Publications
Benchmarking the processing of aircraft tracks with triples mode and self-scheduling
September 20, 2021
Conference Paper
Published in:
2021 IEEE High Performance Extreme Computing Conf., HPEC, 20-24 September 2021.
Summary
As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of Monte Carlo collision risk analysis simulations using probabilistic models of aircraft flight. To train these models, high performance computing resources are required. We've prototyped a high performance computing workflow designed and deployed on the Lincoln Laboratory Supercomputing Center to process billions of observations of aircraft. However, the prototype has various computational and storage bottlenecks that limited rapid or more comprehensive analyses and models. In response, we've developed a novel workflow to take advantage of various job launch and task distribution technologies to improve performance. The workflow was benchmarked using two datasets of observations of aircraft, including a new dataset focused on the environment around aerodromes. Optimizing how the workflow was parallelized drastically reduced the execution time from weeks to days.
Summary
As unmanned aircraft systems (UASs) continue to integrate into the U.S. National Airspace System (NAS), there is a need to quantify the risk of airborne collisions between unmanned and manned aircraft to support regulation and standards development. Developing and certifying collision avoidance systems often rely on the extensive use of...
READ MORE
Benefits of realist ontologies to systems engineering
September 11, 2021
Conference Paper
Published in:
Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO 2021, 11-18 September 2021.
Topic:
R&D area:
R&D group:
Summary
Applied ontologies have been used more and more frequently to enhance systems engineering. In this paper, we argue that adopting principles of ontological realism can increase the benefits that ontologies have already been shown to provide to the systems engineering process. Moreover, adopting Basic Formal Ontology (BFO), an ISO standard for top-level ontologies from which more domain specific ontologies are constructed, can lead to benefits in four distinct areas of systems engineering: (1) interoperability, (2) standardization, (3) testing, and (4) data exploitation. Reaping these benefits in a model-based systems engineering (MBSE) context requires utilizing an ontology's vocabulary when modeling systems and entities within those systems. If the chosen ontology abides by the principles of ontological realism, a semantic standard capable of uniting distinct domains, using BFO as a hub, can be leveraged to promote greater interoperability among systems. As interoperability and standardization increase, so does the ability to collect data during the testing and implementation of systems. These data can then be reasoned over by computational reasoners using the logical axioms within the ontology. This, in turn, generates new data that would have been impossible or too inefficient to generate without the aid of computational reasoners.
Summary
Applied ontologies have been used more and more frequently to enhance systems engineering. In this paper, we argue that adopting principles of ontological realism can increase the benefits that ontologies have already been shown to provide to the systems engineering process. Moreover, adopting Basic Formal Ontology (BFO), an ISO standard...
READ MORE
Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates
September 3, 2021
Journal Article
Published in:
Front Physiol., Vol. 12, September 2021, Article 691074.
Summary
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
Summary
Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First...
READ MORE
Unsupervised Bayesian adaptation of PLDA for speaker verification
August 30, 2021
Conference Paper
Published in:
Interspeech, 30 August - 3 September 2021.
Topic:
R&D area:
Summary
This paper presents a Bayesian framework for unsupervised domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting class labels as latent random variables, Variational Bayes (VB) is used to derive a maximum a posterior (MAP) solution of the adapted PLDA model when labels are missing, referred to as VB-MAP. The VB solution iteratively infers class labels and updates PLDA hyperparameters, offering a systematic framework for dealing with unlabeled data. While presented as a general solution, this paper includes experimental results for domain adaptation in speaker verification. VBMAP estimation is applied to the 2016 and 2018 NIST Speaker Recognition Evaluations (SREs), both of which included small and unlabeled in-domain data sets, and is shown to provide performance improvements over a variety of state-of-the-art domain adaptation methods. Additionally, VB-MAP estimation is used to train a fully unsupervised PLDA model, suffering only minor performance degradation relative to conventional supervised training, offering promise for training PLDA models when no relevant labeled data exists.
Summary
This paper presents a Bayesian framework for unsupervised domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting class labels as latent random variables, Variational Bayes (VB) is used to derive a maximum a posterior (MAP) solution of the adapted PLDA model when labels are missing, referred to as VB-MAP...
READ MORE
A neural network estimation of ankle torques from electromyography and accelerometry
August 26, 2021
Journal Article
Published in:
IEEE Trans. Neural Syst. Rehabilitation Eng., Vol. 29, 2021, pp. 1624-33.
Summary
Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque estimates and sequences of torque predictions from motion capture and ground reaction forces to wearable sensor data using several modern types of neural networks. We use dense feedforward, convolutional, neural ordinary differential equation, and long short-term memory neural networks to learn the mapping for ankle plantarflexion and dorsiflexion torque during standing,walking, running, and sprinting, and consider both single-point torque estimation, as well as the prediction of a sequence of future torques. Our results show that long short-term memory neural networks, which consider incoming data sequentially, outperform dense feedforward, neural ordinary differential equation networks, and convolutional neural networks. Predictions of future ankle torques up to 0.4 s ahead also showed strong positive correlations with the actual torques. The proposed method relies on learning from a motion capture dataset, but once the model is built, the method uses wearable sensors that enable torque estimation without the motion capture data.
