John Adedapo Ojo

Solar energy is the cleanest and most abundant form of energy that can be obtained from the Sun. Solar panels convert this energy to generate solar power, which can be used for various electrical purposes, particularly in rural areas. Maximum solar power can be generated only when the Sun is perpendicular to the panel, which can be achieved only for a few hours when using a fixed solar panel system, hence the development of an automatic solar tracking system. Over the years, different solar tracking systems have been proposed and developed, and a few have been reviewed in the literature. However, the existing review works have not adequately provided a comprehensive survey and taxonomies of these solar tracking systems to show the trends and possible further research direction. This paper aims to bridge these gaps by extensively reviewing these time-based solar tracking systems based on axis rotation and drive types. Lessons learned from the comprehensive review have been highlighted and discussed. Finally, critical open research issues are identified and elaborated.

Spectrum Sensing (SS) is cognitive radio's (CR’s) fundamental and essential mechanism to locate idle spectrum. Multipath effects in wireless channels, on the other hand, reduce CR's sensing accuracy, resulting in a significant risk of missing data. Optimal Spectrum Sensing Technique with Energy Harvesting (OSSTEH) used to solve this problem suffers from poor performance due to only one Primary User (PU) branch used. Hence, in this paper, an enhanced SS with energy harvesting technique is proposed using Maximal Ratio Combiner (MRC). The multiple copies of the PU signal were received using 'N' number of secondary users (SU) antennas and divided into two equal fractions. The first fraction was used for spectrum sensing, while the latter was used for energy harvesting. The first set of SU antennas received a PU signal, which was combined using MRC, and the output of the combiner was used as an input to the energy detector to calculate the received signal's energy. To assess the existence or absence of the PU signal, the acquired energy was compared to the threshold limit. The enhanced technique was evaluated using the probability of detection and signal strength compared with conventional OSSTEH. The enhanced technique gave a high detection rate with an increase in charging rate than the conventional OSSTEH.

Wireless transmission of energy has gained immense attention and importance in the heart of researchers over the years. Many attempts to make it a widely used technology have been done. However, this has not been successful or fruitful. Hence, the need for a comprehensive review of the mechanisms and techniques by harmonizing the findings of various researchers cohesively and adeptly. Thus, this review paper presents a comprehensive evaluation of the research works in wireless power transmission from the time of Andre-Marie Ampere in 1826 till date. The transmission mechanism and techniques used were concisely discussed as well as technologies that have incorporated wireless power transmission into their system. The study closes with a conclusive review of the challenges, and limitations faced, the application, advantages, and disadvantages, of wireless power transmission.

Video-based fire detection (VFD) technologies have received significant attention from both academic and industrial communities recently. However, existing VFD approaches are still susceptible to false alarms due to changes in illumination, camera noise, variability of shape, motion, colour, irregular patterns of smoke and flames, modelling and training inaccuracies. Hence, this work aimed at developing a VSD system that will have a high detection rate, low false-alarm rate and short response time. Moving blocks in video frames were segmented and analysed in HSI colour space, and wavelet energy analysis of the smoke candidate blocks was performed. In addition, Dynamic texture descriptors were obtained using Weber Local Descriptor in Three Orthogonal Planes (WLD-TOP). These features were combined and used as inputs to Support Vector Classifier with radial based kernel function, while post-processing stage employs temporal image filtering to reduce false alarm. The algorithm was implemented in MATLAB 8.1.0.604 (R2013a). Accuracy of 99.30%, detection rate of 99.28% and false alarm rate of 0.65% were obtained when tested with some online videos. The output of this work would find applications in early fire detection systems and other applications such as robot vision and automated inspection.

Gait is a behavioural biometric process that serves to identify persons using their walking style. It is un-obstructive, not easy to conceal and offers distance recognition. Various approaches have been employed to improve the performance and accuracy of gait biometric systems but the performance is yet to measure up to that of other biometric recognition systems. In this work, Gabor wavelets were used to extract Active Gait Differential Image (AGDI) features, while Principal Component Analysis (PCA) was used for feature dimensionality reduction. The classification was performed using Support Vector Machine (SVM) and silhouette images from Chinese Academy of Science Institute of Automation (CASIA) gait dataset was used for testing. The performance was evaluated using accuracy, equal error rate, false acceptance rate and false rejection rate and it gave 99.19%, 1%, 0%, and 2% respectively for the metrics used.

Classification is the process of assigning a class to a group of objects. Moving objects classification can be difficult a task in the presence of dynamic factors like occlusion clutters and shadows. This paper developed a classifier for moving images (video stream) by using a modified adaptive background mixture model method. This system removes shadows and correctly classifies moving objects as human, human group and vehicles. Background Mixture Model is common technique used in Computer Vision, Video object classification is not an exception, many background models have been designed to address different problems ranging from slow start and shadow removal; this paper presents a method which models each pixel as a mixture of Gaussians and using a Maximum A Posteriori approximation to update the model, this paper also introduces a two level shadow removal technique which suppressed shadows in colour and texture consistencies in the classified objects so that the system will not mis- classify moving shadows as objects. This work overcome the problem of slow learning in busy environment and can classify more than one object in view of the camera