A Method to Concatenate Multiple Short Time Series for Evaluating Dynamic Behaviour During Walking

Gait variability is a sensitive metric for assessing functional deficits in individuals with mobility impairments. To correctly represent the temporal evolution of gait kinematics, nonlinear measures require extended and uninterrupted time series. In this study, we present and validate a novel algorithm for concatenating multiple time-series in order to allow the nonlinear analysis of gait data from standard and unrestricted overground walking protocols. The fullbody gait patterns of twenty healthy subjects were captured during five walking trials (at least 5 minutes) on a treadmill under different weight perturbation conditions. The collected time series were cut into multiple shorter time series of varying lengths and subsequently concatenated using a novel algorithm that identifies similar poses in successive time series in order to determine an optimal concatenation time point. After alignment of the datasets, the approach then concatenated the data to provide a smooth transition. Nonlinear measures to assess stability (Largest Lyapunov Exponent, LyE) and regularity (Sample Entropy, SE) were calculated in order to quantify the efficacy of the concatenation approach using intra-class correlation coefficients, standard error of measurement and paired effect sizes. Our results indicate overall good agreement between the full uninterrupted and the concatenated time series for LyE. However, SE was more sensitive to the proposed concatenation algorithm and might lead to false interpretation of physiological gait signals. This approach opens perspectives for analysis of dynamic stability of gait data from physiological overground walking protocols, but also the re-processing and estimation of nonlinear metrics from previously collected datasets

Consistent estimator of ex-post covariation of discretely observed diffusion processes and its application to high frequency financial time series

First chapter of my thesis reviews recent developments in the theory and practice of volatility measurement. We review the basic theoretical framework and describe the main approaches to volatility measurement in continuous time. In this literature the central parameter of interest is the integrated variance and its multivariate counterpart. We describe the measurement of these parameters under ideal circumstances and when the data are subject to measurement error, microstructure issues. We also describe some common applications of this literature. In the second chapter, we propose a new estimator of multivariate ex-post volatility that is robust to microstructure noise and asynchronous data timing. The method is based on Fourier domain techniques. The advantage of this method is that it does not require an explicit time alignment, unlike existing methods in the literature. We derive the large sample properties of our estimator under general assumptions allowing for the number of sample points for different assets to be of different order of magnitude. We show in extensive simulations that our method outperforms the time domain estimator especially when two assets are traded very asynchronously and with different liquidity. In the third chapter, we propose to model high frequency price series by a timedeformed L´evy process. The deformation function is modeled by a piecewise linear function of a physical time with a slope depending on the marks associated with intra-day transaction data. The performance of a quasi-MLE and an estimator based on a permutation-like statistic is examined in extensive simulations. We also consider estimating the deformation function nonparametrically by pulling together many time series. We show that financial returns spaced by equal elapse of estimated deformed time are homogenous. We propose an order execution strategy using the fitted deformation tim

The development of high-speed PIV techniques and their application to jet noise measurement

This thesis describes the design, development and deployment of a high-speed jet flow measurement system. The apparatus was created in response to the need to collect a large quantity of statistically-converged aerodynamic data from a series of commercial turbofan engine models. This acquisition was performed in conjunction with acoustic measurements as part of the ED CoJeN project to investigate jet noise production, and associated noise reduction techniques. Particle Image Velocimetry is a well established flow measurement technique, but its application outside of the laboratory can be limited by a relatively low sample rate and' the need to operate in a hostile environment. This thesis presents a multiple camera technique - used as the basis for the j et measurement system - that is capable of acquiring both time-series PIV data at MHz rates, and continuous, statistically independent measurements at up to 14 Hz. The resultant PIV measurement rig was therefore capable of acquiring time-averaged velocity and turbulence data from the whole of a 110 scale coaxial engine exhaust plume (down to 4m or 20D) in no more than 1 hour. The -500aC Mach:5 0.9 jets were also scanned volumetrically in order to check the spatial alignment of the nozzle and flow streams,.and all PIV measurements were synchronised to simultaneous LDA acquisition, thus enabling the data to be validated. Finally, the cameras were used to acquire novel6-frame time-series data at:5 330 kHz, which was used to calculate time-space correlations within the exhaust. By providing a highly automated and completely remote-controlled system, the exhaust measurements could be repeated over 3 operating conditions and 2 nozzle geometries, thereby providing a comprehensive description of the flow field. The data, having been systematically post-processed, has been shown to agree well with concurrent measurements, and it will now be used to validate CFD models of coaxial jet flow. By improving the quality of computational flow prediction in this way, the time taken to design and test quieter jet engines will be significantly reduced

