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At least 541 records · Page 30

Real-Time Digital Compression Of Television Image Data

Digital encoding/decoding system compresses color television image data in real time for transmission at lower data rates and, consequently, lower bandwidths. Implements predictive coding process, in which each picture element (pixel) predicted from values of prior neighboring pixels, and coded transmission expresses difference between actual and predicted current values. Combines differential pulse-code modulation process with non-linear, nonadaptive predictor, nonuniform quantizer, and multilevel Huffman encoder.

Barnes, Scott P.↗

Parallel processing of real-time dynamic systems simulation on OSCAR (Optimally SCheduled Advanced multiprocessoR)

Parallel processing of real-time dynamic systems simulation on a multiprocessor system named OSCAR is presented. In the simulation of dynamic systems, generally, the same calculation are repeated every time step. However, we cannot apply to Do-all or the Do-across techniques for parallel processing of the simulation since there exist data dependencies from the end of an iteration to the beginning of the next iteration and furthermore data-input and data-output are required every sampling time period. Therefore, parallelism inside the calculation required for a single time step, or a large basic block which consists of arithmetic assignment statements, must be used. In the proposed method, near fine grain tasks, each of which consists of one or more floating point operations, are generated to extract the parallelism from the calculation and assigned to processors by using optimal static scheduling at compile time in order to reduce large run time overhead caused by the use of near fine grain tasks. The practicality of the scheme is demonstrated on OSCAR (Optimally SCheduled Advanced multiprocessoR) which has been developed to extract advantageous features of static scheduling algorithms to the maximum extent.

Kasahara, Hironori↗

Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

Troudet, Terry↗

Digital codec for real-time processing of broadcast quality video signals at 1.8 bits/pixel

The authors present the hardware implementation of a digital television bandwidth compression algorithm which processes standard NTSC (National Television Systems Committee) composite color television signals and produces broadcast-quality video in real time at an average of 1.8 b/pixel. The sampling rate used with this algorithm results in 768 samples over the active portion of each video line by 512 active video lines per video frame. The algorithm is based on differential pulse code modulation (DPCM), but additionally utilizes a nonadaptive predictor, nonuniform quantizer, and multilevel Huffman coder to reduce the data rate substantially below that achievable with straight DPCM. The nonadaptive predictor and multilevel Huffman coder combine to set this technique apart from prior-art DPCM encoding algorithms. The authors describe the data compression algorithm and the hardware implementation of the codec and provide performance results.

Shalkhauser, Mary JO↗

Software Package For Real-Time Graphics

Software package for master graphics interactive console (MAGIC) at Western Aeronautical Test Range (WATR) of NASA Ames Research Center provides general-purpose graphical display system for real-time and post-real-time analysis of data. Written in C language and intended for use on workstation of interactive raster imaging system (IRIS) equipped with level-V Unix operating system. Enables flight researchers to create their own displays on basis of individual requirements. Applicable to monitoring of complicated processes in chemical industry.

Malone, Jacqueline C.↗

Digital CODEC for real-time processing of broadcast quality video signals at 1.8 bits/pixel

Advances in very large scale integration and recent work in the field of bandwidth efficient digital modulation techniques have combined to make digital video processing technically feasible an potentially cost competitive for broadcast quality television transmission. A hardware implementation was developed for DPCM (differential pulse code midulation)-based digital television bandwidth compression algorithm which processes standard NTSC composite color television signals and produces broadcast quality video in real time at an average of 1.8 bits/pixel. The data compression algorithm and the hardware implementation of the codec are described, and performance results are provided.

Shalkhauser, Mary JO↗

Geodetic positioning of the Aerospace Electronics Research Lab (ERL) Osborne Time Transfer Receiver (TTR) using the GPS NAVSTAR Block I satellites

Aerospace has routinely processed the Osborne Time Transfer Receiver (TTR) data for the purpose of monitoring the performance of ground and GPS atomic clocks in near real-time with on-line residual displays and characterizing clock stability with Allan Variance calculations. Recently, Aerospace added the ability to estimate the TTR's location by differentially correcting the TTR's location in the WGS84 reference system. This new feature is exercised on a set of TTR clock phase data and Sub-meter accurate station location estimates of the TTR at the Aerospace Electronic Research Lab (ERL) are obtained.

Liu, Anthony S.↗

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited.

