Learning enhanced 3D models for vehicle tracking

This paper presents an enhanced hypothesis verification strategy for 3D object recognition. A new learning methodology is presented which integrates the traditional dichotomic object-centred and appearance-based representations in computer vision giving improved hypothesis verification under iconic matching. The "appearance" of a 3D object is learnt using an eigenspace representation obtained as it is tracked through a scene. The feature representation implicitly models the background and the objects observed enabling the segmentation of the objects from the background. The method is shown to enhance model-based tracking, particularly in the presence of clutter and occlusion, and to provide a basis for identification. The unified approach is discussed in the context of the traffic surveillance domain. The approach is demonstrated on real-world image sequences and compared to previous (edge-based) iconic evaluation techniques.

Production and evaluation of dry alginate-chitosan microcapsules as an enteric delivery vehicle for probiotic bacteria

This study investigates the production of alginate microcapsules, which have been coated with the polysaccharide chitosan, and evaluates some of their properties with the intention of improving the gastrointestinal viability of a probiotic (Bifidobacterium breve) by encapsulation in this system. The microcapsules were dried by a variety of methods, and the most suitable was chosen. The work described in this Article is the first report detailing the effects of drying on the properties of these microcapsules and the viability of the bacteria within relative to wet microcapsules. The pH range over which chitosan and alginate form polyelectrolyte complexes was explored by spectrophotometry, and this extended into swelling studies on the microcapsules over a range of pHs associated with the gastrointestinal tract. It was shown that chitosan stabilizes the alginate microcapsules at pHs above 3, extending the stability of the capsules under these conditions. The effect of chitosan exposure time on the coating thickness was investigated for the first time by confocal laser scanning microscopy, and its penetration into the alginate matrix was shown to be particularly slow. Coating with chitosan was found to increase the survival of B. breve in simulated gastric fluid as well as prolong its release upon exposure to intestinal pH.

Intake fraction of nonreactive motor vehicle exhaust in HongKong

The intake fraction (iF) of nonreactive constituents of exhaust from mobile vehicles in the urban area of HongKong is investigated using available monitoring data for carbon monoxide (CO) as a tracer of opportunity. Correcting for regional transport of carbon monoxide into HongKong, the annual-average iF for nonreactive motor vehicle emissions within the city is estimated to be around 270 per million. This estimated iF is much higher than values previously reported for vehicle emissions in US urban areas, Helsinki and Beijing, and somewhat lower than those reported for a densely populated street canyon in downtown Manhattan, New York City, or for emissions into indoor environments. The reported differences in intakefractions in various cities mainly result from the differences in local population densities. Our analysis highlights the importance of accounting for the influence of upwind transport of pollutants when using ambient data to estimate iF for an urban area. For vehicleexhaust in HongKong, it is found that the in/near vehicle microenvironment contributes similarly to the indoor home environment when accounting for the overall iF for children and adults. Keywords Intakefraction; Vehicle emission; Regional pollutant transport; Carbon monoxide; Exposure

A framework for English? Or a vehicle for literacy? English teaching in England in the age of the Strategy

This article considers the evolution and impact on schools in England of the "Framework for English" since its introduction in 2001, a national initiative that follows on from the National Literacy Strategy, which focused on primary schools. Whilst acknowledging that the Framework is part of a whole school policy, "The Key Stage Three Strategy", I concentrate on its direct impact on the school subject "English" and on standards within that subject. Such a discussion must incorporate some consideration of the rise of "Literacy" as a dominant term and theme in England (and globally) and its challenge to a politically controversial and much contested curriculum area, i.e. "English". If the Framework is considered within the context of the Literacy drive since the mid-1990s then it can be see to be evolving within a much changed policy context and therefore likely to change substantially in the next few years. In a global context England has been regarded for some time as at the extreme edge of standards-driven policy and practice. It is hoped that the story of "English" in England may be salutary to educators from other countries.

The utility of aerial pan-trapping for assessing insect pollinators across vertical strata

Insect pollinators provide a critical ecosystem service by pollinating many wild flowers and crops. It is therefore essential to be able to effectively survey and monitor pollinator communities across a range of habitats, and in particular, sample the often stratified parts of the habitats where insects are found. To date, a wide array of sampling methods have been used to collect insect pollinators, but no single method has been used effectively to sample across habitat types and throughout the spatial structure of habitats. Here we present a method of ‘aerial pan-trapping’ that allows insect pollinators to be sampled across the vertical strata from the canopy of forests to agro-ecosystems. We surveyed and compared the species richness and abundance of a wide range of insect pollinators in agricultural, secondary regenerating forest and primary forest habitats in Ghana to evaluate the usefulness of this approach. In addition to confirming the efficacy of the method at heights of up to 30 metres and the effects of trap color on catch, we found greatest insect abundance in agricultural land and higher bee abundance and species richness in undisturbed forest compared to secondary forest.

