Results of adaptive predictor studies, part 1
Adaptative predictor studies based on Tiros cloud picture video data, and relative merits of linear and Markov prediction data compression techniques
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Adaptative predictor studies based on Tiros cloud picture video data, and relative merits of linear and Markov prediction data compression techniques
A storyboard display is presented which summarizes work done recently in design and simulation of autonomous video rendezvous and docking systems for spacecraft. This display includes: photographs of the simulation hardware, plots of chase vehicle trajectories from simulations, pictures of the docking aid including image processing interpretations, and drawings of the control system strategy. Viewgraph-style sheets on the display bulletin board summarize the simulation objectives, benefits, special considerations, approach, and results.
Papers on optical pattern recognition are presented, covering topics such as the estimation of satellite pose and motion parameters using a neural net tracker, associative memory, optical implmentation of programmable neural networks, optoelectronic neural networks, dynamic autoassociative neural memory, heteroassociative memory, bilinear pattern recognition processors, optical processing of optical correlation plane data, and a synthetic discriminant function-based nonlinear optical correlator. Other topics include an interactive optical-digital image processor, geometric transformations for video compression and human teleoperator display, quasiconformal remapping for compensation of human visual field defects, hybrid vision for automated spacecraft landing, advanced symbolic and inference optical correlation filters, and a rotationally invariant holographic tracking system. Additional topics include the detection of rotational and scale-varying objects with a programmable joint transform correlator, a single spatial light modulator binary nonlinear optical correlator, optical joint transform correlation, linear phase coefficient composite filters, and binary phase-only filters.
Video/computer subsystem of welding system produces overlay, on video image, containing numerical data on parameters of welding operation being viewed. Operator watches weld bead simultaneously with instantaneous values of such data as elapsed time, welding current, and processed sensor measurements of weld area. Pertinent weld data always available to viewer and correlated in time with image of weld.
The topics are presented in viewgraph form and include: RMS active damping augmentation; potential space station assembly benefits to CSI; LaRC/JSC bridge program; control law design process; draper RMS simulator; MIMO acceleration control laws improve damping; potential load reduction benefit; DRS modified to model distributed accelerations; accelerometer location; Space Shuttle aft cockpit simulator; simulated shuttle video displays; SES test goals and objectives; and SES modifications to support RMS active damping augmentation.
Infrared thermography is a preferred method investigating transition in flight: a) Global and non-intrusive; b) Can also be used to visualize and characterize other fluid mechanic phenomena such as shock impingement, separation etc. F-15 based system was updated with new camera and digital video recorder to support high Reynolds number transition tests. Digital Recording improves image quality and analysis capability and allows for accurate quantitative (temperature) measurements and greater enhancement through image processing allows analysis of smaller scale phenomena.
Engineers at the Marshall Space Flight Center (MSFC) have been developing and testing video-based sensors for automated spacecraft guidance for several years. The next generation of Video Guidance Sensor (VGS) is being designed to be faster and more capable than ever. It will have applications to relative position measurement in any field of endeavor. The system works by sequentially firing two different wavelengths of laser diodes at the target (which has retroreflectors) and processing the two images. Since the target only reflects one wavelength, it shows up well in one image and not at all in the other. Because the target's dimensions are known, the relative positions and attitudes of the target and the sensor can be computed from the spots reflected from the target. The current sensor operates at 5 Hz at ranges from 1 to 110 meters with a 20 deg. field-of-view. The Video Guidance Sensor (VGS) developed over the past several years has performed well in testing and met the objective of being used as the terminal guidance sensor for an automated rendezvous and capture system. The first VGS was successfully tested in closed-loop 3-degree-of-freedom (3-DOF) tests in 1989 and then in 6-DOF open-loop tests in 1992 and closed-loop tests in 1993-4. Development and testing continued, and in 1995 approval was given to test the VGS in an experiment on the Space Shuttle. The VGS flew in 1997 and in 1998, performing well during both flight experiments. During the development and testing before, during, and after the flight experiments, numerous areas for improvement were found. The next generation of VGS is being designed to operate at up to 100 Hz tracking rates and at ranges from 0.5 to 200 meters. In addition to its use as a spacecraft guidance sensor, it could be used as an alignment aid for an operator of a remote system (giving position and attitude feedback data), as a feedback system for a robotic arm, or for automated vehicle guidance. The next generation VGS, with its higher tracking rates, smaller size, and lower power could be used in more places than the original VGS, and by using LED's instead of laser diodes, the system would be eye-safe at any range. More potential applications include aerial station keeping (keeping 2 or more autonomous aircraft within particular relative positions), under-water robotics, and the guidance of ground vehicles in predefined areas equipped with sets of targets.
