Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models
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Narrow Bipolar Events (NBEs) are compact intracloud discharges that produce the most powerful lightning‐related radio frequency signals that have been observed. However, their luminosity is below the threshold for detectability from current and past spaceborne optical sensors. NBEs have been loosely associated with convective intensity, but their occurrence tends to be highly localized in time and space within a thunderstorm, and there remain many questions about whether and to what extent they are significantly related to meteorological processes within thunderstorms. Using the North Alabama Lightning Mapping Array (NALMA), the National Lightning Detection Network, and available Doppler and polarimetric radar data, case studies will be presented for storm events that produced large numbers of NBEs (~10s‐100s) during their lifetimes. NBEs are documented via a method that identifies high peak power (>40‐50 dBW) initial VHF sources within a specific altitude band in the upper levels of thunderstorms. The production of NBEs, including spatial and temporal variability, will be compared to the radar‐inferred kinematic and microphysical structure and evolution of thunderstorms, as well as their NALMA‐ and NLDN‐inferred electrical characteristics. The results should provide new insights into the relationships between NBEs and thunderstorm processes.
A radiation-hard transponder was developed utilizing submicron/nanotechnology from IBM. The device consumes low power and has a low fabrication cost. This device utilizes a Plug-and-Play concept, and can be integrated into intra-satellite networks, supporting SpaceWire and Gigabit Ethernet I/O. A space-qualified, 100-pin package also was developed, allowing space-qualified (class K) transponders to be delivered within a six-month time frame. The novel, optical, radiation-tolerant transponder was implemented as a standalone board, containing the transponder ASIC (application specific integrated circuit) and optical module, with an FPGA (field-programmable gate array) friendly parallel interface. It features improved radiation tolerance; high-data-rate, low-power consumption; and advanced functionality. The transponder utilizes a patented current mode logic library of radiation-hardened-by-architecture cells. The transponder was developed, fabricated, and radhard tested up to 1 MRad. It was fabricated using 90-nm CMOS (complementary metal oxide semiconductor) 9 SF process from IBM, and incorporates full BIT circuitry, allowing a loop back test. The low-speed parallel LVCMOS (lowvoltage complementary metal oxide semiconductor) bus is compatible with Actel FPGA. The output LVDS (low-voltage differential signaling) interface operates up to 1.5 Gb/s. Built-in CDR (clock-data recovery) circuitry provides robust synchronization and incorporates two alarm signals such as synch loss and signal loss. The ultra-linear peak detector scheme allows on-line control of the amplitude of the input signal. Power consumption is less than 300 mW. The developed transponder with a 1.25 Gb/s serial data rate incorporates a 10-to-1 serializer with an internal clock multiplication unit and a 10-1 deserializer with internal clock and data recovery block, which can operate with 8B10B encoded signals. Three loop-back test modes are provided to facilitate the built-in-test functionality. The design is based on a proprietary library of differential current switching logic cells implemented in the standard 90-nm CMOS 9SF technology from IBM. The proprietary low-power LVDS physical interface is fully compatible with the SpaceWire standard, and can be directly connected to the SFP MSA (small form factor pluggable Multiple Source Agreement) optical transponder. The low-speed parallel interfaces are fully compatible with the standard 1.8 V CMOS input/output devices. The utilized proprietary annular CMOS layout structures provide TID tolerance above 1.2 MRad. The complete chip consumes less than 150 mW of power from a single 1.8-V positive supply source.
