Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.

Identifying barriers and opportunities for transitions towards more sustainable agriculture through system analysis: the case of Vereda La Hoya, Colombia

The paper presents the results of studies which investigated farmers’ reasoning and behaviour with regards to the mis‐use of personal protective equipment and pesticide among smallholders in Colombia. First, the research approach is described. In particular, the structured mental models approach and the integrative agent‐centred framework are presented. These approaches permit to understand the farmers’ reasoning and behaviour in a system perspective. Second, the results are summarized. The methods adopted allowed not only for identifying the factors, but also the social dynamics influencing farmers. Finally, suggestions for interventions are provided, which are not limited to a technical fix, but address the underlying social causes of the problem.

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km2 area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.

Modification of enrofloxacin treatment regimens for poultry experimentally infected with Salmonella enterica serovar Typhimurium DT104 to minimize selection of resistance

We hypothesized that higher doses of fluoroquinolones for a shorter duration could maintain efficacy (as measured by reduction in bacterial count) while reducing selection in chickens of bacteria with reduced susceptibility. Chicks were infected with Salmonella enterica serovar Typhimurium DT104 and treated 1 week later with enrofloxacin at the recommended dose for 5 days (water dose adjusted to give 10 mg/kg of body weight of birds or equivalence, i.e., water at 50 ppm) or at 2.5 or 5 times the recommended dose for 2 days or 1 day, respectively. The dose was delivered continuously (ppm) or pulsed in the water (mg/kg) or by gavage (mg/kg). In vitro in sera, increasing concentrations of 0.5 to 8 mu g/ml enrofloxacin correlated with increased activity. In vivo, the efficacy of the 1-day treatment was significantly less than that of the 2- and 5-day treatments. The 2-day treatments showed efficacy similar to that of the 5-day treatment in all but one repeat treatment group and significantly (P < 0.01) reduced the Salmonella counts. Dosing at 2.5x the recommended dose and pulsed dosing both increased the peak antibiotic concentrations in cecal contents, liver, lung, and sera as determined by high-pressure liquid chromatography. There was limited evidence that shorter treatment regimens (in particular the 1-day regimen) selected for fewer strains with reduced susceptibility. In conclusion, the 2-day treatment would overall require a shorter withholding time than the 5-day treatment and, in view of the increased peak antibiotic concentrations, may give rise to improved efficacy, in particular for treating respiratory and systemic infections. However, it would be necessary to validate the 2-day regimen in a field situation and in particular against respiratory and systemic infections to validate or refute this hypothesis.

Observing the in situ chiral modification of Ni nanoparticles using scanning transmission X-ray microspectroscopy

Enantioselective heterogeneous hydrogenation of Cdouble bond; length as m-dashO bonds is of great potential importance in the synthesis of chirally pure products for the pharmaceutical and fine chemical industries. One of the most widely studied examples of such a reaction is the hydrogenation of β-ketoesters and β-diketoesters over Ni-based catalysts in the presence of a chiral modifier. Here we use scanning transmission X-ray microscopy combined with near-edge X-ray absorption fine structure spectroscopy (STXM/NEXAFS) to investigate the adsorption of the chiral modifier, namely (R,R)-tartaric acid, onto individual nickel nanoparticles. The C K-edge spectra strongly suggest that tartaric acid deposited onto the nanoparticle surfaces from aqueous solutions undergoes a keto-enol tautomerisation. Furthermore, we are able to interrogate the Ni L2,3-edge resonances of individual metal nanoparticles which, combined with X-ray diffraction (XRD) patterns showed them to consist of a pure nickel phase rather than the more thermodynamically stable bulk nickel oxide. Importantly, there appears to be no “particle size effect” on the adsorption mode of the tartaric acid in the particle size range ~ 90–~ 300 nm.

