Extracting the maximum value from data obtained by smart sensors will require identifying and leveraging trends and mutual information in raw data, calibration measurements, and telemetry data. Use of machine learning algorithms enables the extraction of information content which is not exploited by current approaches. To reduce power draw of radiometric sensors, we propose an approach using machine learning to produce calibrated radiometer measurements prior to reaching steady state. By enabling calibration during instrument power cycling, or between instrument turn-on and reaching equilibrium, the average power draw of the sensor can be reduced, while reducing gaps in data acquisition and increasing capabilities of existing sensor platforms.
The high global burden of stroke presents unique clinical challenges, and morbidity is high in treatable patients despite prevalent research and development in stroke diagnosis. Differentiation between ischemic and hemorrhagic stroke is the foremost diagnostic step, so precise detection or exclusion of intracranial hemorrhage is a high-impact objective in stroke cases. To develop microwave imaging prototypes, experiments must be performed with material composites with realistic tissue properties, known as phantoms. Relevant factors, methods, and data for hemorrhagic lesion phantoms are presented.
Joesphson parametric amplifiers (JPA), operating in the microwave regime, are a key component for quantum radar. A Josephson junction can combine the functions of local oscillator, mixer, amplifier and detector. For quantum radar based on two-mode squeezed vacuum states (TMSV), the Josephson devices perform squeezed state generation and mixing, for subsequent photon counting and hypothesis testing. In this contribution, we investigate quantum circuit for modeling of JPAs.
This study presents an analytical design of a new compact coplanar waveguide (CPW) fed ultra-wideband (UWB) antenna operable in the spectrum between 23–150 GHz. It exhibits remarkable performance in the mm-Wave Spectrum with simulated radiation efficiency over 90% and gain over 4.5 dBi in the entire band. Moreover, with a 147.2% fractional bandwidth, this newly proposed miniaturized antenna (17 x 15 x 0.787 mm 3 ) is envisaged to be deployed for the higher band of 5G as well as the next generation 6G wireless communication.
This paper presents the conceptual design of an electrically small antenna (ESA) for transmitting arbitrary wideband signals. Using an ESA, the antenna system converts the baseband I/Q of a broadband signal to a circularly polarized waveform. Consequently, the bandwidth of the transmitted signal is orders of magnitude broader than the antenna’s instantaneous bandwidth. The full-wave simulation results of the antenna system are imported into a circuit simulator to evaluate its performance. These simulation results demonstrate that the proposed method for transmitting an arbitrary waveform from an ESA is effective.
The occurrence of Venusian lighting has been debated for decades. One key signature of lightning is the emission of whistler waves, which are electromagnetic plasma waves that propagate along magnetic field lines near the electron cyclotron frequency. Many whistler waves have been detected in Venus's ionosphere and induced magnetosphere, which have sometimes been interpreted as evidence for Venusian lightning. However, other signatures of lightning including optical flashes, are exceedingly rare. Multiple spacecraft, such as Cassini and Parker Solar Probe, have performed gravity assists at Venus and have not detected signatures of Venusian lightning in the broadband radio spectrum. These non-detections and rare optical flashes are consistent with extremely low rates of Venusian lightning. However, the Venusian lightning occurrence rates based on whistler wave observations are orders of magnitude greater than the occurrence rates based on other signatures of lightning. The inconsistency between these different signatures of lightning has sparked decades of debate on the possible existence and occurrence rates of lightning on Venus.
We present a reconfigurable one dimensional leaky wave antenna (LWA) in the terahertz region. The proposed LWA can steer its beam at a fixed operational frequency using liquid crystals (LC). It consists of a rectangular waveguide filled with three layers of dielectric: an LC layer sandwiched between two spacers. The dominant TE 10 mode leaks into the free space through a partially reflecting surface (PRS). Using numerical analysis, we optimized the PRS reactance for maximum gain and beam steering angle and confirmed the results by full-wave simulation.
The use of engineered magnetic composite ground planes has shown promise in mitigating degradations in antenna performance as radiating structures operate close to a metallic ground plane (structure or platform). To address this issue, this paper presents the current results of the synthesis of magnetic nanoparticles and the inclusion of the particles in porous biopolymer scaffolds, as well as the physical and magnetic characterization data for the composites.
Weather radars have been proven to be a valuable asset for accurately characterizing precipitation over large domains. Dual-polarization and dual-frequency measurements from weather radar can be used for a variety of microphysical retrievals that are crucial for accurate parametrization of precipitation characteristics [1]. This study presents a microphysical investigation of a convective precipitation event that occurred near Huntsville, Alabama on December 11, 2021 and leverages on complementary observations from a vertically pointing S-Band Profiler Radar and a scanning dual-polarization C-Band Radar. The ultimate goal is to understand and better resolve the critical atmospheric physical processes governing convective precipitation events that is needed to help guide the development and choice of model physics in NOAA’s operational model forecast systems which is one of the main objectives of the NOAA’s Precipitation Prediction Grand Challenge (PPGC).
To investigate the effects of atmospheric variability on radiofrequency propagation over land, a month-long received power measurement campaign was performed over a 14.5 km path at 3.4 GHz. Results demonstrate a ±3.4 dB variability about the median; the variability was best characterized by a Rician distribution with K = 26 dB. A comprehensive analysis of atmospheric data demonstrated that signal variability was only correlated with the diurnal solar cycle.
The Global Positioning System (GPS) became accessible for general usage in the 1980s and has since prompted applications in various domains and on platforms with special requirements and constraints. This work presents a low-cost, shorted-annular-ring (SAR) reduced-surface-wave patch antenna design, with multipath mitigation characteristics, for high-precision positioning of freight-carrying autonomous rail transport vehicles. Simulation and measurement of radiation patterns are conducted for a single-band (L1) antenna design. The radiation characteristics of the SAR antenna near a side plate that models the freight container are also assessed through full-wave simulations, and recommendations have been made for improved performance.