Software Defined Radio (SDR) technology has been proven in the commercial sector since the early 1990's. Today's rapid advancement in mobile telephone reliability and power management capabilities exemplifies the effectiveness of the SDR technology for the modern communications market. In contrast, the foundations of transponder technology presently qualified for satellite applications were developed during the early space program of the 1960's. Conventional transponders are built to a specific platform and must be redesigned for every new bus while the SDR is adaptive in nature and can fit numerous applications with no hardware modifications. A SDR uses a minimum amount of analog / Radio Frequency (RF) components to up/down-convert the RF signal to/from a digital format. Once the signal is digitized, all processing is performed using hardware or software logic. Typical SDR digital processes include; filtering, modulation, up/down converting and demodulation. NASA Marshall Space Flight Center (MSFC) Programmable Ultra Lightweight System Adaptable Radio (PULSAR) leverages existing MSFC SDR designs and commercial sector enhanced capabilities to provide a path to a radiation tolerant SDR transponder. These innovations (1) reduce the cost of NASA Low Earth Orbit (LEO) and Deep Space standard transponders, (2) decrease power requirements, and (3) commensurately reduce volume. A second pay-off is the increased SDR flexibility by allowing the same hardware to implement multiple transponder types simply by altering hardware logic - no change of hardware is required - all of which will ultimately be accomplished in orbit. Development of SDR technology for space applications will provide a highly capable, low cost transponder to programs of all sizes. The MSFC PULSAR Project results in a Technology Readiness Level (TRL) 7 low-cost telemetry system available to Smallsat and CubeSat missions, as well as other platforms. This paper documents the continued development and verification/validation of the MSFC SDR, called PULSAR, which contributes to advancing the state-of-the-art in transponder design - directly applicable to the SmallSat and CubeSat communities. This paper focuses on lessons learned on the first sub-orbital flight (high altitude balloon) and the follow-on steps taken to validate PULSAR. A sounding rocket launch, currently planned for 03/2015, will further expose PULSAR to the high dynamics of sub-orbital flights. Future opportunities for orbiting satellite incorporation reside in the small satellite missions (FASTSat, CubeSat. etc.).
For the past few years, the Advanced Electrical Systems Branch here at NASA Glenn has been pursuing research in the area of flywheels. The purpose of these pursuits has been t o explore the potential for flywheels to replace current battery-powered systems in space. So far it has been learned that flywheels offer large momentum storage capacity, comparatively small volume, high durability, and near- complete discharge capabilities, all of which are advancements over the existing nickel hydrogen and nickel cadmium batteries. Another significant advantage of flywheels is the potential they offer for combining the function of attitude control with energy storage. During the summer of 2004, I worked with Dr. Barbara Kenny in the Advanced Electrical Systems Branch, supporting the work she is doing by analyzing and testing some new components for the new Generation-2 flywheel. To monitor the speed and angular position of the flywheel rotor, a once-around (OAR) signal along with a sensorless algorithm is used. The OAR signal is used for the magnetic bearings that keep the flywheel suspended for frictionless operation. The sensorless algorithm is used for the flywheel motor/generator control. The OAR is generated from position sensors that monitor a circular plate. The plate has a cut down the middle such that one half of the circle is on a slightly lower level than the other. Every half-turn, or 180, the sensors detect the "cut" on the plate, and trigger the OAR, telling the computer that the rotor has made half a revolution. This, however, doesn't provide needed detailed information about the angular position of the rotor, since it only provides a signal alert every half- revolution. This is enough information for the magnetic bearing control but is insufficient for the motor/generator control. A new resolver was designed such that it would give continuous angle information rather than the 180 degree information of the OAR. The new resolver has two separate observable pieces: a flat middle section to monitor vertical motion, and an angled section around the circumference, which, when observed from above, produces a sine-wave displacement through the entire 360" revolution. My first job when I arrived this summer was to calibrate the sensors that would be mounted on the inside of the flywheel casing to monitor the position (angular and vertical) of the shaft. After calibration, I used the sensors to evaluate voltage outputs created by position differences between two pairs of sensors on the angled portion of the resolver for eight different angular positions, moving the resolver vertically and laterally through its entire potential range of motion. The results of these tests will be used to determine the rotor angular (and axial) position from the sensor readings once the new flywheel unit is assembled. The sensorless algorighm mentioned above consists of two operations: the signal injection method and the back electro-motive force (EMF). The signal injection is meant to work at low speeds, while the back EMF algorithm is meant to work at higher speeds. Both work together to determine the correct estimate of rotor position and speed based on the measured motor/generator current. It was determined that we wanted to know exactly how accurate our estimation methods were, and so a resolver (a commercially available mechanical sensor mounted to the motor/generator shaft to measure rotor position and speed) and a "Resolver to Digital" (R2D) circuit board was purchased to make the comparison to the existing estimation. My work related to the R2D board has included the following: creating two connector cables (one to power the circuit and one to get readable output off the board), writing Simulink code to process the board's output, and building a dSpace panel to control and monitor the circuit. The next step in the process will be to perform tests to compare the estimated rotor position and speed from the sensorless algorithm to the actual rotor and speed from the resolver signal.
