<p> In computer vision, accurately detecting objects of varying sizes is essential for various applications, such as autonomous vehicle navigation and medical imaging diagnostics. Addressing the variance in object sizes presents a significant challenge requiring advanced computational solutions for reliable object recognition and processing. This research introduces a size-adaptive Convolutional Neural Network (CNN) framework to enhance detection performance across different object sizes. By dynamically adjusting the CNN’s configuration based on the observed distribution of object sizes, the framework employs statistical analysis and algorithmic decision-making to improve detection capabilities. Further innovation is presented through the Parameterized-Swish activation function. Distinguished by its dynamic parameters, this function is designed to better adapt to varying input patterns. It exceeds the performance of traditional activation functions by enabling faster model convergence and increasing detection accuracy, showcasing the effectiveness of adaptive activation functions in enhancing object detection systems. The implementation of this model has led to notable performance improvements: a 11.4% increase in mean Average Precision (mAP) and a 40.63% increase in frames per second (FPS) for small objects, demonstrating enhanced detection speed and accuracy. The model has achieved a 48.42% reduction in training time for medium-sized objects while still improving mAP, indicating significant efficiency gains without compromising precision. Large objects have seen a 16.9% reduction in training time and a 76.04% increase in inference speed, showcasing the model’s ability to expedite processing times substantially. Collectively, these advancements contribute to a more than 12% increase in detection efficiency and accuracy across various scenarios, highlighting the model’s robustness and adaptability in addressing the critical challenge of size variance in object detection. </p>

The objective of this study was to provide an adequate model of the k-factor for scientific radio planning in South Africa for terrestrial propagation. An extensive literature survey played an essential role in the research and provided verification and confirmation for the novelty of the research on historical grounds. The approach of the research was initially structured around theoretical analysis of existing data, which resulted from the work of J. W. Nel. The search for analytical models was extended further to empirical studies of primary data obtained from the South African Weather Service. The methodology of the research was based on software technology, which provided new tools and opportunities to process data effectively and to visualise the results in an innovative manner by a means of digital terrain maps (DTMs) and spreadsheet graphics. MINITAB / Thesis (PhD)--University of Pretoria, 2005. / Electrical, Electronic and Computer Engineering / unrestricted