Lau, Sonie↗

An algorithm for attitude identification for SCOLE using two infrared cameras

This report presents an algorithm that incorporates real-time data from two infrared cameras and computes the attitude parameters of the Spacecraft Control Laboratory Experiment (SCOLE), a laboratory apparatus representing an offset-feed antenna attached to the Space Shuttle by a flexible mast. The algorithm utilizes camera position information of three miniature LEDs, mounted on the SCOLE platform, permitting arbitrary camera placement and an on-line attitude extraction. The continuous nature of the algorithm allows identification of the placement of the two cameras with respect to some initial position of the three reference LEDs, followed by on-line six degrees of freedom attitude tracking, regardless of the attitude time history. The report provides a description of the algorithm in the camera identification mode as well as the mode of target tracking. Experimental data from a reduced-size SCOLE-like laboratory model, reflecting the performance of the camera identification and tracking processes, are presented.

Shenhar, J.↗

Dynamic control of robotic welding - On-going research

Two major challenges exist for the development of dynamic control systems: first, the control system must be resourceful enough to provide problem-solving capabilities in unforeseen circumstances; second, it must be rapid enough to respond to dynamic environments. Most conventional control systems do not have the ability to 'step back' and problem-solve, especially in environments with incomplete and uncertain models and data. Orthogonally, commercially available AI systems usually do not respond at the rates required to support 'real-time' control. Hence, control systems are not available that respond to complex dynamic environments within appropriate time constraints. This paper describes work on an AI-based control system designed to address these challenges. A prototype system has been used, in a simulated environment, to control the robotic welding of aerospace components. This AI-based control system has demonstrated the ability to flexibly control a complex process within required time constraints by incorporating higher level reasoning and the ability to deal with uncertainty. Further work is in progress to expand the system's problem-solving capabilities.

Ruokangas, Corinne C.↗

VSTOL ground effects testing with flow visualization and image enhancement

A remotely controlled high-energy fiber-optic light delivery technique is employed to examine the implementation of a laser-light-sheet flow-visualization system. During testing, video data are enhanced in real time using digital image processing techniques. A summary of test results for an advanced VSTOL configuration in ground effect, and techniques for the generation of 3D reconstructions for the flowfield are outlined. The system performed well during all phases of the test and proved to be an extremely useful asset to the overall test program. The most useful application of the flow visualization system was the interactive real-time flow field analysis made during the actual testing.

Kegelman, Jerome T.↗

Real-time decision aiding - Aircraft guidance for wind shear avoidance

Modern control theory and artificial intelligence technology are applied to the Wind Shear Safety Advisor, a conceptual airborne advisory system to help flight crews avoid or survive encounter with hazardous low-altitude wind shear. Numerical and symbolic processes of the system fuse diverse, time-varying data from ground-based and airborne measurements. Simulated wind-shear-encounter scenarios illustrate the need to consider a variety of factors for optimal decision reliability. The wind-shear-encounter simulations show the Wind Shear Safety Advisor's potential for effectively integrating the available information, highlighting the benefits of the computational techniques employed.

Stratton, D. A.↗

A fundamental study of suction for Laminar Flow Control (LFC)

This report covers the period forming the first year of the project. The aim is to experimentally investigate the effects of suction as a technique for Laminar Flow Control. Experiments are to be performed which require substantial modifications to be made to the experimental facility. Considerable effort has been spent developing new high performance constant temperature hot-wire anemometers for general purpose use in the Fluid Mechanics Laboratory. Twenty instruments have been delivered. An important feature of the facility is that it is totally automated under computer control. Unprecedently large quantities of data can be acquired and the results examined using the visualization tools developed specifically for studying the results of numerical simulations on graphics works stations. The experiment must be run for periods of up to a month at a time since the data is collected on a point-by-point basis. Several techniques were implemented to reduce the experimental run-time by a significant factor. Extra probes have been constructed and modifications have been made to the traverse hardware and to the real-time experimental code to enable multiple probes to be used. This will reduce the experimental run-time by the appropriate factor. Hot-wire calibration drift has been a frustrating problem owing to the large range of ambient temperatures experienced in the laboratory. The solution has been to repeat the calibrations at frequent intervals. However the calibration process has consumed up to 40 percent of the run-time. A new method of correcting the drift is very nearly finalized and when implemented it will also lead to a significant reduction in the experimental run-time.