In this work, we present a novel neural network (NN) method for the detection and removal of radio frequency interference (RFI) from the raw digitized signal in the signal processing chain of a typical radio astronomy experiment. The main advantage of our method is that it does not require a training set. Instead, our method relies on the fact that the true signal of interest coming from astronomical sources is thermal and therefore described as a Gaussian random process, which cannot be compressed. We employ a variational encoder/decoder network to find the compressible information in the data stream that can explain the most variance with the fewest degrees of freedom. We demonstrate it on a set of toy problems and stored ring buffers from the Baryon Mapping eXperiment prototype. We find that the RFI subtraction is effective at cleaning simulated time streams: While we find that the power spectra of the RFI-cleaned time streams output by the NN suffer from extra signal consistent with additive noise, we find that it is generally around percent level across the band and sub 10% in contaminated spectral channels even when RFI power is an order of magnitude larger than the signal. We discuss the advantages and limitations of this method and possible implementation in the front end of future radio experiments.
Although a nuclear reactor is a hostile environment for sensing and electrical communications, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure (AMI) has been installed at the Advanced Test Reactor (ATR) nozzle trench area to record acoustic signals that has the ability to capture different operating regime of the reactor. This AMI includes ATR in-pile structural components, coolant, acoustic receivers, primary coolant pumps (PCP) as signal sources, a data acquisition system, and signal-processing algorithms, enabling real-time. This report will discusses development of recursive Fast Fourier Transform approach to process in real-time acoustic signals, application of short time Fast Fourier Transform to the ATR brush data to understand the vibration level and to develop spectrograms for different primary coolant pump combinations. The combination of primary coolant pumps for normal and power axial locator mechanism of ATR are different and generates different signatures. These acoustic signatures were used to develop machine learning approaches to automatically classify different operating regimes. This lay the foundation for predictive analytic framework that can be leverage by ATR to optimize their operation and maintenance. The path forward involves continued engagement with ATR and expanded implementation of AMI and predictive framework at ATR and other facilities within INL and at other experimental reactors.
Surface ion traps are a promising platform for quantum computing due to their potential to store large numbers of ions that can be addressed by electrical and optical control signals in order to implement quantum algorithms. Increasing the power of the quantum computer requires increasing the number of ions, but this poses a significant challenge in that it leads to a non-linear increase in on-chip power dissipation. The primary contributor to this power scaling in current devices is the capacitance between the radio frequency (RF) electrode and the metal plane that shields the silicon substrate from the RF signals applied to it. Silicon has traditionally been chosen for the substrate material for compatibility with the processing required for multi-metal-level traps. In this work, we address these capacitance and fabrication challenges by replacing the commonly used silicon substrate with an insulating sapphire substrate to fabricate a multi-metal-level ion trap, while still employing common semiconductor manufacturing techniques. This change in substrate allows the design to remove the metal shielding from the device design, reducing the capacitance of the RF electrode. The electrical characteristics of these traps were measured, specifically trap impedance, capacitance, and voltage breakdown, and compared to nearly identical silicon trap devices. Finally, we used laser cutting techniques to shape a sapphire wafer into bowtie shapes matching silicon traps previously fabricated at Sandia National Labs to explore solutions for integrating sapphire substrates into non-rectangular ion trap designs.
Size, power, and cost reduced by exploiting commonality. Digital signal processor for Global Positioning System (GPS) receiver set to operate in "code" mode when P code known, or in "codeless" mode when P code not known. In codeless mode, processor performs full-quadrature processing, resulting in signal-to-noise ratio (SNR) 6 dB greater than SNR's of processors not performing at full quadrature.