Multi-vehicle planning using RRT-connect

The problem of planning multiple vehicles deals with the design of an effective algorithm that can cause multiple autonomous vehicles on the road to communicate and generate a collaborative optimal travel plan. Our modelling of the problem considers vehicles to vary greatly in terms of both size and speed, which makes it suboptimal to have a faster vehicle follow a slower vehicle or for vehicles to drive with predefined speed lanes. It is essential to have a fast planning algorithm whilst still being probabilistically complete. The Rapidly Exploring Random Trees (RRT) algorithm developed and reported on here uses a problem specific coordination axis, a local optimization algorithm, priority based coordination, and a module for deciding travel speeds. Vehicles are assumed to remain in their current relative position laterally on the road unless otherwise instructed. Experimental results presented here show regular driving behaviours, namely vehicle following, overtaking, and complex obstacle avoidance. The ability to showcase complex behaviours in the absence of speed lanes is characteristic of the solution developed.

Motion planning of autonomous vehicles in a non-autonomous vehicle environment without speed lanes

Planning is one of the key problems for autonomous vehicles operating in road scenarios. Present planning algorithms operate with the assumption that traffic is organised in predefined speed lanes, which makes it impossible to allow autonomous vehicles in countries with unorganised traffic. Unorganised traffic is though capable of higher traffic bandwidths when constituting vehicles vary in their speed capabilities and sizes. Diverse vehicles in an unorganised exhibit unique driving behaviours which are analysed in this paper by a simulation study. The aim of the work reported here is to create a planning algorithm for mixed traffic consisting of both autonomous and non-autonomous vehicles without any inter-vehicle communication. The awareness (e.g. vision) of every vehicle is restricted to nearby vehicles only and a straight infinite road is assumed for decision making regarding navigation in the presence of multiple vehicles. Exhibited behaviours include obstacle avoidance, overtaking, giving way for vehicles to overtake from behind, vehicle following, adjusting the lateral lane position and so on. A conflict of plans is a major issue which will almost certainly arise in the absence of inter-vehicle communication. Hence each vehicle needs to continuously track other vehicles and rectify plans whenever a collision seems likely. Further it is observed here that driver aggression plays a vital role in overall traffic dynamics, hence this has also been factored in accordingly. This work is hence a step forward towards achieving autonomous vehicles in unorganised traffic, while similar effort would be required for planning problems such as intersections, mergers, diversions and other modules like localisation.

The consumer acceptance of novel vegetable-enriched bread products as a potential vehicle to increase vegetable consumption

Currently UK fruit and vegetable intakes are below recommendations. Bread is a staple food consumed by ~95% of adults in western countries. In addition, bread provides an ideal matrix by which functionality can be delivered to the consumer in an accepted food. Therefore, enriching bread with vegetables may be an effective strategy to increase vegetable consumption. This study evaluated consumer acceptance, purchase intent and intention of product replacement of bread enriched with red beetroot, carrot with coriander, red pepper with tomato or white beetroot (80g vegetable per serving of 200g) compared to white control bread (0g vegetable). Consumers (n=120) rated their liking of the breads overall, as well as their liking of appearance, flavour and texture using nine-point hedonic scales. Product replacement and purchase intent of the breads was rated using five-point scales. The effect of providing consumers with health information about the breads was also evaluated. There were significant differences in overall liking (P<0.0001), as well as liking of appearance (P<0.0001), flavour (P=0.0002) and texture (P=0.04), between the breads. However, the significant differences resulted from the red beetroot bread which was significantly (P<0.05) less liked compared to control bread. There were no significant differences in overall liking between any of the other vegetable-enriched breads compared with the control bread (no vegetable inclusion), apart from the red beetroot bread which was significantly less liked. The provision of health information about the breads did not increase consumer liking of the vegetable-enriched breads. In conclusion, this study demonstrated that vegetable-enriched bread appeared to be an acceptable strategy to increase vegetable intake, however, liking depended on vegetable type.

Vehicle-driver communication using off-the-shelf transceivers

Almost all modern cars can be controlled remotely using a personal communicator (keyfob). However, the degree of interaction between currently available personal communicators and cars is very limited. The communication link is unidirectional and the communication range is limited to a few dozen meters. However, there are many interesting applications that could be supported if a keyfob would be able to support energy efficient bidirectional longer range communication. In this paper we investigate off-the-shelf transceivers in terms of their usability for bidirectional longer range communication. Our evaluation results show that existing transceivers can generally support the required communication ranges but that links tend to be very unreliable. This high unreliability must be handled in an energy efficient way by the keyfob to car communication protocol in order to make off-the-shelf transceivers a viable solution.