A general state-based temporal pattern recognition

Time-series and state-sequences are ubiquitous patterns in temporal logic and are widely used to present temporal data in data mining. Generally speaking, there are three known choices for the time primitive: points, intervals, points and intervals. In this thesis, a formal characterization of time-series and state-sequences is presented for both complete and incomplete situations, where a state-sequence is defined as a list of sequential data validated on the corresponding time-series. In addition, subsequence matching is addressed to associate the state-sequences, where both non-temporal aspects as well as rich temporal aspects including temporal order, temporal duration and temporal gap should be taken into account. Firstly, based on the typed point based time-elements and time-series, a formal characterization of time-series and state-sequences is introduced for both complete and incomplete situations, where a state-sequence is defined as a list of sequential data validated on the corresponding time-series. A time-series is formalized as a tetrad (T, R, Tdur, Tgap), which denotes: the temporal order of time- elements; the temporal relationship between time-elements; the temporal duration of each time-element and the temporal gap between each adjacent pair of time-elements respectively. Secondly, benefiting from the formal characterization of time-series and state-sequences, a general similarity measurement (GSM) that takes into account both non-temporal and rich temporal information, including temporal order as well as temporal duration and temporal gap, is introduced for subsequence matching. This measurement is general enough to subsume most of the popular existing measurements as special cases. In particular, a new conception of temporal common subsequence is proposed. Furthermore, a new LCS-based algorithm named Optimal Temporal Common Subsequence (OTCS), which takes into account rich temporal information, is designed. The experimental results on 6 benchmark datasets demonstrate the effectiveness and robustness of GSM and its new case OTCS. Compared with binary-value distance measurements, GSM can distinguish between the distance caused by different states in the same operation; compared with the real-penalty distance measurements, it can filter out the noise that may push the similarity into abnormal levels. Finally, two case studies are investigated for temporal pattern recognition: basketball zone-defence detection and video copy detection. In the case of basketball zone-defence detection, the computational technique and algorithm for detecting zone-defence patterns from basketball videos is introduced, where the Laplacian Matrix-based algorithm is extended to take into account the effects from zoom and single defender‘s translation in zone-defence graph matching and a set of character-angle based features was proposed to describe the zone-defence graph. The experimental results show that the approach explored is useful in helping the coach of the defensive side check whether the players are keeping to the correct zone-defence strategy, as well as detecting the strategy of the opponent side. It can describe the structure relationship between defender-lines for basketball zone-defence, and has a robust performance in both simulation and real-life applications, especially when disturbances exist. In the case of video copy detection, a framework for subsequence matching is introduced. A hybrid similarity framework addressing both non-temporal and temporal relationships between state-sequences, represented by bipartite graphs, is proposed. The experimental results using real-life video databases demonstrated that the proposed similarity framework is robust to states alignment with different numbers and different values, and various reordering including inversion and crossover

Smart Align—a new tool for robust non-rigid registration of scanning microscope data

AbstractMany microscopic investigations of materials may benefit from the recording of multiple successive images. This can include techniques common to several types of microscopy such as frame averaging to improve signal-to-noise ratios (SNR) or time series to study dynamic processes or more specific applications. In the scanning transmission electron microscope, this might include focal series for optical sectioning or aberration measurement, beam damage studies or camera-length series to study the effects of strain; whilst in the scanning tunnelling microscope, this might include bias-voltage series to probe local electronic structure. Whatever the application, such investigations must begin with the careful alignment of these data stacks, an operation that is not always trivial. In addition, the presence of low-frequency scanning distortions can introduce intra-image shifts to the data. Here, we describe an improved automated method of performing non-rigid registration customised for the challenges unique to scanned microscope data specifically addressing the issues of low-SNR data, images containing a large proportion of crystalline material and/or local features of interest such as dislocations or edges. Careful attention has been paid to artefact testing of the non-rigid registration method used, and the importance of this registration for the quantitative interpretation of feature intensities and positions is evaluated.</jats:p

An assessment of the precision and confidence of aquatic eddy correlation measurements

