Sensitivity of Local Dynamic Stability of Over-Ground Walking to Balance Impairment Due to Galvanic Vestibular Stimulation

Impaired balance control during gait can be detected by local dynamic stability measures. For clinical applications, the use of a treadmill may be limiting. Therefore, the aim of this study was to test sensitivity of these stability measures collected during short episodes of over-ground walking by comparing normal to impaired balance control. Galvanic vestibular stimulation (GVS) was used to impair balance control in 12 healthy adults, while walking up and down a 10 m hallway. Trunk kinematics, collected by an inertial sensor, were divided into episodes of one stroll along the hallway. Local dynamic stability was quantified using short-term Lyapunov exponents (λs), and subjected to a bootstrap analysis to determine the effects of number of episodes analysed on precision and sensitivity of the measure. λs increased from 0.50 ± 0.06 to 0.56 ± 0.08 (p = 0.0045) when walking with GVS. With increasing number of episodes, coefficients of variation decreased from 10 ± 1.3% to 5 ± 0.7% and the number of p values >0.05 from 42 to 3.5%, indicating that both precision of estimates of λs and sensitivity to the effect of GVS increased. λs calculated over multiple episodes of over-ground walking appears to be a suitable measure to calculate local dynamic stability on group level

Topology optimization of multi-story buildings under fully non-stationary stochastic seismic ground motion

Topology optimization has been mainly addressed for structures under static loads using a deterministic setting. Nonetheless, many structural systems are subjected to uncertain dynamic loads, and thus efficient approaches are required to evaluate the optimal topology in such kind of applications. Within this framework, the present paper deals with the topology optimization of multi-story buildings subjected to seismic ground motion. Because of the inherent randomness of the earthquakes, the uncertain system response is determined through a random vibration-based approach in which the seismic ground motion is described as filtered white Gaussian noise with time-varying amplitude and frequency content (i.e., fully non-stationary seismic ground motion). The paper is especially concerned with the assessment of the dynamic response sensitivity for the gradient-based numerical solution of the optimization problem. To this end, an approximated construction of the gradient is proposed in which explicit, exact derivatives with respect to the design variables are computed analytically through direct differentiation for a sub-assembly of elements (up to a single element) resulting from the discretization of the optimizable domain. The proposed strategy is first validated for the simpler case of stationary base excitation by comparing the results with those obtained using an exact approach based on the adjoint method, and its correctness is ultimately verified for the more general case of non-stationary seismic ground motion. Overall, this validation demonstrates that the proposed approach leads to accurate results at low computational effort. Further numerical investigations are finally presented to highlight to what extent the features of the non-stationary seismic ground motion influence the optimal topology

Tree pruning/inspection robot climbing mechanism design, kinematics study and intelligent control : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Mechatronics at Massey University, Manawatu Campus, New Zealand

Forestry plays an important role in New Zealand’s economy as its third largest export earner. To achieve New Zealand Wood Council’s export target of $12 billion by 2022 in forest and improve the current situation that is the reduction of wood harvesting area, the unit value and volume of lumber must be increased. Pruning is essential and critical for obtaining high-quality timber during plantation growing. Powerful tools and robotic systems have great potential for sustainable forest management. Up to now, only a few tree-pruning robotic systems are available on the market. Unlike normal robotic manipulators or mobile robots, tree pruning robot has its unique requirements and features. The challenges include climbing pattern control, anti-free falling, and jamming on the tree trunk etc. Through the research on the available pole and tree climbing robots, this thesis presents a novel mechanism of tree climbing robotic system that could serve as a climbing platform for applications in the forest industry like tree pruning, inspection etc. that requires the installation of powerful or heavy tools. The unique features of this robotic system include the passive and active anti-falling mechanisms that prevent the robot falling to the ground under either static or dynamic situations, the capability to vertically or spirally climb up a tree trunk and the flexibility to suit different sizes of tree trunk. Furthermore, for the convenience of tree pruning and the fulfilment of robot anti-jamming feature, the robot platform while the robot climbs up should move up without tilting. An intelligent platform balance control system with real-time sensing integration was developed to overcome the climbing tilting problem. The thesis also presents the detail kinematic and dynamic study, simulation, testing and analysis. A physical testing model of this proposed robotic system was built and tested on a cylindrical rod. The mass of the prototype model is 6.8 Kg and can take 2.1 Kg load moving at the speed of 42 mm/s. The trunk diameter that the robot can climb up ranges from 120 to 160 mm. The experiment results have good matches with the simulations and analysis. This research established a basis for developing wheel-driven tree or pole climbing robots. The design and simulation method, robotic leg mechanism and the control methodologies could be easily applied for other wheeled tree/pole climbing robots. This research has produced 6 publications, two ASME journal papers and 4 IEEE international conference papers that are available on IEEE Xplore. The published content ranges from robotic mechanism design, signal processing, platform balance control, and robot climbing behavior optimization. This research also brought interesting topics for further research such as the integration with artificial intelligent module and mobile robot for remote tree/forest inspection after pruning or for pest control

How to feed the squerall with RDF and other data nuts?

