In recent years, interest in optical wireless(OW) as a promising complementary technology for RF technology has gained new momentum fueled by significant deployments in solid state lighting technology. This article aims at reviewing and summarizing recent advancements in Short Range Optical Wireless communication, with the main focus on indoor deployment scenarios. This includes a discussion of challenges, potential applications,state of the art, and prospects. We discuss exclusively about the deployment of OWC technique using the existing infrastructure such as LED lighting fixtures, mobile phone cameras and flashes, and headlamps of vehicles, with little or no add-ons, which in turn will enable Internet of Things (IoT). This paper also discusses the challenges and potential of Optical Wireless Communication.
This guide covers preparing for installation, running the installation script, and the steps that should be done after the installation script has completed.
In nearly all videogames, creating smart and complex artificial agents helps ensure an enjoyable and challenging player experience. Using a dodgeball-inspired simulation, we attempt to train a population of robots to develop effective individual strategies against hard-coded opponents. Every evolving robot is controlled by a feedforward artificial neural network, and has a fitness function based on its hits and deaths. We evolved the robots using both standard and real-time NEAT against several teams. We hypothesized that interesting strategies would develop using both evolutionary algorithms, and fitness would increase in each trial. Initial experiments using rtNEAT did not increase fitness substantially, and after several thousand time steps the robots still exhibited mostly random movement. One exception was a defensive strategy against randomly moving enemies where individuals would specifically avoid the area near the center line. Subsequent experiments using the NEAT algorithm were more successful both visually and quantitatively: average fitness improved, and complex tactics appeared to develop in some trials, such as hiding behind the obstacle. Further research could improve our rtNEAT algorithm to match the relative effectiveness of NEAT, or use competitive coevolution to remove the need for hard-coded opponents.