risk by using Serval constraints such as the cost of the project, time of the project, Gravitational Search Algorithm and particle swarm used. The result of the risk response selection shows that The investment (contractor, bank) strategy shows a very good strategy as it saves the cost about 30%, while the Mitigate (pay for advances with interest 0. 1) Strategy show saving the cost 40%   and giving land to contractors show saving the cost 40% finally the BIM strategy show saving the cost 25%. The risk response is an important part and should give a great attention and it must be used sophisticated method to select the optimal strategy, the two techniques both show high efficiency in selecting the strategy but Gravitational Search Algorithm show better performance