A 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 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 (i) concentration level, (ii) selection of independent components(IC), (iii) inter-session variability, (iv) inter-subject variability, and (v) 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, KNN and NB) are examined and compared to determine whether multi-source 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 KNN and NB achieved highest accuracies of 59% and 52% respectively, while TL showed a significant increase with an accuracy of 98% achieved using SSMVEP sessions. In a similarly manner both KNN and NB achieved highest accuracies of 49% and 42% respectively using SSMVEP subjects, while TL showed a significant increase with an accuracy of 64% achieved. Furthermore, when 9 MI subject acquired from BCI competition dataset, both KNN and NB achieved highest accuracies of 68% and 65% respectively, while a significant increase in accuracy was observed when TL is used with accuracy of 99% achieved. In conclusion, the change of alpha rhythm magnitude among sessions significantly affect the IDR across sessions. While component selection across sessions has significant effects due to non-linear and non-stationary nature of EEG signals. Moreover, merging of ICs from different sessions, and inter-subject factor introduce challenges of overfitting resulting in low IDR. The classification methods are also found critical, because some advanced classification methods can improve the classification accuracy.

The devastating effects of rain-induced attenuation on communication links operating above 10 GHz during rainy events can significantly degrade signal quality, leading to interruptions in service and reduced data throughput. Understanding the spatial and seasonal distribution of rain heights is crucial for predicting these attenuation effects and for network performance optimization. This study utilized ten years of atmospheric temperature and geopotential height data at seven pressure levels (1000, 850, 700, 500, 300, 200, and 100 hPa) obtained from the Copernicus Climate Data Store (CDS) to deduce rain heights across nine stations in South Africa. The kernel density estimation (KDE) method was applied to estimate the temporal variation of rain height. A comparison of the measured and estimated rain heights shows a correlation coefficient of 0.997 with a maximum percentage difference of 5.3%. The results show that rain height ranges from a minimum of 3.5 km during winter in Cape Town to a maximum of about 5.27 km during the summer in Polokwane. The spatial variation shows a location-dependent seasonal trend, with peak rain heights prevailing at the low-latitude stations. The seasonal variability indicates that higher rain heights dominate in the regions (Polokwane, Pretoria, Nelspruit, Mahikeng) where there is frequent occurrence of rainfall during the winter season and vice versa. Contour maps of rain heights over the four seasons (autumn, spring, winter, and summer) were also developed for South Africa. The estimated seasonal rain heights show that rain-induced attenuations were grossly underestimated by the International Telecommunication Union (ITU) recommended rain heights at most of the stations during autumn, spring, and summer but fairly overestimated during winter. Durban had a peak attenuation of 15.9 dB during the summer, while Upington recorded the smallest attenuation of about 7.7 dB during winter at a 0.01% time exceedance. Future system planning and adjustments of existing infrastructure in the study stations could be improved by integrating these localized, seasonal radio propagation data in link budget design.

Transfer learning (TL) has been proven to be one of the most significant techniques for cross-subject classification in electroencephalogram (EEG)-based brain-computer interfaces (BCI). Hence, it is widely used to address the challenges of cross-session and cross-subject variability with more accurate intention prediction. In this case, TL utilizes knowledge (signal features) in the source domain(s) to improve the classification in the target domain. However, current existing transfer learning approaches on EEG-based BCI are mostly limited to two-class cross-subject classification problems, while multi-class problems are only implemented with a focus on within-subject classification due to the complexity of multi-class cross-subject classification problems. In this paper, we first extended the transfer learning approaches to a multi-class cross-subject scenario, then investigated the reason for transfer learning performance being poor in multi-class cross-subject classification. Secondly, we address the challenge of significant sessional and subject-to-subject variations originating from both known and unknown factors. It is discovered that such variations have a massive influence on the classification because of the negative transfer (NT) across domains. Based on this discovery, we propose a multi-class transfer learning approach based on multi-source manifold feature transfer learning (MMFT) framework and an enhanced version to minimize the effects of NT. The proposed multi-class transfer learning approach extends the existing MMFT to multi-class cases. Then enhanced multi-class MMFT firstly searches for domains with high transferability and selects only the best combination among source domains (SD), then utilize the best-selected combination of domains for transfer learning. Experimental results illustrate that the proposed multi-class MMFT can be employed in the cross-subject classification of both three-class and four-class problems. Experimental results also demonstrated that the enhanced multi-class MMFT could effectively minimize the effect of negative transfer and significantly increase the prediction rates across individual target domains (TD). The highest classification accuracy (CA) of 98% is obtained by the enhanced multi-class MMFT.

