Search

Airplane produced contrail cirrus has a greenhouse effect in our atmosphere. It has an effect that is as much as or more than any other airplane emission such as airplane produced CO2 Karcher et al. 2018. With air traffic anticipated to increase into the future it is important to understand contrail cirrus formation. We present a new receiver instrument using the 424 GHz (O2) and 448 GHz (H2O) emission lines for thermal and humidity profiling that will provide data to model airplane contrail formation. Such an instrument can be used for guiding aircraft flight paths to reduce the development of contrail cirrus that can have an immediate greenhouse effect in our atmosphere.

A geostationary microwave sounder, capable of providing continuous monitoring of temperature, water vapor, clouds, precipitation, and wind in the presence of clouds and precipitation is now feasible. A design called the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR) has been developed at the Jet Propulsion Laboratory, and the required new technology has been developed and is sufficiently mature that a space mission can be initiated. GeoSTAR can be thought of as “AMSU in GEO,” i.e., it has capabilities in geostationary earth orbit (GEO) similar to those of microwave sounders currently operating in low earth orbit. Having such a capability in GEO will add tremendously to our ability to observe dynamic atmospheric phenomena, such as hurricanes and severe storms, monsoonal moisture flow, and atmospheric rivers. GeoSTAR will make measurements every 15 min or less instead of every 12 h and cover a large portion of the Earth continuously instead of with snapshots in a narrow swath. By tracking water vapor patterns, it is also possible to derive atmospheric wind speed and direction at altitudes from the surface to 10–15 km. All of this can be done regardless of cloud cover and weather conditions. During the latter half of 2020, a detailed study of GeoSTAR and its projected performance was undertaken as one of several such studies commissioned by the National Oceanic and Atmospheric Administration (NOAA) for the purpose of configuring NOAA's next generation of earth environmental satellite systems. We present a summary of our findings, including instrument characteristics, measurement accuracy and precision, and expected impact on weather prediction and applications.

The lived environment, particularly when proximal to roadways, is filled with multi-digit and multi-numbered values corresponding to advertised commodities. The reliable detection of single multi-digit values from natural imagery has been widely studied through the last decade (e.g. Street View House Numbers [SVHN]); however, extraction and assignment of the contextual meaning of those values are far more difficult given the diversity and unstructured nature of advertisements. To operationalize information extracted from detected values in the wild, the contextual meaning that those values represent is critical. To our knowledge, no large-scale visual dataset comprising multi-digit, multi-number values with associated context labels exists; we denote this class of problems as “multi-metric extraction in the wild”. In this work, we focus on the accurate detection and reading of gas prices, and their contextual association to gas grade and payment type. We provide complete annotations for the Gas Prices of America (GPA) dataset, comprising 2,048 training and 512 test images sampled across the United States including over 2,600 signs, 6,000 prices, 27,000 digits, and 7,800 gas grade and payment type labels. With these data, we develop the GasBotty predictor, a composite neural network model, and evaluate it over eight benchmark tasks of increasing difficulty. Finally, we define a new highly stringent, binary-type metric, denoted “All-or-Nothing Accuracy” (ANA), requiring that a predictor perfectly extract and correctly associate all information in a gas sign. Our proposed model achieves 72.9% ANA over the independent test set of 512 images, where conventional state-of-the-art models object detection models and both a uniform random and biased random predictor would all tend towards 0% ANA. GasBotty and the GPA dataset will serve as a valuable benchmark for the development of future multi-metric extraction in the wild systems.

In this article, a machine learning aided electronic warfare (EW) system is presented and the simulation results are discussed. The developed EW system uses an automatic decision tree generator to create engagement protocol and a fuzzy logic model to quantify threat levels. A long-short term memory (LSTM) neural network was also trained to predict the next signal set of multifunction radars. The simulation results demonstrate the effectiveness of the developed EW system’s ability to engage multiple multifunction radars.

The Approximate Message Passing (AMP) algorithm efficiently reconstructs signals which have been sampled with large i.i.d. sub-Gaussian sensing matrices. Central to AMP is its “state evolution,” which guarantees that the difference between the current estimate and ground truth (the “aliasing”) at every iteration obeys a Gaussian distribution that can be fully characterized by a scalar. However, when Fourier coefficients of a signal with non-uniform spectral density are sampled, such as in Magnetic Resonance Imaging (MRI), the aliasing is intrinsically colored, AMP's scalar state evolution is no longer accurate and the algorithm encounters convergence problems. In response, we propose the Variable Density Approximate Message Passing (VDAMP) algorithm, which uses the wavelet domain to model the colored aliasing. We present empirical evidence that VDAMP obeys a “colored state evolution,” where the aliasing obeys a Gaussian distribution that can be fully characterized with one scalar per wavelet subband. A benefit of state evolution is that Stein's Unbiased Risk Estimate (SURE) can be effectively implemented, yielding an algorithm with subband-dependent thresholding that has no free parameters. We empirically evaluate the effectiveness of VDAMP on three variations of Fast Iterative Shrinkage-Thresholding (FISTA) and find that it converges in around 10 times fewer iterations on average than the next-fastest method, and to a comparable mean-squared-error.

This paper presents a novel approach to achieve a very high level of isolation between the two ports of a compact antenna system for communication and radar applications. Orthogonal polarization and a special symmetric arrangement of two-element arrays of transmitter and receiver antennas are used to achieve high isolation levels. Identical rectangular patch antennas are designed for operation at 6 GHz, and are arranged in a square grid configuration with 90° rotational symmetry in this design. A pair of transmitters and a pair of receivers are fed perfectly out of phase with 180° couplers. The entire system is simulated and fabricated. We measure the prototype antenna's isolation, and use the absolute gain measurement technique and standard far field antenna pattern measurement technique to characterize it's realized gain and radiation pattern. It is found that exploiting symmetry in this manner leads to isolation of 63.7 dB. This indicates that carefully designed and constructed systems could reasonably exhibit isolation on the order of 70 dB or more using this method.

This brief presents direct electrical measurement of active defects in the strong-accumulation region of N-type 4H-SiC MOS capacitors, which corresponds to the strong-inversion region of N-channel MOSFETs. The results demonstrate the existence of an active defect in the gate oxide, located very close to the SiC surface, with localized energy levels between 0.13 eV and 0.23 eV above the bottom of the conduction band. The observed spatial and energy localizations indicates that this is a well-defined defect.