Publications
Network performance of pLEO topologies in a high-inclination Walker Delta Satellite Constellation
March 4, 2023
Conference Paper
Published in:
IEEE Aerospace Conf. Proc., 4-11 March 2023, 188722.
Summary
Low-earth-orbit satellite constellations with hundreds to thousands of satellites are emerging as practical alternatives for providing various types of data services such as global networking and large-scale sensing. The network performance of these satellite constellations is strongly dependent on the topology of the inter-satellite links (ISLs) in such systems. This paper studies the effects of six different ISL topologies, coupled with three configurations of ground relay terminals, on path failure rate, path latency, and link transmission efficiency in an example highly-inclined Walker Delta constellation with 360 satellites. These network performance parameters are calculated in the presence of satellite failures in the constellation. Trade-offs between ISL connection density and overall performance are examined and quantified. Topologies with 4 active ISLs per satellite are shown to perform significantly better than topologies requiring fewer, especially as the average number of active ISLs per satellite becomes significantly less than three. Latencies for a topology requiring 3 active ISLs per satellite are shown to be between 15 and 60% higher than for a 4-ISL reference topology. Path availabilities for the 3-ISL topology are shown to be on the order of 30% lower for a benchmark case of 10 satellite failures. The performance of near-minimal topologies (e.g., an average of 2.2 active ISLs per satellite) is much worse. Latency reductions of 10-30% and path failure rate improvements on the order of 45% are shown to be obtainable by the inclusion of 2 to 5 strategically located ground relay stations
Summary
Low-earth-orbit satellite constellations with hundreds to thousands of satellites are emerging as practical alternatives for providing various types of data services such as global networking and large-scale sensing. The network performance of these satellite constellations is strongly dependent on the topology of the inter-satellite links (ISLs) in such systems. This...
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Development of 3D-Printed Individualized Vascular Phantoms for Artificial Intelligence (AI) Enabled Interventional Device Testing
March 1, 2023
Abstract
Published in:
Journal of Vascular and Interventional Radiology 34 (3), S12.
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We developed vascular phantoms mapped from human subjects to test AI-enabled ultrasound-guided vascular cannulation. Translational device prototyping necessitates anatomically accurate models. Commercial phantoms fail to address anatomic variability. Uniformity leads to optimistic AI model and operator performance. Individualized 3D-printed vascular phantoms yield anatomically correct models optimized for AI-device testing.
Summary
We developed vascular phantoms mapped from human subjects to test AI-enabled ultrasound-guided vascular cannulation. Translational device prototyping necessitates anatomically accurate models. Commercial phantoms fail to address anatomic variability. Uniformity leads to optimistic AI model and operator performance. Individualized 3D-printed vascular phantoms yield anatomically correct models optimized for AI-device testing.
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Automated exposure notification for COVID-19
February 14, 2023
Technical Report
Published in:
MIT Lincoln Laboratory Report TR-1288
Summary
Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT's mission was to enhance contact tracing in pandemic response by designing exposure-detection functions in personal digital communication devices that have maximal public health utility while preserving privacy. This report explains and discusses the use of automated exposure notification during the COVID-19 pandemic and to provide some recommendations for those who may try to design and deploy similar technologies in future pandemics.
Summary
Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT's mission was to enhance contact tracing in pandemic response by designing exposure-detection functions in personal digital communication devices that have maximal public health utility while preserving privacy...
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A generative approach to condition-aware score calibration for speaker verification
February 8, 2023
Journal Article
Published in:
IEEE/ACM Trans. Audio, Speech, Language Process., Vol. 31, 2023, pp. 891-901.
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In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative approach to condition-aware score calibration. It proposes a novel generative model for speaker verification trials, each which includes a trial score, a trial label, and the associated pair of speaker embeddings. Trials are assumed to be drawn from a discrete set of underlying signal conditions which are modeled as latent Categorical random variables, so that trial scores and speaker embeddings are drawn from condition-dependent distributions. An Expectation-Maximization (EM) Algorithm for parameter estimation of the proposed model is presented, which does not require condition labels and instead discovers relevant conditions in an unsupervised manner. The generative condition-aware (GCA) calibration transform is then derived as the log-likelihood ratio of a verification score given the observed pair of embeddings. Experimental results show the proposed approach to provide performance improvements on a variety of speaker verification tasks, outperforming static and condition-aware baseline calibration methods. GCA calibration is observed to improve the discriminative ability of the speaker verification system, as well as provide good calibration performance across a range of operating points. The benefits of the proposed method are observed for task-dependent models where signal conditions are known, for universal models which are robust across a range of conditions, and when facing unseen signal conditions.
