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Item Statistical evaluation of factors influencing inter-session and inter-subject variability in EEG-based brain computer interface.(Institute of Electrical and Electronics Engineers (IEEE), 2022-09-19) Maswanganyi, Rito Clifford; Tu, Chunling; Owolawi, Pius Adewale; Du, ShengzhiA cognitive alteration in the form of diverse mental states has a significant impact on the performance of electroencephalography (EEG) based brain computer interface (BCI). Such alterations include a change in concentration levels commonly recognized as being indicated by the alpha rhythm, drowsiness or mental fatigue which occurs during EEG signal acquisition. Change in mental state give rise to a challenge of variability in EEG characteristics across sessions and subjects. Consequently, this variability constitutes to low intention detection rate (IDR) that renders BCI performance unreliable. This study investigates the impact of multiple factors that lead to the poor performance of the EEG-BCI. Five factors 1) concentration level; 2) selection of independent components (IC); 3) inter-session variability; 4) inter-subject variability; and 5) classification methods on the IDR in EEG based BCI. The alpha rhythm, as the indicator of concentration level, is validated, and the relationship between the alpha rhythm and the IDR is studied among sessions. In addition, ICs are examined to determine their effects on the IDR across sessions. The possibility of two sessions to contain similar EEG characteristics is also examined, where both sessions are acquired from the same subject in different days. Moreover, the possibility of two different subjects to containing similar EEG characteristics is examined. Furthermore, to conquer the challenge of variability in EEG dynamics a feature transfer learning (TL) approach is proposed in this study. Furthermore, three classification methods (TL, K-NN and NB) are examined and compared to determine whether multisource neural information can improve the classification accuracy of individual sessions or subjects. Three EEG datasets acquired using different paradigms are used for experiments. The datasets include steady state motion visual evoked potential (SSMVEP), motor imagery (MI) and BCI competition IV-a dataset. Experimental results have shown that selection of independent components has an effect on the IDR. In this case IC-2 and IC-11 achieved a lowest and highest accuracies of 51% and 100% for SSMVEP datasets, while IC-9 and the double-component (IC-2 and IC-13) achieved a lowest and highest accuracies of 40% and 69% for MI datasets respectively. The second experiment demonstrated that higher alpha rhythm, depicted by a lower IDR corresponds to a lower concentration level. While a lower alpha rhythm depicted by a higher IDR corresponds to a higher concentration level. Moreover, variability within sessions can significantly deteriorate intention detection rate across sessions. As such a decline in accuracy from 82% to 61%, and from 56% to 44% was observed across both SSMVEP and MI sessions during inter-session experiment respectively. Integration of samples from different sessions but same subject resulted in a highest accuracy of 65%, 59% and 40% for SSMVEP, MI and BCI competition dataset. Integration of samples from different subjects resulted in a highest accuracy of 65%, 44% and 48% for SSMVEP, BCI competition and MI datasets. When three classifiers are evaluated and compared to determine whether multi-source neural information can improve the classification accuracy of individual sessions and subjects or domains, both K-NN and NB achieved highest 36 accuracies of 59% and 52% respectively, while TL showed a significant increase with an accuracy 37 of 98% achieved using SSMVEP sessions. In a similarly manner both K-NN and NB achieved highest accuracies of 38 49%and 42%respectively using SSMVEP subjects, while TL showed a significant increase with an accuracy of 64% 39 achieved. Furthermore, when 9 MI subjects acquired from BCI competition dataset were used, both K-NN and NB 40 achieved highest accuracies of 68% and 65% respectively, while a significant increase in accuracy was observed 41 when TL is used with accuracy of 99% achieved. In conclusion, the change of alpha rhythm magnitude among 42 sessions significantly affects the IDR across sessions. While component selection across sessions has significant 43 effects due to non-linear and non-stationary nature of EEG signals. Moreover, merging of ICs from different sessions, 44 and inter-subject factor introduce challenges of overfitting resulting in low IDR. The classification methods are also 45 found critical, because some advanced classification methods can improve the classification accuracy.Item A hierarchical RCNN for vehicle and vehicle license plate detection and recognition.