The quantification of benthic fluxes with the aquatic eddy correlation (EC) technique is based on simultaneous measurement of the current velocity and a targeted bottom water parameter (e. g., O-2, temperature). High-frequency measurements (64Hz) are performed at a single point above the seafloor using an acoustic Doppler velocimeter (ADV) and a fast-responding sensor. The advantages of aquatic EC technique are that 1) it is noninvasive, 2) it integrates fluxes over a large area, and 3) it accounts for in situ hydrodynamics. The aquatic EC has gained acceptance as a powerful technique; however, an accurate assessment of the errors introduced by the spatial alignment of velocity and water constituent measurements and by their different response times is still needed. Here, this paper discusses uncertainties and biases in the data treatment based on oxygen EC flux measurements in a large-scale flume facility with well-constrained hydrodynamics. These observations are used to review data processing procedures and to recommend improved deployment methods, thus improving the precision, reliability, and confidence of EC measurements. Specifically, this study demonstrates that 1) the alignment of the time series based on maximum cross correlation improved the precision of EC flux estimations; 2) an oxygen sensor with a response time of <0.4 s facilitates accurate EC fluxes estimates in turbulence regimes corresponding to horizontal velocities <11 cm s(-1); and 3) the smallest possible distance (<1 cm) between the oxygen sensor and the ADV's sampling volume is important for accurate EC flux estimates, especially when the flow direction is perpendicular to the sensor's orientation

Oxidized polyethylene films for orienting polar molecules for linear dichroism spectroscopy

Stretched polyethylene (PE) films have been used to orient small molecules for decades by depositing solutions on their surface and allowing the solvent to evaporate leaving the analyte absorbed on the polymer film. However, the non-polar hydrophobic nature of PE is an obstacle to aligning polar molecules and biological samples. In this work PE film was treated with oxygen plasma in order to increase surface hydrophilicity. Different treatment conditions were evaluated using contact angle measurement and X-ray photoelectron spectroscopy. Treated PE (PEOX) films are shown to be able to align molecules of different polarities including progesterone, 1-pyrenecarboxaldehyde, 4′,6-diamidino-2-phenylindole (DAPI) and anthracene. The degree of alignment of each molecule was studied by running series of linear dichroism (LD) experiments and the polarizations of electronic transition moments were determined. For the first time optimal conditions (such as stretching factor and concentration of the sample) for stretched film LD were determined. PEOX aligning ability was compared to that of normal PE films. Progesterone showed a slightly better alignment on PEOX than PE. 1-Pyrenecarboxaldehyde oriented differently on the two different films which enabled transition moment assignment for this low symmetry molecule. DAPI (which does not align on PE) aligned well on PEOX and enabled us to obtain better LD data than had previously been collected with polyvinyl alcohol. Anthracene alignment and formation of dimers and higher order structures were studied in much more detail than previously possible, showing a variety of assemblies on PE and PEOX films

A network model of interpersonal alignment in dialog

In dyadic communication, both interlocutors adapt to each other linguistically, that is, they align interpersonally. In this article, we develop a framework for modeling interpersonal alignment in terms of the structural similarity of the interlocutors’ dialog lexica. This is done by means of so-called two-layer time-aligned network series, that is, a time-adjusted graph model. The graph model is partitioned into two layers, so that the interlocutors’ lexica are captured as subgraphs of an encompassing dialog graph. Each constituent network of the series is updated utterance-wise. Thus, both the inherent bipartition of dyadic conversations and their gradual development are modeled. The notion of alignment is then operationalized within a quantitative model of structure formation based on the mutual information of the subgraphs that represent the interlocutor’s dialog lexica. By adapting and further developing several models of complex network theory, we show that dialog lexica evolve as a novel class of graphs that have not been considered before in the area of complex (linguistic) networks. Additionally, we show that our framework allows for classifying dialogs according to their alignment status. To the best of our knowledge, this is the first approach to measuring alignment in communication that explores the similarities of graph-like cognitive representations. Keywords: alignment in communication; structural coupling; linguistic networks; graph distance measures; mutual information of graphs; quantitative network analysi

Noise considerations when determining phase of large-signal microwave measurements

Advances in microwave instrumentation now make it feasible to accurately measure not only the magnitude spectrum, but also the phase spectrum of wide-bandwidth signals. In a practical measurement, the spectrum is measured over a finite window of time. The phase spectrum is related to the position of this window, causing the spectrum to differ between measurements of an identical waveform. It is difficult to compare multiple measurements with different window positions or to incorporate them into a model. Several methods have been proposed for determining the phase spectrum such that multiple measurements can be effectively compared and utilized in models. The methods are reviewed in terms of the information required to determine the phase and compared in terms of their robustness in the presence of measurement noise