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Students in the twenty-first century are accustomed to using technology in all aspects of their lives and have never known a world without it; the classroom is no exception. Augmented reality (AR) is a technology that bridges the virtual and physical worlds to make learning more engaging and enjoyable. In this paper, we present a mobile application aimed at novice learners that makes use of technology for the teaching and learning of computer system engineering concepts. Currently, students typically learn about finite-state machine (FSM) concepts from lectures, tutorials, and practical hands-on experience combined with commercial timing simulation tools. We aimed to enhance these traditional, lecture-based instruction and information delivery methods. We developed an AR-based FSM visualization tool called AR4FSM to help students more easily grasp concepts through immersion and natural interaction with an FSM. We used a blend of multimedia information, such as text, images, sound, and animations superimposed on real-world-state machine diagrams, presenting the information in an interactive and compelling way. An experiment with 60 students showed that the app was perceived positively by the students and helped to deliver FSM-related concepts in a way that was easier to understand than traditional, lecture-based teaching methods. This instruction methodology not only engaged the students but also motivated them to learn the material. The findings of this study have inspired us to use this application to teach FSM topics in the classroom.

This paper presents a new method for coordinated motion planning of multiple mobile agents. The position in 2-D of each mobile agent is mapped to a complex number and a time varying polynomial contains information regarding the current positions of all mobile agents, the degree of the polynomial being the number of mobile agents and the roots of the polynomial representing the position in 2-D of the mobile agents at a given time. This polynomial is constructed by finding a path parameterized in time from the initial to the goal polynomial which represent the initial and goal positions of the mobile agents so that the discriminant variety or the set of polynomials with multiple roots is avoided in polynomial space. This is equivalent to saying that there is no collision between any two agents in going from initial position to goal position.

Use of steel lazy wave risers has increased as oil and gas developments are happening in deeper waters or in parts of the world with no pipeline infrastructure. These developments utilize FPSO’s with offloading capabilities necessary for these developments. However, due to more severe motions compared to other floating platforms, traditional catenary form of risers are unsuitable for such developments and this is the reason Steel lazy wave risers (SLWR) are required. SLWRs have shown to have better strength and fatigue performance and lower tensions at the hang-off compared to steel catenary risers. A suitable Lazy-Wave form of the catenary riser is achieved by targeted placement of a custom configured buoyancy section. The strength and fatigue performance of steel lazy wave risers are governed by parameters such as length to start of this buoyancy section, length of the buoyancy section, hang-off angle and the buoyancy factor. Achieving these key performance drivers for a SLWR takes several iterations throughout the design process. In this paper, genetic algorithm which is an artificial intelligence optimization tool has been used to automate the generation of an optimized configuration of a steel lazy wave riser. This will enable the riser designer to speed up the riser design process to achieve the best location, coverage and size of the buoyancy section. The results that the genetic algorithm routine produces is compared to a parametric study of steel lazy wave risers varying the key performance drivers. The parametric analysis uses a regular wave time domain analysis and captures trends of change in strength and fatigue performance with change in steel lazy wave parameters.

Steel Lazy wave risers are being increasingly used for deep water applications due to better strength and fatigue performance in the touchdown zone compared to steel catenary risers. Several parameters govern the design of steel lazy wave risers including the length of the catenary from hang-off to start of buoyancy section and the length of the buoyancy section. In this paper, a parametric study is performed to investigate the trends in strength and fatigue performance of steel lazy wave risers with change in configuration parameters. A normative cost assessment is also performed to show the impact of these design variables on overall cost of the system. Dynamic analysis is performed to check the change in strength and fatigue performance of steel lazy wave risers as the configuration parameters are changed. The results from the parametric study will assist in designing steel lazy wave risers which satisfy the strength and fatigue design criteria.