Gesture recognition (GR) has many applications for human-computer interaction (HCI) in the healthcare, home, and business arenas. However, the common techniques to realize gesture recognition using video processing are computationally intensive and expensive. In this work, we propose to task existing visible light communications (VLC) systems with gesture recognition. Different finger movements are identified by training on the light transitions between fingers using the long short-term memory (LSTM) neural network. This paper describes the design and implementation of the gesture recognition technique for a practical VLC system operating over a distance of 48 cm. The platform uses a single low-cost light-emitting diode (LED) and photo-diode sensor at the receiver side. The system recognizes gestures from interruptions in the direct light transmission, and is therefore suitable for high-speed communication. Gesture recognition accuracies were conducted for five gestures, and results demonstrate that the proposed system is able to accurately identify the gestures in up to 88% of cases.

Automated valet parking (AVP) systems aim to use automated driving technology to park a vehicle from the passenger's boarding/exiting location to a parking space in a parking lot and recall the vehicle from the parking space to the boarding/exiting location. For multiple vehicles to move efficiently through a parking lot, travel must be mediated between each vehicle. We have proposed a driving control method that improves the efficiency of AVP by using the Spatio-Temporal Grid Reservation mechanism. We previously compared it with an autonomous driving, which showed that the proposed method improved the efficiency of vehicle movement when vehicles are entering small parking lots. In this paper, we modified our previous method to accommodate the increasing number of vehicles in a parking lot and created Spatio-Temporal Grid Model as a vehicle movement model. we evaluate this model by comparing it with Conflict Zone Model and MAPF Model created by the methods proposed in related studies, in addition to an autonomous driving model. We performed simulations varying the percentage of vehicles arriving at the parking lot and showed that Spatio-Temporal grid model improves the efficiency of vehicle movement when vehicles are entering and exiting the lot.

Hand gestures are a natural and efficient means to control systems and are one of the promising but challenging areas of human–machine interaction (HMI). We propose a system to recognize gestures by processing interrupted patterns of light in a visible light communications (VLC) system. Our solution is aimed at the emerging light communication systems and can facilitate the human–computer interaction for services in health-care, robot systems, commerce and the home. The system exploits existing light communications infrastructure using low-cost and readily available components. Different finger sequences are detected using a probabilistic neural network (PNN) trained on light transitions between fingers. A novel pre-processing of the sampled light on a photodiode is described to facilitate the use of the PNN with limited complexity. The contributions of this work include the development of a sensing technique for light communication systems, a novel PNN pre-processing methodology to convert the light sequences into manageable size matrices along with hardware implementation showing the proof of concept under natural lighting conditions. Despite the modest complexity our system could correctly recognize gestures with an accuracy of 73%, demonstrating the potential of this technology. We show that the accuracy depends on the PNN pre-processing matrix size and the Gaussian spread function. The emerging IEEE 802.11bb ‘Li-Fi’ standard is expected to bring the light communications infrastructure into virtually every room across the world and a methodology to exploit a system for gesture sensing is expected to be of considerable interest and value to society.

Aerocapture maneuvers refer to a single atmospheric crossing to deplete orbital energy and establish a closed orbit. During the atmospheric flight, adjusting the spacecraft’s vertical lift component in an optimal manner, bang-bang bank control, will minimize the propulsion fuel consumption required to establish the target orbit. However, such methods have been suffering from the performance’s oversensitivity to the control’s instantaneous switching time and poor robustness. To address these problems, we propose a new numerical predictor-corrector guidance algorithm based on the saturation function profile in this paper. The saturation function is used to basically simulate the bang-bang control structure, which enhances the algorithm’s robustness by reducing its dependence on the relevant parameters without losing too much optimality. Monte Carlo simulations in both Earth and Mars scenarios demonstrate the robustness, accuracy, and near-optimal performance of the proposed guidance method.

Vehicle-to-Everything (V2X) communications provide opportunities for information sharing among vehicles, edge servers, and cloud services. By the collection and extraction of sensing information from vehicles, such as communication quality or free space size, the edge server in V2X communications can improve its sensing and perception coverage. However, the collection of sensing data from vehicles consumes a large amount of wireless resources and computing resources at the edge server. The objective of this study is to extract object sensing information from vehicles, including the minimum or maximum of the sensing values, with low resource consumption and with high scalability. We propose a method that transforms the extraction of sensing information into a two-level procedure that includes (1) the local sharing and extraction of sensing information among vehicles and (2) the efficient extraction of sensing information at the edge server. Moreover, hybrid communication methods are employed at vehicles, with a short range of communication between vehicles to reduce the consumption of wireless resources for the local sharing of sensing data. The evaluation results show that the proposed method highly reduces the number of reports from the vehicles to the edge server, with a small amount of network resource consumption and scalability.