A Bayesian Framework for the Assessment of Vision-based Weed and Fruit Detection and Classification Algorithms

This paper proposes new metrics and a performance-assessment framework for vision-based weed and fruit detection and classification algorithms. In order to compare algorithms, and make a decision on which one to use fora particular application, it is necessary to take into account that the performance obtained in a series of tests is subject to uncertainty. Such characterisation of uncertainty seems not to be captured by the performance metrics currently reported in the literature. Therefore, we pose the problem as a general problem of scientific inference, which arises out of incomplete information, and propose as a metric of performance the(posterior) predictive probabilities that the algorithms will provide a correct outcome for target and background detection. We detail the framework through which these predicted probabilities can be obtained, which is Bayesian in nature. As an illustration example, we apply the framework to the assessment of performance of four algorithms that could potentially be used in the detection of capsicums (peppers).

Modelling the impact of traffic incidents on travel time reliability

Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics.

Signal Characterization using Fractal Dimension

Fractal Dimensions (FD) are popular metrics for characterizing signals. They are used as complexity measuresin signal analysis applications in various fields. However, proper interpretation of such analyses has not been thoroughly addressed. In this paper, we study the effect of various signal properties on FD and interpret results in terms of classical signal processing concepts such as amplitude, frequency,number of harmonics, noise power and signal bandwidth. We have used Higuchi’s method for estimating FDs. This study helps in gaining a better understanding of the FD complexity measure for various signal parameters. Our results indicate that FD is a useful metric in estimating various signal properties. As an application of the FD measure in real world scenario, the FD is used as a feature in discriminating seizures from seizure free intervals in intracranial EEG data recordings and the FD feature has given good discrimination performance.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

The Connor-Davidson Resilience Scale (CD-RISC): Dimensionality and age-related measurement invariance with Australian cricketers

Objective This study explored the dimensionality and measurement invariance of the 25-item Connor-Davidson Resilience Scale (CD-RISC; Connor & Davidson, 2003) across samples of adult (n = 321; aged 20–36) and adolescent (n = 199; aged 12–18) Australian cricketers. Design Cross-sectional, self-report survey Methods An online, multi-section questionnaire. Results Confirmatory factor and item level analyses supported the psychometric superiority of a revised 10-item, unidimensional model of resilience over the original 25-item, five-factor measurement model. Positive and moderate correlations with hardiness as well as negative and moderate correlations with burnout components were evidenced thereby providing support for the convergent validity of the unidimensional model. Measurement invariance analyses of the unidimensional model across the two age-group samples supported configural (i.e., same factor structure across groups), metric (i.e., same pattern of factor loadings across the groups), and partial scalar invariance (i.e., mostly the same intercepts across the groups). Conclusion Evidence for a psychometrically sound measure of resilient qualities of the individual provides an important foundation upon which researchers can identify the antecedents to and outcomes of resilience in sport contexts.

Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI).

Varying the spatial distribution of applied nitrogen (N) fertilizer to match demand in crops has been shown to increase profits in Australia. Better matching the timing of N inputs to plant requirements has been shown to improve nitrogen use efficiency and crop yields and could reduce nitrous oxide emissions from broad acre grains. Farmers in the wheat production area of south eastern Australia are increasingly splitting N application with the second timing applied at stem elongation (Zadoks 30). Spectral indices have shown the ability to detect crop canopy N status but a robust method using a consistent calibration that functions across seasons has been lacking. One spectral index, the canopy chlorophyll content index (CCCI) designed to detect canopy N using three wavebands along the "red edge" of the spectrum was combined with the canopy nitrogen index (CNI), which was developed to normalize for crop biomass and correct for the N dilution effect of crop canopies. The CCCI-CNI index approach was applied to a 3-year study to develop a single calibration derived from a wheat crop sown in research plots near Horsham, Victoria, Australia. The index was able to predict canopy N (g m-2) from Zadoks 14-37 with an r2 of 0.97 and RMSE of 0.65 g N m-2 when dry weight biomass by area was also considered. We suggest that measures of N estimated from remote methods use N per unit area as the metric and that reference directly to canopy %N is not an appropriate method for estimating plant concentration without first accounting for the N dilution effect. This approach provides a link to crop development rather than creating a purely numerical relationship. The sole biophysical input, biomass, is challenging to quantify robustly via spectral methods. Combining remote sensing with crop modelling could provide a robust method for estimating biomass and therefore a method to estimate canopy N remotely. Future research will explore this and the use of active and passive sensor technologies for use in precision farming for targeted N management.

Modeling and analysis of energy quantization effects on single electron inverter performance

In this paper, for the first time, the effects of energy quantization on single electron transistor (SET) inverter performance are analyzed through analytical modeling and Monte Carlo simulations. It is shown that energy quantization mainly changes the Coulomb blockade region and drain current of SET devices and thus affects the noise margin, power dissipation, and the propagation delay of SET inverter. A new analytical model for the noise margin of SET inverter is proposed which includes the energy quantization effects. Using the noise margin as a metric, the robustness of SET inverter is studied against the effects of energy quantization. A compact expression is developed for a novel parameter quantization threshold which is introduced for the first time in this paper. Quantization threshold explicitly defines the maximum energy quantization that an SET inverter logic circuit can withstand before its noise margin falls below a specified tolerance level. It is found that SET inverter designed with CT:CG=1/3 (where CT and CG are tunnel junction and gate capacitances, respectively) offers maximum robustness against energy quantization.

