Characterization of a Contact-Stylus Surface Digitization Method Using Collaborative Robots: Accuracy Evaluation in the Context of Shoulder Replacement or Resurfacing

Total shoulder arthroplasty (TSA) is the third most common joint replacement. While robot-assisted hip and knee replacement technologies have enjoyed extensive development, this has been limited in the upper limb. This work focused on quantifying the localization accuracy of a robotic system, and evaluating its efficacy in the context of TSA. A collaborative robot was fitted with a stylus tip to perform manual surface digitizations using the robot’s encoder output. In the first experiment, two precision-machined master cubes, representing the working volume around a glenoid structure, were used for system validation. Next, cadaveric glenoids were digitized and compared to a ‘gold standard’ laser scanner. Digitization errors were 0.37±0.27 mm, showing that collaborative robotics can be used for osseous anatomy digitization. This thesis presents two novel concepts: 1) use of collaborative robotics for manually operated surface digitizing, and 2) optical fiducial technique, allowing registration between a laser scanner and stylus digitizer

Coexistence of Wi-Fi and 5G NR-U in the Unlicensed Band

The communications industry continues to evolve to meet the ever-growing demands of fast connectivity and higher energy-efficiency and has emerged the concept of Internet of Things (IoT) systems. IoT devices can be run on Wi-Fi or cellular network, helping businesses to receive higher return on investments. As billions of devices on cellular networks operate on the limited licensed spectrum, it is becoming scarcer. Mobile network operators are investigating to access the immense unlicensed spectrum, on which Wi-Fi is prominently operated. Managing this coexistence between the cellular and Wi-Fi networks poses several challenges. One challenge is the spectrum sharing that affects the network capacity and the spectrum efficiency by properly allocating the available resources for each technology. A second challenge is to maintain the quality of service (QoS) while maximizing the aggregated throughput. A final challenge is to reduce the power consumption of cellular base stations by creating a sleep/wakeup policy, thereby lowering the capital and operating expenses for the mobile network operators. To this end, this thesis proposes various optimization modeling for the coexistence mechanisms in the unlicensed spectrum, as well as intelligent techniques to manage the increasing power consumption with increased usage. First, this thesis develops optimization modeling techniques to properly allocate resources for the coexistence of the Wi-Fi and cellular networks by improving the aggregate throughput, while maintaining the minimum required power consumption. Next, this thesis implements the coexistence mechanism by simulating real-time traffic information to maximize the aggregate throughput, while satisfying the QoS for each user. Finally, this thesis investigates the use of machine learning techniques to predict the traffic behaviour of base stations; this will determine the sleep/wakeup schedule, thereby minimizing the power consumption while maintaining the QoS for each cellular user