Cloud-based license plate recognition (LPR) systems have emerged as essential tools in modern traffic management and security applications. Determining the best approach remains paramount in the field of computer vision. This study presents a comparative analysis of various versions of the YOLO (You Only Look Once) object detection models, namely, YOLO 5, 7, 8, and 9, applied to LPR tasks in a cloud computing environment. Using live video, we performed experiments on YOLOv5, YOLOv7, YOLOv8, and YOLOv9 models to detect number plates in real time. According to the results, YOLOv8 is reported the most effective model for real-world deployment due to its strong cloud performance. It achieved an accuracy of 78% during cloud testing, while YOLOv5 showed consistent performance with 71%. YOLOv7 performed poorly in cloud testing (52%), indicating potential issues, while YOLOv9 reported 70% accuracy. This tight alignment of results shows consistent, although modest, performance across scenarios. The findings highlight the evolution of the YOLO architecture and its impact on enhancing LPR accuracy and processing efficiency. The results provide valuable insights into selecting the most appropriate YOLO model for cloud-based LPR systems, balancing the trade-offs between real-time performance and detection precision. This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimizations of LPR systems in cloud environments.

The increasing demand for sustainable livestock products necessitates a re-evaluation of animal production and breeding practices. Contemporary breeding programs now integrate animal phenotypic behaviors due to their considerable influence on productivity, health, and welfare, which ultimately impact industry yield and economic outcomes. Monitoring animal behavior manually is challenging and subjective, especially in continuous or large-scale operations, as it is time-consuming and labor-intensive. Consequently, computer vision technology has attracted attention for its objectivity, non-invasiveness, and capacity for continuous monitoring. However, recognizing livestock behavior using computer vision remains difficult due to complex scenes and varying conditions, hindering its widespread adoption in the industry. Deep learning technology has emerged as a promising solution, mitigating some of these challenges and enhancing the recognition of livestock behaviors. This paper reviews recent advancements in computer vision methods for detecting behaviors in livestock such as cattle with an emphasis on behaviors critical for health, welfare, and productivity. It investigates the development of both traditional computer vision and deep learning techniques for image segmentation, identification, and behavior recognition. The review explores the development of research trends in livestock behavior recognition, focusing on improvements in reliable identification algorithms, the analysis of behaviors at different growth stages, the measurement of behavioral data, and the design of systems to evaluate welfare, health, growth, and development.

One of the world’s challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques can be used to improve road traffic signal control and reduce real-time traffic delays at intersections without the requirement for substantial infrastructure analysis. Long wait times and significant energy consumption are just two of the problems of the current traffic signal control system. To optimal efficiency, the traffic signal’s duration must be dynamically changed to account for current traffic volume. To lessen congestion, the approach taken in this research focuses on modifying traffic signal time determined by the density of vehicles at the crossroads. The main purpose of this article is to demonstrate heavy traffic and emergency vehicle prioritization from all directions at the traffic intersection for a speedy passage. Using the Pygame tool, the proposed method in this study, which includes a mechanism for estimating traffic density and prioritization by counting vehicles at a traffic junction, is demonstrated. The vehicle throughput for the adaptive traffic light built using Pygame is compared with the vehicle pass rate for the adaptive traffic light built using Simulation of Urban Mobility (SUMO). The simulation results show that the adaptive traffic light built using Pygame achieves 90% throughput compared to the adaptive traffic light built using SUMO. A Two-Dimensional Convolutional Neural Network (2D-CNN) is implemented using Tensorflow for vehicle classification. The 2D-CNN model demonstrated 96% accuracy in classifying vehicles using the test dataset. Additionally, emergency vehicles, such as ambulances, are given priority for quick passing.