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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Data processing assessment for the Lunar Geoscience Observer imaging spectrometer

On the Lunar Geoscience Observer project, a Visible and Infrared Mapping Spectrometer instrument has been proposed. This instrument will have science data input rates in the hundreds of kilobits per second (kbps) and an average telemetry output data rate of 4 kbps. Techniques that can be used to reduce the throughput of the instrument are editing, summing and averaging, data compression, data preprocessing, pattern recognition and snapshot data taking. Due to instrument limitations in the buffer memory size and processing speeds, a careful selection of the available techniques must be made.

Irigoyen, R. E.↗
NASA NTRS1993

Application guide for universal source encoding for space

Lossless data compression was studied for many NASA missions. The Rice algorithm was demonstrated to provide better performance than other available techniques on most scientific data. A top-level description of the Rice algorithm is first given, along with some new capabilities implemented in both software and hardware forms. Systems issues important for onboard implementation, including sensor calibration, error propagation, and data packetization, are addressed. The latter part of the guide provides twelve case study examples drawn from a broad spectrum of science instruments.

Yeh, Pen-Shu↗
NASA NTRS2000

TE/TM Simulations of Interferometric Measurements

Interferometric synthetic aperture radar (IFSAR) measurements at X-, C-, L-, and P-band are used to derive ground topography at meter level resolution. Interpretation of the derived topography requires attention due to the complex interaction of the radar signal with ground cover. The presence of penetrable surfaces such as vegetation, and tree canopies poses a challenge since the depth of penetration depends on a number of parameters such as the operating radar frequency, polarization, incident angle, as well as terrain structure. The dependence of the reconstructed topography on polarization may lead to the characterization of the ground cover. Simulation of interferometric measurements is useful for interpretation of the derived topography (B. Houshmand, Proceedings of URSI, 314, 1997). In this talk , time domain simulations for interferometric measurement for TE- and TM- polarization are presented. Time domain simulation includes the effects of the surface material property as well geometry comparable the radar signal wavelength (B. Houshmand, Proceedings of the URSI, 25, 1998). The IFSAR simulation is carried out in two steps. First, the forward scattering data is generated based on full wave analysis. Next, the electromagnetic information is inverted to generate surface topography. This inversion is based on the well known IFSAR processing technique which is composed of signal compression, and formation of an interferogram. The full wave forward scattering data is generated by the scattered-field formulation of the FDTD algorithm. The simulation is carried out by exciting the computational domain by a radar signal. The scattered field is then computed and translated to the receiving interferometric antennas using the time-domain Huygen's principle. The inversion process starts by compressing the time-domain data. The range compressed data from both receivers are then coregistered to form an interferogram. The resulting interferogram is then related to the ground topography using the radar imaging geometry. In this talk, the simulation results are compared with the C-band TM IFSAR derived topography, and the TE/TM SAR images at L-Band.

Houshmand, Bijan↗
NASA NTRS2014

Distributed Transforms for Efficient Data Gathering in Sensor Networks

Devices, systems, and techniques for data collecting network such as wireless sensors are disclosed. A described technique includes detecting one or more remote nodes included in the wireless sensor network using a local power level that controls a radio range of the local node. The technique includes transmitting a local outdegree. The local outdegree can be based on a quantity of the one or more remote nodes. The technique includes receiving one or more remote outdegrees from the one or more remote nodes. The technique includes determining a local node type of the local node based on detecting a node type of the one or more remote nodes, using the one or more remote outdegrees, and using the local outdegree. The technique includes adjusting characteristics, including an energy usage characteristic and a data compression characteristic, of the wireless sensor network by selectively modifying the local power level and selectively changing the local node type.

Ortega, Antonio↗
NASA NTRS1987

Noiseless coding for the magnetometer

Future unmanned space missions will continue to seek a full understanding of magnetic fields throughout the solar system. Severely constrained data rates during certain portions of these missions could limit the possible science return. This publication investigates the application of universal noiseless coding techniques to more efficiently represent magnetometer data without any loss in data integrity. Performance results indicated that compression factors of 2:1 to 6:1 can be expected. Feasibility for general deep space application was demonstrated by implementing a microprocessor breadboard coder/decoder using the Intel 8086 processor. The Comet Rendezvous Asteroid Flyby mission will incorporate these techniques in a buffer feedback, rate-controlled configuration. The characteristics of this system are discussed.