The simulated effects of the Radio Frequency Identification (RFID) tag emissions on ILS Localizer and ILS Glide Slope functions match the analytical models developed in support of DO-294B provided that the measured peak power levels are adjusted for 1) peak-to-average power ratio, 2) effective duty cycle, and 3) spectrum analyzer measurement bandwidth. When these adjustments are made, simulated and theoretical results are in extraordinarily good agreement. The relationships hold over a large range of potential interference-to-desired signal power ratios, provided that the adjusted interference power is significantly higher than the sum of the receiver noise floor and the noise-like contributions of all other interference sources. When the duty-factor adjusted power spectral densities are applied in the evaluation process described in Section 6 of DO-294B, most narrowband guidance and communications radios performance parameters are unaffected by moderate levels of RFID interference. Specific conclusions and recommendations are provided.
This is the final report for the early career award for Prof. Egemen Kolemen that aimed to develop the physics and technical basis of real-time transient – i.e. disruptions and Edge Localized Modes (ELMs) – avoidance and feedback control at the DIII-D nuclear fusion facility for the ITER era. During this project, we reconstructed high quality real-time DIII-D equilibria using kinetic information from the motional Stark effect (MSE), Thomson Scattering (TS), and Charge Exchange Recombination (CER) spectroscopy. These equilibria were analyzed in real-time to determine the low-n ideal MHD stability and the pedestal stability thresholds, and to obtain plasma response models. These calculations were used to understand which physical process evolves to lead to the onset of disruption in ITER-relevant scenarios at DIII-D, and how disruption can be predicted using stability analysis as opposed to the current methods of correlation of various signals, which are descriptive but lack predictive power. In the disruption studies, we focused on tearing modes, since these constitute the vast majority of disruptions in this regime. With this knowledge, real-time disruption avoidance algorithms were developed and tested. The enhanced situation awareness and physics insight were used in advanced real-time feedback control of pedestal and core profiles, which allowed the pursuit and sustainment of transient-free high-confinement regimes that cannot be achieved via feed-forward design based on in-between shot analysis.
Seismic full wave inversion (FWI) is a widely used non-linear seismic imaging method used to reconstruct subsurface velocity images, however it is time consuming, has high computational cost and depend heavily on human interaction. Recently, deep learning has accelerated it’s use in several data-driven techniques, however most deep learning techniques suffer from overfitting and stability issues. In this work, we propose an edge computing-based data-driven inversion technique based on supervised deep convolutional neural network to accurately reconstruct the subsurface velocities. Deep learning based data-driven technique depends mostly on bulk data training. In this work, we train our deep convolutional neural network (DCN) (UNet and InversionNet) on the raw seismic data and their corresponding velocity models during the training phase to learn the non-linear mapping between the seismic data and velocity models. The trained network is then used to estimate the velocity models from new input seismic data during the prediction phase. The prediction phase is performed on a resource-constrained edge device such as Raspberry Pi. Raspberry Pi provides real-time and on-device computational power to execute the inference process. In addition, we demonstrate robustness of our models to perform inversion in the presence on noise by performing both noise-aware and no-noise training and feeding the resulting trained models with noise at different signal-to-noise (SNR) ratio values. We make great efforts to achieve very feasible inference times on the Raspberry Pi for both models. Specifically, the inference times per prediction for UNet and InversionNet models on Raspberry Pi were 22 and 4 s respectively whilst inference times for both models on the GPU were 2 and 18 s which are very comparable. Finally, we have designed a user-friendly interactive graphical user interface (GUI) to automate the model execution and inversion process on the Raspberry Pi.
Performance characteristics from on-orbit tests of the transmitter experiment package (TEP) for the Communications Technology Satellite (CTS) are presented. The TEP consists of a power processing system (PPS), an output stage tube (OST) and a variable conductance heat pipe system (VCHPS), all of which are described. Test results presented include efficiencies, RF output power, frequency response, and performance with single and multiple (two) carriers, frequency-modulated by video signals.
The form and flow of information from signal reception to recorded bits at each station, through cross-correlation, phase-tracking, phase calibration, and bandwidth synthesis, to final observable formation are traced. In this process the data volume is greatly compressed for each observation, from approx. 10 to the 9th power bits on the station tapes to only four final observables: amplitude, delay, phase-delay rate and RF phase. After phase calibration and propagation media calibrations, the final delay observable is shown to be the sum of a geometric delay and a clock synchronization offset.
