Towards autonomous driving: a machine learning-based pedestrian detection system using 16-layer LiDAR

The advent of driverless and automated vehicle technologies opens up a new era of safe and comfortable transportation. However, one of the most important features that an autonomous vehicle requires, is a reliable pedestrian detection mechanism. Many solutions have been proposed in the literature to achieve this technology, ranging from image processing algorithms applied on a camera feed, to filtering LiDAR scans for points that are reflected off pedestrians. To this extent, this paper proposes a machine learning-based pedestrian detection mechanism using a 16-layer Velodyne Puck LITE LiDAR. The proposed mechanism compensates for the low resolution of the LiDAR through the use of linear interpolation between layers, effectively introducing 15 pseudo-layers to help obtain timely detection at practical distances. The pedestrian candidates are then classified u sing a Support Vector Machine ( SVM), and the algorithm is verified by accuracy testing using real LiDAR frames acquired under different road scenarios

Seasat SAR test of the Virginian Sea Wave Climate Model

Coastal wave refraction imaged by the Seasat Synthetic Aperture Radar is compared to simulations produced by the Virginian Sea Wave Climate Model. Seasat SAR passes 974 at Cape Hatteras, and 974 and 1404 at Long Island, were examined using OFT and ZTS methods. Results generally confirm the validity of linear wave theory in modeling of shallow-water wave refraction --roughly half the deviations between VSWCM and SAR data for direction and wavelength are within 2 degrees and 10 meters. Convergence of wave orthogonals is found in predicted caustic regions. Available bathymetric data were adequate for the analysis. Some details in the pattern of deviations near Cape Hatteras suggested current shear and tidal effects associated with the Gulf Stream

Building a prototype VANET testbed to explore communication dynamics in highly mobile environments

Applications for VANETs will require seamless communication between vehicle-to-infrastructure and vehicle-to-vehicle. However, this is challenging because this is a highly mobile environment. Therefore, traditional handover techniques are inadequate due to the high velocity of the vehicle and the small coverage radius of Road-side Units. Hence in order to have seamless communication for these applications, a proactive approach needs to be carefully investigated. This requires measurements from a real testbed in order to enhance our understanding of the communication dynamics. This paper is about building and evaluating a prototype VANET network on the Middlesex University Hendon Campus, London to explore these issues. The testbed is being used to investigate better propagation models, road-critical safety applications as well as algorithms for traffic management. In addition, the Network Dwell Time of vehicles travelling in the coverage of the RSUs is measured to explore proactive handover and resource allocation mechanisms

National Evaluation of the Healthy Communities Challenge Fund: The Healthy Towns Programme in England

Background and aims     This research reported here presents findings from an evaluation of the development and implementation of the Healthy Community Challenge Fund (otherwise known as the ‘Healthy Towns’ programme). A key aim of the research has been to inform the development of future environmental and systems‐based ‘whole town’ approaches to obesity prevention. The overall aim of the Healthy Towns programme was to pilot and stimulate novel ‘whole town’ approaches that tackle the ‘obesogenic’ environment in order to reduce obesity, with a particular focus on improving diet and increasing physical activity. Through a competitive tender process, nine towns were selected that represented urban areas across England ranging from small market towns to areas of large cities. The fund provided £30 million over the period 2008‐2011, divided amongst the nine towns. The amounts awarded ranged from £900,000 to £4.85 million. Towns were instructed to be innovative and were given freedom to develop a locally‐specific programme of interventions. This report supplements local process and impact evaluations undertaken by each town (not reported here) by taking an overall view of the programme’s development and implementation. Our evaluation therefore addressed the following research questions: 1. What kinds of interventions were delivered across the Healthy Towns programme? 2. Were environmental and infrastructural interventions equitably delivered? 3. How was the Healthy Towns programme theorised and translated into practice? 4. How was evidence used in the selection and design of interventions? 5. What are the barriers and facilitators to the implementation of a systems approach to obesity prevention

Differences in structural and pain phenotypes in the sodium monoiodoacetate and meniscal transection models of osteoarthritis

This study was funded by Arthritis Research UK Grant number 18769.Peer reviewedPublisher PD

Concern level assessment: building domain knowledge into a visual system to support network-security situation awareness

Information officers and network administrators require tools to help them achieve situation awareness about potential network threats. We describe a response to mini-challenge 1 of the 2012 IEEE VAST challenge in which we developed a visual analytic solution to a network security situation awareness problem. To support conceptual design, we conducted a series of knowledge elicitation sessions with domain experts. These provided an understanding of the information they needed to make situation awareness judgements as well as a characterisation of those judgements in the form of production rules which define a parameter we called the ‘Concern Level Assessment’ (CLA). The CLA was used to provide heuristic guidance within a visual analytic system called MSIEVE. An analysis of VAST challenge assessment sessions using M-SIEVE provides some evidence that intelligent heuristics like this can provide useful guidance without unduly dominating interaction and understanding

M-Sieve: a visualisation tool for supporting network security analysts

The Middlesex Spatial Interactive Visualisation Environment (M-Sieve) is a spatiotemporal visual analytics tool for exploring computer network activity. M-Sieve allows the user to filter and visualize data through facets to explore and find patterns. To help guide exploration, we developed a set of rules which are used to derive a variable we call the ‘Concern Level Assessment’ (CLA). The CLA is based on attributes of nodes on the network. The rules were developed by eliciting inferences from network security domain experts. The combination of M-Sieve and the CLA allowed us to address the problem presented by the VAST 2012 Competition - Mini Challenge 1