arXiv subjects

David Hall

Publications and source records attributed to David Hall.

46 records · Page 3Linked to original sources

arXiv2020

Characterisation of the Particle-Induced Background of XMM-Newton EPIC-pn: Short and Long Term Variability

The particle-induced background of X-ray observatories is produced by Galactic Cosmic Ray (GCR) primary protons, electrons, and He ions. Events due to direct interaction with the detector are usually removed by on board processing. The interactions of these primary particles with the detector environment produce secondary particles that mimic X-ray events from celestial sources and are much more difficult to identify. The filter wheel closed data from the XMM-Newton EPIC-pn camera in small window mode (SWM) contains both the X-ray-like background events and the events due to direct interactions with the primary particles. From this data we demonstrate that X-ray-like background events are spatially correlated with the primary particle interaction. This result can be used to further characterise and reduce the non-X-ray background in silicon-based X-ray detectors in current and future missions. We also show that spectrum and pattern fractions of secondary particle events are different from those produced by cosmic X-rays.

astro-ph.HE↗

arXiv2019

From Google Maps to a Fine-Grained Catalog of Street trees

Up-to-date catalogs of the urban tree population are important for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on dedicated airborne LiDAR or hyperspectral campaigns, trees are still mostly mapped manually in practice. We present a fully automated tree detection and species recognition pipeline to process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information (viewpoints, scales) from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, public tree inventory data. In addition, we introduce a change tracker to keep urban tree inventories up-to-date. Changes of individual trees are recognized at city-scale by comparing street-level images of the same tree location at two different times. Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. In a later, separate module, track trees over time and identify the type of change. We believe this is the first work to exploit publicly available image data for fine-grained tree mapping at city-scale, respectively over many thousands of trees. Experiments in the city of Pasadena, California, USA show that we can detect > 70% of the street trees, assign correct species to > 80% for 40 different species, and correctly detect and classify changes in > 90% of the cases.

cs.CV↗

arXiv2019

An energy consistent discretization of the nonhydrostatic equations in primitive variables

We derive a formulation of the nonhydrostatic equations in spherical geometry with a Lorenz staggered vertical discretization. The combination conserves a discrete energy in exact time integration when coupled with a mimetic horizontal discretization. The formulation is a version of Dubos and Tort (2014) rewritten in terms of primitive variables. It is valid for terrain following mass or height coordinates and for both Eulerian or vertically Lagrangian discretizations. The discretization relies on an extension to Simmons and Burridge (1981) vertical differencing which we show obeys a discrete derivative product rule. This product rule allows us to simplify the treatment of the vertical transport terms. Energy conservation is obtained via a term-by-term balance in the kinetic, internal and potential energy budgets, ensuring an energy-consistent discretization with no spurious sources of energy. We demonstrate convergence with respect to time truncation error in a spectral element code with a HEVI IMEX timestepping algorithm

math.NA↗

arXiv2019

The Probabilistic Object Detection Challenge

We introduce a new challenge for computer and robotic vision, the first ACRV Robotic Vision Challenge, Probabilistic Object Detection. Probabilistic object detection is a new variation on traditional object detection tasks, requiring estimates of spatial and semantic uncertainty. We extend the traditional bounding box format of object detection to express spatial uncertainty using gaussian distributions for the box corners. The challenge introduces a new test dataset of video sequences, which are designed to more closely resemble the kind of data available to a robotic system. We evaluate probabilistic detections using a new probability-based detection quality (PDQ) measure. The goal in creating this challenge is to draw the computer and robotic vision communities together, toward applying object detection solutions for practical robotics applications.

cs.RO↗

arXiv2018

A Rapidly Deployable Classification System using Visual Data for the Application of Precision Weed Management

In this work we demonstrate a rapidly deployable weed classification system that uses visual data to enable autonomous precision weeding without making prior assumptions about which weed species are present in a given field. Previous work in this area relies on having prior knowledge of the weed species present in the field. This assumption cannot always hold true for every field, and thus limits the use of weed classification systems based on this assumption. In this work, we obviate this assumption and introduce a rapidly deployable approach able to operate on any field without any weed species assumptions prior to deployment. We present a three stage pipeline for the implementation of our weed classification system consisting of initial field surveillance, offline processing and selective labelling, and automated precision weeding. The key characteristic of our approach is the combination of plant clustering and selective labelling which is what enables our system to operate without prior weed species knowledge. Testing using field data we are able to label 12.3 times fewer images than traditional full labelling whilst reducing classification accuracy by only 14%.

cs.CV↗

arXiv2017

Comparing simulations and test data of a radiation damaged charge-couple device for the Euclid mission

