General Dynamics

About: General Dynamics is a company organization based out in Fairfax, Virginia, United States. It is known for research contribution in the topics: Signal & Propellant. The organization has 5722 authors who have published 5819 publications receiving 85768 citations. The organization is also known as: GD & General Dynamics Corporation.

Aggregate Flow Model for Air-Traffic Management

Abstract: Traditionally, models used in air-traffic control and flow management are based on simulating the trajectories of individual aircraft. This approach results in models with a large number of states, which are intrinsically susceptible to errors and difficult for designing and implementing optimal strategies for traffic flow management. This paper outlines an innovative approach for the development of linear-time-variant dynamic traffic flow system models based on historical data about the behavior of air traffic. The resulting low-order, linear, robust models can be used both for the analysis and synthesis of traffic flow management techniques for current and future systems.

Higher-ordered and intraconnected bidirectional associative memories

Abstract: The relationship between autocorrelators and heterocorrelators is examined. A review of the encoding algorithms, recall operations, stability proofs, and capacity arguments of first- and higher-order autocorrelator, commonly referred to as Hopfield associative memories (HAMs), and referred to as bidirectional associated memories (BAMs), is presented. Higher-ordered BAM's and first- and higher-ordered intraconnected BAM's are introduced. The encoding, recall, and stability procedures of each are discussed. Simulation results that compare the storage capacity and storage efficiency of three heterocorrelators, the heteroassociative HAM (HHAM), the BAM, and the intraconnected BAM (IBAM), are presented. The simulation suites were conducted for first-order, direct second-order, and general second-order heterocorrelators with a total pattern dimensionality of 4, 10, 20, 50, and 100. Each suite consisted of between 150 and 1000 separate program executions. >

Demonstration of a Reconfigurable Entangled Radio-Frequency Photonic Sensor Network

Abstract: Quantum metrology takes advantage of nonclassical resources such as entanglement to achieve a sensitivity level below the standard quantum limit. To date, almost all quantum-metrology demonstrations are restricted to improving the measurement performance at a single sensor, but a plethora of applications require multiple sensors that work jointly to tackle distributed sensing problems. Here, we propose and experimentally demonstrate a reconfigurable sensor network empowered by continuous-variable (CV) multipartite entanglement. Our experiment establishes a connection between the entanglement structure and the achievable quantum advantage in different distributed sensing problems. The demonstrated entangled sensor network is composed of three sensor nodes each equipped with an electro-optic transducer for the detection of radio-frequency (RF) signals. By properly tailoring the CV multipartite entangled states, the entangled sensor network can be reconfigured to maximize the quantum advantage in distributed RF sensing problems such as measuring the angle of arrival of an RF field. The rich physics of CV multipartite entanglement unveiled by our work would open a new avenue for distributed quantum sensing and would lead to applications in ultrasensitive positioning, navigation, and timing.