Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the other hand, has an accuracy of roughly 87% when it comes to classifying potholes in low-light conditions. These findings are crucial in the selection of feature extraction filters for use by ML classifiers in the development of a robust autonomous pothole detection and classification system for improved navigation on anomalous roads and possible integration into self-driving cars.

The development of wireless technology has facilitated the wide deployment of mobile communication systems. The beauty of wireless communication is that all nooks and corners can be reached at a cheaper and faster rate when compared with wireline. Wireless is now dominating the telecommunications market. Initially, the dawn of wireless was seen as the dawn of communications to poor countries and rural areas which were poorly covered by wireline devices due to high cost. Currently, the story has changed. Both the wired and unwired environments are clamoring for wireless connectivity. Considering the hype of R&D in broadband technologies and easy acceptance in the market place, wireline communications may soon die a natural death. However, wireless communications faces a few challenges. One of them is that the radio frequency (RF) carrier signals used in these communication systems degrades as it travels through the air interface due to attenuation and interference. As a result, the range of coverage may not be as planned leading to very weak reception or even dead zones where no communication can be done. This problem has resulted in the development of cellular signal boosters that help in receiving the weak signal, amplifying and then re-transmitting it to reach the uncovered areas. Boosters are now giving hope to the frustrated wireless users such as indoor users and those at the fringes of a cell site. These boosters are diverse in make, range, method of operation, deployment and cost. In this paper, a survey of various signal booster designs, deployment and performance is presented. It is hoped that this will serve as a one-stop shop for researchers and developers in the important field of wireless signal boosters and extenders, who wish to know what is available and existing challenges.