(Institute of Advanced Engineering and Science, 2021-06-30) Tu, Chunling; Du, ShengzhiVehicle and vehicle license detection obtained incredible achievements during recent years that are also popularly used in real traffic scenarios, such as intelligent traffic monitoring systems, auto parking systems, and vehicle services. Computer vision attracted much attention in vehicle and vehicle license detection, benefit from image processing and machine learning technologies. However, the existing methods still have some issues with vehicle and vehicle license plate recognition, especially in a complex environment. In this paper, we propose a multivehicle detection and license plate recognition system based on a hierarchical region convolutional neural network (RCNN). Firstly, a higher level of RCNN is employed to extract vehicles from the original images or video frames. Secondly, the regions of the detected vehicles are input to a lower level (smaller) RCNN to detect the license plate. Thirdly, the detected license plate is split into single numbers. Finally, the individual numbers are recognized by an even smaller RCNN. The experiments on the real traffic database validated the proposed method. Compared with the commonly used all-in-one deep learning structure, the proposed hierarchical method deals with the license plate recognition task in multiple levels for sub-tasks, which enables the modification of network size and structure according to the complexity of sub-tasks. Therefore, the computation load is reduced.Item Intelligent metaheuristic-based handover algorithm for vehicular ad hoc networks.(Scientific Research Publishing (SCIRP), 2023-02-21) Oladosu, Gbenga O.; Tu, Chunling; Awolawi, Pius A.; Mathonsi, Topside E.Recently, Vehicular Ad hoc Networks (VANETs) are becoming increasingly popular. VANETs are a subcategory of Mobile Ad hoc Networks (MANETs) in which nodes represent vehicles equipped with On-Board Units (OBUs). The fundamental reason is that VANETs improve safety for road users by providing vehicles with real-time road-related information. However, the increasing number of vehicles being introduced into these networks causes handover delays, and end-to-end delays, among other things. Therefore, the Quality of Service (QoS) is affected. This article proposes an Intelligent Metaheuristic-based Handover Algorithm (IMHA) to improve QoS in VANETs. The proposed IMHA is designed and implemented by integrating two of the most popular and recent optimization methods, namely disturbance Particle Swarm Optimization (d-PSO) and Ant Colony Optimization (ACO), wherein dPSO assigns different priority levels to vehicles on the road to ensure safety meanwhile ACO determines the most profitable routes from the source to the destination. Furthermore, the Congestion Problem Reduction (CPR) algorithm is implemented in the IMHA to define the requests to process in priority order. The ACO and d-PSO hybrid methods have been tested and evaluated in real-world VANETs, giving us more confidence in their performance and robustness. Network Simulator 2 (NS-2) is used to simulate the proposed algorithm. Based on the outcomes, IHMA reduces end-to-end, and handover delays and improves throughput at different vehicle velocities and network packet sizes. Consequently, this proposed solution guarantees improved QoS in VANETs. The experiment results show the proposed method outperforms existing handover algorithms, with a throughput of 92%, an end-to-end delay of 0.8 seconds, a handover delay and a computation time of less than 2.0 seconds, and an average memory usage of 60%.Item On the performance of recon gurable intelligent surface in cooperative decode-and-forward relaying for hybrid RF/FSO systems.(The Electromagnetics Academy, 2022-05-24) Odeyemi, Kehinde O.; Owolawi, Pius A.; Olakanmi, Oladayo O.Recon gurable intelligent surface (RIS) has been suggested as a promising solution to prevent wireless communication systems from transmission blockage. In this paper, the performance of recon gurable intelligent surface in cooperative decode-and-forward relaying for hybrid radio frequency (RF)/free space optical (FSO) system is evaluated where parallel transmission of information occurs on the system downlink. In this network, the RF links in the system are assumed to follow Nakagami-m distributions while the FSO link is subjected to Gamma-Gamma distribution. Thus, the exact closed- form expressions of the system outage probability and average bit error rate are obtained to quantify the system performance. The accuracy of these expressions is justi ed by Monte-Carlo simulations. Also, to get more physical insight from the derived outage probability expression, the asymptotic outage probability under the condition of higher signal-to-noise ratio (SNR) is provided. In addition, the results illustrate that the system and channel parameters signi cantly affect the performance of the concerned system. Furthermore, the results show that RIS-hybrid downlink system offers better performance than hybrid downlink system without RIS. Under the RIS system, the results demonstrate that RIS-hybrid downlink system outperforms RIS-FSO downlink system.Item Intelligent metaheuristic-based handover algorithm for vehicular ad hoc networks.