The VIS instrument on board the Euclid mission is a weak-lensing experiment that depends on very precise shape measurements of distant galaxies obtained by a large CCD array. Due to the harsh radiative environment outside the Earth's atmosphere, it is anticipated that the CCDs over the mission lifetime will be degraded to an extent that these measurements will only be possible through the correction of radiation damage effects. We have therefore created a Monte Carlo model that simulates the physical processes taking place when transferring signal through a radiation-damaged CCD. The software is based on Shockley-Read-Hall theory, and is made to mimic the physical properties in the CCD as closely as possible. The code runs on a single electrode level and takes three dimensional trap position, potential structure of the pixel, and multi-level clocking into account. A key element of the model is that it also takes device specific simulations of electron density as a direct input, thereby avoiding to make any analytical assumptions about the size and density of the charge cloud. This paper illustrates how test data and simulated data can be compared in order to further our understanding of the positions and properties of the individual radiation-induced traps.

astro-ph.IM↗

arXiv2017

Towards Unsupervised Weed Scouting for Agricultural Robotics

Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different set of weed species present within them. In order to overcome this limitation, this paper works towards developing a clustering approach to weed scouting which can be utilized in any field without the need for prior species knowledge. We demonstrate our system using challenging data collected in the field from an agricultural robotics platform. We show that considerable improvements can be made by (i) learning low-dimensional (bottleneck) features using a deep convolutional neural network to represent plants in general and (ii) tying views of the same area (plant) together. Deploying this algorithm on in-field data collected by AgBotII, we are able to successfully cluster cotton plants from grasses without prior knowledge or training for the specific plants in the field.

cs.CV↗

arXiv2016

Fine-Grained Classification of Pedestrians in Video: Benchmark and State of the Art

A video dataset that is designed to study fine-grained categorisation of pedestrians is introduced. Pedestrians were recorded "in-the-wild" from a moving vehicle. Annotations include bounding boxes, tracks, 14 keypoints with occlusion information and the fine-grained categories of age (5 classes), sex (2 classes), weight (3 classes) and clothing style (4 classes). There are a total of 27,454 bounding box and pose labels across 4222 tracks. This dataset is designed to train and test algorithms for fine-grained categorisation of people, it is also useful for benchmarking tracking, detection and pose estimation of pedestrians. State-of-the-art algorithms for fine-grained classification and pose estimation were tested using the dataset and the results are reported as a useful performance baseline.

cs.CV↗

arXiv2016

Technology advancement of the CCD201-20 EMCCD for the WFIRST coronagraph instrument: sensor characterization and radiation damage

The Wide Field InfraRed Survey Telescope-Astrophysics Focused Telescope Asset (WFIRST-AFTA) mission is a 2.4-m class space telescope that will be used across a swath of astrophysical research domains. JPL will provide a high-contrast imaging coronagraph instrument - one of two major astronomical instruments. In order to achieve the low noise performance required to detect planets under extremely low flux conditions, the electron multiplying charge-coupled device (EMCCD) has been baselined for both of the coronagraph's sensors - the imaging camera and integral field spectrograph. JPL has established an EMCCD test laboratory in order to advance EMCCD maturity to technology readiness level-6. This plan incorporates full sensor characterization, including read noise, dark current, and clock-induced charge. In addition, by considering the unique challenges of the WFIRST space environment, degradation to the sensor's charge transfer efficiency will be assessed, as a result of damage from high-energy particles such as protons, electrons, and cosmic rays. Science-grade CCD201-20 EMCCDs have been irradiated to a proton fluence that reflects the projected WFIRST orbit. Performance degradation due to radiation displacement damage is reported, which is the first such study for a CCD201-20 that replicates the WFIRST conditions. In addition, techniques intended to identify and mitigate radiation-induced electron trapping, such as trap pumping, custom clocking, and thermal cycling, are discussed.

astro-ph.IM↗

arXiv2014

An improved model of Charge Transfer Inefficiency and correction algorithm for the Hubble Space Telescope

Charge-Coupled Device (CCD) detectors, widely used to obtain digital imaging, can be damaged by high energy radiation. Degraded images appear blurred, because of an effect known as Charge Transfer Inefficiency (CTI), which trails bright objects as the image is read out. It is often possible to correct most of the trailing during post-processing, by moving flux back to where it belongs. We compare several popular algorithms for this: quantifying the effect of their physical assumptions and tradeoffs between speed and accuracy. We combine their best elements to construct a more accurate model of damaged CCDs in the Hubble Space Telescope's Advanced Camera for Surveys/Wide Field Channel, and update it using data up to early 2013. Our algorithm now corrects 98% of CTI trailing in science exposures, a substantial improvement over previous work. Further progress will be fundamentally limited by the presence of read noise. Read noise is added after charge transfer so does not get trailed - but it is incorrectly untrailed during post-processing.

astro-ph.IM↗