Summary
In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative...
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Dissociating COVID-19 from other respiratory infections based on acoustic, motor coordination, and phonemic patterns
January 28, 2023
Journal Article
Published in:
Sci. Rep., Vol. 13, No. 1, January 2023, 1567.
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In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based and standard acoustic features extracted from recordings of simple speech tasks could aid in detecting the presence of COVID-19. We further hypothesized that these features would aid in characterizing the effect of COVID-19 on speech production systems. A protocol, consisting of a variety of speech tasks, was administered to 12 individuals with COVID-19 and 15 individuals with other viral infections at University Hospital Galway. From these recordings, we extracted a set of acoustic time series representative of speech production subsystems, as well as their univariate statistics. The time series were further utilized to derive correlation-based features, a proxy for speech production motor coordination. We additionally extracted phoneme-based features. These features were used to create machine learning models to distinguish between the COVID-19 positive and other viral infection groups, with respiratory- and laryngeal-based features resulting in the highest performance. Coordination-based features derived from harmonic-to-noise ratio time series from read speech discriminated between the two groups with an area under the ROC curve (AUC) of 0.94. A longitudinal case study of two subjects, one from each group, revealed differences in laryngeal based acoustic features, consistent with observed physiological differences between the two groups. The results from this analysis highlight the promise of using nonintrusive sensing through simple speech recordings for early warning and tracking of COVID-19.
Summary
In the face of the global pandemic caused by the disease COVID-19, researchers have increasingly turned to simple measures to detect and monitor the presence of the disease in individuals at home. We sought to determine if measures of neuromotor coordination, derived from acoustic time series, as well as phoneme-based...
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An emotion-driven vocal biomarker-based PTSD screening tool
January 1, 2023
Journal Article
Published in:
IEEE Open J. Eng. Med. Biol., early access, 2023.
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This paper introduces an automated post-traumatic stress disorder (PTSD) screening tool that could potentially be used as a self-assessment or inserted into routine medical visits for PTSD diagnosis and treatment. Methods: With an emotion estimation algorithm providing arousal (excited to calm) and valence (pleasure to displeasure) levels through discourse, we select regions of the acoustic signal that are most salient for PTSD detection. Our algorithm was tested on a subset of data from the DVBIC-TBICoE TBI Study, which contains PTSD Check List Civilian (PCL-C) assessment scores. Results: Speech from low-arousal and positive-valence regions provide the best discrimination for PTSD. Our model achieved an AUC (area under the curve) equal to 0.80 in detecting PCL-C ratings, outperforming models with no emotion filtering (AUC = 0.68). Conclusions: This result suggests that emotion drives the selection of the most salient temporal regions of an audio recording for PTSD detection.
Summary
This paper introduces an automated post-traumatic stress disorder (PTSD) screening tool that could potentially be used as a self-assessment or inserted into routine medical visits for PTSD diagnosis and treatment. Methods: With an emotion estimation algorithm providing arousal (excited to calm) and valence (pleasure to displeasure) levels through discourse, we...
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Noninvasive monitoring of simulated hemorrhage and whole blood resuscitation
December 14, 2022
Journal Article
Published in:
Biosensors, Vol. 12, No. 12, 2022, Art. No. 1168.
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Summary
Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators because of physiological mechanisms that compensate for blood loss and thus do not provide an accurate assessment of volume status. As an alternative, machine learning (ML) algorithms that operate on an arterial blood pressure (ABP) waveform have been shown to provide an effective early indicator. However, these ML approaches lack physiological interpretability. In this paper, we evaluate and compare the performance of ML models trained on nine ABP-derived features that provide physiological insight, using a database of 13 human subjects from a lower-body negative pressure (LBNP) model of progressive central hypovolemia and subsequent progressive restoration to normovolemia (i.e., simulated hemorrhage and whole blood resuscitation). Data were acquired at multiple repressurization rates for each subject to simulate varying resuscitation rates, resulting in 52 total LBNP collections. This work is the first to use a single ABP-based algorithm to monitor both simulated hemorrhage and resuscitation. A gradient-boosted regression tree model trained on only the half-rise to dicrotic notch (HRDN) feature achieved a root-mean-square error (RMSE) of 13%, an R2 of 0.82, and area under the receiver operating characteristic curve of 0.97 for detecting decompensation. This single-feature model's performance compares favorably to previously reported results from more-complex black box machine learning models. This model further provides physiological insight because HRDN represents an approximate measure of the delay between the ABP ejected and reflected wave and therefore is an indication of cardiac and peripheral vascular mechanisms that contribute to the compensatory response to blood loss and replacement.