(Oxford University Press (OUP), 2023-09-09) Oladosu, Gbenga O.; Tu, Chunling; Owolawi, Pius A.; Mathonsi, Topside E.Recently, Vehicular Ad hoc Networks (VANETs) are becoming increasingly popular. VANETs are a subcategory of Mobile Ad hoc Networks (MANETs) in which nodes represent vehicles equipped with On-Board Units (OBUs). The fundamental reason is that VANETs improve safety for road users by providing vehicles with real-time road-related information. However, the increasing number of vehicles being introduced into these networks causes handover delays, and end-to-end delays, among other things. Therefore, the Quality of Service (QoS) is affected. This article proposes an Intelligent Metaheuristic-based Handover Algorithm (IMHA) to improve QoS in VANETs. The proposed IMHA is designed and implemented by integrating two of the most popular and recent optimization methods, namely disturbance Particle Swarm Optimization (d-PSO) and Ant Colony Optimization (ACO), wherein dPSO assigns different priority levels to vehicles on the road to ensure safety meanwhile ACO determines the most profitable routes from the source to the destination. Furthermore, the Congestion Problem Reduction (CPR) algorithm is implemented in the IMHA to define the requests to process in priority order. The ACO and d-PSO hybrid methods have been tested and evaluated in real-world VANETs, giving us more confidence in their performance and robustness. Network Simulator 2 (NS-2) is used to simulate the proposed algorithm. Based on the outcomes, IHMA reduces end-to-end and handover delays and improves throughput at different vehicle velocities and network packet sizes. Consequently, this proposed solution guarantees improved QoS in VANETs. The experiment results show the proposed method outperforms existing handover algorithms, with a throughput of 92%, an end-to-end delay of 0.8 seconds, a handover delay and a computation time of less than 2.0 seconds, and an average memory usage of 60%.Item Enhanced resource allocation algorithm for heterogeneous wireless networks.(Fuji Technology Press Ltd., 2020-09-14) Mathonsi, Topside E.; Tshilongamulenzhe, Tshimangadzo Mavin; Buthelezi, Bongisizwe ErasmusIn heterogeneous wireless networks, service providers typically employ multiple radio access technologies to satisfy the requirements of quality of service (QoS) and improve the system performance. However, many challenges remain when using modern cellular mobile communications radio access technologies (e.g., wireless local area network, long-term evolution, and fifth generation), such as inefficient allocation and management of wireless network resources in heterogeneous wireless networks (HWNs). This problem is caused by the sharing of available resources by several users, random distribution of wireless channels, scarcity of wireless spectral resources, and dynamic behavior of generated traffic. Previously, resource allocation schemes have been proposed for HWNs. However, these schemes focus on resource allocation and management, whereas traffic class is not considered. Hence, these existing schemes significantly increase the end-to-end delay and packet loss, resulting in poor user QoS and network throughput in HWNs. Therefore, this study attempts to solve the identified problem by designing an enhanced resource allocation (ERA) algorithm to address the inefficient allocation of available resources vs. QoS challenges. Computer simulation was performed to evaluate the performance of the proposed ERA algorithm by comparing it with a joint power bandwidth allocation algorithm and a dynamic bandwidth allocation algorithm. On average, the proposed ERA algorithm demonstrates a 98.2% bandwidth allocation, 0.75 s end-to-end delay, 1.1% packet loss, and 98.9% improved throughput performance at a time interval of 100 sItem Intelligent metaheuristic-based handover algorithm for vehicular ad hoc networks.(Oxford University Press (OUP), 2023-09-09) Oladosu, Gbenga O.; Tu, Chunling; Owolawi, Pius A.; Mathonsi, Topside E.Recently, Vehicular Ad hoc Networks (VANETs) are becoming increasingly popular. VANETs are a subcategory of Mobile Ad hoc Networks (MANETs) in which nodes represent vehicles equipped with On-Board Units (OBUs). The fundamental reason is that VANETs improve safety for road users by providing vehicles with real-time road-related information. However, the increasing number of vehicles being introduced into these networks causes handover delays, and end-to-end delays, among other things. Therefore, the Quality of Service (QoS) is affected. This article proposes an Intelligent Metaheuristic-based Handover Algorithm (IMHA) to improve QoS in VANETs. The proposed IMHA is designed and implemented by integrating two of the most popular and recent optimization methods, namely disturbance Particle Swarm Optimization (d-PSO) and Ant Colony Optimization (ACO), wherein dPSO assigns different priority levels to vehicles on the road to ensure safety meanwhile ACO determines the most profitable routes from the source to the destination. Furthermore, the Congestion Problem Reduction (CPR) algorithm is implemented in the IMHA to define the requests to process in priority order. The ACO and d-PSO hybrid methods have been tested and evaluated in real-world VANETs, giving us more confidence in their performance and robustness. Network Simulator 2 (NS-2) is used to simulate the proposed algorithm. Based on the outcomes, IHMA reduces end-to-end and handover delays and improves throughput at different vehicle velocities and network packet sizes. Consequently, this proposed solution guarantees improved QoS in VANETs. The experiment results show the proposed method outperforms existing handover algorithms, with a throughput of 92%, an end-to-end delay of 0.8 seconds, a handover delay and a computation time of less than 2.0 seconds, and an average memory usage of 60%.