Watmuff, Jonathan H.↗

On recursive least-squares filtering algorithms and implementations

In many real-time signal processing applications, fast and numerically stable algorithms for solving least-squares problems are necessary and important. In particular, under non-stationary conditions, these algorithms must be able to adapt themselves to reflect the changes in the system and take appropriate adjustments to achieve optimum performances. Among existing algorithms, the QR-decomposition (QRD)-based recursive least-squares (RLS) methods have been shown to be useful and effective for adaptive signal processing. In order to increase the speed of processing and achieve high throughput rate, many algorithms are being vectorized and/or pipelined to facilitate high degrees of parallelism. A time-recursive formulation of RLS filtering employing block QRD will be considered first. Several methods, including a new non-continuous windowing scheme based on selectively rejecting contaminated data, were investigated for adaptive processing. Based on systolic triarrays, many other forms of systolic arrays are shown to be capable of implementing different algorithms. Various updating and downdating systolic algorithms and architectures for RLS filtering are examined and compared in details, which include Householder reflector, Gram-Schmidt procedure, and Givens rotation. A unified approach encompassing existing square-root-free algorithms is also proposed. For the sinusoidal spectrum estimation problem, a judicious method of separating the noise from the signal is of great interest. Various truncated QR methods are proposed for this purpose and compared to the truncated SVD method. Computer simulations provided for detailed comparisons show the effectiveness of these methods. This thesis deals with fundamental issues of numerical stability, computational efficiency, adaptivity, and VLSI implementation for the RLS filtering problems. In all, various new and modified algorithms and architectures are proposed and analyzed; the significance of any of the new method depends crucially on specific application.

Hsieh, Shih-Fu↗

Bank Terminals

In the photo, employees of the UAB Bank, Knoxville, Tennessee, are using Teller Transaction Terminals manufactured by SCI Systems, Inc., Huntsville, Alabama, an electronics firm which has worked on a number of space projects under contract with NASA. The terminals are part of an advanced, computerized financial transaction system that offers high efficiency in bank operations. The key to the system's efficiency is a "multiplexing" technique developed for NASA's Space Shuttle. Multiplexing is simultaneous transmission of large amounts of data over a single transmission link at very high rates of speed. In the banking application, a small multiplex "data bus" interconnects all the terminals and a central computer which stores information on clients' accounts. The data bus replaces the maze-of wiring that would be needed to connect each terminal separately and it affords greater speed in recording transactions. The SCI system offers banks real-time data management through constant updating of the central computer. For example, a check is immediately cancelled at the teller's terminal and the computer is simultaneously advised of the transaction; under other methods, the check would be cancelled and the transaction recorded at the close of business. Teller checkout at the end of the day, conventionally a time-consuming matter of processing paper, can be accomplished in minutes by calling up a summary of the day's transactions. SCI manufactures other types of terminals for use in the system, such as an administrative terminal that provides an immediate printout of a client's account, and another for printing and recording savings account deposits and withdrawals. SCI systems have been installed in several banks in Tennessee, Arizona, and Oregon and additional installations are scheduled this year.

Source record↗

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗

Insights Into On-Demand Transit: A Case Study of Houston METRO's curb2curb Transit Services: Preprint

As modern on-demand transit (ODT) deployments increase, the insights gained from studying real-world systems prove more useful. In this study, a shared ODT system called "METRO curb2curb" in the suburbs of Houston, Texas was studied, with a particular focus on the Missouri City zone, which has the highest ridership out of the four total zones in which the system operates. The Missouri City zone spans 18 square miles with an average daily ridership of nearly 400 as of November 2024. In order to gain a passenger perspective, data collection was performed through using the ODT system first-hand, completing 50 rides in March and April of 2023. These rides included a mix of three different types of on-demand travel accommodations - 1.) advance bookings (30 min - 2 hours ahead), 2.) as-soon-as-possible / real-time dispatch bookings, and 3.) no-reservation trips at two designated "anchor points." Wait and travel time were collected for each ride. Anecdotal findings related to ease of locating the driver/vehicle, the process of booking a ride, and popular trip purposes were also documented. This study provides an overview of the METRO curb2curb system, including its history, service provider, service area, ridership, booking options, wait time, and travel time. Intricacies related to different definitions of wait time are included. Finally, relevant considerations for automating the on-demand transit system and discussed.

33 ADVANCED PROPULSION SYSTEMS↗