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At least 73 records · Page 4

Perseverance’s Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) Investigation

The Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC) is a robotic arm-mounted instrument on NASA’s Perseverance rover. SHERLOC has two primary boresights. The Spectroscopy boresight generates spatially resolved chemical maps using fluorescence and Raman spectroscopy coupled to microscopic images (10.1 μm/pixel). The second boresight is a Wide Angle Topographic Sensor for Operations and eNgineering (WATSON); a copy of the Mars Science Laboratory (MSL) Mars Hand Lens Imager (MAHLI) that obtains color images from microscopic scales (∼13 μm/pixel) to infinity. SHERLOC Spectroscopy focuses a 40 μs pulsed deep UV neon-copper laser (248.6 nm), to a ∼100 μm spot on a target at a working distance of ∼48 mm. Fluorescence emissions from organics, and Raman scattered photons from organics and minerals, are spectrally resolved with a single diffractive grating spectrograph with a spectral range of 250 to ∼370 nm. Because the fluorescence and Raman regions are naturally separated with deep UV excitation (<250 nm), the Raman region ∼ 800 – 4000 cm−1 (250 to 273 nm) and the fluorescence region (274 to ∼370 nm) are acquired simultaneously without time gating or additional mechanisms. SHERLOC science begins by using an Autofocus Context Imager (ACI) to obtain target focus and acquire 10.1 μm/pixel greyscale images. Chemical maps of organic and mineral signatures are acquired by the orchestration of an internal scanning mirror that moves the focused laser spot across discrete points on the target surface where spectra are captured on the spectrometer detector. ACI images and chemical maps (< 100 μm/mapping pixel) will enable the first Mars in situ view of the spatial distribution and interaction between organics, minerals, and chemicals important to the assessment of potential biogenicity (containing CHNOPS). Single robotic arm placement chemical maps can cover areas up to 7x7 mm in area and, with the < 10 min acquisition time per map, larger mosaics are possible with arm movements. This microscopic view of the organic geochemistry of a target at the Perseverance field site, when combined with the other instruments, such as Mastcam-Z, PIXL, and SuperCam, will enable unprecedented analysis of geological materials for both scientific research and determination of which samples to collect and cache for Mars sample return.

Rohit Bhartia↗

NASA Tech Briefs, July 2008

Topics covered include: Torque Sensor Based on Tunnel-Diode Oscillator; Shaft-Angle Sensor Based on Tunnel-Diode Oscillator; Ground Facility for Vicarious Calibration of Skyborne Sensors; Optical Pressure-Temperature Sensor for a Combustion Chamber; Impact-Locator Sensor Panels; Low-Loss Waveguides for Terahertz Frequencies; MEMS/ECD Method for Making Bi(2-x)Sb(x)Te3 Thermoelectric Devices; Low-Temperature Supercapacitors; Making a Back-Illuminated Imager with Back-Side Contact and Alignment Markers; Compact, Single-Stage MMIC InP HEMT Amplifier; Nb(x)Ti(1-x)N Superconducting-Nanowire Single-Photon Detectors; Improved Sand-Compaction Method for Lost-Foam Metal Casting; Improved Probe for Evaluating Compaction of Mold Sand; Polymer-Based Composite Catholytes for Li Thin-Film Cells; Using ALD To Bond CNTs to Substrates and Matrices; Alternating-Composition Layered Ceramic Barrier Coatings; Variable-Structure Control of a Model Glider Airplane; Axial Halbach Magnetic Bearings; Compact, Non-Pneumatic Rock-Powder Samplers; Biochips Containing Arrays of Carbon-Nanotube Electrodes; Nb(x)Ti(1-x)N Superconducting-Nanowire Single-Photon Detectors; Neon as a Buffer Gas for a Mercury-Ion Clock; Miniature Incandescent Lamps as Fiber-Optic Light Sources; Bidirectional Pressure-Regulator System; and Prism Window for Optical Alignment. Single-Grid-Pair Fourier Telescope for Imaging in Hard-X Rays and gamma Rays Range-Gated Metrology with Compact Optical Head Lossless, Multi-Spectral Data Compressor for Improved Compression for Pushbroom-Typetruments.