The theoretical basis and hardware implementation of a versatile device which uses digital signal-processing methods to achieve signal modulation are reported. The carrier-modulation process is formulated as a simple quadrature equation; a generalized description of the signal constellation to be processed is derived; the pulse-shaping scheme is explained; and the system architecture is described in detail and illustrated with diagrams. Experimental results obtained with a breadboard transmitter based on this modulation technique are presented in extensive graphs. The power spectra and eye diagrams generated for different QPSK, 8PSK, and MSK modulation schemes are shown to be in good agreement with theoretical predictions. It is suggested that this modulator could be readily implemented in VLSI and mass produced.
High resolution radar data for the lunar surface were acquired over 14 sites in June and November 1990 using the new 10 MHz data taking system at the Arecibo Observatory. The raw data collected for each site covers an area approximately 100 by 400 km and will be processed using delay-Doppler techniques into images of backscatter cross section with three fo four independent looks. All observations transmitted a circularly polarized signal and both senses of circular polarization were received containing the polarized and depolarized component of the backscatter signal. The relative power in these two polarizations provides useful information on properties of the surface, in particular surface roughness. The effort to date focused on the initial data analysis with new software written to perform a full synthetic aperture focusing on the raw radar data. This analysis will involve the use of complementary high resolution optical and topographic data sets to aid interpretation of surface scattering mechanisms.
The NASA Glenn Research Center is investigating and developing suitable reconfigurable radio architectures for future NASA missions. This effort is examining software-based open-architectures for space based transceivers, as well as common hardware platform architectures. The Joint Tactical Radio System's (JTRS) Software Communications Architecture (SCA) is a candidate for the software approach, but may need modifications or adaptations for use in space. An in-house SCA compliant waveform development focuses on increasing understanding of software defined radio architectures and more specifically the JTRS SCA. Space requirements put a premium on size, mass, and power. This waveform development effort is key to evaluating tradeoffs with the SCA for space applications. Existing NASA telemetry links, as well as Space Exploration Initiative scenarios, are the basis for defining the waveform requirements. Modeling and simulations are being developed to determine signal processing requirements associated with a waveform and a mission-specific computational burden. Implementation of the waveform on a laboratory software defined radio platform is proceeding in an iterative fashion. Parallel top-down and bottom-up design approaches are employed.
The term "Passive Acoustic Monitoring System" (PAMS) describes a developmental sensing-and-data-acquisition system for recording underwater sounds. The sounds (more precisely, digitized and preprocessed versions from acoustic transducers) are subsequently analyzed by a combination of data processing and interpretation to identify and/or, in some cases, to locate the sources of those sounds. PAMS was originally designed to locate the sources such as fish of species that one knows or seeks to identify. The PAMS unit could also be used to locate other sources, for example, marine life, human divers, and/or vessels. The underlying principles of passive acoustic sensing and analyzing acoustic-signal data in conjunction with temperature and salinity data are not new and not unique to PAMS. Part of the uniqueness of the PAMS design is that it is the first deep-sea instrumentation design to provide a capability for studying soniferous marine animals (especially fish) over the wide depth range described below. The uniqueness of PAMS also lies partly in a synergistic combination of advanced sensing, packaging, and data-processing design features with features adapted from proven marine instrumentation systems. This combination affords a versatility that enables adaptation to a variety of undersea missions using a variety of sensors. The interpretation of acoustic data can include visual inspection of power-spectrum plots for identification of spectral signatures of known biological species or artificial sources. Alternatively or in addition, data analysis could include determination of relative times of arrival of signals at different acoustic sensors arrayed at known locations. From these times of arrival, locations of acoustic sources (and errors in those locations) can be estimated. Estimates of relative locations of sources and sensors can be refined through analysis of the attenuation of sound in the intervening water in combination with water-temperature and salinity data acquired by instrumentation systems other than PAMS. A PAMS is packaged as a battery-powered unit, mated with external sensors, that can operate in the ocean at any depth from 2 m to 1 km. A PAMS includes a pressure housing, a deep-sea battery, a hydrophone (which is one of the mating external sensors), and an external monitor and keyboard box. In addition to acoustic transducers, external sensors can include temperature probes and, potentially, underwater cameras. The pressure housing contains a computer that includes a hard drive, DC-to- DC power converters, a post-amplifier board, a sound card, and a universal serial bus (USB) 4-port hub.