Rice, Robert F.↗
NASA NTRS1990

Data compression in digitized lines

The problem of data compression is very important in digital photogrammetry, computer assisted cartography, and GIS/LIS. In addition, it is also applicable in many other fields such as computer vision, image processing, pattern recognition, and artificial intelligence. Consequently, there are many algorithms available to solve this problem but none of them are considered to be satisfactory. In this paper, a new method of finding critical points in a digitized curve is explained. This technique, based on the normalized symmetric scattered matrix, is good for both critical points detection and data compression. In addition, the critical points detected by this algorithm are compared with those by zero-crossings.

Thapa, Khagendra↗
NASA NTRS1995

KRESKA: A compression system for small and very large images

An effective lossless compression system for grayscale images is presented using finite context variable order Markov models. A new method to accurately estimate the probability of the escape symbol is proposed. The choice of the best model order and rules for selecting context pixels are discussed. Two context precision and two symbol precision techniques to handle noisy image data with Markov models are introduced. Results indicate that finite context variable order Markov models lead to effective lossless compression systems for small and very large images. The system achieves higher compression ratios than some of the better known image compression techniques such as lossless JPEG, JBIG, or FELICS.

Ohnesorge, Krystyna W.↗
NASA NTRS2004

Real time on-chip sequential adaptive principal component analysis for data feature extraction and image compression

In this paper, we present a new, simple, and optimized hardware architecture sequential learning technique for adaptive Principle Component Analysis (PCA) which will help optimize the hardware implementation in VLSI and to overcome the difficulties of the traditional gradient descent in learning convergence and hardware implementation.

PCA feature extraction image compression real time↗
NASA NTRS2011

Image Segmentation, Registration, Compression, and Matching

A novel computational framework was developed of a 2D affine invariant matching exploiting a parameter space. Named as affine invariant parameter space (AIPS), the technique can be applied to many image-processing and computer-vision problems, including image registration, template matching, and object tracking from image sequence. The AIPS is formed by the parameters in an affine combination of a set of feature points in the image plane. In cases where the entire image can be assumed to have undergone a single affine transformation, the new AIPS match metric and matching framework becomes very effective (compared with the state-of-the-art methods at the time of this reporting). No knowledge about scaling or any other transformation parameters need to be known a priori to apply the AIPS framework. An automated suite of software tools has been created to provide accurate image segmentation (for data cleaning) and high-quality 2D image and 3D surface registration (for fusing multi-resolution terrain, image, and map data). These tools are capable of supporting existing GIS toolkits already in the marketplace, and will also be usable in a stand-alone fashion. The toolkit applies novel algorithmic approaches for image segmentation, feature extraction, and registration of 2D imagery and 3D surface data, which supports first-pass, batched, fully automatic feature extraction (for segmentation), and registration. A hierarchical and adaptive approach is taken for achieving automatic feature extraction, segmentation, and registration. Surface registration is the process of aligning two (or more) data sets to a common coordinate system, during which the transformation between their different coordinate systems is determined. Also developed here are a novel, volumetric surface modeling and compression technique that provide both quality-guaranteed mesh surface approximations and compaction of the model sizes by efficiently coding the geometry and connectivity/topology components of the generated models. The highly efficient triangular mesh compression compacts the connectivity information at the rate of 1.5-4 bits per vertex (on average for triangle meshes), while reducing the 3D geometry by 40-50 percent. Finally, taking into consideration the characteristics of 3D terrain data, and using the innovative, regularized binary decomposition mesh modeling, a multistage, pattern-drive modeling, and compression technique has been developed to provide an effective framework for compressing digital elevation model (DEM) surfaces, high-resolution aerial imagery, and other types of NASA data.

Yadegar, Jacob↗
NASA NTRS1989

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↗
NASA NTRS2012

Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System

Modern hyperspectral imaging systems are able to acquire far more data than can be downlinked from a spacecraft. Onboard data compression helps to alleviate this problem, but requires a system capable of power efficiency and high throughput. Software solutions have limited throughput performance and are power-hungry. Dedicated hardware solutions can provide both high throughput and power efficiency, while taking the load off of the main processor. Thus a hardware compression system was developed. The implementation uses a field-programmable gate array (FPGA). The implementation is based on 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), page 26, which achieves excellent compression performance and has low complexity. This algorithm performs predictive compression using an adaptive filtering method, and uses adaptive Golomb coding. The implementation also packetizes the coded data. The FL algorithm is well suited for implementation in hardware. In the FPGA implementation, one sample is compressed every clock cycle, which makes for a fast and practical realtime solution for space applications. Benefits of this implementation are: 1) The underlying algorithm achieves a combination of low complexity and compression effectiveness that exceeds that of techniques currently in use. 2) The algorithm requires no training data or other specific information about the nature of the spectral bands for a fixed instrument dynamic range. 3) Hardware acceleration provides a throughput improvement of 10 to 100 times vs. the software implementation. A prototype of the compressor is available in software, but it runs at a speed that does not meet spacecraft requirements. The hardware implementation targets the Xilinx Virtex IV FPGAs, and makes the use of this compressor practical for Earth satellites as well as beyond-Earth missions with hyperspectral instruments.

