A general pose estimation algorithm in a multi-Kinect system

 Microsoft Kinect which has been primarily aimed at the computer gaming industry has been used in bio-kinematic research related implementations. A multi-Kinect system can be useful in exploiting spatial diversity to increase measurement accuracy. One of the main problems in deploying multi-Kinect systems is to estimate the pose, including the position and orientation of each Kinect. In this paper, a singular value decomposition (SVD) least-squares algorithm is extended to a more generic time-series based approach to solve this pose estimation problem utilising 3D positions of one or more joints in skeletons obtained from a multi-Kinect system. Additionally, computer simulations are performed to demonstrate the use and to evaluate the efficiency of the proposed algorithm. The former is further validated with a commercial Vicon system.

Estimation of accumulated lethality under pressure-assisted thermal processing

A study was conducted to develop an integrated process lethality model for pressure-assisted thermal processing (PATP) taking into consideration the lethal contribution of both pressure and heat on spore inactivation. Assuming that the momentary inactivation rate was dependent on the survival ratio and momentary pressure-thermal history, a differential equation was formulated and numerically solved using the Runge-Kutta method. Published data on combined pressure-heat inactivation of Bacillus amyloliquefaciens spores were used to obtain model kinetic parameters that considered both pressure and thermal effects. The model was experimentally validated under several process scenarios using a pilot-scale high-pressure food processor. Using first-order kinetics in the model resulted in the overestimation of log reduction compared to the experimental values. When the n th-order kinetics was used, the computed accumulated lethality and the log reduction values were found to be in reasonable agreement with the experimental data. Within the experimental conditions studied, spatial variation in process temperature resulted up to 3.5 log variation in survivors between the top and bottom of the carrier basket. The predicted log reduction of B. amyloliquefaciens spores in deionized water and carrot purée had satisfactory accuracy (1.07-1.12) and regression coefficients (0.83-0.92). The model was also able to predict log reductions obtained during a double-pulse treatment conducted using a pilot-scale high-pressure processor. The developed model can be a useful tool to examine the effect of combined pressure-thermal treatment on bacterial spore lethality and assess PATP microbial safety. © 2013 Springer Science+Business Media New York.

Indirect estimation of semiparametric binary choice models*

One of the most cited studies within the field of binary choice models is that of Klein and Spady (1993), in which the authors propose a semiparametric estimator for use when the distribution of the error term is unknown. However, although theoretically appealing, the estimator has been found to be difficult to implement, and therefore not very attractive from an applied point of view. The current study offers an indirect inference-based solution to this problem. The new estimator is not only simple with good small-sample properties, but also consistent and asymptotically normal.

Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Reduced order functional observers with application to partial state estimation of linear systems with input delay

Reduced order multi-functional observer design for multi-input multi-utput (MIMO) linear time-invariant (LTI) systems with constant delayed inputs is studied. This research is useful in the input estimation of LTI systems with actuator delay, as well as system monitoring and fault detection of these systems. Two approaches for designing an asymptotically stable functional observer for the system are proposed: delay-dependent and delay-free. The delay-dependent observer is infinite-dimensional, while the delay-free structure is finite-dimensional. Moreover, since the delay-free observer does not require any information on the time delay, it is more practical in real applications. However, the delay-dependent observer contains less restrictive assumptions and covers more variety of systems. The proposed observer design schemes are novel, simple to implement, and have improved numerical features compared to some of the other available approaches to design (unknown-input) functional observers. In addition, the proposed observers usually possess lower order than ordinary Luenberger observers, and the design schemes do not need the observability or detectability requirements of the system. The necessary and sufficient conditions of the existence of an asymptoticobserver in each scenario are explored. The extensions of the proposed observers to systems with multiple delayed-inputs are also discussed. Several numerical examples and simulation results are employed to support our theories.

