PyCBC Live: Rapid Detection of Gravitational Waves from Compact Binary Mergers

We introduce an efficient and straightforward technique for rapidly detecting gravitational waves from compact binary mergers. We show that this method achieves the low latencies required to alert electromagnetic partners of candidate binary mergers, aids in data monitoring, and makes use of multidetector networks for sky localization. This approach was instrumental to the analysis of gravitational-wave candidates during the second observing run of Advanced LIGO, including the period of coincident operation with Advanced Virgo, and in particular the analysis of the first observed binary neutron star merger GW170817, where it led to the first tightly localized sky map ($31~\mathrm{deg}^2$) used to identify AT 2017gfo. Operation of this analysis also enabled the initial discovery of GW170104 and GW170608 despite non-nominal observing of the instrument.Comment: 10 pages, 5 figures, submitted to Physical Review

Smart Sensing in Advanced Manufacturing Processes: Statistical Modeling and Implementations for Quality Assurance and Automation

With recent breakthroughs in sensing technology, data informatics and computer networks, smart manufacturing with intertwined advanced computation, communication and control techniques promotes the transformation of conventional discrete manufacturing processes into the new paradigm of cyber-physical manufacturing systems. The cybermanufacturing systems should be predictive and instantly responsive to incident prevention for quality assurance. Thus, providing viable in-process monitoring approaches for real-time quality assurance is one essential research topic in cybermanufacturing system to allow a closed-loop control of the processes, ensure the quality of products, and consequently improve the whole shop floor efficiency. However, thus far, such in-process monitoring tools are still underdeveloped on the following counts: • For precision/ultraprecision machining processes, most sensor-based change detection approaches are reticent to the anomalies since they largely root in the stationary assumption whilst the underlying dynamics under precision machining processes exhibit intermittent patterns. Therefore, existing approaches are feeble to detect subtle variations which are detrimental to the process; • For shaping processes that realize complicated geometries, currently there is no viable tool to allow a noncontact monitoring on surface morphology evolution that measures critical dimensioning criteria in real time. • For precision machining processes, we aim to present advanced smart sensing approaches towards characterizations of the process, specifically, microdynamics reflecting the fundamental cutting mechanisms as well as variations of microstructure of the material surfaces. To address these gaps, this dissertation achieves the following contributions: • For precision and ultraprecision machining processes, an in-situ anomaly detection approach is provided which allows instant prevention from surface deterioration. The method could be applied to various (ultra)precision processes of which most underlying systems are unknow and always exhibit intermittency. Extensive experimental studies suggest that the developed model can detect in-situ anomalies of the underlying dynamic intermittency; • For shaping processes that require noncontact in-process monitoring, a vision-based monitoring approach is presented which rapidly measures the geometric features during forming process on sheet-based workpieces. Investigations into laser origami sheet forming processes suggest that the presented approach can provide precise geometric measurements as feedback in real time for the control loop of the sheeting forming processes in cybermanufacturing systems. • As for smart sensing for precision machining, an advanced in-process sensing/ monitoring approach [including implementations of Acoustic Emission (AE) sensor, the associated data acquisition system and developed advanced machine/deep learning methods] is introduced to connect the AE characteristics to microdynamics of the precision machining of natural fiber reinforced composites. The presented smart sensing framework shows potentials towards real-time estimations/predictions of microdynamics of the machining processes using AE features

Photodetectors

In this book some recent advances in development of photodetectors and photodetection systems for specific applications are included. In the first section of the book nine different types of photodetectors and their characteristics are presented. Next, some theoretical aspects and simulations are discussed. The last eight chapters are devoted to the development of photodetection systems for imaging, particle size analysis, transfers of time, measurement of vibrations, magnetic field, polarization of light, and particle energy. The book is addressed to students, engineers, and researchers working in the field of photonics and advanced technologies

Advanced vision based vehicle classification for traffic surveillance system using neural networks

Master's thesis in Cybernetics and signal processingThis master thesis focus on traffic monitoring, which are of importance to fulfill planning and traffic management of road networks. An important requirement is data interpretation accuracy to provide adequate characteristic data from the acquired vision-data. A vision-based system has been developed, using new methods and technologies to achieve an automated traffic monitoring system, without the use of additional sensors. The thesis is based upon Erik Sudland’s master thesis from 2016, which investigated available litterateur containing adequate algorithms for traffic monitoring. However in the current master thesis, methods have been further analyzed and experimentally optimized on vision-data from real traffic situations. In addition, a new classification method based upon neural networks has been implemented and verified with successful result

