Kraken Camera Development and Fielding Overview
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This paper describes a novel application and evaluation of programmable networking in High-Energy Physics (HEP): a complete parser for the custom packet format used by Fermilab’s DUNE experiment. Notably, this parser is implemented on a Tofino programmable network switch and evaluated on the FABRIC testbed by using network traffic generated by the ICEBERG DUNE prototype. The parsed network traffic consists of Jumbo Ethernet frames that contain digitizations of sensor readings from ICEBERG’s detector.This work is an early investigation into providing in-network processing support for HEP experiments. The paper describes DUNE’s custom packet format, the challenges encountered when implementing a parser for that format, and an exploration of the techniques that are needed to overcome those challenges. We identify performance bottlenecks and discuss directions for future research.
Small, lightweight camera systems use solid state imaging devices in the form of phototransistor mosaic sensors instead of vidicon tubes for light sensing and image conversion. The digital logic circuits scan the sensor mosaic at 60 frames per second to produce pictures composed of a series of dots rather than lines.
Intelligent Vision Systems, Inc. (InVision) needed image acquisition technology that was reliable in bad weather for its TDS-200 Traffic Detection System. InVision researchers used information from NASA Tech Briefs and assistance from Johnson Space Center to finish the system. The NASA technology used was developed for Earth-observing imaging satellites: charge coupled devices, in which silicon chips convert light directly into electronic or digital images. The TDS-200 consists of sensors mounted above traffic on poles or span wires, enabling two sensors to view an intersection; a "swing and sway" feature to compensate for movement of the sensors; a combination of electronic shutter and gain control; and sensor output to an image digital signal processor, still frame video and optionally live video.
A digital video camera includes an image sensor capable of producing a frame of video data representing an image viewed by the sensor, an image memory for storing video data such as previously recorded frame data in a video frame location of the image memory, a read circuit for fetching the previously recorded frame data, an encryption circuit having an encryption key input connected to receive the previously recorded frame data from the read circuit as an encryption key, an un-encrypted data input connected to receive the frame of video data from the image sensor and an encrypted data output port, and a write circuit for writing a frame of encrypted video data received from the encrypted data output port of the encryption circuit to the memory and overwriting the video frame location storing the previously recorded frame data.
Digitizer and storage system allow rapid random access to video data by computer. RAPID (random-access picture digitizer) uses two commercially-available, charge-injection, solid-state TV cameras as sensors. It can continuously update its memory with each frame of video signal, or it can hold given frame in memory. In either mode, it generates composite video output signal representing digitized image in memory.
The instrument has a focal plane array of infrared sensors of the integrating type such as a multiplexed device in which a charge is built up on a capacitor which is proportional to the total number of photons which that sensor is exposed to between read-out cycles. The infrared sensors of the array are manufactured as part of an overall array which is part of a micro-electronic device. The sensor achieves greater sensitivity by applying a local offset to the output of each sensor before it is converted into a digital word. The offset which is applied to each sensor will typically be the sensor's average value so that the digital signal which is periodically read from each sensor of the array corresponds to the portion of the signal which is varying in time. With proper synchronization between the cyclical loading of the test object and the frame rate of the infrared array the output of the A/D converted signal will correspond to the stress field induced temperature variations. A digital lock-in operation may be performed on the output of each sensor in the array. This results in a test instrument which can rapidly form a precise image of the thermoelastic stresses in an object.
Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.
The present conference on digital signal processing, association and filtering techniques, and multiple-sensor/multiple-tracking techniques, discusses single-frame velocity estimation, efficient target extraction for laser radar imagery, precision target tracking for small extended objects, IR clutter partitioning for matched filter design, the maximum-likelihood approach to gamma circumvention, position estimation for optical point targets using staring detector arrays, and a multiple-scan signal processing technique for area-moving target indication. Also discussed are a proportional integral estimator, the prediction of track purity in tracking performance evaluations, synchronization and fault-tolerance in a distributed tracker, the benefits of soft sensors and probabilistic fusion, and testing track initiation algorithms fusing two-dimensional tracks.
Robust and accurate perception is important for modern intelligent transportation systems (ITS), which use sensors of various modalities for data fusion to create a digital twin of an intersection. Sensor calibration is an important process that creates a unified coordinate frame for the sensor output data so that it can be used for data fusion. Classical approaches for sensor calibration are time-consuming, require an overlapping field of view for feature matching, and are not feasible for ITS application as they cause disruptions in the flow of traffic. In this paper, we present a spatiotemporal automatic calibration approach to calibrate multiple infrastructure lidar, radar, and cameras installed at a traffic intersection. The approach uses global navigation satellite system (GNSS) positioning information shared by connected vehicles, and when the vehicle is detected by the sensor, we match the sensor detections with the GNSS coordinates. The proposed algorithm is evaluated with a real-world dataset utilizing detections from two radars, cameras, and lidars with a test vehicle instrumented with a post-processing kinematic (PPK)-corrected GNSS driving past the sensors installed at a four-way traffic intersection. The experimental results show that the proposed automatic calibration approach can achieve the transformation with a root mean squared error of less than 0.5 for radar and lidar and less than 2 for camera detections. The ability to rapidly calibrate sensors not only benefits initial installations, but can also be used for system health monitoring, while utilizing available connected vehicle data to test the real-time sensor fidelity and operational status.