Source record↗

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

Figueiredo, Marco A.↗

Performance of a multistep fluorescence-gated proportional counter for hard X-ray astronomy

Results from the first flight of our proportional counter in an imaging telescope led us to rebuild the detector. We have used a Penning gas mixture (xenon + 1 percent isobutylene) and introduced a preamplification region to improve the energy resolution. We have rebuilt the pressure vessel making novel use of molybdenum as the housing material in order to reduce the residual instrument background, particularly in the fluorescence-gated mode for which the detector design has been optimized. We have also increased the sensitive gas depth from 9 to 14 cm to further increase the sensitivity to both fluorescent pairs and conventional singles. Our calibrations have shown that the overall energy resolution of the detector has been enhanced by a factor of 2, and we predict that the sensitivity at float will increase by a factor of 3 in the 50-70 keV energy band.

Dietz, K. L.↗

Next Generation UAS Based Spectral Systems for Environmental Monitoring

This presentation provides information on the development of a small Unmanned Aerial System(UAS) with a low power, high performance Intelligent Payload Module (IPM) and a hyperspectral imager to enable intelligent gathering of science grade vegetation data over agricultural fields at about 150 ft. The IPM performs real time data processing over the image data and then enables the navigation system to move the UAS to locations where measurements are optimal for science. This is important because the small UAS typically has about 30 minutes of battery power and therefore over large agricultural fields, resource utilization efficiency is important. The key innovation is the shrinking of the IPM and the cross communication with the navigation software to allow the data processing to interact with desired way points while using Field Programmable Gate Arrays to enable high performance on large data volumes produced by the hyperspectral imager.

onboard processing↗

Beyond the supercomputer

A NASA-directed development of massively parallel processor (MPP) computers is outlined, noting intended applications for data processing for near term earth resource and environment mapping, radar, and television transmissions. The MPP is designed to perform 100 billion operations/sec to obtain satisfactory image processing, while separate processing units correct distortions, register images, calculate correlation functions, and classify multispectral characteristics. Arrays of 1s and 0s will be manipulated in analog-to-digital conversions generating separate planes corresponding to powers of binaries. Data wires are replaced by fiber-optic tubes or thousands of wires, and single logic gates are replaced by thousands of logic gates and every memory element by thousands of memory elements. Features of the interconnections and the images control processor units are detailed, along with implementation of sliders for program flexibility.

Schaefer, D. H.↗

Optical computing and neural networks; Proceedings of the Meeting, National Chiao Tung Univ., Hsinchu, Taiwan, Dec. 16, 17, 1992

The present conference discusses optical neural networks, photorefractive nonlinear optics, optical pattern recognition, digital and analog processors, and holography and its applications. Attention is given to bifurcating optical information processing, neural structures in digital halftoning, an exemplar-based optical neural net classifier for color pattern recognition, volume storage in photorefractive disks, and microlaser-based compact optical neuroprocessors. Also treated are the optical implementation of a feature-enhanced optical interpattern-associative neural network model and its optical implementation, an optical pattern binary dual-rail logic gate module, a theoretical analysis for holographic associative memories, joint transform correlators, image addition and subtraction via the Talbot effect, and optical wavelet-matched filters. (No individual items are abstracted in this volume)

Hsu, Ken-Yuh↗

Subframe Burst Gating for Raman Spectroscopy in Combustion

We describe an architecture for spontaneous Raman scattering utilizing a frame-transfer CCD sensor operating in a subframe burst-gating mode to realize time-resolved combustion diagnostics. The technique permits all-electronic optical gating with microsecond shutter speeds 5 J.Ls) without compromising optical throughput or image fidelity. When used in conjunction with a pair of orthogonally polarized excitation lasers, the technique measures single-shot vibrational Raman scattering that is minimally contaminated by problematic optical background noise.

Kojima, Jun↗

Interference-free optical detection for Raman spectroscopy

An architecture for spontaneous Raman scattering (SRS) that utilizes a frame-transfer charge-coupled device (CCD) sensor operating in a subframe burst gating mode to realize time-resolved combustion diagnostics is disclosed. The technique permits all-electronic optical gating with microsecond shutter speeds (<5 .mu.s), without compromising optical throughput or image fidelity. When used in conjunction with a pair of orthogonally-polarized excitation lasers, the technique measures time-resolved vibrational Raman scattering that is minimally contaminated by problematic optical background noise.

