Engineering topics
Ogut, Mehmet
Publications and source records attributed to Ogut, Mehmet.
PV Validation Hub
The Validation Hub will be a clearinghouse for the transfer of novel algorithms and software from the PV research community to industry. Potential algorithms tested in the Hub could include the estimation of various PV loss factors and the detection of various operational issues. The primary function of the Hub will be for developers to submit executable code which will run on hosted data sets. Developers will receive private reports on the accuracy and performance (e.g., run-time) of the submitted algorithms, and public high level summaries will be hosted. These summaries will indicate the organization who submitted the algorithm (e.g., links to GitHub pages, documentation websites, etc.), high-level accuracy metrics, and standardized performance metrics. These results will be stored in a publicly available database, accessible through the Hub, with the ability for users to sort and filter the results. In short, the Hub will be presented to public users as a collection of interactive leaderboards, organized around specific analysis tasks pertinent to the PV data science community. These tasks include things such as the estimation of various PV loss factors and the detection of various operational issues. We will present progress on the development of this hub, including preliminary results of comparative validation of PV data science algorithms and progress towards building the platform itself.
Development of SMAP Retrievals for Forested Regions: SMAPVEX19-22 and SMAPVEX22-Boreal
The retrieval of soil moisture (SM) under forest canopy has long been an important goal for low frequency remote sensing. The NASA Soil Moisture Active Passive (SMAP) mission is engaged at three separate experiment sites to improve its SM retrieval algorithm in forested areas. Two of the sites are located in the deciduous forest region in Massachusetts and New York, US and one is located in southern boreal forest zone in Saskatchewan, Canada. Each site has a SM measurement network of about 20 stations spread out over an area of about 30 km, which covers the SMAP radiometer footprint. In 2022, intensive observations will be carried out at each site which involve deployments of an airborne instrument, which is similar to the SMAP instrument, and intensive manual measurements of SM, surface and vegetation. The measurements also include tower-based radiometer observations with ground truth measurements within the instrument footprint. Here we show some early results using the networks and SMAP measurements to analyze the sensitivity of the SMAP L-band measurements to SM changes in forested area and the impact of the vegetation to the signal. The results suggest an upper limit for vegetation attenuation accounting for surface roughness effect and relate that to the values used in the current SMAP SM products.
Benchmarking and Testing of Qualcomm Snapdragon System-on-Chip for JPL Space Applications and Missions
As some space missions become more challenging due to new environments, greater distances, or more limited size, weight, and power (SWaP) constraints, spacecraft avionics must adapt to allow the spacecraft to be more autonomous and agile---eliminating the Spacecraft-Earth-Spacecraft feedback loop whenever possible. Prime examples of such missions include Aerobots (such as Ingenuity with extremely low SWaP constraints and demanding signal/image processing during flight) and landers in possibly hostile environments (such as a Europa lander mission, with limited communication capacity, high latency, and constrained power budget). To address these challenges, JPL worked with Qualcomm to demonstrate the use of their Snapdragon 801 system-on-chip (SoC) onboard the Ingenuity Helicopter on Mars. The Qualcomm Snapdragon SoC contains various subsystems, including an ARM cluster, a Graphics processing unit, a Digital Signal Processing subsystem, a Neural Processing Engine, Image Signal Processing subsystem, among others. Since the success of Ingenuity, JPL is continuing to work with Qualcomm to address other applications of the Snapdragon SoC technology. This includes the deployment of two 855 Snapdragon development boards onboard the International Space Station (ISS) for successful in-situ benchmarking of applications in space (beyond those tested on Ingenuity). In this paper, we will examine the performance of various applications that have been identified to benefit from greater onboard computational capability. These applications include (among others): machine vision algorithms that are expected to be critical in autonomous entry-descent-and-landing scenarios and real-time Aerobot flight navigation; Hyperspectral compression algorithms; Synthetic Aperture Radar Processing along with various instrument processing algorithms. We discuss how the infusion of Qualcomm's Snapdragon SoC is capable of enabling missions that may not have been able to achieve their goals with traditional flight computing. In addition, we also show that for some algorithms, the software implementation on the Snapdragon SoC outperforms traditional FPGA implementations.
Using Intelligent Targeting to Increase the Science Return of a Smart Ice Storm Hunting Radar
No abstract provided
Using Intelligent Targeting to increase the science return of a Smart Ice Storm Hunting Radar
Smart Ice Cloud Sensing (SMICES) is a small-sat concept in which a radar intelligently targets ice storms based on information collected by a lookahead radiometer. Often space observations are performed by continuously collecting data from an instrument aimed at nadir (e.g. directly below the space platform). However, if the platform has the ability to assess science utility of features being overflown, an intelligent measurement scheme can improve science return. This can be achieved by controlling the on/off state of the instrument if it is not able to continuously operate (e.g. due to energy or thermal constraints), and by allowing the instrument to view off nadir if it has pointing capabilities.In the case of SMICES, power constraints and the rarity of storms means that with blind nadir targeting SMICES would collect a limited amount of ice storm radar data. The algorithms proposed acquire measurements to maximize acquired high interest storms while concurrently collecting a background sampling of all features. We use a cloud classification system to identify five different cloud types. Six algorithms ranging from “blind” to more selective are described and results from evaluation on a dataset of 13 ground swaths covering 72,399,600 km2 of data are presented. This data is from high quality science simulations that contain all five cloud types and multiple storms. When utilizing the radiometer’s lookahead and the full range of the radar the results show a 23.7x and 1.9x increase over the base algorithm in the most and second most important cloud types respectively.
The Smart Ice Cloud Sensing (smices) Smallsat Instrument Artificial Intelligence Strategies
No abstract provided
Multiyear sea ice thickness estimation using wideband P/L-band radiometric measurements
A new wideband radiometer covering P/L-band was developed at the Jet Propulsion Laboratory for polar ocean salinity and seasonal sea-ice thickness measurements. The instrument was deployed on the US Coast Guard Cutter Healy for an Arctic Ocean research cruise from September 13, 2018 to October 20, 2018. This work shows the first results relating sea ice thickness obtained from the measurements taken with the wideband P/L-band radiometer during the campaign. Results from the Artic cruise campaign were also used to study wideband spectral properties of salinity. In addition to this paper, Salinity and wide-band calibration challenges are presented in two other companion papers.