Summary
Estimations of human joint torques can provide clinically valuable information to inform patient care, plan therapy, and assess the design of wearable robotic devices. Predicting joint torques into the future can also be useful for anticipatory robot control design. In this work, we present a method of mapping joint torque...
READ MORE
Geographic source estimation using airborne plant environmental DNA in dust
August 10, 2021
Journal Article
Published in:
Scientific Reports
Topic:
R&D area:
R&D group:
Summary
Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis of dust components, we developed a pipeline that utilizes the airborne plant environmental DNA (eDNA) in settled dust to estimate geographic origin. Metabarcoding of settled airborne eDNA was used to identify plant species whose geographic distributions were then derived from occurrence records in the USGS Biodiversity in Service of Our Nation (BISON) database. The distributions for all plant species identified in a sample were used to generate a probabilistic estimate of the sample source. With settled dust collected at four U.S. sites over a 15-month period, we demonstrated positive regional geolocation (within 600 km2 of the collection point) with 47.6% (20 of 42) of the samples analyzed. Attribution accuracy and resolution was dependent on the number of plant species identified in a dust sample, which was greatly affected by the season of collection. In dust samples that yielded a minimum of 20 identified plant species, positive regional attribution was achieved with 66.7% (16 of 24 samples). For broader demonstration, citizen-collected dust samples collected from 31 diverse U.S. sites were analyzed, and trace plant eDNA provided relevant regional attribution information on provenance in 32.2% of samples. This showed that analysis of airborne plant eDNA in settled dust can provide an accurate estimate regional provenance within the U.S., and relevant forensic information, for a substantial fraction of samples analyzed.
Summary
Information obtained from the analysis of dust, particularly biological particles such as pollen, plant parts, and fungal spores, has great utility in forensic geolocation. As an alternative to manual microscopic analysis of dust components, we developed a pipeline that utilizes the airborne plant environmental DNA (eDNA) in settled dust to...
READ MORE
Demand and capacity modeling for advanced air mobility
August 2, 2021
Conference Paper
Published in:
AIAA Aviation 2021 Conf., 2-6 August 2021.
Topic:
R&D area:
Summary
Advanced Air Mobility encompasses emerging aviation technologies that transport people and cargo between local, regional, or urban locations that are currently underserved by aviation and other transportation modalities. The disruptive nature of these technologies has pushed industry, academia, and governments to devote significant investments to understand their impact on airspace risk, operational procedures, and passengers. A flexible framework was designed to assess the operational viability of these technologies and the sensitivity to a variety of assumptions. This framework is used to simulate air taxi traffic within New York City by replacing a portion of the city's taxi requests with trips taken with electric vertical takeoff and landing vehicles and evaluate the sensitivity of passenger trip time to a variety of system wide assumptions. In particular, the paper focuses on the impact of the passenger capacity, landing site vehicle capacity, and fleet size. The operation density is then compared with the current air traffic to assess operation constraints that will challenge the network UAM operations.
Summary
Advanced Air Mobility encompasses emerging aviation technologies that transport people and cargo between local, regional, or urban locations that are currently underserved by aviation and other transportation modalities. The disruptive nature of these technologies has pushed industry, academia, and governments to devote significant investments to understand their impact on airspace...
READ MORE
Near-term regional climate change over Bangladesh
July 5, 2021
Journal Article
Published in:
Clim. Dyn., Vol. 57, July 2021, pp. 3055-73.
Summary
Bangladesh stands out as a climate change hot spot due to its unique geography, climate, high population density, and limited adaptation capacity. Mounting evidence suggests that the country is already suffering from the effects of climate change which may get worse without aggressive action. Here, we use an ensemble of high-resolution (10 km) regional climate model simulations to project near-term change in climate extremes, mainly heat waves and intense rainfall, for the period (2021–2050). Near-term climate projections represent a valuable input for designing sound adaptation policies. Our climate projections suggest that heatwaves will become more frequent and severe in Bangladesh under the business-as-usual scenario (RCP8.5). In particular, extremes of wet-bulb temperature (a temperature and humidity metric important in evaluating humid heat stress) in the western part of Bangladesh including Bogra, Ishurdi, and Jessore are likely to exceed the extreme danger threshold (according to U.S. National Weather Service criterion), which has rarely been observed in the current climate. The return periods of extreme heat waves are also significantly shortened across the country. In addition, country-averaged rainfall is projected to increase by about 6% during the summer months, with the largest increases (above 10%) in the eastern mountainous areas, such as Sylhet and Chittagong. Meanwhile, insignificant changes in extreme rainfall are simulated. Our results suggest that Bangladesh is particularly susceptible
Summary
Bangladesh stands out as a climate change hot spot due to its unique geography, climate, high population density, and limited adaptation capacity. Mounting evidence suggests that the country is already suffering from the effects of climate change which may get worse without aggressive action. Here, we use an ensemble of...
