A Novel Framework for Real-time Traffic Flow Parameter Estimation from Aerial Videos

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

Electrodynamic heart model construction and ECG simulation

Objectives: In this paper, we present a unified electrodynamic heart model that permits simulations of the body surface potentials generated by the heart in motion. The inclusion of motion in the heart model significantly improves the accuracy of the simulated body surface potentials and therefore also the 12-lead ECG. Methods: The key step is to construct an electromechanical heart model. The cardiac excitation propagation is simulated by an electrical heart model, and the resulting cardiac active forces are used to calculate the ventricular wall motion based on a mechanical model. The source-field point relative position changes during heart systole and diastole. These can be obtained, and then used to calculate body surface ECG based on the electrical heart-torso model. Results: An electromechanical biventricular heart model is constructed and a standard 12-lead ECG is simulated. Compared with a simulated ECG based on the static electrical heart model, the simulated ECG based on the dynamic heart model is more accordant with a clinically recorded ECG, especially for the ST segment and T wave of a V1-V6 lead ECG. For slight-degree myocardial ischemia ECG simulation, the ST segment and T wave changes can be observed from the simulated ECG based on a dynamic heart model, while the ST segment and T wave of simulated ECG based on a static heart model is almost unchanged when compared with a normal ECG. Conclusions: This study confirms the importance of the mechanical factor in the ECG simulation. The dynamic heart model could provide more accurate ECG simulation, especially for myocardial ischemia or infarction simulation, since the main ECG changes occur at the ST segment and T wave, which correspond with cardiac systole and diastole phases.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

A comparison of microphone and speech recognition engine efficacy for mobile data entry

The research presented in this paper is part of an ongoing investigation into how best to incorporate speech-based input within mobile data collection applications. In our previous work [1], we evaluated the ability of a single speech recognition engine to support accurate, mobile, speech-based data input. Here, we build on our previous research to compare the achievable speaker-independent accuracy rates of a variety of speech recognition engines; we also consider the relative effectiveness of different speech recognition engine and microphone pairings in terms of their ability to support accurate text entry under realistic mobile conditions of use. Our intent is to provide some initial empirical data derived from mobile, user-based evaluations to support technological decisions faced by developers of mobile applications that would benefit from, or require, speech-based data entry facilities.

Into the Bends of Time and Musical Forces in Jazz: Group Interaction and Double-time in “My Foolish Heart” as performed by the Bill Evans Trio with Scott LaFaro and Paul Motian.

Into the Bends of Time is a 40-minute work in seven movements for a large chamber orchestra with electronics, utilizing real-time computer-assisted processing of music performed by live musicians. The piece explores various combinations of interactive relationships between players and electronics, ranging from relatively basic processing effects to musical gestures achieved through stages of computer analysis, in which resulting sounds are crafted according to parameters of the incoming musical material. Additionally, some elements of interaction are multi-dimensional, in that they rely on the participation of two or more performers fulfilling distinct roles in the interactive process with the computer in order to generate musical material. Through processes of controlled randomness, several electronic effects induce elements of chance into their realization so that no two performances of this work are exactly alike. The piece gets its name from the notion that real-time computer-assisted processing, in which sound pressure waves are transduced into electrical energy, converted to digital data, artfully modified, converted back into electrical energy and transduced into sound waves, represents a “bending” of time.

The Bill Evans Trio featuring bassist Scott LaFaro and drummer Paul Motian is widely regarded as one of the most important and influential piano trios in the history of jazz, lauded for its unparalleled level of group interaction. Most analyses of Bill Evans’ recordings, however, focus on his playing alone and fail to take group interaction into account. This paper examines one performance in particular, of Victor Young’s “My Foolish Heart” as recorded in a live performance by the Bill Evans Trio in 1961. In Part One, I discuss Steve Larson’s theory of musical forces (expanded by Robert S. Hatten) and its applicability to jazz performance. I examine other recordings of ballads by this same trio in order to draw observations about normative ballad performance practice. I discuss meter and phrase structure and show how the relationship between the two is fixed in a formal structure of repeated choruses. I then develop a model of perpetual motion based on the musical forces inherent in this structure. In Part Two, I offer a full transcription and close analysis of “My Foolish Heart,” showing how elements of group interaction work with and against the musical forces inherent in the model of perpetual motion to achieve an unconventional, dynamic use of double-time. I explore the concept of a unified agential persona and discuss its role in imparting the song’s inherent rhetorical tension to the instrumental musical discourse.