Deglaciation of the Caucasus Mountains, Russia/Georgia, in the 21st century observed with ASTER satellite imagery and aerial photograpy

Changes in the map area of 498 glaciers located on the Main Caucasus ridge (MCR) and on Mt. Elbrus in the Greater Caucasus Mountains (Russia and Georgia) were assessed using multispectral ASTER and panchromatic Landsat imagery with 15 m spatial resolution in 1999/2001 and 2010/2012. Changes in recession rates of glacier snouts between 1987–2001 and 2001–2010 were investigated using aerial photography and ASTER imagery for a sub-sample of 44 glaciers. In total, glacier area decreased by 4.7 ± 2.1% or 19.2 ± 8.7 km2 from 407.3 ± 5.4 km2 to 388.1 ± 5.2 km2. Glaciers located in the central and western MCR lost 13.4 ± 7.3 km2 (4.7 ± 2.5%) in total or 8.5 km2 (5.0 ± 2.4%) and 4.9 km2 (4.1 ± 2.7%) respectively. Glaciers on Mt. Elbrus, although located at higher elevations, lost 5.8 ± 1.4 km2 (4.9 ± 1.2%) of their total area. The recession rates of valley glacier termini increased between 1987–2000/01 and 2000/01–2010 (2000 for the western MCR and 2001 for the central MCR and Mt.~Elbrus) from 3.8 ± 0.8, 3.2 ± 0.9 and 8.3 ± 0.8 m yr−1 to 11.9 ± 1.1, 8.7 ± 1.1 and 14.1 ± 1.1 m yr−1 in the central and western MCR and on Mt. Elbrus respectively. The highest rate of increase in glacier termini retreat was registered on the southern slope of the central MCR where it has tripled. A positive trend in summer temperatures forced glacier recession, and strong positive temperature anomalies in 1998, 2006, and 2010 contributed to the enhanced loss of ice. An increase in accumulation season precipitation observed in the northern MCR since the mid-1980s has not compensated for the effects of summer warming while the negative precipitation anomalies, observed on the southern slope of the central MCR in the 1990s, resulted in stronger glacier wastage.

Vehicle subtype, make and model classification from side profile video

This paper addresses the challenging domain of vehicle classification from pole-mounted roadway cameras, specifically from side-profile views. A new public vehicle dataset is made available consisting of over 10000 side profile images (86 make/model and 9 sub-type classes). 5 state-of-the-art classifiers are applied to the dataset, with the best achieving high classification rates of 98.7% for sub-type and 99.7- 99.9% for make and model recognition, confirming the assertion made that single vehicle side profile images can be used for robust classification.

Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.

MACHINE VISION FOR AUTOMATICVISUAL INSPECTION OF WOODENRAILWAY SLEEPERS USING UNSUPERVISED NEURAL NETWORKS

The motivation for this thesis work is the need for improving reliability of equipment and quality of service to railway passengers as well as a requirement for cost-effective and efficient condition maintenance management for rail transportation. This thesis work develops a fusion of various machine vision analysis methods to achieve high performance in automation of wooden rail track inspection.The condition monitoring in rail transport is done manually by a human operator where people rely on inference systems and assumptions to develop conclusions. The use of conditional monitoring allows maintenance to be scheduled, or other actions to be taken to avoid the consequences of failure, before the failure occurs. Manual or automated condition monitoring of materials in fields of public transportation like railway, aerial navigation, traffic safety, etc, where safety is of prior importance needs non-destructive testing (NDT).In general, wooden railway sleeper inspection is done manually by a human operator, by moving along the rail sleeper and gathering information by visual and sound analysis for examining the presence of cracks. Human inspectors working on lines visually inspect wooden rails to judge the quality of rail sleeper. In this project work the machine vision system is developed based on the manual visual analysis system, which uses digital cameras and image processing software to perform similar manual inspections. As the manual inspection requires much effort and is expected to be error prone sometimes and also appears difficult to discriminate even for a human operator by the frequent changes in inspected material. The machine vision system developed classifies the condition of material by examining individual pixels of images, processing them and attempting to develop conclusions with the assistance of knowledge bases and features.A pattern recognition approach is developed based on the methodological knowledge from manual procedure. The pattern recognition approach for this thesis work was developed and achieved by a non destructive testing method to identify the flaws in manually done condition monitoring of sleepers.In this method, a test vehicle is designed to capture sleeper images similar to visual inspection by human operator and the raw data for pattern recognition approach is provided from the captured images of the wooden sleepers. The data from the NDT method were further processed and appropriate features were extracted.The collection of data by the NDT method is to achieve high accuracy in reliable classification results. A key idea is to use the non supervised classifier based on the features extracted from the method to discriminate the condition of wooden sleepers in to either good or bad. Self organising map is used as classifier for the wooden sleeper classification.In order to achieve greater integration, the data collected by the machine vision system was made to interface with one another by a strategy called fusion. Data fusion was looked in at two different levels namely sensor-level fusion, feature- level fusion. As the goal was to reduce the accuracy of the human error on the rail sleeper classification as good or bad the results obtained by the feature-level fusion compared to that of the results of actual classification were satisfactory.