Reliability analysis of structures is crucial to ensure efficient operations in industrial assets. Nowadays, industries benefit from condition monitoring (CM) equipment to evaluate their products' health. Current commercial reliability software can not accurately analyze the reliability of structures under time-varying conditions. The intern has previously developed an algorithm for accurate reliability analysis of different structures. We have also validated that algorithm for bearings under time-varying working conditions.
Light is a powerful sensing tool that humans are very familiar with. We are, however, only sensitive to a visible light. Much more can be learned from other types of light. Terahertz light is very low energy light that allows very sensitive non-invasive measurements to be made on many types of samples including biological and polymers. This makes it a compelling tool and currently there is exponential growth in Terahertz light technologies.
Unmanned Aerial Vehicles (UAV) or drones have numerous applications in different industries. However, drones have limitations such as short flight time, limited flight range, and navigational inaccuracy in regions with unreliable GPS signal. To mitigate these limitations, using a group of drones to cooperatively perform the mission is proposed. The resulting parallel operation increases the efficiency and reduces the operational time. In addition, we propose a cooperative localization to navigate the group of UAVs as a back-up for GPS navigation.
Laboratory pedagogy is considered absolutely critical for physics and engineering education ranging from high school to university. Among all universities worldwide, a well-known first-year physics experiment demonstration is a spring-coupled pendulums system. It demonstrates the coherent coupling; the underlying physics is reflected from classical mechanics to advanced quantum systems. It has excellent application from a tuned mass damper that reduced the unwanted vibration in skyscrapers and precision machining to data transformation of a quantum computer.
Rolling element bearings act as the heart of rotating machines. Any imperfection in their condition can lead to an abrupt failure which can be catastrophic. For more than two decades, implementing sensors for condition monitoring of bearings has been beneficial in preventing abrupt failure and fault detection. These sensors require a noise-free atmosphere to have optimal performance, however, noise and vibration from other machines are an unpreventable part of an industrial environment.
As the number of patients with stroke and Parkinson's Disease (PD) increases, it is essential to obtain treatment progress data efficiently for the home rehabilitation. For the therapy of hand disabilities, a system is required to collect data, process and control hand motions during rehabilitation. Current rehabilitation devices that are available in the market are costly and not portable. Existing hand training devices use contact-based sensing approaches that are expensive and inaccurate.
Bolus covers patient’s skins to correct varying surface contours for desired dose distribution in cancer care. The existing method of bolus shaping is a manual process by cutting the bolus material into 2D pieces and wrapping the pieces on the targeted body area, which is inaccuracy and time-consuming. An accurate and efficient bolus shaping method is proposed to increase the bolus shaping accuracy and reduce air gaps by applying a 3D-2D-3D process. In order to improve the efficiency, a model retrieval system will be developed based on feature extracting and image-based matching methods.
Upper limb rehabilitation devices mirror the skeletal structure of patients’ limbs and moves patients’ arms for recovery in rehabilitation exercises. Existing rehabilitation devices in the market have problems of adaptability and portability, which cannot meet different requirements of rehabilitation. This project will improve the design and build a prototype the device. The detail design will be improved in dimensions and specifications based on relation of design parameters and functions. The device prototype will be made by 3D printing.
The safe storage of grains is crucial for the food supply worldwide; for example, the storage loss is estimated to be between 2% to 30% depending on different geographic locations. In this project, an advanced signal processing algorithm (a deep learning approach) is developed to enhance the identification process of the moisture contents (MC) of grain bins from the measured electromagnetic data. This deep learning approach for grain bin monitoring will significantly accelerate the identification process of the MC as compared to existing techniques.
This study will develop a novel smart rehabilitation device and gaming software that is versatile and will provide highly effective, individualized exercise programs with embedded electronic records, timely feedback, and support for safe independent use in community centers and private homes. Through this study we will further gain deeper insights into the type of person who will find “therapeutic” games compelling, fun, and effective, and why this works.