Drug use in Australian nightlife settings: estimation of prevalence and validity of self-report

AIMS: This study aimed to 1) estimate the prevalence of illicit drug use in night-time entertainment districts across five major cities in Australia; and 2) validate self-reported drug use using biochemical marker oral swabs. DESIGN: Street intercept surveys and oral drug swabs conducted over a seven-month period during 2011-2012. SETTING: The night-time entertainment districts of three metropolitan cities (Sydney, Melbourne and Perth) and two regional cities (Wollongong and Geelong) in Australia, between the hours of 10 pm and 5 am. PARTICIPANTS: 7,340 individuals agreed to participate in the survey (a 93% response rate). More than half (62%) of the sample was male, with a median age of 22 years (range 18-73). MEASUREMENTS: Patrons were approached in thoroughfares, and while entering and leaving licensed venues. Data collected included demographics and current session alcohol and other substance use. Drug swabs (n = 401) were performed with a sub-sample of participants. FINDINGS: Approximately 9% (95% CI, 7% to 12%) of participants self-reported consumption of illicit or non-prescribed pharmaceutical drugs prior to interview; of those, 81% identified psychostimulants as the drug used. One in five drug swabs returned a positive result, with psychostimulants the most commonly detected drugs (15%; 95% CI, 12%-19%). Kappa statistics indicate agreement between self-report of any illicit drug and a positive drug swab is in the slight range (κ = 0.12 (95% CI, .05 to .20) p = .000). CONCLUSIONS: Self-report findings suggest drug use in the nightlife in Australia is common, though still very much a minority past-time. Drug swabs indicate a higher prevalence of use (20%) than self-report (9%), which suggests that self-reported drug use may not be reliable in this context. This article is protected by copyright. All rights reserved.

Prediction interval estimation of electricity prices using PSO-tuned support vector machines

Uncertainty of the electricity prices makes the task of accurate forecasting quite difficult for the electricity market participants. Prediction intervals (PIs) are statistical tools which quantify the uncertainty related to forecasts by estimating the ranges of the future electricity prices. Traditional approaches based on neural networks (NNs) generate PIs at the cost of high computational burden and doubtful assumptions about data distributions. In this work, we propose a novel technique that is not plagued with the above limitations and it generates high-quality PIs in a short time. The proposed method directly generates the lower and upper bounds of the future electricity prices using support vector machines (SVM). Optimal model parameters are obtained by the minimization of a modified PI-based objective function using a particle swarm optimization (PSO) technique. The efficiency of the proposed method is illustrated using data from Ontario, Pennsylvania-New Jersey-Maryland (PJM) interconnection day-ahead and real-time markets.

Unveiling the diversification dynamics of Australasian predaceous diving beetles in the Cenozoic

During the Cenozoic, Australia experienced major climatic shifts that have had dramatic ecological consequences for the modern biota. Mesic tropical ecosystems were progressively restricted to the coasts and replaced by arid-adapted floral and faunal communities. Whilst the role of aridification has been investigated in a wide range of terrestrial lineages, the response of freshwater clades remains poorly investigated. To gain insights into the diversification processes underlying a freshwater radiation, we studied the evolutionary history of the Australasian predaceous diving beetles of the tribe Hydroporini (147 described species). We used an integrative approach including the latest methods in phylogenetics, divergence time estimation, ancestral character state reconstruction, and likelihood-based methods of diversification rate estimation. Phylogenies and dating analyses were reconstructed with molecular data from seven genes (mitochondrial and nuclear) for 117 species (plus 12 outgroups). Robust and well-resolved phylogenies indicate a late Oligocene origin of Australasian Hydroporini. Biogeographic analyses suggest an origin in the East Coast region of Australia, and a dynamic biogeographic scenario implying dispersal events. The group successfully colonized the tropical coastal regions carved by a rampant desertification, and also colonized groundwater ecosystems in Central Australia. Diversification rate analyses suggest that the ongoing aridification of Australia initiated in the Miocene contributed to a major wave of extinctions since the late Pliocene probably attributable to an increasing aridity, range contractions and seasonally disruptions resulting from Quaternary climatic changes. When comparing subterranean and epigean genera, our results show that contrasting mechanisms drove their diversification and therefore current diversity pattern. The Australasian Hydroporini radiation reflects a combination of processes that promoted both diversification, resulting from new ecological opportunities driven by initial aridification, and a subsequent loss of mesic adapted diversity due to increasing aridity.

Estimation of factor-augmented panel regressions with weakly influential factors

The use of factor-augmented panel regressions has become very popular in recent years. Existing methods for such regressions require that the common factors are strong, such that their cumulative loadings rise proportionally to the number of cross-sectional units, which of course need not be the case in practice. Motivated by this, the current paper offers an indepth analysis of the effect of non-strong factors on two of the most popular estimators for factor-augmented regressions, namely, principal components (PC) and common correlated effects (CCE).

