- API data.nasa.gov | Last Updated 2018-07-19T04:44:14.000Z
This data set contains Spectroscopic, Continuum and Engineering data taken by the Microwave Instrument for the Rosetta Orbiter (MIRO) during the second cruise phase of the mission. The data consist primarily of observations of comet 9P/Tempel 1 before, during and after the planned collision of the Deep Impact mission impactor spacecraft with that comet in July 2005. The data are presented as a series of binary table files.
- API data.nasa.gov | Last Updated 2018-07-19T08:08:25.000Z
<p>Collision avoidance for unmanned aerial systems (UAS) traveling at high relative speeds is a challenging task. It requires both the detection of a possible collision and deployment of an appropriate maneuver to avoid it, to be done in few seconds or less. NASA Langley and Boston University are engaged in a collaborative effort to design neuromorphic optic flow algorithms to avoid collisions and embed these algorithms in small, low-weight, and low-power customized hardware solutions in UAS.<p/><p>Using biologically-inspired neuromorphic optic flow algorithms is a novel approach in collision avoidance for UAS. Traditional computer vision algorithms rely on solving nonlinear partial differential equation systems to estimate optic flow which is a computationally expensive task. Neuromorphic algorithms instead make use of lessons learned in biology to solve these problems in a more efficient manner. An example is the fly's motion detector, which can be modeled by a system that uses a set of locally calculated, parallel spatio-temporal correlations for a set of velocities determined by the input sampling rates and flying speeds. Correlation results are interpreted as likelihood for a motion direction and speed. Stages of obstacle detection and tracking can temporally and spatially integrate these likelihoods to increase the signal-to-noise ratio, and in turn the detection rate. In addition to its computational efficiency, the proposed neuromorphic solution is more stable and noise tolerant than solving a nonlinear optimization problem. Even if single computational nodes get corrupted due to functional or structural failures in the hardware, the performance of appropriately designed parallel, distributed neuromorphic algorithms degrades gracefully. Neuromorphic algorithms are commonly implemented using software running on general-purpose multicore/graphic processing unit systems. This approach, though flexible, can have significant overhead in terms of power, performance, and is not easily portable across platforms, therefore reducing its scope of applicability. In the second phase, we will port the neuromorphic algorithms to field programmable gate arrays (FPGAs) and application specific integrated chips (ASICs). This will allow us to meet demanding performance requirements needed in UAS such as fast processing, low weight, low power consumption, as well as robustness to hardware failure.</p>
- API data.nasa.gov | Last Updated 2018-07-19T11:09:46.000Z
The proposed research activity is focused on the development of fuel and computationally efficient guidance and control algorithms for spacecraft swarms. The guidance and control methods developed in this research will address swarms with the following characteristics: swarms will have hundreds to thousands of agents, each agent will have limited control, sensing, and communication capabilities, and the swarms will be under the influence of highly coupled, nonlinear dynamics. The proposed research will also deliver an implementation of the guidance and control algorithms using both a testbed of tens to hundreds of autonomous helicopters and a realistic simulator that captures the exact orbital dynamics. One challenge unique to the spacecraft swarm is to meet the optimal and robust performance requirement of the desired swarm behaviors governed by the highly nonlinear orbital dynamics as well as the attitude dynamics. The relatively limited control, sensing, communication, and computation capabilities of the spacecraft will further complicate the complexity of the guidance and control problems. In the field of robotics and multivehicle control, graphs have been used to solve flocking and consensus control problems with similar objectives to those of spacecraft swarms. However, this work cannot be directly applied to the guidance and control problem of spacecraft swarms. One key difference is the complexity of the dynamic models of spacecraft swarm dynamics. The algorithms developed for simple planar motions of mobile robots or aerial vehicles cannot automatically ensure either fuel-efficient or collision-free maneuvers for the swarm dynamics in the presence of various orbital perturbations. In other words, when we derive the guidance and control algorithms and conduct verification and validation (V&V), we should consider the highly nonlinear coupled time-varying dynamics with various environmental, sensor, actuator, and communication uncertainties. The field of formation flying has been very popular for the past decade and many guidance and control algorithms have been developed. Unfortunately, the size of a swarm is several orders of magnitude larger than most spacecraft formations. For this reason, the fully centralized approaches typically used in formation flying are computationally expensive because they require that the location and path of each spacecraft be known at all times. Therefore, a decentralized approach is much more computationally feasible. In this case, each spacecraft only needs to know the locations and paths of its neighboring spacecraft. Spacecraft swarms have many potential uses in interferometry and communications but they cannot perform any functions until more efficient guidance and control algorithms. Swarms will greatly outperform monolithic spacecraft in many areas due to their versatility and durability. For this reason it is critical that fuel efficient and computationally practical guidance and control approaches be developed and tested. Additionally, these algorithms can be modified for use in other fields such as robotics and multivehicle control.
