Human Motion Analysis Based on Sequential Modeling of Radar Signal and Stereo Image Features

Falls are one of the greatest threats to elderly health in their daily living routines and activities. Therefore, it is very important to detect falls of an elderly in a timely and accurate manner, so that immediate response and proper care can be provided, by sending fall alarms to caregivers. Radar is an effective non-intrusive sensing modality which is well suited for this purpose, which can detect human motions in all types of environments, penetrate walls and fabrics, preserve privacy, and is insensitive to lighting conditions. Micro-Doppler features are utilized in radar signal corresponding to human body motions and gait to detect falls using a narrowband pulse-Doppler radar. Human motions cause time-varying Doppler signatures, which are analyzed using time-frequency representations and matching pursuit decomposition (MPD) for feature extraction and fall detection. The extracted features include MPD features and the principal components of the time-frequency signal representations. To analyze the sequential characteristics of typical falls, the extracted features are used for training and testing hidden Markov models (HMM) in different falling scenarios. Experimental results demonstrate that the proposed algorithm and method achieve fast and accurate fall detections. The risk of falls increases sharply when the elderly or patients try to exit beds. Thus, if a bed exit can be detected at an early stage of this motion, the related injuries can be prevented with a high probability. To detect bed exit for fall prevention, the trajectory of head movements is used for recognize such human motion. A head detector is trained using the histogram of oriented gradient (HOG) features of the head and shoulder areas from recorded bed exit images. A data association algorithm is applied on the head detection results to eliminate head detection false alarms. Then the three dimensional (3D) head trajectories are constructed by matching scale-invariant feature transform (SIFT) keypoints in the detected head areas from both the left and right stereo images. The extracted 3D head trajectories are used for training and testing an HMM based classifier for recognizing bed exit activities. The results of the classifier are presented and discussed in the thesis, which demonstrates the effectiveness of the proposed stereo vision based bed exit detection approach.

Modelagem e análise de desempenho de sistema para geração de energia elétrica através de ondas marítimas.

Mediante a crescente necessidade de aumento na oferta de energia elétrica devido à constante elevação na demanda mundial, esta dissertação avalia o desempenho de um sistema conversor de energia de ondas marítimas em energia elétrica. O sistema em análise é o de coluna de água oscilante com turbina de dupla ação instalado na costa. Utiliza-se um modelo regular de ondas como perturbação à dinâmica de uma câmara semi-submersa gerando fluxo de ar através de uma turbina à ar de dupla ação. O sistema final é não linear e com parâmetros variantes no tempo. A dissertação investiga possibilidades para o aumento do rendimento da turbina em diferentes condições de mar através do método de simulação numérica. Após a modelagem física e matemática do sistema escolhido, inicia-se a síntese de um controlador proporcional derivativo para controle da pressão de ar na turbina em torno da pressão ideal de trabalho da mesma. A análise inclui o comparativo entre os resultados do sistema com e sem controlador e a avaliação de robustez utilizando ondas com amplitude variável. O trabalho apresenta ainda propostas de otimização do sistema para trabalhar em condições similares a região de Pecém no Brasil. Pelos resultados obtidos nas simulações, conclui-se que o rendimento e a robustez do sistema podem melhorar utilizando um sistema controlado. O rendimento do sistema poderá ainda ser otimizado para a região de instalação.

Australian water mass variations from GRACE data linked to Indo-Pacific climate variability

Time-variable gravity data from the Gravity Recovery And Climate Experiment (GRACE) mission are used to study total water content over Australia for the period 2002–2010. A time-varying annual signal explains 61% of the variance of the data, in good agreement with two independent estimates of the same quantity from hydrological models. Water mass content variations across Australia are linked to Pacific and Indian Ocean variability, associated with El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), respectively. From 1989, positive (negative) IOD phases were related to anomalously low (high) precipitation in southeastern Australia, associated with a reduced (enhanced) tropical moisture flux. In particular, the sustained water mass content reduction over central and southern regions of Australia during the period 2006–2008 is associated with three consecutive positive IOD events.

Inflation persistence in Central and Eastern European countries. Bruegel Working Paper 2013/04, 22 July 2013

This paper studies inflation persistence with time-varying coefficient autoregressions for twelve central European countries,in comparison with the United States and the euro area. Inflation persistence tends to be higher in times of high inflation. Since the oil price shocks, inflation persistence has declined both in the US and euro-area. In most central and eastern European countries, for which our study covers 1993-2012, inflation persistence has also declined, with the main exceptions of the Czech Republic, Slovakia and Slovenia, where persistence seems to be rather stable.

Phase relationships between orbital forcing and the composition of air trapped in Antarctic ice cores

Orbital tuning is central for ice core chronologies beyond annual layer counting, available back to 60 ka (i.e. thousands of years before 1950) for Greenland ice cores. While several complementary orbital tuning tools have recently been developed using δ¹⁸Oatm, δO₂⁄N₂ and air content with different orbital targets, quantifying their uncertainties remains a challenge. Indeed, the exact processes linking variations of these parameters, measured in the air trapped in ice, to their orbital targets are not yet fully understood. Here, we provide new series of δO₂∕N₂ and δ¹⁸Oatm data encompassing Marine Isotopic Stage (MIS) 5 (between 100 and 160 ka) and the oldest part (340–800 ka) of the East Antarctic EPICA Dome C (EDC) ice core. For the first time, the measurements over MIS 5 allow an inter-comparison of δO₂∕N₂ and δ¹⁸Oatm records from three East Antarctic ice core sites (EDC, Vostok and Dome F). This comparison highlights some site-specific δO₂∕N₂ variations. Such an observation, the evidence of a 100 ka periodicity in the δO₂∕N₂ signal and the difficulty to identify extrema and mid-slopes in δO2∕N2 increase the uncertainty associated with the use of δO₂∕N₂ as an orbital tuning tool, now calculated to be 3–4 ka. When combining records of δ¹⁸Oatm and δO₂∕N₂ from Vostok and EDC, we find a loss of orbital signature for these two parameters during periods of minimum eccentricity (∼ 400 ka, ∼ 720–800 ka). Our data set reveals a time-varying offset between δO₂∕N₂ and δ¹⁸Oatm records over the last 800 ka that we interpret as variations in the lagged response of δ¹⁸Oatm to precession. The largest offsets are identified during Terminations II, MIS 8 and MIS 16, corresponding to periods of destabilization of the Northern polar ice sheets. We therefore suggest that the occurrence of Heinrich–like events influences the response of δ¹⁸Oatm to precession.