The increasing use of highly augmented digital flight-control systems in modern military helicopters prompted an examination of the influence of rotor dynamics and other high-order dynamics on control-system performance. A study was conducted at NASA Ames Research Center to correlate theoretical predictions of feedback gain limits in the roll axis with experimental test data obtained from a variable-stability research helicopter. Feedback gains, the break frequency of the presampling sensor filter, and the computational frame time of the flight computer were systematically varied. The results, which showed excellent theoretical and experimental correlation, indicate that the rotor-dynamics, sensor-filter, and digital-data processing delays can severely limit the usable values of the roll-rate and roll-attitude feedback gains.
The increasing use of highly augmented digital flight-control systems in modern military helicopters prompted an examination of the influence of the influence of rotor dynamics and other high-order dynamics on control-system performance. A study was conducted at NASA Ames Research Center to correlate theoretical predictions of feedback gain limits in the roll axis with experimental test data obtained from a variable-stability research helicopter. Feedback gains, the break frequency of the presampling sensor filter, and the computational frame time of the flight computer were systematically varied. The results, which showed excellent theoretical and experimental correlation, indicate that the rotor-dynamics, sensor-filter, and digital-data processing delays can severely limit the usable values of the roll-rate and roll-attitude feedback gains.
Proposed system stabilizes jittering television image and/or measures jitter to extract information on motions of objects in image. Alternative version, system controls lateral motion on camera to generate stereoscopic views to measure distances to objects. In another version, motion of camera controlled to keep object in view. Heart of system is digital image-data processor called "jitter-miser", which includes frame buffer and logic circuits to correct for jitter in image. Signals from motion sensors on camera sent to logic circuits and processed into corrections for motion along and across line of sight.
Single-camera, single-view videogrammetry has been used to determine static aeroelastic deformation of a slotted flap configuration on a semispan model at the National Transonic Facility (NTF). Deformation was determined by comparing wind-off to wind-on spatial data from targets placed on the main element, shroud, and flap of the model. Digitized video images from a camera were recorded and processed to automatically determine target image plane locations that were then corrected for sensor, lens, and frame grabber spatial errors. The videogrammetric technique has been established at NASA facilities as the technique of choice when high-volume static aeroelastic data with minimum impact on data taking is required. The primary measurement at the NTF with this technique in the past has been the measurement of static aeroelastic wing twist on full span models. The first results using the videogrammetric technique for the measurement of component deformation during semispan testing at the NTF are presented.
Single-camera, single-view videogrammetry has been used for the first time to determine static aeroelastic deformation of a slotted flap configuration on a semispan model at the National Transonic Facility (NTF). Deformation was determined by comparing wind-off to wind-on spatial data from targets placed on the main element, shroud, and flap of the model. Digitized video images from a camera were recorded and processed to automatically determine target image plane locations that were then corrected for sensor, lens, and frame grabber spatial errors. The videogrammetric technique used for the measurements presented here has been established at NASA facilities as the technique of choice when high-volume static aeroelastic data with minimum impact on data taking is required. However, the primary measurement at the NTF with this technique in the past has been the measurement of the static aeroelastic wing twist of the main wing element on full span models rather than for the measurement of component deformation. Considerations for using the videogrammetric technique for semispan component deformation measurements as well as representative results are presented.
A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.
A computer-implemented method for compressing video data comprises receiving a sequence of video data values, each video data value being a digital value from a successive one of a plurality of pixels that form a video sensor, the sequence of video data values resulting from successive frames of video captured by the video sensor; extracting the video data values for each pixel in turn to create a plurality of pixel data streams, each pixel data stream including the video data value for each frame of captured video for the pixel; and applying data compression to each pixel data stream to create compressed data for each pixel data stream.
NASA's Marshall Space Flight Center flew on the STS-87 mission an active sensor system, the Video Guidance Sensor (VGS), to demonstrate its functioning in space and to collect performance data. The VGS was designed to provide near-range sensor data as part of an automatic rendezvous and docking system. The sensor determines the relative positions and attitudes between the active sensor and the passive target. The VGS uses laser diodes to illuminate retro-reflectors in the target, a solid-state camera to detect the return from the target, and a frame grabber and digital signal processor to convert the video information into the relative positions and attitudes. The system is designed to operate with the target within a relative azimuth of +/- 9.5 degrees and a relative elevation of +/- 7.5 degrees. The system will acquire and track the target within that field-of-view anywhere from 1.5 meters to 110 meters range, and is designed to acquire at relative attitudes of +/- 10 degrees in pitch and yaw and at any roll angle. The data is output from the sensor at 5 Hz, and the target and sensor software have been designed to permit two independent sensors to operate simultaneously (in order to allow for redundancy). The data from the flight experiment includes raw video data from the VGS camera, relative position and attitude measurements from the VGS to the target, independent hand-held laser ranges from the Shuttle Aft Flight Deck to the target, and Remote Manipulator System position data to correlate with the VGS data. The experiment was quite successful and returned much useful information. The experience gained from the design and flight of this experiment will lead to improved video sensors in the future.