Publications
Radar-optimized wind turbine siting
January 1, 2022
Journal Article
Published in:
IEEE Trans. Sustain. Energy, Vol. 13, No. 1, January 2022, pp. 403-13.
Summary
A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's orientation relative to a radar site, the impacts on that radar can be minimized. The proposed changes to wind farm siting are relatively minor and do not have a significant effect on wind turbine density. With proper optimization of radar clutter mitigation, radar tracking performance above such wind farms can be significantly increased. Both present-day and potential future or upgraded radar systems are analyzed. The reduction in radar performance due to wind turbine clutter is approximately halved using this method. The developed method is robust with respect to controlled variations in wind turbine placement caused by potential obstructions.
Summary
A method for analyzing wind turbine-radar interference is presented. A model is used to derive layouts for siting wind turbines that reduces their impact on radar systems, potentially allowing for increased wind turbine development near radar sites. By choosing a specific wind turbine grid stagger based on a wind farm's...
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AI-enabled, ultrasound-guided handheld robotic device for femoral vascular access
December 18, 2021
Journal Article
Published in:
Biosensors, Vol. 11, No. 12, 18 December 2021, 522.
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Summary
Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions. However, central access is normally performed by highly experienced critical care physicians in a hospital setting. We developed a handheld AI-enabled interventional device, AI-GUIDE (Artificial Intelligence Guided Ultrasound Interventional Device), capable of directing users with no ultrasound or interventional expertise to catheterize a deep blood vessel, with an initial focus on the femoral vein. AI-GUIDE integrates with widely available commercial portable ultrasound systems and guides a user in ultrasound probe localization, venous puncture-point localization, and needle insertion. The system performs vascular puncture robotically and incorporates a preloaded guidewire to facilitate the Seldinger technique of catheter insertion. Results from tissue-mimicking phantom and porcine studies under normotensive and hypotensive conditions provide evidence of the technique's robustness, with key performance metrics in a live porcine model including: a mean time to acquire femoral vein insertion point of 53 plus or minus 36 s (5 users with varying experience, in 20 trials), a total time to insert catheter of 80 plus or minus 30 s (1 user, in 6 trials), and a mean number of 1.1 (normotensive, 39 trials) and 1.3 (hypotensive, 55 trials) needle insertion attempts (1 user). These performance metrics in a porcine model are consistent with those for experienced medical providers performing central vascular access on humans in a hospital.
Summary
Hemorrhage is a leading cause of trauma death, particularly in prehospital environments when evacuation is delayed. Obtaining central vascular access to a deep artery or vein is important for administration of emergency drugs and analgesics, and rapid replacement of blood volume, as well as invasive sensing and emerging life-saving interventions...
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Keeping Safe Rust safe with Galeed
December 6, 2021
Conference Paper
Published in:
Annual Computer Security Applications Conf., ACSAC, December 2021, pp. 824-36.
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Summary
Rust is a programming language that simultaneously offers high performance and strong security guarantees. Safe Rust (i.e., Rust code that does not use the unsafe keyword) is memory and type safe. However, these guarantees are violated when safe Rust interacts with unsafe code, most notably code written in other programming languages, including in legacy C/C++ applications that are incrementally deploying Rust. This is a significant problem as major applications such as Firefox, Chrome, AWS, Windows, and Linux have either deployed Rust or are exploring doing so. It is important to emphasize that unsafe code is not only unsafe itself, but also it breaks the safety guarantees of ‘safe’ Rust; e.g., a dangling pointer in a linked C/C++ library can access and overwrite memory allocated to Rust even when the Rust code is fully safe. This paper presents Galeed, a technique to keep safe Rust safe from interference from unsafe code. Galeed has two components: a runtime defense to prevent unintended interactions between safe Rust and unsafe code and a sanitizer to secure intended interactions. The runtime component works by isolating Rust’s heap from any external access and is enforced using Intel Memory Protection Key (MPK) technology. The sanitizer uses a smart data structure that we call pseudo-pointer along with automated code transformation to avoid passing raw pointers across safe/unsafe boundaries during intended interactions (e.g., when Rust and C++ code exchange data). We implement and evaluate the effectiveness and performance of Galeed via micro- and macro-benchmarking, and use it to secure a widely used component of Firefox.
Summary
Rust is a programming language that simultaneously offers high performance and strong security guarantees. Safe Rust (i.e., Rust code that does not use the unsafe keyword) is memory and type safe. However, these guarantees are violated when safe Rust interacts with unsafe code, most notably code written in other programming...
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CoSPA data product description
November 1, 2021
Project Report
Published in:
MIT Lincoln Laboratory Report ATC-449
Summary
This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically expressed as rectangular arrays whose elements contain a data value coinciding with uniformly-spaced observations or computed results on a 2-D surface. Gridded data arrays map to the earth's surface through a map projection, for example, Lambert Conformal or Lambert Azimuthal Equal-Area. CoSPA generates only gridded products; there are no non-gridded data for CoSPA.
Summary
This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically...
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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...
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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.
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...
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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...
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Unsupervised Bayesian adaptation of PLDA for speaker verification
August 30, 2021
Conference Paper
Published in:
Interspeech, 30 August - 3 September 2021.
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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...
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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...
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Demand and capacity modeling for advanced air mobility
August 2, 2021
Conference Paper
Published in:
AIAA Aviation 2021 Conf., 2-6 August 2021.
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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...
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