- API data.nasa.gov | Last Updated 2018-07-19T08:37:34.000Z
<p>Evaluate Current State of the Art; Define Critical Performance Requirements; Select Components; Smart Initiator or Smart Connector; Perform Detailed Cost/Benefit Analysis; Develop System Architecture; Build Prototype System; Qualify Prototype System to TRL 6; Develop Full Qualification Plan for implementation on JPL Flight Project.</p>
- API data.nasa.gov | Last Updated 2018-07-19T08:39:46.000Z
<p>An emerging area in microwave remote sensing is to use global navigation satellite service (GNSS) signals, like GPS, to form a bi-static radar by deploying a receive-only (i.e. passive) instrument to measure signals reflected from the Earth's surface. It is possible, and has been demonstrated using XM Satellite radio, to use other existing space borne transmitters. To this end, we propose to demonstrate the use of these so-called signals-of-opportunity (SOP) to perform bi-static active microwave remote sensing of land surfaces. Specially, we will demonstrate the exploitation of geostationary satellite transmissions within the direct broadcast service (DBS) to sense changes in a reflecting surface (e.g., the ground). While past research within the community has focused on using reflected GPS signals to sense ocean winds and soil moisture, there is a paucity of investigation using higher frequencies. We see potential in using multiple satellite downlink frequencies to sense surface properties.<p/><p>We propose to demonstrate the use of these so-called signals-of-opportunity (SOP) to perform bi-static active microwave remote sensing of land surfaces. Specially, we will demonstrate the exploitation of geostationary satellite transmissions within the direct broadcast service (DBS) to sense changes in a reflecting surface (e.g., the ground). •Develop a RF receiver/interferometer system to receive broadcast signals of DirecTV satellites to perform bi-static active microwave remote sensing of the Earth's surface. •Demonstrate the concept of using signals-of-opportunity (SOp) to perform active bi-static active microwave remote sensing. •Perform proof-of-concept ground measurements useful for sensing soil moisture and vegetation, snow, and sea ice.</p>
- API data.nasa.gov | Last Updated 2018-09-05T23:04:12.000Z
The current International Space Station (ISS) ECG (electrocardiogram) system for donning the biomedical sensors is time consuming and inconvenient, requiring shaving, application of electrodes, and signal checks. A more efficient ECG system will save crew time and reduce the overhead of stowing additional supplies. Additionally, the current ECG hardware requires dedicated ISS power and significant volume, but advances in microelectronics has significantly reduced the volume and power required for ECG applications. The Biosensors-EMSD (Exploration Medical System Demonstration) will demonstrate the integration of small, battery powered, easy to use biomedical sensors and data acquisition devices that will have the ability to measure, store, and transmit physiologic parameters during operational and ambulatory scenarios. Specific Aims: 1. Demonstrate that commercial off the shelf (COTS) and emerging technologies satisfy exploration physiological monitoring requirements and operational requirements 2. Reduce the time required of an on-orbit crew and ground personnel to store, access, transfer, and process physiological data 3. Provide a mechanism for interfacing biomedical sensor technology with a common data management framework and architecture to enable the EMSD objectives. The functionality of the ECG system will be verified through a ground demonstration and an ISS flight demonstration, both as part of the Exploration Medical System Demonstration. The project will begin with a market survey of available COTS ECG systems that meet physiological monitoring requirements followed by a direct COTS procurement. The ECG system will then be tested and verified for proper capabilities by CMO analogs. Ground testing will require CMO analogs to don the ECG system and execute a series of predetermined tasks while a variety of ECG data and video is collected. ECG data and video will be examined to ensure data quality, appropriate data routing, and to demonstrate system efficiency. Flight testing will be similar to ground testing, but may not be as comprehensive given in-flight resource limitations. The availability of more varied medical condition simulations, more extensive supply of power, fewer time and space limitations, and enhanced system characterization capabilities will allow the ground demonstration to expand the on-orbit objectives by assessing system effectiveness and performance.