Engineering thin films of magnetic alloys and semiconductor oxides at the nanoscale

The thesis aims to exploit properties of thin films for applications such as spintronics, UV detection and gas sensing. Nanoscale thin films devices have myriad advantages and compatibility with Si-based integrated circuits processes. Two distinct classes of material systems are investigated, namely ferromagnetic thin films and semiconductor oxides. To aid the designing of devices, the surface properties of the thin films were investigated by using electron and photon characterization techniques including Auger electron spectroscopy (AES), X-ray photoelectron spectroscopy (XPS), grazing incidence X-ray diffraction (GIXRD), and energy-dispersive X-ray spectroscopy (EDS). These are complemented by nanometer resolved local proximal probes such as atomic force microscopy (AFM), magnetic force microscopy (MFM), electric force microscopy (EFM), and scanning tunneling microscopy to elucidate the interplay between stoichiometry, morphology, chemical states, crystallization, magnetism, optical transparency, and electronic properties. Specifically, I studied the effect of annealing on the surface stoichiometry of the CoFeB/Cu system by in-situ AES and discovered that magnetic nanoparticles with controllable areal density can be produced. This is a good alternative for producing nanoparticles using a maskless process. Additionally, I studied the behavior of magnetic domain walls of the low coercivity alloy CoFeB patterned nanowires. MFM measurement with the in-plane magnetic field showed that, compared to their permalloy counterparts, CoFeB nanowires require a much smaller magnetization switching field , making them promising for low-power-consumption domain wall motion based devices. With oxides, I studied CuO nanoparticles on SnO2 based UV photodetectors (PDs), and discovered that they promote the responsivity by facilitating charge transfer with the formed nanoheterojunctions. I also demonstrated UV PDs with spectrally tunable photoresponse with the bandgap engineered ZnMgO. The bandgap of the alloyed ZnMgO thin films was tailored by varying the Mg contents and AES was demonstrated as a surface scientific approach to assess the alloying of ZnMgO. With gas sensors, I discovered the rf-sputtered anatase-TiO2 thin films for a selective and sensitive NO2 detection at room temperature, under UV illumination. The implementation of UV enhances the responsivity, response and recovery rate of the TiO2 sensor towards NO2 significantly. Evident from the high resolution XPS and AFM studies, the surface contamination and morphology of the thin films degrade the gas sensing response. I also demonstrated that surface additive metal nanoparticles on thin films can improve the response and the selectivity of oxide based sensors. I employed nanometer-scale scanning probe microscopy to study a novel gas senor scheme consisting of gallium nitride (GaN) nanowires with functionalizing oxides layer. The results suggested that AFM together with EFM is capable of discriminating low-conductive materials at the nanoscale, providing a nondestructive method to quantitatively relate sensing response to the surface morphology.

Quantification of variable palmar ligaments around the triquetrum-hamate joint determined by lunate type

