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Equipment indicates that system can rapidly and accurately process metallurgical and materials-processing data for wide range of applications. Advantages include increase in contract between areas on image, ability to analyze images via operator-written programs, and space available for storing images.
Land-observing satellites with multiple thematic mappers will produce data at rates of 100 to 300 Mbps. When coupled with a high daily scene production rate, these rates will require new approaches to ground processing. Consideration is given here to future downlink rates and data volumes, and requirements peculiar to the future user community are discussed. The advanced technologies required to attain an operational system in the years 1985-1990 are considered, together with advances foreseen in communications, mass storage, bulk memories, and data processing. Using advanced devices, a centralized data processing system capable of handling the 100 Mbps data rate is described. New approaches, among them a parallel pipelined calibration front-end, real-time browse image production, a high bandwidth optical disk archive, regional image broadcast and massively parallel product production, are considered. A distributed system capable of handling the 300 Mbps data rate is then described. Designs for a hub system and a regional processing center are presented.
The development of hardware and software for an interactive, minicomputer based processing and display system for atmospheric and oceanographic information extraction and image data analysis is described. The major applications of the system are discussed as well as enhancements planned for the future.
The design of the N-ROSS scatterometer data system and the development of key processing algorithms are described. The data products and parts of the data system to be directly validated are listed. The main features of the Data Management Subsystem, which delivers data to science users and supports system validation are outlined.
Reducing the time that it takes to have useful satellite information is very important because timely access allows for more informed decision making. This is especially true in time critical situations like disaster response and financial market analysis. One way to achieve reductions in the overall time between information capture and delivery to use the direct broadcast from weather satellites. In this work, we describe a state driven satellite information system that captures a satellite’s direct broadcast signal and uses cloud-based resources to provide end-user controlled processing. The system takes advantage of the reliability and customizability of Amazon Web Services to provide fast and reliable access to a system that takes the direct broadcast signal and leverages the CSPP software as well as dynamically supplied end-user processing modules to produce a user desired information product. Finally, we describe the development process and how a flexible design allowed for changes as the capabilities of the processing platform evolved and the lessons we learned from the process.
The UMET-1 is a microprocessor-based portable system for automatic real-time processing of flight data transmitted from the standard RAWINSONDE upper atmosphere meteorological balloonsonde. The first 'target system' is described which was designed to receive data from a mobile tracking and telemetry receiving station (TRADAT), as the balloonsonde ascends to apogee. After balloon-burst, the UMET-1 produces user-ready hardcopy.
This paper describes two processing algorithms that can be implemented optically: the Radon transform and angular correlation. These two algorithms can be combined in one optical processor to extract all the basic geometric and amplitude features from objects embedded in video imagery. We show that the internal amplitude structure of objects is recovered by the Radon transform, which is a well-known result, but, in addition, we show simulation results that calculate angular correlation, a simple but unique algorithm that extracts object boundaries from suitably threshold images from which length, width, area, aspect ratio, and orientation can be derived. In addition to circumventing scale and rotation distortions, these simulations indicate that the features derived from the angular correlation algorithm are relatively insensitive to tracking shifts and image noise. Some optical architecture concepts, including one based on micro-optical lenslet arrays, have been developed to implement these algorithms. Simulation test and evaluation using simple synthetic object data will be described, including results of a study that uses object boundaries (derivable from angular correlation) to classify simple objects using a neural network.
Architecture for step-oriented pipeline data processing is disclosed utilizing a plurality of cascaded modules, each module including a programmable general purpose processor and a read/write random access memory. The memory of each module, shared with the next module in cascade serves as an output memory for the processor and the input memory for the next processor. An additional memory is provided to serve as the input memory of the first module, and each module is provided with a memory, which may be a read-out memory, to store a program for the processor. Each module is further provided with a logic network for resolving a potential memory sharing conflict by awarding priority to the processor of the module.
Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.
Topics related to robot operated materials processing in space (RoMPS) are presented in view graph form. Some of the areas covered include: (1) mission requirements; (2) automation management system; (3) Space Transportation System (STS) Hitchhicker Payload; (4) Spacecraft Command Language (SCL) scripts; (5) SCL software components; (6) RoMPS EasyLab Command & Variable summary for rack stations and annealer module; (7) support electronics assembly; (8) SCL uplink packet definition; (9) SC-4 EasyLab System Memory Map; (10) Servo Axis Control Logic Suppliers; and (11) annealing oven control subsystem.
The results of the free space reactor experimental work are summarized. Overall, the objectives were achieved and the unit can be confidently scaled to the EPSDU size based on the experimental work and supporting theoretical analyses. The piping and instrumentation of the fluidized bed reactor was completed.
Technical activities are reported in the design of process, facilities, and equipment for producing silicon at a rate and price comensurate with production goals for low cost solar cell modules. The silane-silicone process has potential for providing high purity poly-silicon on a commercial scale at a price of fourteen dollars per kilogram by 1986, (1980 dollars). Commercial process, economic analysis, process support research and development, and quality control are discussed.
The Microelectronic Systems Branch (MSB) at Goddard Space Flight Center (GSFC) has developed a Spacecraft Data Simulator (SDS) to support the development, test, and verification of prototype and production Level Zero Processing (LZP) systems. Based on a disk array system, the SDS is capable of generating large test data sets up to 5 Gigabytes and outputting serial test data at rates up to 80 Mbps. The SDS supports data formats including NASA Communication (Nascom) blocks, Consultative Committee for Space Data System (CCSDS) Version 1 & 2 frames and packets, and all the Advanced Orbiting Systems (AOS) services. The capability to simulate both sequential and non-sequential time-ordered downlink data streams with errors and gaps is crucial to test LZP systems. This paper describes the system architecture, hardware and software designs, and test data designs. Examples of test data designs are included to illustrate the application of the SDS.
An electronic computing device including at least one processing unit that implements a specific fault signal upon experiencing an associated fault, a control unit that generates a specific recovery signal upon receiving the fault signal from the at least one processing unit, and at least one input memory unit. The recovery signal initiates specific recovery processes in the at least one processing unit. The input memory buffers input data signals input to the at least one processing unit that experienced the fault during the recovery period.
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The system is discussed which was developed to process digitized telemetry data from the intensity monitoring spectrometer flown on the Orbiting Geophysical Observatory (OGO-F) Satellite. Functional descriptions and operating instructions are included for each program in the system.
The work describes a scientist/user oriented interactive program for processing one-dimensional arrays. It is shown that the program is oriented toward processing spectrophotometric astronomical data and can also be used for general I-D array processing. Further, the program has totally free format input with a sophisticated decoding capability which can cope with typographical plus other possible mistakes. Finally, a description of the program is given to provide information on implementing a large-scale data-reduction facility.
The development of a cost effective process sequence that has the potential for the production of flat plate photovoltaic modules which meet the price goal in 1986 of 70 cents or less per Watt peak is described. The major accomplishments include (1) an improved AR coating technique; (2) the use of sand blast back clean-up to reduce clean up costs and to allow much of the Al paste to serve as a back conductor; and (3) the development of wave soldering for use with solar cells. Cells were processed to evaluate different process steps, a cell and minimodule test plan was prepared and data were collected for preliminary Samics cost analysis.