Thesis submitted in fulfilment of the requirements for the degree Master of Technology: Electrical Engineering at the Cape Peninsula University of Technology, 2012 / This research project addresses the unique challenges of extensive farming in terms of monitoring and controlling remote equipment or events. Poorly maintained roads and escalating fuel costs increase difficulty of farming and the time spent on physically monitoring remote sites further reduces financial yields. The research showed that there are very few solutions that implement wireless or electronic technology to overcome the challenges associated with these isolated and arid areas and that a low-cost, long range wireless telemetry solution that is easy to use would be beneficial for the extensive farming industry. It was therefore the aim of this project to develop a remote monitoring and controlling solution that implements wireless technology to convey information of activities around the farm utilising electronic means. To be able to successfully develop a wireless telemetry solution that will accurately meet the needs of this specific sector of industry, market research was conducted. To guide the research, the QFD (quality function deployment) process for product development has been implemented. The research consisted out of various aspects including a survey, financial considerations and international comparisons. The research also aided in the understanding of the day-to-day activities and also the physical parameters of extensive farms. Also, currently available technologies and products were evaluated to establish whether similarities exist that will aid in the development of a new product. The development process was based on the results obtained in the market research and resulted in a wireless telemetry solution that overcame all the design challenges and proved to be technically feasible, successfully addressing the application requirements. Zigbee technology was utilized for wireless communication because it provided an off-the-shelf solution with a number of readily available development platforms from various technology providers. A communication range of up to 6 kilometres with a transmitted power of 11dBm was achieved for point-to-point communication and a mesh network topology has been implemented for even longer range and complete coverage on farms. Various types of measurements have been catered for, with custom-designed instrumentation which enabled measurements such as water levels, movement and analogue signals. Also, a basic user interface was developed to enable the user to monitor or control the equipment or events remotely from a personal computer, locally or even over the internet. The results of this research project showed that by carefully selecting available technologies and understanding the application, it is possible to develop a solution that addresses the monitoring and controlling needs associated with extensive farming. The wireless telemetry system that was developed resulted in a saving equal to 10% of the total expenses of the farms per year. The telemetry system is therefore a financially feasible solution with a payback period of less than 1 year and far below the initial estimated budget. Without the need to physically monitoring equipment and events, an increase in productivity and the expansion of the overall enterprise is a further benefit added unto the monetary savings. In addition to the financial benefits of implementing new wireless technology, this is an opportunity to contribute to a cleaner and more sustained future as a legacy for the next generation by reducing the carbon footprint of the farm.

Rheumatoid Arthritis (RA) is a disease in which the body has "turned on itself", with its immune system attacking mobility. In RA, an immune mechanism attacks and destroys the joints and limits mobility, in some circumstances to the point of needing replacement of joints. The aim of this research is the development of a less costly, widely accessible, passive sensing technology that provides a quantitative assessment of RA and that monitors the therapeutic effectiveness on joint-debilitating diseases. The proposed solution relies on a quantitative evaluation of human gestures. Such a quantitative assessment supports the comparison between the motion capabilities of a patient and that of a healthy person, using a kinematic model of the human skeleton. Criteria for the classification of severity were established, and tables were generated to classify the levels of severity as a function of the measurements extracted from processed videos of a subject performing predefined movements. This research project, while contributing a new tool to the process of classification of RA level of severity, opens the way for using widely accessible digital imaging for diagnosing and monitoring the evolution of the illness. Replacing MRI or HRUS with a cheaper and more accessible technology would have a major impact on health care services. From the clinical point of view, the proposed techniques based on digital images processing combined with a monitoring approach based on infrared images that was previously developed may provide a utility of care for patients with RA, as well as an alternative and automated approach for early detection of RA and active inflammation at a critical time.

The purpose of this project is to explore the FBG (Fiber Bragg Grating) technology and create a force sensor. The result can be used as a basis for further projects.The project starts with force and strain measurements. The project then evolves to incorporate multiple FBG sensors. An uncommon method of writing the FBG withcoating is tested, which results in a FBG with most of the coating left.The result is a multi-FBG sensor. And even though the individual FBG is not linear the sum shows fantastic linearity with R-square of 0.99999. The change in wavelength is 1328pm/N. A common issue in the strain measurement is discussed and proof is provided. This shows that the reference value of the FBG is 1.12pm/μstrain instead of 1.21pm/μstrain. This is important if the FBG is mounted in a structure, because then the material proprieties will be dominating. Another result is that the peaks of Fabry Perot grating pair are linear but with different coecients.

The oil and gas industry faces a lack of compact drilling devices capable of performing horizontal drilling maneuvers in depleted or abandoned wells in order to enhance oil recovery. The purpose of this project was to design and develop a smart sensing system that can be later implemented in compact drilling devices used to perform horizontal drilling to enhance oil recovery in wells. A smart sensor is the combination of a sensing element (sensor) and a microprocessor. Hence, a smart sensing system is an arrangement that consists of different sensors, where one or more have smart capabilities. The sensing system was built and tested in a laboratory setting. For this, a test bench was used as a case study to simulate the operation from a micro-drilling device. The smart sensing system integrated the sensors essential for the direct operational measurements required for the robot. The focus was on selecting reliable and sturdy components that can handle the operation Down the Hole (DTH) on the final lateral micro-drilling robot. The sensing system's recorded data was sent to a microcontroller, where it was processed and then presented visually to the operator through a User Interface (UI) developed in a cloud-based framework. The information was filtered, processed, and sent to a controller that executed commands and sent signals to the test bench’s actuators. The smart sensing system included novel modules and sensors suitable for the operation in a harsh environment such as the one faced in the drilling process. Furthermore, it was designed as an independent, flexible module that can be implemented in test benches with different settings and early robotic prototypes. The outcome of this project was a sensing system able to provide robotic drilling devices with flexibility while providing accurate and reliable measurements during their operation.

<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>