Dynamic D-Vine copula model with applications to Value-at-Risk (VaR)

Regular vine copulas are multivariate dependence models constructed from pair-copulas (bivariate copulas). In this paper, we allow the dependence parameters of the pair-copulas in a D-vine decomposition to be potentially time-varying, following a nonlinear restricted ARMA(1,m) process, in order to obtain a very flexible dependence model for applications to multivariate financial return data. We investigate the dependence among the broad stock market indexes from Germany (DAX), France (CAC 40), Britain (FTSE 100), the United States (S&P 500) and Brazil (IBOVESPA) both in a crisis and in a non-crisis period. We find evidence of stronger dependence among the indexes in bear markets. Surprisingly, though, the dynamic D-vine copula indicates the occurrence of a sharp decrease in dependence between the indexes FTSE and CAC in the beginning of 2011, and also between CAC and DAX during mid-2011 and in the beginning of 2008, suggesting the absence of contagion in these cases. We also evaluate the dynamic D-vine copula with respect to Value-at-Risk (VaR) forecasting accuracy in crisis periods. The dynamic D-vine outperforms the static D-vine in terms of predictive accuracy for our real data sets.

An evaluation of driver behavior at signalized intersections. Final report.

Arizona Department of Transportation, Phoenix

Efficacy of red and yellow turn arrows in traffic signals. Final report.

Federal Highway Administration, Washington, D.C.

Investigating responses of calcium carbonate nucleation rates in artificial seawater to changes in saturation states due to ocean acidification

Senior thesis written for Oceanography 445

Proton pump inhibitors and the risk of fractures in older adults: a population-based retrospective cohort study

Thesis (Master's)--University of Washington, 2016-06

Hypertension and diabetes treatments and risk of adverse outcomes among breast cancer patients

Thesis (Ph.D.)--University of Washington, 2016-06

A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies

This paper presents a metafrontier production function model for firms in different groups having different technologies. The metafrontier model enables the calculation of comparable technical efficiencies for firms operating under different technologies. The model also enables the technology gaps to be estimated for firms under different technologies relative to the potential technology available to the industry as a whole. The metafrontier model is applied in the analysis of panel data on garment firms in five different regions of Indonesia, assuming that the regional stochastic frontier production function models have technical inefficiency effects with the time-varying structure proposed by Battese and Coelli ( 1992).

Consumption habit and international stock returns

We use the consumption-based asset pricing model with habit formation to study the predictability and cross-section of returns from the international equity markets. We find that the predictability of returns from many developed countries' equity markets is explained in part by changing prices of risks associated with consumption relative to habit at the world as well as local levels. We also provide an exploratory investigation of the cross-sectional implications of the model under the complete world market integration hypothesis and find that the model performs mildly better than the traditional consumption-based model. the unconditional and conditional world CAPMs and a three-factor international asset pricing model. (C) 2004 Elsevier B.V. All rights reserved.

Enhanced detection-guided NLMS estimation of sparse FIR-modeled signal channels

In various signal-channel-estimation problems, the channel being estimated may be well approximated by a discrete finite impulse response (FIR) model with sparsely separated active or nonzero taps. A common approach to estimating such channels involves a discrete normalized least-mean-square (NLMS) adaptive FIR filter, every tap of which is adapted at each sample interval. Such an approach suffers from slow convergence rates and poor tracking when the required FIR filter is "long." Recently, NLMS-based algorithms have been proposed that employ least-squares-based structural detection techniques to exploit possible sparse channel structure and subsequently provide improved estimation performance. However, these algorithms perform poorly when there is a large dynamic range amongst the active taps. In this paper, we propose two modifications to the previous algorithms, which essentially remove this limitation. The modifications also significantly improve the applicability of the detection technique to structurally time varying channels. Importantly, for sparse channels, the computational cost of the newly proposed detection-guided NLMS estimator is only marginally greater than that of the standard NLMS estimator. Simulations demonstrate the favourable performance of the newly proposed algorithm. © 2006 IEEE.

Filtering duplicate items over distributed data streams

In recent years many real time applications need to handle data streams. We consider the distributed environments in which remote data sources keep on collecting data from real world or from other data sources, and continuously push the data to a central stream processor. In these kinds of environments, significant communication is induced by the transmitting of rapid, high-volume and time-varying data streams. At the same time, the computing overhead at the central processor is also incurred. In this paper, we develop a novel filter approach, called DTFilter approach, for evaluating the windowed distinct queries in such a distributed system. DTFilter approach is based on the searching algorithm using a data structure of two height-balanced trees, and it avoids transmitting duplicate items in data streams, thus lots of network resources are saved. In addition, theoretical analysis of the time spent in performing the search, and of the amount of memory needed is provided. Extensive experiments also show that DTFilter approach owns high performance.