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Capacity bounds for frequency-hopped BPSK

Published in:
2021 IEEE Military Communications Conf., MILCOM, 29 November - 2 December 2021.

Summary

In some channels, such as the frequency-hop channel, the transmission may undergo abrupt transitions in phase. This can require the receiver to re-estimate the phase on each hop, or for the system to utilize modulation techniques that lend themselves to noncoherent detection. How well the receiver can estimate the phase depends on the channel signal-to-noise ratio and how long phase coherence can be assumed. Although prior work has shown that using any reference symbols to aid the phase estimation process is suboptimal with respect to capacity, their presence may be useful in practice as they can simplify the receiver processing. In this paper, the effects of per-pulse phase uncertainty are examined for systems using binary modulation. Both the fraction of the transmission that may be devoted to reference symbols without substantially reducing the overall channel capacity and the point at which it is better to forego coherent processing in favor of noncoherent demodulation are examined.
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Summary

In some channels, such as the frequency-hop channel, the transmission may undergo abrupt transitions in phase. This can require the receiver to re-estimate the phase on each hop, or for the system to utilize modulation techniques that lend themselves to noncoherent detection. How well the receiver can estimate the phase...

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Application of complex split-activation feedforward networks to beamforming

Published in:
55th Asilomar Conf. on Signals, Systems and Computers, ACSSC, 31 October - 3 November 2021.

Summary

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real sequences. In this paper, neural networks using complex numbers to represent the complex baseband RF signals are considered. A complex backpropagation approach that calculates gradients and a Jacobian, allows for fast optimization of the neural networks. Through simulations, it is shown that complex neural networks require less training samples than their real counterparts and may generalize better in dynamic environments.
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Summary

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real...

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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates

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.
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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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Using oculomotor features to predict changes in optic nerve sheath diameter and ImPACT scores from contact-sport athletes

Summary

There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential for oculomotor features to complement existing diagnostic tools, such as measurements of Optic Nerve Sheath Diameter (ONSD) and Immediate Post-concussion Assessment and Cognitive Testing (ImPACT). Thirty-one high school American football and soccer athletes were tracked through the course of a sports season. Given the high risk of repetitive head impacts associated with both soccer and football, our hypotheses were that (1) ONSD and ImPACT scores would worsen through the season and (2) oculomotor features would effectively capture both neurophysiological changes reflected by ONSD and neuro-functional status assessed via ImPACT. Oculomotor features were used as input to Linear Mixed-Effects Regression models to predict ONSD and ImPACT scores as outcomes. Prediction accuracy was evaluated to identify explicit relationships between eye movements, ONSD, and ImPACT scores. Significant Pearson correlations were observed between predicted and actual outcomes for ONSD (Raw = 0.70; Normalized = 0.45) and for ImPACT (Raw = 0.86; Normalized = 0.71), demonstrating the capability of oculomotor features to capture neurological changes detected by both ONSD and ImPACT. The most predictive features were found to relate to motor control and visual-motor processing. In future work, oculomotor models, linking neural structures to oculomotor function, can be built to gain extended mechanistic insights into neurophysiological changes observed through seasons of participation in contact sports.
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Summary

There is mounting evidence linking the cumulative effects of repetitive head impacts to neuro-degenerative conditions. Robust clinical assessment tools to identify mild traumatic brain injuries are needed to assist with timely diagnosis for return-to-field decisions and appropriately guide rehabilitation. The focus of the present study is to investigate the potential...

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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

Summary

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with highresolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.
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Summary

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states...

READ MORE

Detecting virus exposure during the pre-symptomatic incubation period using physiological data

Summary

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Using high-resolution physiological data from non-human primate studies of Ebola and Marburg viruses, we pre-processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm. In most subjects detection is achieved well before the onset of fever; subject cross-validation lead to 52±14h mean early detection (at >0.90 area under the receiver-operating characteristic curve). Cross-cohort tests across pathogens and exposure routes also lead to successful early detection (28±16h and 43±22h, respectively). We discuss which physiological indicators are most informative for early detection and options for extending this capability to lower data resolution and wearable, non-invasive sensors.
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Summary

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning method to better detect asymptomatic states during...