Advances in Data Management methods have resulted in a wide array of storage solutions having varying query capabilities and supporting different data formats. Traditionally, heterogeneous data was transformed off-line into a unique format and migrated to a unique data management system, before being uniformly queried. However, with the increasing amount of heterogeneous data sources, many of which are dynamic, modern applications prefer accessing directly the original fresh data. Addressing this requirement, we designed and developed Squerall, a software framework that enables the querying of original large and heterogeneous data on-the-fly without prior data transformation. Squerall is built from the ground up with extensibility in consideration, e.g., supporting more data sources. Here, we explain Squerall’s extensibility aspect and demonstrate step-by-step how to add support for RDF data, a new extension to the previously supported range of data sources

Design of robust scheduling methodologies for high performance computing

Scientific applications are often large, complex, computationally-intensive, and irregular. Loops are often an abundant source of parallelism in scientific applications. Due to the ever-increasing computational needs of scientific applications, high performance computing (HPC) systems have become larger and more complex, offering increased parallelism at multiple hardware levels. Load imbalance, caused by irregular computational load per task and unpredictable computing system characteristics (system variability), often degrades the performance of applications. Besides, perturbations, such as reduced computing power, network latency availability, or failures, can severely impact the performance of the applications. System variability and perturbations are only expected to increase in future extreme-scale computing systems. Extrapolating the current failure rate to Exascale would result in a failure every 20 minutes. Such failure rate and perturbations would render the computing systems unusable. This doctoral thesis improves the performance of computationally-intensive scientific applications on HPC systems via robust load balancing. Robust scheduling ensures and maintains improved load balanced execution under unpredictable application and system characteristics. A number of dynamic loop self-scheduling (DLS) techniques have been introduced and successfully used in scientific applications between the 1980s and 2000s. These DLS techniques are not fault-tolerant as they were originally introduced. In this thesis, we identify three major research questions to achieve robust scheduling (1) How to ensure that the DLS techniques employed in scientific applications today adhere to their original design goals and specifications? (2) How to select a DLS technique that will achieve improved performance under perturbations? (3) How to tolerate perturbations during execution and maintain a load balanced execution on HPC systems? To answer the first question, we reproduced the original experiments that introduced the DLS techniques to verify their present implementation. Simulation is used to reproduce experiments on systems from the past. Realistic simulation induces a similar analysis and conclusions to the analysis of the native results. To this end, we devised an approach for bridging the native and simulative executions of parallel applications on HPC systems. This simulation approach is used to reproduce scheduling experiments on past and present systems to verify the implementation of DLS techniques. Given the multiple levels of parallelism offered by the present HPC systems, we analyzed the load imbalance in scientific applications, from computer vision, astrophysics, and mathematical kernels, at both thread and process levels. This analysis revealed a significant interplay between thread level and process level load balancing. We found that dynamic load balancing at the thread level propagates to the process level and vice versa. However, the best application performance is only achieved by two-level dynamic load balancing. Next, we examined the performance of applications under perturbations. We found that the most robust DLS technique does not deliver the best performance under various perturbations. The most efficient DLS technique changes by changing the application, the system, or perturbations during execution. This signifies the algorithm selection problem in the DLS. We leveraged realistic simulations to address the algorithm selection problem of scheduling under perturbations via a simulation assisted approach (SimAS), which answers the second question. SimAS dynamically selects DLS techniques that improve the performance depending on the application, system, and perturbations during the execution. To answer the third question, we introduced a robust dynamic load balancing (rDLB) approach for the robust self-scheduling of scientific applications under failures (question 3). rDLB proactively reschedules already allocated tasks and requires no detection of perturbations. rDLB tolerates up to P −1 processor failures (P is the number of processors allocated to the application) and boosts the flexibility of applications against nonfatal perturbations, such as reduced availability of resources. This thesis is the first to provide insights into the interplay between thread and process level dynamic load balancing in scientific applications. Verified DLS techniques, SimAS, and rDLB are integrated into an MPI-based dynamic load balancing library (DLS4LB), which supports thirteen DLS techniques, for robust dynamic load balancing of scientific applications on HPC systems. Using the methods devised in this thesis, we improved the performance of scientific applications by up to 21% via two-level dynamic load balancing. Under perturbations, we enhanced their performance by a factor of 7 and their flexibility by a factor of 30. This thesis opens up the horizons into understanding the interplay of load balancing between various levels of software parallelism and lays the ground for robust multilevel scheduling for the upcoming Exascale HPC systems and beyond

