Planetary Radio Interferometry and Doppler Experiment (PRIDE) Technique: a Test Case of the Mars Express Phobos Fly-by. 2. Doppler tracking: Formulation of observed and computed values, and noise budget

Context. Closed-loop Doppler data obtained by deep space tracking networks (e.g., NASA's DSN and ESA's Estrack) are routinely used for navigation and science applications. By "shadow tracking" the spacecraft signal, Earth-based radio telescopes involved in Planetary Radio Interferometry and Doppler Experiment (PRIDE) can provide open-loop Doppler tracking data when the dedicated deep space tracking facilities are operating in closed-loop mode only. Aims. We explain in detail the data processing pipeline, discuss the capabilities of the technique and its potential applications in planetary science. Methods. We provide the formulation of the observed and computed values of the Doppler data in PRIDE tracking of spacecraft, and demonstrate the quality of the results using as a test case an experiment with ESA's Mars Express spacecraft. Results. We find that the Doppler residuals and the corresponding noise budget of the open-loop Doppler detections obtained with the PRIDE stations are comparable to the closed-loop Doppler detections obtained with the dedicated deep space tracking facilities

Online Visual Robot Tracking and Identification using Deep LSTM Networks

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar

Fingers of a Hand Oscillate Together: Phase Syncronisation of Tremor in Hover Touch Sensing

When using non-contact finger tracking, fingers can be classified as to which hand they belong to by analysing the phase relation of physiological tremor. In this paper, we show how 3D capacitive sensors can pick up muscle tremor in fingers above a device. We develop a signal processing pipeline based on nonlinear phase synchronisation that can reliably group fingers to hands and experimentally validate our technique. This allows significant new gestural capabilities for 3D finger sensing without additional hardware

Markerless Motion Capture in the Crowd

This work uses crowdsourcing to obtain motion capture data from video recordings. The data is obtained by information workers who click repeatedly to indicate body configurations in the frames of a video, resulting in a model of 2D structure over time. We discuss techniques to optimize the tracking task and strategies for maximizing accuracy and efficiency. We show visualizations of a variety of motions captured with our pipeline then apply reconstruction techniques to derive 3D structure.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

Learning to Detect and Track Cells for Quantitative Analysis of Time-Lapse Microscopic Image Sequences

© 2015 IEEE.Studying the behaviour of cells using time-lapse microscopic imaging requires automated processing pipelines that enable quantitative analysis of a large number of cells. We propose a pipeline based on state-of-the-art methods for background motion compensation, cell detection, and tracking which are integrated into a novel semi-automated, learning based analysis tool. Motion compensation is performed by employing an efficient nonlinear registration method based on powerful discrete graph optimisation. Robust detection and tracking of cells is based on classifier learning which only requires a small number of manual annotations. Cell motion trajectories are generated using a recent global data association method and linear programming. Our approach is robust to the presence of significant motion and imaging artifacts. Promising results are presented on different sets of in-vivo fluorescent microscopic image sequences