Chiemela Onunka

Autonomous marine craft navigation: On the study of radar obstacle detection

Technological advancements over the years have increased the use of Radar technology in the field of robotics, especially in marine robotics to aid obstacle detection algorithms. Obstacle detection comprises of an analytical process in which different algorithms are applied to the field of study to determine the range of objects that are within the reach of a robot. Radar signal analysis and target detection in conjunction with target tracking are attributes required for autonomous marine navigation. The paper presents a model which converts optimal estimates of radar range values for each range spectra into multiple targets down-range and also presents an approach for power range spectra (range bins) prediction using the radar range equation with adequate information of the signal-to-noise ratio (SNR) of the radar. Obstacle detection in the presence of noise raises certain probabilities of false alarms. Target characteristics are simulated and these are fluctuating targets and non fluctuating targets. Analytical models, simulations and techniques of obstacle detection for autonomous marine craft navigation using a continuous wave radar system were points of discussion in the paper.

Fuel properties comparison of species of microalgae and selected second-generation oil feedstocks

Comparative investigation and assessment of microalgal technology as a biodiesel production option was studied alongside other second generation feedstocks. This was carried out by comparing fuel properties of species of Chlorella vulgaris, Duneliella spp., Synechococus spp. and Senedesmus spp. with the feedstock of Jatropha (ex-basirika variety), Hura crepitans, rubber and Natal mahogany seed oils. The microalgae were cultivated using a photo-bioreactor (New Brunsink set-up model BF-115 Bioflo/CelliGen made in the USA) with operating parameters: 14 l capacity, working volume of 7.5 l media, including 10% inoculum, at optical density of 3.144 @ 540 nm and light intensity of 200 lux, for 23 and 16 days respectively. Various produced/accumulated biomasses were harvested by draining, flocculation, centrifugation and drying, and then subjected to lipid extraction processes. The oils extracted from the algae and feedstocks were characterised and used to produce biodiesel fuels, by the transesterification method, using a modified optimisation protocol. The fuel properties of the final biodiesel products were evaluated for chemo-physical and fuel properties. Results revealed Chlorella vulgaris as the best strain for biomass cultivation, having the highest lipid productivity (5.2 mgl−1h−1), the highest rate of CO2 absorption (17.85 mgl−1min−1) and the average carbon sequestration in the form of CO2 was 76.6%. The highest biomass productivity was 35.1 mgl−1h−1 (Chlorella), while Senedesmus had the least output (3.75 mgl−1h−1, 11.73 mgl−1min−1). All species had good pH value adaptation, ranging from 6.5 to 8.5. The fuel properties of the microalgal biodiesel in comparison with Jatropha, rubber, Hura and Natal mahogany were within ASTM specification and AGO used as control. Fuel cultivation from microalgae is feasible and will revolutionise the biodiesel industry.

USV attitude estimation: an approach using quaternion in direction cosine matrix

Positioning and navigation data for unmanned surface vehicles (USVs) are extracted using the Global Positioning System (GPS) and the Inertial Navigation System (INS) integrated with an inertial measurement unit (IMU). The integration of quaternion with direction cosine matrix (DCM) with the aim of obtaining high accuracy with complete system independence has been effectively used to supply position and attitude information for autonomous navigation of marine crafts. A DCM integrated with a quaternion provided an advanced technique for precise USV attitude estimation and position determination using low-cost sensors. This paper presents the implementation of an INS developed by the integration of DCM and quaternion.

Brainwave Variability Identification in Robotic Arm Control Strategy

Neuronal activity, the fundamental source for bio-electric signals expresses the variability of brainwaves in humans. Brainwave and specific EEG spectral analysis are important in bio-electric signal variability identification. Recent researches in neuro-robotics rely on the use of brain computer interface (BCI) technology in developing robotic commands. Brainwave variability identification provides different levels of robot control signal development and optimization.