Photoplethysmography-Based Biomedical Signal Processing

In this dissertation, photoplethysmography-based biomedical signal processing methods are developed and analyzed. The developed methods solve problems concerning the estimation of the heart rate during physical activity and the monitoring of cardiovascular health. For the estimation of heart rate during physical activity, two methods are presented that are very accurate in estimating the instantaneous heart rate at the wrist and, at the same time, are computationally efficient so that they can easily be integrated into wearables. In the context of cardiovascular health monitoring, a method for the detection of atrial fibrillation using the video camera of a smartphone is proposed that achieves a high detection rate of atrial fibrillation (AF) on a clinical pre-study data set. Further monitoring of cardiovascular parameters includes the estimation of blood pressure (BP), pulse wave velocity (PWV), and vascular age index (VAI), for which an approach is presented that requires only a single photoplethysmographic (PPG) signal. Heart rate estimation during physical activity using PPG signals constitutes an important research focus of this thesis. In this work, two computationally efficient algorithms are presented that estimate the heart rate from two PPG signals using a three axis accelerometer. In the first approach, adaptive filters are applied to estimate motion artifacts that severely deteriorate the signal quality. The non-stationary relationship between the measured acceleration signals and the artifacts is modeled as a linear system. The outputs of the adaptive filters are combined to further enhance the signal quality and a constrained heart rate tracker follows the most probable high energy continuous line in the spectral domain. The second approach is modest in computational complexity and very fast in execution compared to existing approaches. It combines correlation-based fundamental frequency indicating functions and spectral combination to enhance the correlated useful signal and suppress uncorrelated noise. Additional harmonic noise damping further reduces the impact of strong motion artifacts and a spectral tracking procedure uses a linear least squares prediction. Both approaches are modest in computational complexity and especially the second approach is very fast in execution, as it is shown on a widely used benchmark data set and compared to state-of-the-art methods. The second research focus and a further major contribution of this thesis lies in the monitoring of the cardiovascular health with a single PPG signal. Two methods are presented, one for detection of AF and one for the estimation of BP, PWV, and VAI. The first method is able to detect AF based on a smartphone filming the finger placed on the video camera. The algorithm transforms the video into a PPG signal and extracts features which are then used to discriminate between AF and normal sinus rhythm (NSR). Perfect detection of AF is already achieved on a data set of 326 measurements (including 20 with AF) that were taken at a clinical pre-study using an appropriate pair of features whereby a decision is formed through a simple linear decision equation. The second method aims at estimating cardiovascular parameters from a single PPG signal without the conventional use of an additional electrocardiogram (ECG). The proposed method extracts a large number of features from the PPG signal and its first and second order difference series, and reconstructs missing features by the use of matrix completion. The estimation of cardiovascular parameters is based on a nonlinear support vector regression (SVR) estimator and compared to single channel PPG based estimators using a linear regression model and a pulse arrival time (PAT) based method. If the training data set contains the person for whom the cardiovascular parameters are to be determined, the proposed method can provide an accurate estimate without further calibration. All proposed algorithms are applied to real data that we have either recorded ourselves in our biomedical laboratory, that have been recorded by a clinical research partner, or that are freely available as benchmark data sets

Technology for the Future: In-Space Technology Experiments Program, part 2

The purpose of the Office of Aeronautics and Space Technology (OAST) In-Space Technology Experiments Program In-STEP 1988 Workshop was to identify and prioritize technologies that are critical for future national space programs and require validation in the space environment, and review current NASA (In-Reach) and industry/ university (Out-Reach) experiments. A prioritized list of the critical technology needs was developed for the following eight disciplines: structures; environmental effects; power systems and thermal management; fluid management and propulsion systems; automation and robotics; sensors and information systems; in-space systems; and humans in space. This is part two of two parts and contains the critical technology presentations for the eight theme elements and a summary listing of critical space technology needs for each theme

Research and Technology 1996: Innovation in Time and Space

As the NASA Center responsible for assembly, checkout, servicing, launch, recovery, and operational support of Space Transportation System elements and payloads, the John F. Kennedy Space Center is placing increasing emphasis on its advanced technology development program. This program encompasses the efforts of the Engineering Development Directorate laboratories, most of the KSC operations contractors, academia, and selected commercial industries - all working in a team effort within their own areas of expertise. This edition of the Kennedy Space Center Research and Technology 1996 Annual Report covers efforts of all these contributors to the KSC advanced technology development program, as well as our technology transfer activities