Modulator for tone and binary signals

Tones and binary information are transmitted as phase variations on a carrier wave of constant amplitude and frequency. The carrier and tones are applied to a balanced modulator for deriving an output signal including a pair of sidebands relative to the carrier. The carrier is phase modulated by a digital signal so that it is + or - 90 deg out of phase with the predetermined phase of the carrier. The carrier is combined in an algebraic summing device with the phase modulated signal and the balanced modulator output signal. The output of the algebraic summing device is hard limited to derive a constant amplitude and frequency signal having very narrow bandwidth requirements. At a receiver, the tones and binary data are detected with a phase locked loop having a voltage controlled oscillator driving a pair of orthogonal detection channels

An evaluation of aft-end ignition for solid propellant rocket motors

Performance evaluation of solid propellant rocket motor ignition to determine igniter design and parameters to avoid overpressurizatio

Memory based on abstraction for dynamic fitness functions

Copyright @ Springer-Verlag Berlin Heidelberg 2008.This paper proposes a memory scheme based on abstraction for evolutionary algorithms to address dynamic optimization problems. In this memory scheme, the memory does not store good solutions as themselves but as their abstraction, i.e., their approximate location in the search space. When the environment changes, the stored abstraction information is extracted to generate new individuals into the population. Experiments are carried out to validate the abstraction based memory scheme. The results show the efficiency of the abstraction based memory scheme for evolutionary algorithms in dynamic environments.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant No. EP/E060722/1

An evaluation of the post-ignition unblocking behavior of solid propellant aft-end ignition systems Final report

Determining postignition interactions between igniter and main motor flow by aft-end heated air simulation of solid propellant exhaus

A comparison between conventional and LANDSAT based hydrologic modeling: The Four Mile Run case study

Models designed to support the hydrologic studies associated with urban water resources planning require input parameters that are defined in terms of land cover. Estimating the land cover is a difficult and expensive task when drainage areas larger than a few sq. km are involved. Conventional and LANDSAT based methods for estimating the land cover based input parameters required by hydrologic planning models were compared in a case study of the 50.5 sq. km (19.5 sq. mi) Four Mile Run Watershed in Virginia. Results of the study indicate that the LANDSAT based approach is highly cost effective for planning model studies. The conventional approach to define inputs was based on 1:3600 aerial photos, required 110 man-days and a total cost of $14,000. The LANDSAT based approach required 6.9 man-days and cost$2,350. The conventional and LANDSAT based models gave similar results relative to discharges and estimated annual damages expected from no flood control, channelization, and detention storage alternatives

Online planning for multi-robot active perception with self-organising maps

© 2017, Springer Science+Business Media, LLC, part of Springer Nature. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has a runtime complexity that is polynomial in the number of nodes to be observed and the magnitude of the relative weighting of rewards. We show empirically the runtime is sublinear in the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Exploration objectives for online tasks where the environment is only partially known in advance are modelled by introducing goal regions in unexplored space. Online replanning is performed efficiently by adapting previous solutions as new information becomes available. Simulations were performed using a 3D point-cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for online active perception tasks with continuous sets of candidate viewpoints and long planning horizons

Multi-robot path planning for budgeted active perception with self-organising maps

© 2016 IEEE. We propose a self-organising map (SOM) algorithm as a solution to a new multi-goal path planning problem for active perception and data collection tasks. We optimise paths for a multi-robot team that aims to maximally observe a set of nodes in the environment. The selected nodes are observed by visiting associated viewpoint regions defined by a sensor model. The key problem characteristics are that the viewpoint regions are overlapping polygonal continuous regions, each node has an observation reward, and the robots are constrained by travel budgets. The SOM algorithm jointly selects and allocates nodes to the robots and finds favourable sequences of sensing locations. The algorithm has polynomial-bounded runtime independent of the number of robots. We demonstrate feasibility for the active perception task of observing a set of 3D objects. The viewpoint regions consider sensing ranges and self-occlusions, and the rewards are measured as discriminability in the ensemble of shape functions feature space. Simulations were performed using a 3D point cloud dataset from a real robot in a large outdoor environment. Our results show the proposed methods enable multi-robot planning for budgeted active perception tasks with continuous sets of candidate viewpoints and long planning horizons

Motion states inference through 3D shoulder gait analysis and Hierarchical Hidden Markov Models

Automatically inferring human intention from walking movements is an important research concern in robotics and other fields of study. It is generally derived from temporal motion of limb position relative to the body. These changes can also be reected in the change of stance and gait. Conventional systems relying on gait are usually based on tracking the lower body motion (hip, foot) and are extracted from monocular camera data. However, such data can be inaccessible in crowded environments where occlusions of the lower body are prevalent. This paper proposes a novel approach to utilize upper body 3D-motion and Hierarchical Hidden Markov Models to estimate human ambulatory states, such as quietly standing, starting to walk (gait initiation), walking (gait cycle), or stopping (gait termination). Methods have been tested on real data acquired through a motion capture system where foot measurements (heels and toes) were used as ground truth data for labeling the states to train and test the models. Current results demonstrate the feasibility of using such a system to infer lower-body motion states and sub-states through observations of 3D shoulder motion online. Our results enable applications in situations where only upper body motion is readily observable