Factory communication system for customer-based production execution: An empirical study on the manufacturing system entropy

The emergence of the fourth industrial revolution, has seen the development of information technology infrastructure that supports factories of the future. Cyber physical systems (CPS) are the dominant focus in the fourth industrial revolution. CPS are advanced manufacturing systems that offer connectivity between physical and virtual systems. The paper presents the model of an advanced communication system that is intelligently networked and responds to customised production. The entropic measure of the system was investigated by determining the structural and operational entropy of the system. While system performance was assessed based on its adherence to a schedule.

Modelling Radar Signal Error Performance under Atmospheric Refraction and Clutter Attenuation

Radar signal error performance was modelled in the presence of atmospheric refraction and clutter attenuation. The models presented in the paper exploited prior information on atmospheric refraction properties and conditions such as partial pressure, water vapour, atmospheric temperature and the associated clutter. The atmospheric properties and characteristics were used to model random and bias errors experienced in radar systems. Errors which were associated with azimuth, elevation and target velocity were considered in the performance analysis. Range resolution and Doppler resolution were key mechanisms which were implemented in the analysis of the radar signal error performance. The radar error performance was analysed using residual error, signal-to-clutter + noise ratio and thermal noise error. Errors from azimuth, elevation and target velocity were combined in investigating the total effect of errors in determining the desired signal-to-clutter + noise ratio. The results discussed in the paper enhances target detection and tracking towards optimising the navigation system of autonomous and semi-autonomous robotic systems using radars.

A stability optimization model for shaft rotor-bearing systems

The paper presents the modelling of unbalance in a shaft rotor-bearing system using energy methods and finite element analysis strategies. The modelling process brought about the review of three types of unbalance in the shaft rotor-bearing system. They include mass unbalances in the rotor disk, and misalignment in the flexible coupling, which includes parallel and angular misalignment. Each component in the unbalanced system was defined and modelled using energy methods. The forcing function causing unbalance introduced through the flexible coupling was also defined and modelled. Each component and the forcing function were integrated into the global shaft rotor-bearing unbalance system model. The output model was solved using numerical simulation analysis in Matlab. The result yielded a stability optimization model for managing balance in shaft rotor-bearing systems.

Encoding/Decoding Expressive EEG Signal Variability Using IAF/ASDM Technique towards EEG-Controlled Robotic System Development

EEG-controlled mechatronic and robotic systems provides additional flexibility and control option for both the disabled and able body persons. Providing adaptive control option through EEG as the source control signal requires efficient embedded technology system for EEG feature and artefact extraction toward robot motion control. The encoding and decoding of EEG signal allows for efficient EEG artefact extraction and selection in embedded systems.

Complex augmentation in autonomie EEG-Cayley neural network: Integrating bipartite-trivalent graph with Erdos-Renyi in EEG network modelling

Cayley graph is used in representing the complex augmentation of autonomie EEG neural network with bipartite, trivalent and Erdos-Renyi models. The augmentation was used in determining an efficient communication, data and information transmission in EEG neural network. The geometric properties of EEG neural network augmented in autonomie Cayley neural network is used in the processing and transmission of EEG data. The correlation between directed communication path and optimum information transfer path ensured that EEG data and information were transmitted effortlessly to the end effector and end user. EEG network centrality revealed the geometric property of the neural network. The paper proposed the use of Cayley diagrams and graphs in the representation of autonomie EEG neural networks.

Wireless autonomic neural network in EEG signal extraction management

The use of autonomic neural network in the control of smart machines using bio-signals has created the need for advancements in autonomic neural network in EEG signal extraction management. The advancements made in using human cognition for robotic control have increased possibilities once imagined in the field of robotics. The focus of the study is on the application of wireless autonomic neural network in EEG signal extraction. The paper discusses the importance of autonomic neural network in brainwave extraction, transmission and management for robotic control. The prediction and extraction process is an important component of the EEG autonomic neural network.