READ MORE
Applicability and surrogacy of uncorrelated airspace encounter models at low altitudes
July 1, 2021
Journal Article
Published in:
J. Air Transport., Vol. 29, No. 3, July-September 2021, pp. 137-41.
Topic:
R&D area:
R&D group:
Summary
National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments and flight testing. For example, an update to the Traffic Collision Avoidance System (TCAS) mandated for manned aircraft was validated in part using this approach [1]. For many aviation safety studies, manned aircraft behavior is represented using the MIT Lincoln Laboratory statistical encounter models [2–5]. The original models [2–4] were developed from 2008 to 2013 to support safety simulations for altitudes above 500 feet above ground level (AGL). However, these models were not sufficient to assess the safety of smaller unmanned aerial systems (UAS) operations below 500 feet AGL and fully support the ASTM F38 and RTCA SC-147 standards efforts. In response, newer models [5–7] with altitude floors below 500 feet AGL have been in development since 2018. Many of the models assume that aircraft behavior is uncorrelated and not dependent on air traffic services or nearby aircraft. The models were trained using observations of cooperative aircraft equipped with transponders, but data sources and assumptions vary. The newer models are organized by aircraft types of fixed-wing multi-engine, fixed-wing single engine, and rotorcraft, whereas the original models do not consider aircraft type. Our research objective was to compare the various uncorrelated models of conventional aircraft and identify how the models differ. Particularly if models of rotorcraft were sufficiently different from models of fixed-wing aircraft to require type-specific models. The scope of this work was limited to altitudes below 5000 feet AGL, the expected altitude ceiling for many new airspace entrants. The scope was also informed by the Federal Aviation Administration (FAA) UAS Integration Office and Alliance for System Safety of UAS through Research Excellence (ASSURE). The primary contribution is guidance on which uncorrelated models to leverage when evaluating the performance of a collision avoidance system designed for low-altitude operations, such as prescribed by the ASTM F3442 detect and avoid standard for smaller UAS [8]. We also address which models can be surrogates for non-cooperative aircraft without transponders. All models and software used are publicly available under open source licenses [9].
Summary
National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the...
READ MORE
The need for spectrum and the impact on weather observations
July 1, 2021
Journal Article
Published in:
Bull. Am. Meteorol. Soc., Vol. 102, No. 7, July 2021, E1402-7.
Topic:
Summary
One of the most significant challenges—and potential opportunities—for the scientific community is society's insatiable need for the radio spectrum. Wireless communication systems have profoundly impacted the world's economies and its inhabitants. Newer technological uses in telemedicine, Internet of Things, streaming services, intelligent transportation, etc., are driving the rapid development of 5G/6G (and beyond) wireless systems that demand ever-increasing bandwidth and performance. Without question, these wireless technologies provide an important benefit to society with the potential to mitigate the economic divide across the world. Fundamental science drives the development of future technologies and benefits society through an improved understanding of the world in which we live. Often, these studies require use of the radio spectrum, which can lead to an adversarial relationship between ever evolving technology commercialization and the quest for scientific understanding. Nowhere is this contention more acute than with atmospheric remote sensing and associated weather forecasts (Saltikoff et al. 2016; Witze 2019), which was the theme for the virtual Workshop on Spectrum Challenges and Opportunities for Weather Observations held in November 2020 and hosted by the University of Oklahoma. The workshop focused on spectrum challenges for remote sensing observations of the atmosphere, including active (e.g., weather radars, cloud radars) and passive (e.g., microwave imagers, radiometers) systems for both spaceborne and ground-based applications. These systems produce data that are crucial for weather forecasting—we chose to primarily limit the workshop scope to forecasts up to 14 days, although some observations (e.g., satellite) cover a broader range of temporal scales. Nearly 70 participants from the United States, Europe, South America, and Asia took part in a concentrated and intense discussion focused not only on current radio frequency interference (RFI) issues, but potential cooperative uses of the spectrum ("spectrum sharing"). Equally important to the workshop's international makeup, participants also represented different sectors of the community, including academia, industry, and government organizations. Given the importance of spectrum challenges to the future of scientific endeavor, the U.S. National Science Foundation (NSF) recently began the Spectrum Innovation Initiative (SII) program, which has a goal to synergistically grow 5G/6G technologies with crucial scientific needs for spectrum as an integral part of the design process. The SII program will accomplish this goal in part through establishing the first nationwide institute focused on 5G/6G technologies and science. The University of California, San Diego (UCSD), is leading an effort to compete for NSF SII funding to establish the National Center for Wireless Spectrum Research. As key partners in this effort, the University of Oklahoma (OU) and The Pennsylvania State University (PSU) hosted this workshop to bring together intellectual leaders with a focus on impacts of the spectrum revolution on weather observations and numerical weather prediction.
Summary
One of the most significant challenges—and potential opportunities—for the scientific community is society's insatiable need for the radio spectrum. Wireless communication systems have profoundly impacted the world's economies and its inhabitants. Newer technological uses in telemedicine, Internet of Things, streaming services, intelligent transportation, etc., are driving the rapid development of...
READ MORE