Nguyen, Quang-Viet↗

Method and apparatus for reflection mode imaging

A volume is scanned with a raster scan about a center of rotation using a transmitter/receiver at a selected range while gating a range window on the receiver with a selected range differential. The received signals are then demodulated to obtain signals representative of a property within the volume being scanned such as the density of a tumor. The range is varied until the entire volume has been scanned at all ranges to be displayed. An imaging display is synchronously scanned together with the raster scan to display variations of the property on the display. A second transmitter/receiver with associated equipment may be offset from the first and variations displayed from each of the transmitter/receivers on its separate display. The displays may then be combined stereoscopically to provide a three-dimensional image representative of variations of the property.

Heyser, Richard C.↗

Rapid Corner Detection Using FPGAs

In order to perform precision landings for space missions, a control system must be accurate to within ten meters. Feature detection applied against images taken during descent and correlated against the provided base image is computationally expensive and requires tens of seconds of processing time to do just one image while the goal is to process multiple images per second. To solve this problem, this algorithm takes that processing load from the central processing unit (CPU) and gives it to a reconfigurable field programmable gate array (FPGA), which is able to compute data in parallel at very high clock speeds. The workload of the processor then becomes simpler; to read an image from a camera, it is transferred into the FPGA, and the results are read back from the FPGA. The Harris Corner Detector uses the determinant and trace to find a corner score, with each step of the computation occurring on independent clock cycles. Essentially, the image is converted into an x and y derivative map. Once three lines of pixel information have been queued up, valid pixel derivatives are clocked into the product and averaging phase of the pipeline. Each x and y derivative is squared against itself, as well as the product of the ix and iy derivative, and each value is stored in a WxN size buffer, where W represents the size of the integration window and N is the width of the image. In this particular case, a window size of 5 was chosen, and the image is 640 480. Over a WxN size window, an equidistance Gaussian is applied (to bring out the stronger corners), and then each value in the entire window is summed and stored. The required components of the equation are in place, and it is just a matter of taking the determinant and trace. It should be noted that the trace is being weighted by a constant k, a value that is found empirically to be within 0.04 to 0.15 (and in this implementation is 0.05). The constant k determines the number of corners available to be compared against a threshold sigma to mark a valid corner. After a fixed delay from when the first pixel is clocked in (to fill the pipeline), a score is achieved after each successive clock. This score corresponds with an (x,y) location within the image. If the score is higher than the predetermined threshold sigma, then a flag is set high and the location is recorded.

Morfopoulos, Arin C.↗

Absolute Position Encoders With Vertical Image Binning

Improved optoelectronic patternrecognition encoders that measure rotary and linear 1-dimensional positions at conversion rates (numbers of readings per unit time) exceeding 20 kHz have been invented. Heretofore, optoelectronic pattern-recognition absoluteposition encoders have been limited to conversion rates <15 Hz -- too low for emerging industrial applications in which conversion rates ranging from 1 kHz to as much as 100 kHz are required. The high conversion rates of the improved encoders are made possible, in part, by use of vertically compressible or binnable (as described below) scale patterns in combination with modified readout sequences of the image sensors [charge-coupled devices (CCDs)] used to read the scale patterns. The modified readout sequences and the processing of the images thus read out are amenable to implementation by use of modern, high-speed, ultra-compact microprocessors and digital signal processors or field-programmable gate arrays. This combination of improvements makes it possible to greatly increase conversion rates through substantial reductions in all three components of conversion time: exposure time, image-readout time, and image-processing time.

Leviton, Douglas B.↗

Large area CCD image sensors for scientific applications

The designs of the 512 x 512 pixel and 2048 x 2048 pixel CCD sensors developed for scientific imaging are described. Both sensors are manufactured using the three phase, three level polysilicon gate technology, and the architecture of the chips is series-parallel-series. The components of the two different on-chip amplifiers are examined. The sensors are thinned and operated in the back side illumination mode to ensure the highest quantum efficiency. The performances of a number of front side illuminated devices and two thinned rear illuminated chips are evaluated. It is observed that the sensors equal or exceed their proposed charge transfer efficiency of 0.99999, the noise level of 2 e- and 20 e- at 50 kp/s, the quantum efficiency of 40 percent at 400 microns and 70 percent at 700 microns, and of the well capacity of greater than 500 ke-/pixel.