Feasible estimation in cointegrated panels

In this paper we propose a simple procedure for data dependent determination of the number of lags and leads to use in feasible estimation of cointegrated panel regressions. Results from Monte Carlo simulations suggests that the feasible estimators considered enjoys excellent precision in terms of root mean squared error and reasonable power with effective size hovering close to the nominal level. The good performance of the feasible estimators is verified empirically through an application to the long run money demand.

Localization and degmentation of 3D intervertebral discs in MR Images by data driven estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

The use of kernel density estimation to examine associations between neighborhood destination intensity and walking and physical activity

BACKGROUND: Local destinations have previously been shown to be associated with higher levels of both physical activity and walking, but little is known about how the distribution of destinations is related to activity. Kernel density estimation is a spatial analysis technique that accounts for the location of features relative to each other. Using kernel density estimation, this study sought to investigate whether individuals who live near destinations (shops and service facilities) that are more intensely distributed rather than dispersed: 1) have higher odds of being sufficiently active; 2) engage in more frequent walking for transport and recreation. METHODS: The sample consisted of 2349 residents of 50 urban areas in metropolitan Melbourne, Australia. Destinations within these areas were geocoded and kernel density estimates of destination intensity were created using kernels of 400m (meters), 800m and 1200m. Using multilevel logistic regression, the association between destination intensity (classified in quintiles Q1(least)-Q5(most)) and likelihood of: 1) being sufficiently active (compared to insufficiently active); 2) walking≥4/week (at least 4 times per week, compared to walking less), was estimated in models that were adjusted for potential confounders. RESULTS: For all kernel distances, there was a significantly greater likelihood of walking≥4/week, among respondents living in areas of greatest destinations intensity compared to areas with least destination intensity: 400m (Q4 OR 1.41 95%CI 1.02-1.96; Q5 OR 1.49 95%CI 1.06-2.09), 800m (Q4 OR 1.55, 95%CI 1.09-2.21; Q5, OR 1.71, 95%CI 1.18-2.48) and 1200m (Q4, OR 1.7, 95%CI 1.18-2.45; Q5, OR 1.86 95%CI 1.28-2.71). There was also evidence of associations between destination intensity and sufficient physical activity, however these associations were markedly attenuated when walking was included in the models. CONCLUSIONS: This study, conducted within urban Melbourne, found that those who lived in areas of greater destination intensity walked more frequently, and showed higher odds of being sufficiently physically active-an effect that was largely explained by levels of walking. The results suggest that increasing the intensity of destinations in areas where they are more dispersed; and or planning neighborhoods with greater destination intensity, may increase residents' likelihood of being sufficiently active for health.

Estimation of changes in volume of individual lower-limb muscles using magnetic resonance imaging (during bed-rest)

Muscle size in the lower limb is commonly assessed in neuromuscular research as it correlates with muscle function and some approaches have been assessed for their ability to provide valid estimates of muscle volume. Work to date has not examined the ability of different measurement approaches (such as cross-sectional area (CSA) measures on magnetic resonance (MR) imaging) to accurately track changes in muscle volume as a result of an intervention, such as exercise, injury or disuse. Here we assess whether (a) the percentage change in muscle CSA in 17 lower-limb muscles during 56 days bed-rest, as assessed by five different algorithms, lies within 0.5% of the muscle volume change and (b) the variability of the outcome measure is comparable to that of muscle volume. We find that an approach selecting the MR image with the highest muscle CSA and then a series of CSA measures, the number of which depended upon the muscle considered, immediately distal and proximal, provided an acceptable estimate of the muscle volume change. In the vastii, peroneal, sartorius and anterior tibial muscle groups, accurate results can be attained by increasing the spacing between CSA measures, thus reducing the total number of MR images and hence the measurement time. In the two heads of biceps femoris, semimembranosus and gracilis, it is not possible to reduce the number of CSA measures and the entire muscle volume must be evaluated. Using these approaches one can reduce the number of CSA measures required to estimate changes in muscle volume by ~60%. These findings help to attain more efficient means to track muscle volume changes in interventional studies.

New results in common functional state estimation for two linear systems with unknown inputs

This paper presents new developments in common functional observers for two systems. We improve an existing common functional observer scheme by reducing its order, and then investigate its existence conditions in terms of the original system matrices. These conditions have never been explored and they enable the users to know at the outset the class of systems for which the scheme is applicable. They also show that both observers can be designed independently of each other which significantly simplifies the design process. A numerical simulation verifies the findings.