Invasion impacts on biodiversity: responses of ant communities to infestation by cat’s claw creeper, Macfadyena unguis-cati (Bignoniaceae) in subtropical Australia

Ants are the dominant soil faunal group in many if not most terrestrial ecosystems, and play a key role in soil structure and function. This study documents the impacts of invasion by the exotic cat’s claw creeper vine, Macfadyena unguis-cati (L.) Gentry (Bignoniaceae) on surface-situated (epigaeic) and subterranean (hypogaeic) ant communities in subtropical SE Queensland Australia where it is a major environmental weed of riparian areas, rainforest communities and remnant natural vegetation, smothering standing vegetation and causing canopy collapse. Soil ants were sampled in infested and uninfested areas at eight sites spanning both riparian and non-riparian habitats in subtropical SE Queensland. Patterns of ant species composition and functional grouping in response to patch invasion status, landscape type and habitat stratum were investigated using ANOVA and non-metric multidimensional scaling ordination. The epigaeic and subterranean strata supported markedly different ant assemblages, and ant communities also differed between riparian and non-riparian habitats. However, M. unguis-cati invasion had a surprisingly limited impact. There was a tendency for ant abundance and species richness to be lower in infested patches, and overall species composition was different between infested and uninfested patches, but these differences were relatively small, and did not occur consistently across sites. There were changes in functional group composition that conformed to known functional group responses to environmental change, but these were similarly limited and inconsistent across sites. Our study has shown that ant communities are surprisingly resilient to invasion by M. unguis-cati, and serves as a warning against making assumptions about invasion impacts based on visual appearances.

Estimating winter crop area across seasons and regions using time-sequential MODIS imagery.

The wheat grain industry is Australia's second largest agricultural export commodity. There is an increasing demand for accurate, objective and near real-time crop production information by industry. The advent of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite platform has augmented the capability of satellite-based applications to capture reflectance over large areas at acceptable pixel scale, cost and accuracy. The use of multi-temporal MODIS-enhanced vegetation index (EVI) imagery to determine crop area was investigated in this article. Here the rigour of the harmonic analysis of time-series (HANTS) and early-season metric approaches was assessed when extrapolating over the entire Queensland (QLD) cropping region for the 2005 and 2006 seasons. Early-season crop area estimates, at least 4 months before harvest, produced high accuracy at pixel and regional scales with percent errors of -8.6% and -26% for the 2005 and 2006 seasons, respectively. In discriminating among crops at pixel and regional scale, the HANTS approach showed high accuracy. The errors for specific area estimates for wheat, barley and chickpea were 9.9%, -5.2% and 10.9% (for 2005) and -2.8%, -78% and 64% (for 2006), respectively. Area estimates of total winter crop, wheat, barley and chickpea resulted in coefficient of determination (R(2)) values of 0.92, 0.89, 0.82 and 0.52, when contrasted against the actual shire-scale data. A significantly high coefficient of determination (0.87) was achieved for total winter crop area estimates in August across all shires for the 2006 season. Furthermore, the HANTS approach showed high accuracy in discriminating cropping area from non-cropping area and highlighted the need for accurate and up-to-date land use maps. The extrapolability of these approaches to determine total and specific winter crop area estimates, well before flowering, showed good utility across larger areas and seasons. Hence, it is envisaged that this technology might be transferable to different regions across Australia.

Light thresholds derived from seagrass loss in the coastal zone of the northern Great Barrier Reef, Australia

There is a world-wide trend for deteriorating water quality and light levels in the coastal zone, and this has been linked to declines in seagrass abundance. Localized management of seagrass meadow health requires that water quality guidelines for meeting seagrass growth requirements are available. Tropical seagrass meadows are diverse and can be highly dynamic and we have used this dynamism to identify light thresholds in multi-specific meadows dominated by Halodule uninervis in the northern Great Barrier Reef, Australia. Seagrass cover was measured at similar to 3 month intervals from 2008 to 2011 at three sites: Magnetic Island (MI) Dunk Island (DI) and Green Island (GI). Photosynthetically active radiation was continuously measured within the seagrass canopy, and three light metrics were derived. Complete seagrass loss occurred at MI and DI and at these sites changes in seagrass cover were correlated with the three light metrics. Mean daily irradiance (I-d) above 5 and 8.4 mol m(-2) d(-1) was associated with gains in seagrass at MI and DI, however a significant correlation (R = 0.649, p < 0.05) only occurred at MI. The second metric, percent of days below 3 mol m(-2) d(-1), correlated the most strongly (MI, R = -0.714, p < 0.01 and DI, R = -0.859, p = <0.001) with change in seagrass cover with 16-18% of days below 3 mol m(-2) d(-1) being associated with more than 50% seagrass loss. The third metric, the number of hours of light saturated irradiance (H-sat) was calculated using literature-derived data on saturating irradiance (E-k). H-sat correlated well (R = 0.686, p <0.01; and DI, R = 0.704, p < 0.05) with change in seagrass abundance, and was very consistent between the two sites as 4 H-sat was associated with increases in seagrass abundance at both sites, and less than 4 H-sat with more than 50% loss. At the third site (GI), small seasonal losses of seagrass quickly recovered during the growth season and the light metrics did not correlate (p > 0.05) with change in percent cover, except for I-d which was always high, but correlated with change in seagrass cover. Although distinct light thresholds were observed, the departure from threshold values was also important. For example, light levels that are well below the thresholds resulted in more severe loss of seagrass than those just below the threshold. Environmental managers aiming to achieve optimal seagrass growth conditions can use these threshold light metrics as guidelines; however, other environmental conditions, including seasonally varying temperature and nutrient availability, will influence seagrass responses above and below these thresholds. (C) 2012 Published by Elsevier Ltd.

