The intersection between control algorithms and the environment pose multiple issues regarding safe and reliable operations of remote-controlled and autonomous quadcopters for commercial and defense applications. This is particularly true in urban environments, which can pose significant problems to navigation and safety. We are developing a new platform for the development and testing of control schemes for quad-copters in urban environments, with emphasis on the intersection of drone and environmental physics, the uncertainty inherent in each, and control algorithms employed. As our basis, we are using Unreal Engine, which provides exibility for physics and controls used, in addition to state-of-the-art visualization, environmental interactions (e.g. collision simulation) and user interface tools. We incorporate the open-source, open-architecture PixHawk PX4 software platform, with the object of transitioning control algorithms to hardware in the future. Finally, we convert models of actual cities from MapBox and OpenStreetMap for use in Unreal Engine. We conclude with a demonstration of human-controlled drone ight in a section of Chicago, IL with light, uni-directional winds.
Mobile ad hoc networks (MANETs) pose challenges distinct from commercial wireless networking, including a need for decentralized routing. We consider the barrage relay network (BRN) routing algorithm for unicast communications, beginning with a simple connected graph model (CGM) and then more physically motivated models. We show geometrically and through simulation that lower bound on the domain of utilized nodes nodes converges to an elliptical region in the high-density limit. In this bound, the number of utilized nodes scales with the (1.5)-power of the distance between source and destination. We conclude with recommendations on how to reduce load while maintaining reliability.
The battlefield of the future is projected to include regions with high-densities of small, networked devices. As a step toward developing decentralized, multi-path routing protocols for these scenarios, we consider the geometric problem of assembling a geographic representation of the network based solely on information about relative positions of local neighbors. We present two algorithms to be used in series. 1. The assembly of the network from relative positions only. 2. The determination of the geographic extents of the network, and the position of each node within those extents. With this information, the nodes can then assemble a position-based routing protocol, such as the Ad Hoc On-Demand Distance Vector (AODV) routing protocol. We further consider the effects of positional noise on the accuracy of the constructed geography.
KEYWORDS: Analytics, Sensors, Data centers, Cameras, Analytical research, Monte Carlo methods, Data storage, Image processing, Defense and security, Electrical engineering
With the rise of small, networked sensors, the volume of data generated increasingly require curation by AI to analyze which events are of sufficient importance to report to human operators. We consider the ultimate limit of edge computing, when it is impractical to employ external resources for the curation, but individual devices have insufficient computing resources to perform the analytics themselves. In a previous paper we introduced a decenralized method that distributes the analytics over the network of devices, employing simulated annealing, based on physics-inspired Metropolis Monte Carlo. If the present paper we discuss the capability of this method to balance the energy consumption of the placement on a network of heterogeneous resources. We introduce the balanced utilization index (BUI), an adaptation of Jain’s Fairness Index, to measure this balance.
KEYWORDS: Data modeling, Statistical modeling, Interference (communication), Carrier dynamics, Polarization, Gallium nitride, Monte Carlo methods, Process modeling, Semiconductors, General packet radio service
Monte Carlo (MC) methods have been shown to capture the ultrafast dynamics of carriers and resulting luminescence in photo-excited GaN. These calculations have assumed a homogeneous material under uniform illumination. We investigate the development of surrogate models to represent the carrier dynamics with fewer degrees of freedom, with the ultimate goal of computing the response of structured materials under inhomogeneous illumination profiles. These models are presently based on the extraction of mean and variance information from single data sets, through the use of fits to s piece-wise linear basis. Two models are then developed, one based on the mean and variance directly, and one on a Gaussian process regression model. New carrier populations with similar statistical profiles are then generated from these models to determine the amount of data reduction that can be obtained.
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