Correction of weather radar observations which are partially or fully blocked is an indispensable step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments such as the western United States. In this paper, we propose a deep learning framework based on generative adversarial networks (GANs), for restoring partial beam blockage regions in polarimetric radar observations. Two S-band Weather Surveillance Radar – 1988 Doppler (WSR-88D) stations are used in this study: KFWS and KDAX, which are located in northern Texas and northern California, respectively. We intentionally create partial beam blocked regions for the KFWS radar data characterized by convective precipitation in training the deep learning model. The trained model is tested using independent precipitation events in the same domain and a mountainous region in Northern California. The results show that for both domains the continuity of precipitation system is greatly improved after applying the deep learning-based inpainting approach. For KFWS radar data, the deep learning model is superior to conventional interpolation approaches. Although ground truth is not available for the blocked KDAX radar data in Northern California, the repaired observations demonstrated a great potential for enhanced quantitative applications.
We apply machine learning (ML) techniques to identify the modes in rectangular waveguides from images of 2D modal field patterns injected with uniform, exponential, correlated exponential, and Gaussian noise distributions. A binary classifier is used to identify either transverse electric (TE) or transverse magnetic (TM) modes, and a Multi-class classifier is used to identify the mode numbers. Signal to noise ratios of 1, 0.1, and 0.01 are used to show the effectiveness of each model. Results show accuracy scores up to 99.95%. Several examples demonstrate that noisy modal patterns (unidentifiable to human eyes) may be successfully classified by the ML model.
Accurate nowcasting (0–6 hours) of landfalling hurricanes plays an important role in early weather warning system and real-time decision making for weather disaster monitoring and mitigation. In the past few years, a number of studies have proposed using deep learning methods to improve nowcasting skill such as convolutional recurrent neural network (ConvRNN) and trajectory gated recurrent unit (TrajGRU). As self-attention module is able to retrieve the global spatial context, this paper develops a self-attention-based gated recurrent unit (SaGRU) for hurricane nowcasting using multi-radar observations. The nowcasting problem is first transformed into an image-to-image prediction problem. Five consecutive radar images of previous observations are used as input to the nowcasting model and actual observations from “future” sequences are used as targets in the training stage. In particular, 21 hurricane events, which made landfall in the United States during 2015 and 2020 is collected for model training and hurricane Harvey is selected for independent test. Compared with TrajGRU, the evaluation results of nowcasting system show that out SaGRU performs very well in predicting hurricane-induced rainfall.
The significance of nearby-term approximation in the construction of method of moment (MoM) impedance matrix is studied in 2D electrodynamic problems. The nearby-term approximation is particularly emphasized for TE polarization scenarios, but the reason has not been clearly addressed. In this work, we revisit the formulations of two levels of approximation used for the off-diagonal matrix terms and apply them to solve a 2D TE scattering problem. It is observed that nearby-term approximation enormously improves the accuracy of near-diagonal terms of the impedance matrix and thus is crucial for the accuracy of MoM results.
This document is a brief summary of the work done at Montana Technological University to electrically characterize sand-casts. This research was sponsored by the Combat Ca-pabilities Development Command Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-20-2-0163. The views and conclusions contained in this document are those of the authors, and should not be interpreted as representing the official policies, either expressed or implied, of the Combat Capabilities Development Command Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Near-field scanning is a well established technique to asses radiated electromagnetic interference. For stochastic electromagnetic (EM) fields, techniques based on field correlation analysis are required for accurate modeling. This requires two-probe scanning for experimental characterization. In this contribution, we investigate requirements for a near-field scan over a distributed source to assess the EM far-field radiation based on the scan.
This paper presents a machine learning time-series approach to predicting the geomagnetic field at hourly resolution. The analysis uses hourly data corresponding to the horizontal component of the geomagnetic field from the Resolute Geomagnetic Observatory in Canada’s polar zone (74.690° N, 265.105° E ). Data from Jan 1, 1982, through Dec 31, 1999, is used to estimate the model. The model is evaluated from Jan 1, 2002, to Dec 31, 2005. The results indicate that the geomagnetic field’s hourly average can be predicted with moderate accuracy.
A deployable ultra-wideband, Tightly Coupled Dipole Array, with Integrated Balun (TCDA-IB) is presented for CubeSat-based Ice Sounding Radar. A swarm of Low-Earth Orbit (LEO) CubeSats employing TCDA-IB can form the spaceborne sounding radar using synthetic aperture concepts. The proposed radar offers fine-resolution mapping of the polar icebergs at a reduced cost. In this perspective, we introduce packable linear sub-arrays that operate across 184 MHz to 735 MHz (4:1) with VSWR < 3. Two orthogonal linear arrays are folded across the four sides of a 12U CubeSat chassis. This array also introduces an extended bent ground plane that maintains a stable gain performance across the entire bandwidth. Finally, a novel tapered balun is realized to retain the TCDA bandwidth.
The latest data acquisition system running at the Jicamarca Radio Observatory (JRO) for the main radar has been used for more than seven years now. Although there are no major inconveniences on the performance, there have been some problems with internal interference which are related to the PCB design that uses old discrete components. A new design is proposed for the digital receivers, giving it more lifetime and flexibility for future modifications. The JESD204B protocol is ideal for radar applications, a JESD204B ADC together with an FPGA was proposed for the new design, making it capable of a wider bandwidth which could adapt the system to an SDR device in the future with the proper software. This paper will present the new PCB design, the IP cores implemented for the FPGA and some preliminary tests with development boards.