The ligaments of the wrist are highly variable and poorly described, which is more obvious on the ulnar side of the wrist. Previous studies highlighted the potential differences within the ligaments of the wrist but no consensus has been reached. Poor tissue description and inconsistent use of terminology hindered the reproducibility of the results. Improved understanding of the morphological variations between carpal bones may facilitate improved understanding of the ligamentous structure within the wrist. This study aims to identify the potential variations between carpal bones that could be used to separate palmar ligamentous patterns around the triquetrum-hamate joint into subgroups within the sample population. Investigations were performed following a detailed nomenclature and a clear definition of ligamentous structures to facilitate detailed description and reproducible results. Quantitative analyses were conducted using 3D modelling technique. Histological sections were then analysed to identify the structure of each ligamentous attachment. Variable patterns of ligamentous attachments were identified. Differences were not only obvious between samples but also between the right and left hands of the same person. These identifications suggested that the palmar ligamentous patterns around the triquetrum-hamate joint are best described as a spectrum with a higher affinity of the triquetrum-hamate-capitate ligament and the lunate-triquetrum ligament to be associated with type I lunate wrists on one extreme and type II lunate wrists with the palmar triquetrum-hamate ligament, triquetrum-hamate-capitate ligament and palmar radius-lunate-triquetrum ligament attachments at the other extreme. Histological analyses confirmed pervious established work regarding the mechanical role of ligaments in wrist joint biomechanics. Also, there were no significant differences between the quantitative data obtained from the Genelyn-embalmed and unembalmed specimens (p>0.05). The current study demonstrated variable ligamentous patterns that suggest different bone restraints and two different patterns of motion. These findings support previous suggestions regarding separating the midcarpal joint into two distinct functional types. Type I wrists were identified with ligamentous attachments that are suggestive of rotating/translating hamate whilst type II wrists identified with ligamentous attachments that are suggestive of flexing/extending hamate motion based upon the patterns of the ligamentous attachments in relation to the morphological features of the underlying lunate type of the wrist. This opens the horizon for particular consideration and/or modification of surgical procedures, which may enhance the clinical management of wrist dysfunction.

Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments

Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.

Directly training spiking neural networks for cyber-physical systems: from supervised to reinforcement learning

Spiking Neural Networks (SNNs) are bio-inspired Artificial Neural Networks (ANNs) utilizing discrete spiking signals, akin to neuron communication in the brain, making them ideal for real-time and energy-efficient Cyber-Physical Systems (CPSs). This thesis explores their potential in Structural Health Monitoring (SHM), leveraging low-cost MEMS accelerometers for early damage detection in motorway bridges. The study focuses on Long Short-Term SNNs (LSNNs), although their complex learning processes pose challenges. Comparing LSNNs with other ANN models and training algorithms for SHM, findings indicate LSNNs' effectiveness in damage identification, comparable to ANNs trained using traditional methods. Additionally, an optimized embedded LSNN implementation demonstrates a 54% reduction in execution time, but with longer pre-processing due to spike-based encoding. Furthermore, SNNs are applied in UAV obstacle avoidance, trained directly using a Reinforcement Learning (RL) algorithm with event-based input from a Dynamic Vision Sensor (DVS). Performance evaluation against Convolutional Neural Networks (CNNs) highlights SNNs' superior energy efficiency, showing a 6x decrease in energy consumption. The study also investigates embedded SNN implementations' latency and throughput in real-world deployments, emphasizing their potential for energy-efficient monitoring systems. This research contributes to advancing SHM and UAV obstacle avoidance through SNNs' efficient information processing and decision-making capabilities within CPS domains.

Seismic motion simulation based on Cassino Parallel Manipulator

In this paper we present a study of feasibility by using Cassino Parallel Manipulator (CaPaMan) as an earthquake simulator. We propose a suitable formulation to simulate the frequency, amplitude and acceleration magnitude of seismic motion by means of the movable platform motion by giving a suitable input motion. In this paper we have reported numerical simulations that simulate the three principal earthquake types for a seismic motion: one at the epicenter (having a vertical motion), another far from the epicenter (with the motion on a horizontal plane), and a combined general motion (with a vertical and horizontal motion).

