HERA Inclusive Diffraction and Factorisation Tests

HERA measurements of diffractive ep scattering - the quasi-elastic scattering of the photon in the proton colour field - are summarised. Emphasis is placed on the most recent data.Comment: 9 pages, proceedings of PHOTON'0

Listening for Sirens: Locating and Classifying Acoustic Alarms in City Scenes

This paper is about alerting acoustic event detection and sound source localisation in an urban scenario. Specifically, we are interested in spotting the presence of horns, and sirens of emergency vehicles. In order to obtain a reliable system able to operate robustly despite the presence of traffic noise, which can be copious, unstructured and unpredictable, we propose to treat the spectrograms of incoming stereo signals as images, and apply semantic segmentation, based on a Unet architecture, to extract the target sound from the background noise. In a multi-task learning scheme, together with signal denoising, we perform acoustic event classification to identify the nature of the alerting sound. Lastly, we use the denoised signals to localise the acoustic source on the horizon plane, by regressing the direction of arrival of the sound through a CNN architecture. Our experimental evaluation shows an average classification rate of 94%, and a median absolute error on the localisation of 7.5{\deg} when operating on audio frames of 0.5s, and of 2.5{\deg} when operating on frames of 2.5s. The system offers excellent performance in particularly challenging scenarios, where the noise level is remarkably high.Comment: 6 pages, 9 figure

The Hadronic Final State at HERA

The hadronic final state in electron-proton collisions at HERA has provided a rich testing ground for development of the theory of the strong force, QCD. In this review, over 200 publications from the H1 and ZEUS Collaborations are summarised. Short distance physics, the measurement of processes at high energy scales, has provided rigorous tests of perturbative QCD and constrained the structure of the proton as well as allowing precise measurements of the strong coupling constant to be made. Non-perturbative or low energy processes have also been investigated and results on hadronisation interpreted together with those from other experiments. Searches for exotic QCD objects, such as pentaquarks, glueballs and instantons have been performed. The subject of diffraction has been re-invigorated through its precise measurement, such that it can now be described by perturbative QCD. After discussion of HERA, the H1 and ZEUS detectors and the techniques used to reconstruct differing hadronic final states, the above subject areas are elaborated. The major achievements are then condensed further in a final section summarising what has been learned.Comment: 60 pages, 65 figures, submitted to Reviews of Modern Physics. Updated version includes comments to the text from journal referee

HERA Diffractive Structure Function Data and Parton Distributions

Recent diffractive structure function measurements by the H1 and ZEUS experiments at HERA are reviewed. Various data sets, obtained using systematically different selection and reconstruction methods, are compared. NLO DGLAP QCD fits are performed to the most precise H1 and ZEUS data and diffractive parton densities are obtained in each case. Differences between the Q^2 dependences of the H1 and ZEUS data are reflected as differences between the diffractive gluon densities.Comment: Contributed to the Proceedings of the Workshop on HERA and the LHC, DESY and CERN, 2004-200

Radar-only ego-motion estimation in difficult settings via graph matching

Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation. In this work, we propose a radar-only odometry pipeline that is highly robust to radar artifacts (e.g., speckle noise and false positives) and requires only one input parameter. We demonstrate its ability to adapt across diverse settings, from urban UK to off-road Iceland, achieving a scan matching accuracy of approximately 5.20 cm and 0.0929 deg when using GPS as ground truth (compared to visual odometry's 5.77 cm and 0.1032 deg). We present algorithms for keypoint extraction and data association, framing the latter as a graph matching optimization problem, and provide an in-depth system analysis.Comment: 6 content pages, 1 page of references, 5 figures, 4 tables, 2019 IEEE International Conference on Robotics and Automation (ICRA

Distant Vehicle Detection Using Radar and Vision

For autonomous vehicles to be able to operate successfully they need to be aware of other vehicles with sufficient time to make safe, stable plans. Given the possible closing speeds between two vehicles, this necessitates the ability to accurately detect distant vehicles. Many current image-based object detectors using convolutional neural networks exhibit excellent performance on existing datasets such as KITTI. However, the performance of these networks falls when detecting small (distant) objects. We demonstrate that incorporating radar data can boost performance in these difficult situations. We also introduce an efficient automated method for training data generation using cameras of different focal lengths

Geometric Multi-Model Fitting with a Convex Relaxation Algorithm

We propose a novel method to fit and segment multi-structural data via convex relaxation. Unlike greedy methods --which maximise the number of inliers-- this approach efficiently searches for a soft assignment of points to models by minimising the energy of the overall classification. Our approach is similar to state-of-the-art energy minimisation techniques which use a global energy. However, we deal with the scaling factor (as the number of models increases) of the original combinatorial problem by relaxing the solution. This relaxation brings two advantages: first, by operating in the continuous domain we can parallelize the calculations. Second, it allows for the use of different metrics which results in a more general formulation. We demonstrate the versatility of our technique on two different problems of estimating structure from images: plane extraction from RGB-D data and homography estimation from pairs of images. In both cases, we report accurate results on publicly available datasets, in most of the cases outperforming the state-of-the-art