Modular and offsite construction methods are being increasingly adopted due to the advantages they offer in terms of project completion time, quality, and energy-efficiency. Despite these advantages, the current state of monitoring systems in modular construction factories highly relies on labor-intensive, subjective, and error-prone observational methods. A large body of research has aimed to automate the monitoring process using an array of sensors, such as IMUs and RFIDs, during the past two decades. Recently, computer vision-based methods have gained increasing interest as a non-intrusive technology to monitor the process inside modular construction factories. However, partial occlusion challenges have impeded their practical application on a large scale. This challenge is specifically important for monitoring the installation of subassemblies since they can obstruct the view of the monitoring camera, especially those that enable long-term monitoring like closed-circuit television (CCTV) fixed-view surveillance cameras. Here, this paper aims to address this challenge by proposing a novel computer vision-based method to monitor the installation of new subassemblies inside modular factories in highly occluded scenes. The proposed methodology identifies the subassemblies in the CCTV video footage using computer vision, analyzes the occlusions using BIM and ray casting techniques, and estimates the progress of assembly by comparing the BIM model with the detected subassemblies in the video. The proposed methodology was successfully validated on surveillance videos captured from a volumetric modular construction factory in the U.S., achieving 93% accuracy in identifying the installation of subassemblies. The results from this research show that the integration of BIM and computer vision is a promising method for monitoring the installation processes inside modular factories under severe occlusion.
This paper presents an intelligent adaptive control system for the control of a solid-liquid interface of a crystal while it is growing via directional solidification inside a multizone transparent furnace. The task of the process controller is to establish a user-specified axial temperature profile and to maintain a desirable interface shape. Both single-input-single-output and multi-input-multi-output adaptive pole placement algorithms have been used to control the temperature. Also described is an intelligent measurement system to assess the shape of the crystal while it is growing. A color video imaging system observes the crystal in real time and determines the position and the shape of the interface. This information is used to evaluate the crystal growth rate, and to analyze the effects of translational velocity and temperature profiles on the shape of the interface. Creation of this knowledge base is the first step to incorporate image processing into furnace control.
The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.
Accurate, precise, and rapid particle tracking in three dimensions remains a challenge; yet, its achievement will significantly enhance our understanding of living systems. We developed a multifocal microscopy (MFM) that allows snapshot acquisition of the imaging data, and an associated image processing approach, that together allow simultaneous 3D tracking of many fluorescent particles with nanoscale resolution. The 3D tracking was validated by measuring a known trajectory of a fluorescent bead with an axial accuracy of 19 nm through an image depth (axial range) of 3 pm and 4 nm precision of axial localization through an image depth of 4 pm. A second test obtained a uniform axial probability distribution and Brownian dynamics of beads diffusing in solution. We also validated the MFM approach by imaging fluorescent beads immobilized in gels and comparing the 3D localizations to their "ground truth" positions obtained from a confocal microscopy z-stack of finely spaced images. Finally, we applied our MFM and image processing approach to obtain 3D trajectories of insulin granules in pseudoislets of MIN6 cells to demonstrate its compatibility with complex biological systems. Our study demonstrates that multifocal microscopy allows rapid (video rate) and simultaneous 3D tracking of many "particles" with nanoscale accuracy and precision in a wide range of systems, including over spatial scales relevant to whole live cells.
The programming and algorithms needed for implementing a full-field, 3-D velocimeter for laminar flow systems and the appropriate hardware to fully implement this ultimate system are discussed. It appears that imaging using a synched pair of video cameras and digitizer boards with synched rails for camera motion will provide a viable solution to the laminar tracking problem. The algorithms given here are simple, which should speed processing. On a heavily loaded VAXstation 3100 the particle identification can take 15 to 30 seconds, with the tracking taking less than one second. It seeems reasonable to assume that four image pairs can thus be acquired and analyzed in under one minute.
Real-time monitor system provides bar-chart displays of significant operating parameters developed for National Full-Scale Aerodynamic Complex at Ames Research Center. Designed to gather and process sensory data on operating conditions of wind tunnels and models, and displays data for test engineers and technicians concerned with safety and validation of operating conditions. Bar-chart video monitor displays data in as many as 50 channels at maximum update rate of 2 Hz in format facilitating quick interpretation.
This video highlights the celebration of NASA Lewis Research Center's 50th anniversary celebrations. To commemorate this event, employees designed and manufactured a statue that contains a time capsule. The design process is shown, as well as the unveiling ceremony which features speeches by the center director and local dignitaries.