- API data.nasa.gov | Last Updated 2018-07-19T08:02:03.000Z
(1) Identify and evaluate CO2/CO separation technologies that are compatible with the high operating temperatures (700-850oC) of the Solid Oxide Electrolysis process. (2) Identify and evaluate CO2 Acquisition technology options. (3) MARCO POLO Atmospheric Processing Module (APM): verify the operation of the CO2 pump and the associated storage system, complete setup and testing of the Sabatier subsystem and operate it with the CO2 freezers to ready the APM for a potential analog demonstration with other components of MARCO POLO at KSC and/or JSC.
- API data.nasa.gov | Last Updated 2018-09-07T17:41:59.000Z
<p style="margin-left:0in; margin-right:0in">A novel data processing accelerator intellectual property (IP) Radiation-Hardened-by-Design (RHBD) core for use in NASA future missions is proposed. The core is an artificial neural network accelerator based upon work done at Google, IBM, and others. The IP core is known as a tensor core and follows an architecture of matrix multipliers, accumulators, register files, and fast and abundant memory access. The tensor core will be developed to be Advanced Microcontroller Bus Architecture (AMBA) bus compliant and will feature an architectural approach to easily expand the data processing elements when more die area is available. The core will be developed on the trusted Global Foundries (GF) 32nm Silicon on Insulator (SOI) process. There is extensive development currently occurring at this process technology, including NASA’s future High Performance Spaceflight Computing (HPSC) platform. The core is proposed as an effort to develop a data processing acceleration to decrease the down-link data bandwidth of future space missions. If more processing can be accomplished <em>in situ</em>, a given mission can be expected to require less data bandwidth, a problem that is becoming more critical with the ever increasing number of active missions. The IP core will be developed to be incorporated into other development at the 32nm process. The IP core will also be structured in such a way as to be incorporated into Micro-RDC’s future Reticle Programmable System on Chip (RPSoC) platform. The RPSoC is an active future platform, under development with funding from NASA and the Air Force, for digital and mixed signal designs to lower the cost of development at 32nm and to decrease lead-time from design inception to product delivery. The tensor core will be featured on this platform as a data acceleration core. The core will have RHBD techniques throughout the FEOL and BEOL to ensure that no data will be corrupted within the artificial neural network configuration or the data path.</p>
Two-Dimensional Differential Deposition and Erosion for Thin-Shell Figure Correction, and Non-Distorting, High-Energy X-ray Multilayersdata.nasa.gov | Last Updated 2018-09-07T17:46:47.000Z
We propose to continue our development of two-dimensional differential deposition and erosion, a novel methods for high-throughput surface height error correction in thin-shell cylindrical mirror substrates. We also propose to develop non-distorting X-ray reflective multilayer coatings for use above 80 keV. Our specific research objectives are: (a) develop two-dimensional control of film deposition and erosion to correct both low- and mid-frequency surface height errors in cylindrical, thin-shell mirror substrates, and (b) develop high-efficiency, non-distorting, zero net-stress, and stress balanced, reflective multilayer coatings for use above 80 keV.
- API data.nasa.gov | Last Updated 2018-07-20T07:11:07.000Z
This project is focusing on the strategic, routine incorporation of medium-resolution satellite imagery into operational agricultural assessments for the global crop market. Automated algorithms for rapid extraction of field-level crop area statistics from Landsat and Landsat-class imagery (including Landsat 5 TM, Landsat 7 ETM+, AWiFS, ASTER, SPOT, LDCM, etc.) are under development. For prototype development, the project is collaborating with the Production Estimates and Crop Assessment Division of the USDA Foreign Agricultural Service. The Phase I prototype algorithms, based on Bayesian Probability Theory, incorporate multiple lines of evidence in the form of prior and conditional probabilities and implement an innovative approach to supervised image classification allowing for automated class delineation. The knowledge-based expert classifiers prototyped during Phase I were tested and validated at selected pilot sites across the globe. The results of the Phase I work have clearly demonstrated the technical feasibility of the GDA approach to automated crop area assessment with medium resolution imagery. Development undertaken during Phase I resulted in a robust, fully functional set of modules that are capable of processing large volumes of data and allow for accurate crop detection, area estimation, and crop acreage change assessment with minimal user intervention. The prototype algorithms were tested on a range of test sites, sensors, crop types, and crop conditions. A non-rigorous validation study proved the reliability and accuracy of the prototype algorithms. The overall results of the project will enhance global agricultural production estimates by improving the timeliness and accuracy of field-level crop area estimates.