Summary
Hemorrhage is the leading cause of preventable death from trauma. Accurate monitoring of hemorrhage and resuscitation can significantly reduce mortality and morbidity but remains a challenge due to the low sensitivity of traditional vital signs in detecting blood loss and possible hemorrhagic shock. Vital signs are not reliable early indicators...
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Rate control with autoregressive forecasting for high frequency communication
November 28, 2022
Conference Paper
Published in:
2022 IEEE Military Communications Conf., MILCOM, 28 November - 2 December 2022.
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Summary
This work introduces a data-driven framework for rate control and applies it to high frequency (HF) communication systems that propagate via the Earth’s ionosphere. The rate control approach uses statistical techniques to forecast channel state with an autoregressive (AR) model, which has previously been applied to different forms of wireless fading, including "medium" timescale fading at HF. The objective of rate control is to maximize the data rate while constraining the rate of packets decoded in error. We show that under ideal assumptions, the rate controller selects the rate by backing off from the forecast average signal-to-noise ratio (SNR) by a factor of sigmaQ^-1(Beta), where sigma^2 correlates with fading variance, Beta denotes a constraint on decoder errors, and Q(.) is the complementary cumulative distribution function of the Gaussian distribution. Simulation results on an HF channel model show that compared with naive schemes, AR forecasting provides a good balance between achieving high rate and ensuring reliability.
Summary
This work introduces a data-driven framework for rate control and applies it to high frequency (HF) communication systems that propagate via the Earth’s ionosphere. The rate control approach uses statistical techniques to forecast channel state with an autoregressive (AR) model, which has previously been applied to different forms of wireless...
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Contingent routing using orbital geometry in proliferated low-earth-orbit satellite networks
November 28, 2022
Conference Paper
Published in:
2022 IEEE Military Communications Conf., MILCOM, 28 November - 2 December 2022.
Summary
Optimum adaptive routing in proliferated low-earth-orbit (pLEO) satellite networks requires intensive computation. The very small size, light weight, and low power of individual satellites in such networks makes a centralized, terrestrial, SDN-like approach to routing computation an attractive solution. However, it is highly desirable to have a distributed backup routing capability onboard each satellite that can maintain service if the central computational node(s) fail or lose their pathway(s) to upload routing data frequently to each satellite. This paper presents a routing algorithm based on orbital geometry that has a very low computation and storage requirements and is suitable as a backup routing capability in the event of failure of a centralized routing calculation node or nodes. Path failure rate, path latency, and link resource usage are simulated for a 360-satellite Walker Delta constellation with 4 inter-satellite link (ISL) terminals per satellite, and with up to 10% of the satellites having failed. For the fully intact satellite constellation, path failure rate is zero (identical to a shortest path routing algorithm), while mean latency and average link resource usage are shown to be approximately 12% and 13% higher, respectively, than with shortest path routing. With 10 random satellite failures in the constellation, the geometric algorithm has a path failure rate of less than 0.5%, while the mean latency and link resource usage are approximately 12% and 16% higher, respectively, than with shortest path routing.
Summary
Optimum adaptive routing in proliferated low-earth-orbit (pLEO) satellite networks requires intensive computation. The very small size, light weight, and low power of individual satellites in such networks makes a centralized, terrestrial, SDN-like approach to routing computation an attractive solution. However, it is highly desirable to have a distributed backup routing...
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Backdoor poisoning of encrypted traffic classifiers
November 28, 2022
Conference Paper
Published in:
IEEE Intl. Conf. Data Mining Workshops, ICDMW, 28 Nov. - 1 Dec. 2022.
Summary
Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks against an encrypted network traffic classifier. We consider attacks based on padding network packets, which has the benefit of preserving the functionality of the network traffic. In particular, we consider a handcrafted attack, as well as an optimized attack leveraging universal adversarial perturbations. We find that poisoning attacks can be extremely successful if the adversary has the ability to modify both the labels and the data (dirty label attacks) and somewhat successful, depending on the attack strength and the target class, if the adversary perturbs only the data (clean label attacks).
Summary
Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks...
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