Design energy input spectra for high seismicity regions based on Turkish registers

This work proposes design energy spectra in terms of an equivalent velocity, intended for regions with design peak acceleration 0.3 g or higher. These spectra were derived through linear and nonlinear dynamic analyses on a number of selected Turkish strong ground motion records. In the long and mid period ranges the analyses are linear, given the relative insensitivity of the spectra to structural parameters other than the fundamental period; conversely, in the short period range, the spectra are more sensitive to the structural parameters and, hence, nonlinear analyses are required. The selected records are classified in eight groups with respect to soil type (stiff or soft soil), the severity of the earthquake in terms of surface magnitude Ms(Ms≤ 5.5 and Ms> 5.5) and the relevance of the near-source effects (impulsive or vibratory). For each of these groups, median and characteristic spectra are proposed; such levels would respectively correspond to 50 and 95 % percentiles. These spectra have an initial linear growing branch in the short period range, a horizontal branch in the mid period range and a descending branch in the long period range. Empirical criteria for estimating the hysteretic energy from the input energy are suggested. The proposed design spectra are compared with those obtained from other studies.

Self-tuning algorithm for dsp-based motion control in cnc applications

This thesis describes the development of an adaptive control algorithm for Computerized Numerical Control (CNC) machines implemented in a multi-axis motion control board based on the TMS320C31 DSP chip. The adaptive process involves two stages: Plant Modeling and Inverse Control Application. The first stage builds a non-recursive model of the CNC system (plant) using the Least-Mean-Square (LMS) algorithm. The second stage consists of the definition of a recursive structure (the controller) that implements an inverse model of the plant by using the coefficients of the model in an algorithm called Forward-Time Calculation (FTC). In this way, when the inverse controller is implemented in series with the plant, it will pre-compensate for the modification that the original plant introduces in the input signal. The performance of this solution was verified at three different levels: Software simulation, implementation in a set of isolated motor-encoder pairs and implementation in a real CNC machine. The use of the adaptive inverse controller effectively improved the step response of the system in all three levels. In the simulation, an ideal response was obtained. In the motor-encoder test, the rise time was reduced by as much as 80%, without overshoot, in some cases. Even with the larger mass of the actual CNC machine, decrease of the rise time and elimination of the overshoot were obtained in most cases. These results lead to the conclusion that the adaptive inverse controller is a viable approach to position control in CNC machinery.

The reliability of video based time motion analysis

Time motion analysis is extensively used to assess the demands of team sports. At present there is only limited information on the reliability of measurements using this analysis tool. The aim of this study was to establish the reliability of an individual observer's time motion analysis of rugby union. Ten elite level rugby players were individually tracked in Southern Hemisphere Super 12 matches using a digital video camera. The video footage was subsequently analysed by a single researcher on two occasions one month apart. The test-retest reliability was quantified as the typical error of measurement (TEM) and rated as either good (10% TEM). The total time spent in the individual movements of walking, jogging, striding, sprinting, static exertion and being stationary had moderate to poor reliability (5.8-11.1% TEM). The frequency of individual movements had good to poor reliability (4.3-13.6% TEM), while the mean duration of individual movements had moderate reliability (7.1-9.3% TEM). For the individual observer in the present investigation, time motion analysis was shown to be moderately reliable as an evaluation tool for examining the movement patterns of players in competitive rugby. These reliability values should be considered when assessing the movement patterns of rugby players within competition.

FMRI 3D registration based on Fourier space subsets using neural networks

In this work, we present a neural network (NN) based method designed for 3D rigid-body registration of FMRI time series, which relies on a limited number of Fourier coefficients of the images to be aligned. These coefficients, which are comprised in a small cubic neighborhood located at the first octant of a 3D Fourier space (including the DC component), are then fed into six NN during the learning stage. Each NN yields the estimates of a registration parameter. The proposed method was assessed for 3D rigid-body transformations, using DC neighborhoods of different sizes. The mean absolute registration errors are of approximately 0.030 mm in translations and 0.030 deg in rotations, for the typical motion amplitudes encountered in FMRI studies. The construction of the training set and the learning stage are fast requiring, respectively, 90 s and 1 to 12 s, depending on the number of input and hidden units of the NN. We believe that NN-based approaches to the problem of FMRI registration can be of great interest in the future. For instance, NN relying on limited K-space data (possibly in navigation echoes) can be a valid solution to the problem of prospective (in frame) FMRI registration.