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Distributed multi-modal sensor system for searching a foliage-covered region

Summary

We designed and constructed a system that includes aircraft, ground vehicles, and throwable sensors to search a semiforested region that was partially covered by foliage. The system contained 4 radio-controlled (RC) trucks, 2 aircraft, and 30 SensorMotes (throwable sensors). We also investigated communications links, search strategies, and system architecture. Our system is designed to be low-cost, contain a variety of sensors, and distributed so that the system is robust even if individual components are lost.
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Summary

We designed and constructed a system that includes aircraft, ground vehicles, and throwable sensors to search a semiforested region that was partially covered by foliage. The system contained 4 radio-controlled (RC) trucks, 2 aircraft, and 30 SensorMotes (throwable sensors). We also investigated communications links, search strategies, and system architecture. Our...

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Circuit-fed tile-approach configuration for millimeter-wave spatial power combining

Published in:
IEEE Trans. Microw. Theory Tech., Vol. 50, No. 1, Part 1, January 2002, pp. 17-21.

Summary

In this paper, a circuit-fed spatially combined transmitter array is described for operation at 44 GHz. The array contains 256 elements where each element consists of a monolithic-microwave integrated-circuit amplifier and a circularly polarized microchip patch antenna. The array is constructed using 16-element tile-approach subarrays. Each subarray is a two RF-level (three-dimensional) multichip module containing integrated microstrip patch antennas. The basic construction of the transmitter array resembles tile-approach phased arrays; however, the implementation has been tailored for the power-combining application. The peak performance at 43.5 GHz is equivalent isotropic radiated power of 40.6 dBW (11570 W), effective transmitted power (Peff) of 5.9 W, dc-to-RF efficiency of 7.3%, and system gain of 35 dB.
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Summary

In this paper, a circuit-fed spatially combined transmitter array is described for operation at 44 GHz. The array contains 256 elements where each element consists of a monolithic-microwave integrated-circuit amplifier and a circularly polarized microchip patch antenna. The array is constructed using 16-element tile-approach subarrays. Each subarray is a two...

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Comparison of two flat reflector-type designs for dual-polarization, dual-band operation

Published in:
IEEE Antennas and Propagation Society Int. Synp. 2001 Digest, Vol. 2, 8-13 July 2001, pp. 288-291.

Summary

The parabolic reflector remains an essential antenna for high-gain applications. This is a result of its desirable characteristics based on geometric optics. These include relative frequency independence for sufficiently large apertures and high aperture efficiency. However, the parabolic reflector occupies a large volume. This may be aesthetically unappealing on the sides of buildings and structures. Also, from a mobile user perspective, a desirable characteristic is having a large aperture during operation while having a small volume when packed away and not in use. The parabolic reflector is typically constructed of multiple petals for mobile uses, but it does not pack into as small a volume as a flat, thin antenna would due to the curvature of the paraboloid. Therefore, the primary goal of the antennas studied in this work is developing flat reflector antennas to utilize the advantages of large reflector apertures while remaining capable of packing into a small volume. In addition, system requiremenls dictated dual-band, dual-polarized operation. Two flat reflectors are compared: a reflectarray and a zoned reflector. While each design is inherently narrow-band, methods of achieving dual-band operation were employed.
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Summary

The parabolic reflector remains an essential antenna for high-gain applications. This is a result of its desirable characteristics based on geometric optics. These include relative frequency independence for sufficiently large apertures and high aperture efficiency. However, the parabolic reflector occupies a large volume. This may be aesthetically unappealing on the...

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Accurate modeling of dual dipole and slot elements used with photomixers for coherent terahertz output power

Summary

Accurate circuit models derived from electromagnetic simulations have been used to fabricate photomixer sources with optimized high-impedance antennas. Output powers on the order of 1 uW were measured for various designs spanning 0.6-2.7 THz. The improvement in output power ranged from 3 to 10 dB over more conventionally designed photomixers using broad-band log-spiral antennas. Measured data on single dipoles, twin dipoles, and twin slots are in good agreement with the characteristics predicted by the design simulations.
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Summary

Accurate circuit models derived from electromagnetic simulations have been used to fabricate photomixer sources with optimized high-impedance antennas. Output powers on the order of 1 uW were measured for various designs spanning 0.6-2.7 THz. The improvement in output power ranged from 3 to 10 dB over more conventionally designed photomixers...

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