Geometrical interpretation of Inönü-Wigner contractions

The Inönü-Wigner contractions which interrelate the Lie algebras of the isometry groups of metric spaces are discussed with reference to deformations of the absolutes of the spaces. A general formula is derived for the Lie algebra commutation relations of the isometry group for anyN-dimensional metric space. These ideas are illustrated by a discussion of important particular cases, which interrelate the four-dimensional de Sitter, Poincaré, and Galilean groups.

Exploring metrics to express energy expenditure of physical activity in youth

Background Several approaches have been used to express energy expenditure in youth, but no consensus exists as to which best normalizes data for the wide range of ages and body sizes across a range of physical activities. This study examined several common metrics for expressing energy expenditure to determine whether one metric can be used for all healthy children. Such a metric could improve our ability to further advance the Compendium of Physical Activities for Youth. Methods A secondary analysis of oxygen uptake (VO2) data obtained from five sites was completed, that included 947 children ages 5 to 18 years, who engaged in 14 different activities. Resting metabolic rate (RMR) was computed based on Schofield Equations [Hum Nutr Clin Nut. 39(Suppl 1), 1985]. Absolute oxygen uptake (ml.min-1), oxygen uptake per kilogram body mass (VO2 in ml.kg-1.min-1), net oxygen uptake (VO2 – resting metabolic rate), allometric scaled oxygen uptake (VO2 in ml.kg-0.75.min-1) and YOUTH-MET (VO2.[resting VO2] -1) were calculated. These metrics were regressed with age, sex, height, and body mass. Results Net and allometric-scaled VO2, and YOUTH-MET were least associated with age, sex and physical characteristics. For moderate-to-vigorous intensity activities, allometric scaling was least related to age and sex. For sedentary and low-intensity activities, YOUTH-MET was least related to age and sex. Conclusions No energy expenditure metric completely eliminated the influence of age, physical characteristics, and sex. The Adult MET consistently overestimated EE. YOUTH-MET was better for expressing energy expenditure for sedentary and light activities, whereas allometric scaling was better for moderate and vigorous intensity activities. From a practical perspective, The YOUTH-MET may be the more feasible metric for improving of the Compendium of Physical Activities for Youth.

Liikennepolttoaineeksi tarkoitettu ohraetanoli, Suomen ohramarkkinat ja ilmastonmuutos

The aim of the thesis was to analyze the use of barley as an input for bioethanol production and the impacts the use has on the Finnish barley markets. Two main research questions were formulated. First, privately and socially optimal bioethanol production levels were examined. In the social optimum, the climate benefits of bioethanol production were considered. It was calculated that the production and use of bioethanol created smaller CO2 -emissions when compared with the production and use of gasoline. Second, the impacts of bioethanol production on farmland allocation and agricultural production were analyzed. In more detail, the second aim was to analyze the farmland allocation between wheat and barley cultivation and green set aside in the private and social optimum. An analytical model was produced to analyze the barley markets in Finland. To provide an empirical counterpart to this model, existing research data on bioethanol production and barley cultivation was used. The aim of the model was to analyze the supply and the demand as well as market equilibrium of barley. Furthermore, the model provided a framework for analyzing the differences between the private optimum and social optimum of bioethanol production in Finland. The demand for barley consists of animal feed demand and bioethanol demand. On the supply side, a heterogeneous model of farmland quality was used. With this framework, it is possible to analyze farmland allocation between barley and wheat cultivation and green set aside and how the climate benefits of bioethanol production affects the allocation. Moreover, the relative changes in barley price between the private and social optimum were analyzed. Based on the empirical analysis, the private optimum for barley based bioethanol production is 58 691 metric tons. However, the social optimum for barley based bioethanol production is 72 736 metric tons. The portion of farmland that is allocated to barley cultivation is increased if the climate benefits of bioethanol production are considered. In the private optimum, 1/19 of the total farmland is allocated to barley cultivation whereas in social optimum the share increases to 7/19. Furthermore, the increase in barley price between private and social optimum is rather modest. Total increase in price is only about 1,8 percent.