With the high speed networking capabilities, multiple media options, and massive amounts of information that exist in electronic format today, the concept of a 'virtual' library or 'library without walls' is becoming viable. In virtual library environment, the information processed goes beyond the traditional definition of documents to include the results of scientific and technical research and development (reports, software, data) recorded in any format or media: electronic, audio, video, or scanned images. Network access to information must include tools to help locate information sources and navigate the networks to connect to the sources, as well as methods to extract the relevant information. Graphical User Interfaces (GUI's) that are intuitive and navigational tools such as Intelligent Gateway Processors (IGP) will provide users with seamless and transparent use of high speed networks to access, organize, and manage information. Traditional libraries will become points of electronic access to information on multiple medias. The emphasis will be towards unique collections of information at each library rather than entire collections at every library. It is no longer a question of whether there is enough information available; it is more a question of how to manage the vast volumes of information. The future equation will involve being able to organize knowledge, manage information, and provide access at the point of origin.
This research project is intended to develop an efficient system for reliably transmitting image and video data over low bit rate noisy channels. The basic ideas behind the proposed approach are the following: employ statistical-based image modeling to facilitate pre- and post-processing and error detection, use spare redundancy that the source compression did not remove to add robustness, and implement coded modulation to improve bandwidth efficiency and noise rejection. Over the last six months, progress has been made on various aspects of the project. Through our studies of the integrated system, a list-based iterative Trellis decoder has been developed. The decoder accepts feedback from a post-processor which can detect channel errors in the reconstructed image. The error detection is based on the Huber Markov random field image model for the compressed image. The compression scheme used here is that of JPEG (Joint Photographic Experts Group). Experiments were performed and the results are quite encouraging. The principal ideas here are extendable to other compression techniques. In addition, research was also performed on unequal error protection channel coding, subband vector quantization as a means of source coding, and post processing for reducing coding artifacts. Our studies on unequal error protection (UEP) coding for image transmission focused on examining the properties of the UEP capabilities of convolutional codes. The investigation of subband vector quantization employed a wavelet transform with special emphasis on exploiting interband redundancy. The outcome of this investigation included the development of three algorithms for subband vector quantization. The reduction of transform coding artifacts was studied with the aid of a non-Gaussian Markov random field model. This results in improved image decompression. These studies are summarized and the technical papers included in the appendices.
An overview is presented of the development, use, and current flight-test status of a highly instrumented North American Rockwell OV-10A Bronco as a wake-vortex-hazards research aircraft. A description of the operational requirements and measurements criteria, the resulting instrumentation systems and aircraft modifications, system-calibration and research flights completed to date, and current flight status are included. These experiments are being conducted by the National Aeronautics and Space Administration as part of an effort to provide the technology to safely improve the capacity of the nation's air transportation system and specifically to provide key data in understanding and predicting wake vortex decay, transport characteristics, and the dynamics of encountering wake turbulence. The OV-10A performs several roles including meteorological measurements platform, wake-decay quantifier, and trajectory-quantifier for wake encounters. Extensive research instrumentation systems include multiple airdata sensors, video cameras with cockpit displays, aircraft state and control-position measurements, inertial aircraft-position measurements, meteorological measurements, and an on-board personal computer for real-time processing and cockpit display of research data. To date, several of the preliminary system check flights and two meteorological-measurements deployments have been completed. Several wake encounter and wake-decay-measurements flights are planned for the fall of 1995.
Detect Damage in Blades Electronic holography can show damaged regions in fan blades at 30 frames/sec. The electronic holograms are transformed by finite-element-model-trained artificial neural networks to visualize the damage. The trained neural networks are linked with video and graphics to visualize the bending-induced strain distribution, which is very sensitive to damage. By contrast, it is very difficult to detect damage by viewing the raw, speckled, characteristic fringe patterns. For neural-network visualization of damage, 2 frames or 2 fields are used, rather than the 12 frames normally used to compute the displacement distribution from electronic holograms. At the NASA Lewis Research Center, finite element models are used to compute displacement and strain distributions for the vibration modes of undamaged and cracked blades. A model of electronic time-averaged holography is used to transform the displacement distributions into finite-element-resolution characteristic fringe patterns. Then, a feedforward neural network is trained with the fringe-pattern/strain-pattern pairs, and the neural network, electronic holography, and video are implemented on a workstation. Now that the neural networks have been tested successfully at 30 frames/sec on undamaged and cracked cantilevers, the electronic holography and neural-network processing are being adapted for onsite damage inspection of twisted fan blades and rotormounted blades. Our conclusion is that model-trained neural nets are effective when they are trained with good models whose application is well understood. This work supports the aeromechanical testing portion of the Advanced Subsonic Technology Project.