Blouke, M. M.↗

FPGA Coprocessor for Accelerated Classification of Images

An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.

Pingree, Paula J.↗

Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager

Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx Virtex IV LX25 device, and ported to a Xilinx prototype board. The current implementation has a critical path of 29.5 ns, which dictated a clock speed of 33 MHz. The critical path delay is end-to-end measurement between the uncompressed input data and the output compression data stream. The implementation compresses one sample every clock cycle, which results in a speed of 33 Msample/s. The implementation has a rather low device use of the Xilinx Virtex IV LX25, making the total power consumption of the implementation about 1.27 W.

Keymeulen, Didlier↗

Influence of Molecular Weight on the Mechanical Performance of a Thermoplastic Glassy Polyimide

Mechanical Testing of an advanced thermoplastic polyimide (LaRC-TM-SI) with known variations in molecular weight was performed over a range of temperatures below the glass transition temperature. The physical characterization, elastic properties and notched tensile strength were all determined as a function of molecular weight and test temperature. It was shown that notched tensile strength is a strong function of both temperature and molecular weight, whereas stiffness is only a strong function of temperature. A critical molecular weight (Mc) was observed to occur at a weight-average molecular weight (Mw) of approx. 22000 g/mol below which, the notched tensile strength decreases rapidly. This critical molecular weight transition is temperature-independent. Furthermore, inelastic analysis showed that low molecular weight materials tended to fail in a brittle manner, whereas high molecular weight materials exhibited ductile failure. The microstructural images supported these findings.

Nicholson, Lee M.↗

Acceleration of Stereo Correlation in Verilog

To speed up vision processing in low speed, low power devices, embedding FPGA hardware is becoming an effective way to add processing capability. FPGAs offer the ability to flexibly add parallel and/or deeply pipelined computation to embedded processors without adding significantly to the mass and power requirements of an embedded system. This paper will discuss the JPL stereo vision system, and describe how a portion of that system was accelerated by using custom FPGA hardware to process the computationally intensive portions of JPL stereo. The architecture described takes full advantage of the ability of an FPGA to use many small computation elements in parallel. This resulted in a 16 times speedup in real hardware over using a simple linear processor to compute image correlation and disparity.

machine vision↗

Video Guidance Sensor and Time-of-Flight Rangefinder

A proposed video guidance sensor (VGS) would be based mostly on the hardware and software of a prior Advanced VGS (AVGS), with some additions to enable it to function as a time-of-flight rangefinder (in contradistinction to a triangulation or image-processing rangefinder). It would typically be used at distances of the order of 2 or 3 kilometers, where a typical target would appear in a video image as a single blob, making it possible to extract the direction to the target (but not the orientation of the target or the distance to the target) from a video image of light reflected from the target. As described in several previous NASA Tech Briefs articles, an AVGS system is an optoelectronic system that provides guidance for automated docking of two vehicles. In the original application, the two vehicles are spacecraft, but the basic principles of design and operation of the system are applicable to aircraft, robots, objects maneuvered by cranes, or other objects that may be required to be aligned and brought together automatically or under remote control. In a prior AVGS system of the type upon which the now-proposed VGS is largely based, the tracked vehicle is equipped with one or more passive targets that reflect light from one or more continuous-wave laser diode(s) on the tracking vehicle, a video camera on the tracking vehicle acquires images of the targets in the reflected laser light, the video images are digitized, and the image data are processed to obtain the direction to the target. The design concept of the proposed VGS does not call for any memory or processor hardware beyond that already present in the prior AVGS, but does call for some additional hardware and some additional software. It also calls for assignment of some additional tasks to two subsystems that are parts of the prior VGS: a field-programmable gate array (FPGA) that generates timing and control signals, and a digital signal processor (DSP) that processes the digitized video images. The additional timing and control signals generated by the FPGA would cause the VGS to alternate between an imaging (direction-finding) mode and a time-of-flight (range-finding mode) and would govern operation in the range-finding mode.

Bryan, Thomas↗