Event-based tracking of human hands

This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space)

Human-Structure Dynamic Interaction during Short-Distance Free Falls

The dynamic interactions of falling human bodies with civil structures, regardless of their potentially critical effects, have sparsely been researched in contact biomechanics. The physical contact models suggested in the existing literature, particularly for short-distant falls in home settings, assume the human body falls on a “rigid” (not vibrating) ground. A similar assumption is usually made during laboratory-based fall tests, including force platforms. Based on observations from a set of pediatric head-first free fall tests, the present paper shows that the dynamics of the grounded force plate are not always negligible when doing fall test in a laboratory setting. By using a similar analogy for lightweight floor structures, it is shown that ignoring the dynamics of floors in the contact model can result in an up to 35% overestimation of the peak force experienced by a falling human. A nonlinear contact model is suggested, featuring an agent-based modelling approach, where the dynamics of the falling human and the impact object (force plate or a floor structure here) are each modelled using a single-degree-of-freedom model to simulate their dynamic interactions. The findings of this research can have wide applications in areas such as impact biomechanics and sports science

Dynamic nuclear polarization in InGaAs/GaAs and GaAs/AlGaAs quantum dots under nonresonant ultralow-power optical excitation

We study experimentally the dependence of dynamic nuclear spin polarization on the power of nonresonant optical excitation in two types of individual neutral semiconductor quantum dots: InGaAs/GaAs and GaAs/AlGaAs. We show that the mechanism of nuclear spin pumping via second-order recombination of optically forbidden (“dark”) exciton states recently reported in InP/GaInP quantum dots [E. A. Chekhovich et al., Phys. Rev. B 83, 125318 (2011)] is relevant for material systems considered in this work. In the InGaAs/GaAs dots this nuclear spin polarization mechanism is particularly pronounced, resulting in Overhauser shifts up to ∼80 μeV achieved at ultralow optical excitation power, ∼1000 times smaller than the power required to saturate ground state excitons. The Overhauser shifts observed at ultralow power pumping in the interface GaAs/AlGaAs dots are generally found to be smaller (up to ∼40 μeV). Furthermore in GaAs/AlGaAs we observe dot-to-dot variation and even sign reversal of the Overhauser shift which is attributed to the dark-bright exciton mixing originating from electron-hole exchange interaction in dots with reduced symmetry. Nuclear spin polarization degrees reported in this work under ultralow-power optical pumping are comparable to those achieved by techniques such as resonant optical pumping or above-gap pumping with high-power circularly polarized light. Dynamic nuclear polarization via second-order recombination of “dark” excitons may become a useful tool in single quantum dot applications, where manipulation of the nuclear spin environment or electron spin is required

Software-Architecture Recovery from Machine Code

In this paper, we present a tool, called Lego, which recovers object-oriented software architecture from stripped binaries. Lego takes a stripped binary as input, and uses information obtained from dynamic analysis to (i) group the functions in the binary into classes, and (ii) identify inheritance and composition relationships between the inferred classes. The information obtained by Lego can be used for reengineering legacy software, and for understanding the architecture of software systems that lack documentation and source code. Our experiments show that the class hierarchies recovered by Lego have a high degree of agreement---measured in terms of precision and recall---with the hierarchy defined in the source code

Human gesture classification by brute-force machine learning for exergaming in physiotherapy

In this paper, a novel approach for human gesture classification on skeletal data is proposed for the application of exergaming in physiotherapy. Unlike existing methods, we propose to use a general classifier like Random Forests to recognize dynamic gestures. The temporal dimension is handled afterwards by majority voting in a sliding window over the consecutive predictions of the classifier. The gestures can have partially similar postures, such that the classifier will decide on the dissimilar postures. This brute-force classification strategy is permitted, because dynamic human gestures show sufficient dissimilar postures. Online continuous human gesture recognition can classify dynamic gestures in an early stage, which is a crucial advantage when controlling a game by automatic gesture recognition. Also, ground truth can be easily obtained, since all postures in a gesture get the same label, without any discretization into consecutive postures. This way, new gestures can be easily added, which is advantageous in adaptive game development. We evaluate our strategy by a leave-one-subject-out cross-validation on a self-captured stealth game gesture dataset and the publicly available Microsoft Research Cambridge-12 Kinect (MSRC-12) dataset. On the first dataset we achieve an excellent accuracy rate of 96.72%. Furthermore, we show that Random Forests perform better than Support Vector Machines. On the second dataset we achieve an accuracy rate of 98.37%, which is on average 3.57% better then existing methods