Increased frequency of extreme La Niña events under greenhouse warming

The El Niño/Southern Oscillation is Earth’s most prominent source of interannual climate variability, alternating irregularly between El Niño and La Niña, and resulting in global disruption of weather patterns, ecosystems, fisheries and agriculture1, 2, 3, 4, 5. The 1998–1999 extreme La Niña event that followed the 1997–1998 extreme El Niño event6 switched extreme El Niño-induced severe droughts to devastating floods in western Pacific countries, and vice versa in the southwestern United States4, 7. During extreme La Niña events, cold sea surface conditions develop in the central Pacific8, 9, creating an enhanced temperature gradient from the Maritime continent to the central Pacific. Recent studies have revealed robust changes in El Niño characteristics in response to simulated future greenhouse warming10, 11, 12, but how La Niña will change remains unclear. Here we present climate modelling evidence, from simulations conducted for the Coupled Model Intercomparison Project phase 5 (ref. 13), for a near doubling in the frequency of future extreme La Niña events, from one in every 23 years to one in every 13 years. This occurs because projected faster mean warming of the Maritime continent than the central Pacific, enhanced upper ocean vertical temperature gradients, and increased frequency of extreme El Niño events are conducive to development of the extreme La Niña events. Approximately 75% of the increase occurs in years following extreme El Niño events, thus projecting more frequent swings between opposite extremes from one year to the next.

Evaluation of the inclusion of a mixture of organic acids or lactulose into the feed of pigs experimentally challenged with Salmonella Typhimurium

A mixture of organic acids and lactulose for preventing or reducing colonization of the gut by Salmonella Typhimurium was evaluated in pigs. A total of 63 4-week-old commercial piglets were randomly distributed into three different experimental dietary groups: a plain diet without additives (PD) and the same diet supplemented with either 0.4% (w/v) formic acid and 0.4% lactic acid (w/v) (AC) or 1% (w/v) lactulose (LC). After 7 days of adaptation, two-thirds of the pigs (14 from each diet) were challenged with a 2-mL oral dose of 10(8) CFU/mL of Salmonella Typhimurium, leaving the remaining animals unchallenged (UC). After 4 and 10 days post-challenge, pigs were euthanized and the ileum and caecum content were aseptically sampled to (a) quantify lactic, formic, and short-chain fatty acids (SCFA), (b) quantify bacterial populations and Salmonella by fluorescence in situ hybridization and (c) qualitatively analyse bacterial populations through denaturing gradient gel electrophoresis (DGGE). Modification of fermentation products and counts of some of the bacterial groups analysed in the challenged pigs receiving the treatments AC and LC were minimal. Treatments only influenced the bacterial diversity after 10 days post-challenge, with AC generating a lower number of DGGE bands than UC(P < 0.05). Neither the inclusion of a mixture of 0.4% (w/v) formic and 0.4% (w/v) lactic acids nor of 1% (w/v) lactulose in the feed influenced numbers of Salmonella in the ileum and caecum of experimentally challenged pigs. (C) 2009 Elsevier B.V. All rights reserved.

Investigation into the performance of an algorithm for deriving concise descriptions of optic flow fields for real-time control

This paper describes a region-based algorithm for deriving a concise description of a first order optical flow field. The algorithm described achieves performance improvements over existing algorithms without compromising the accuracy of the flow field values calculated. These improvements are brought about by not computing the entire flow field between two consecutive images, but by considering only the flow vectors of a selected subset of the images. The algorithm is presented in the context of a project to balance a bipedal robot using visual information.

A cluster-based implementation of a fault-tolerant parallel reduction algorithm using swarm-array computing

Recent research in multi-agent systems incorporate fault tolerance concepts. However, the research does not explore the extension and implementation of such ideas for large scale parallel computing systems. The work reported in this paper investigates a swarm array computing approach, namely ‘Intelligent Agents’. In the approach considered a task to be executed on a parallel computing system is decomposed to sub-tasks and mapped onto agents that traverse an abstracted hardware layer. The agents intercommunicate across processors to share information during the event of a predicted core/processor failure and for successfully completing the task. The agents hence contribute towards fault tolerance and towards building reliable systems. The feasibility of the approach is validated by simulations on an FPGA using a multi-agent simulator and implementation of a parallel reduction algorithm on a computer cluster using the Message Passing Interface.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Simulated annealing technique for fast learning of SOM networks

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensional data. Several experiments are used to compare the proposed approach with the original algorithm and some of its modification and speed-up techniques.

Bayesian estimation of the double hurdle model in the presence of fixed costs

We present a model of market participation in which the presence of non-negligible fixed costs leads to random censoring of the traditional double-hurdle model. Fixed costs arise when household resources must be devoted a priori to the decision to participate in the market. These costs, usually of time, are manifested in non-negligible minimum-efficient supplies and supply correspondence that requires modification of the traditional Tobit regression. The costs also complicate econometric estimation of household behavior. These complications are overcome by application of the Gibbs sampler. The algorithm thus derived provides robust estimates of the fixed-costs, double-hurdle model. The model and procedures are demonstrated in an application to milk market participation in the Ethiopian highlands.