Genetic Algorithm (GA)

The human machine interface research in the light of modern fast computers and advanced sensors is taking new heights. The classification and processing of neural activity in the brain accessed by Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), EEG Electroencephalogram (EEG) etc., are peeling off new paradigms for pattern recognition in human brain-machine interaction applications. In the present paper, an effective novel scheme based upon a synergetic approach employing the Genetic Algorithm (GA), Support Vector Machine and Wavelet packet transform for motor imagery classification and optimal Channel selection is proposed. GA with SVM acting as the objective function is employed for simultaneous selection of features and channels optimally. The binary population of GA is uniquely represented in three-dimensional structure and a new cross-over operator for GA are introduced. The new modified cross-over operator is proposed for the modified three-dimensional population. The ‘data set I’ of ‘BCI Competition IV’ is taken for evaluation of the efficacy of the proposed scheme. For subject ‘a’ accuracy is 88.9 6.9 with 10 channels, for subject ‘b’ accuracy is 79.20±5.36with 11 channels, for subject ‘f’ accuracy is 90.50±3.56 with 13 channels, and for subject ‘g’ accuracy is 92.23±3.21with 12 channels. The proposed scheme outperforms in terms of classification accuracy for subjects ‘a, b, f, g’ and in terms of number of channels for subject ‘a’ and that for subject ‘b’ is same as reported earlier in literature. Therefore, proposed scheme contributes a significant development in terms of new three-dimensional representation of binary population for GA as well as significant new modification to the GA operators. The efficacy of the scheme is evident from the results presented in the paper for dataset under consideration.

  Background: Unmanned Aerial Vehicles (UAVs) utilizing and active interface with 5G networks has become the new frontier to tackling problems of latency and energy efficiency, interference, and resource management. Although prior researches explained the benefits of UAV integrated networks; overall assessment of various parameters and cases is still scarce. Objective: The article seeks to assess the performance of UAV integrated 5G network in terms of latency, power, signal quality, task coordination and coverage optimization and to ascertain the efficiency of optimization algorithms in the improvement of the integrated 5G network. Methods: Emulations were done in MATLAB and NS3 platforms in urban / suburban / emergency call settings. Latency, power consumption, SINR, and completion time were the performance indicator chosen in the paper. Optimization algorithms: Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), and the Multi-Objective Evolutionary Algorithm (MOEA) is evaluated in terms of Convergence time and Solution quality. Results : UAV-aided networks showed 36.7% and 29.2 % improvement in latency and energy consumption, while 33.6 % enhancement in SINR. MOEA offered the best results with 98.3% solution quality, and the PSO being the most convergence oriented. Minor deviations between simulation and real results highlight the need for adaptive mechanisms. Conclusion: The results presented focus on the enough potential of UAV-assisted 5G networks and their potential influence on improving performances in case of different criteria. Further research should focus on successfully implementing and deploying the proposed solutions and broadening the context of study to include 6G technologies.