Aranki, Nazeeh I.↗
NASA NTRS1978

Applications of dynamic measurement techniques to wind-tunnel testing

The uses of laser Doppler velocimeter, hot wire, and surface hot film techniques in the study of turbulent flows are described, and data obtained in compressible flows are discussed. Applications are illustrated with measurements of wind tunnel freestream turbulence characteristics and with data obtained in transitional, turbulent, and separated shear flows. A new method which was developed for the study of time dependent and unsteady turbulent flows is also presented.

Owen, F. K.↗
NASA NTRS1989

The Voyager 2 mission to Neptune

Voyager 2 and its twin, Voyager 1, were launched in 1977. Both spacecraft investigated Jupiter's and Saturn's systems. Voyager 2 continued on to fly past Utranus in 1986 and Neptune in 1989, while Voyager 1 headed out of the solar system. The mission at Neptune presented many engineering and scientific challenges. Neptune is about 30 Astronomical Units (AU) from the sun and earth, resulting in extremely low lights levels (nearly 1000 times lower than at earth) and in communication distances of nearly 4.5 billion kilometers. To compensate for the long communication distances, several new techniques were developed. As at Uranus, an onboard backup computer compressed the imaging data. In addition, the data return was further improved by electronically arraying and expanding several receiving antennas. As a result, the data rates from Neptune were about the same as they were from Saturn, even though the distance was three times greater. Several changes were made in the onboard software to optimize Voyager's operations at the very low light levels at Neptune. Finally, to obtain the maximum information from the Neptune encounter, a trajectory was selected which passed within just 5000 kilometers of Neptune's atmosphere, but which also posed several possible environmental hazards.

Haynes, Norman R.↗
NASA NTRS2011

Analysis of Compression Algorithm in Ground Collision Avoidance Systems (Auto-GCAS)

Automatic Ground Collision Avoidance Systems (Auto-GCAS) utilizes Digital Terrain Elevation Data (DTED) stored onboard a plane to determine potential recovery maneuvers. Because of the current limitations of computer hardware on military airplanes such as the F-22 and F-35, the DTED must be compressed through a lossy technique called binary-tree tip-tilt. The purpose of this study is to determine the accuracy of the compressed data with respect to the original DTED. This study is mainly interested in the magnitude of the error between the two as well as the overall distribution of the errors throughout the DTED. By understanding how the errors of the compression technique are affected by various factors (topography, density of sampling points, sub-sampling techniques, etc.), modifications can be made to the compression technique resulting in better accuracy. This, in turn, would minimize unnecessary activation of A-GCAS during flight as well as maximizing its contribution to fighter safety.

Schmalz, Tyler↗
NASA NTRS1972

Rank-k Maximal Statistics for Divergence and Probability of Misclassification

A technique is developed for selecting from n-channel multispectral data some k combinations of the n-channels upon which to base a given classification technique so that some measure of the loss of the ability to distinguish between classes, using the compressed k-dimensional data, is minimized. Information loss in compressing the n-channel data to k channels is taken to be the difference in the average interclass divergences (or probability of misclassification) in n-space and in k-space.

Decell, H. P., Jr.↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗
NASA NTRS1988

Autonomous image data reduction by analysis and interpretation

Image data is a critical component of the scientific information acquired by space missions. Compression of image data is required due to the limited bandwidth of the data transmission channel and limited memory space on the acquisition vehicle. This need becomes more pressing when dealing with multispectral data where each pixel may comprise 300 or more bytes. An autonomous, real time, on-board image analysis system for an exploratory vehicle such as a Mars Rover is developed. The completed system will be capable of interpreting image data to produce reduced representations of the image, and of making decisions regarding the importance of data based on current scientific goals. Data from multiple sources, including stereo images, color images, and multispectral data, are fused into single image representations. Analysis techniques emphasize artificial neural networks. Clusters are described by their outlines and class values. These analysis and compression techniques are coupled with decision-making capacity for determining importance of each image region. Areas determined to be noise or uninteresting can be discarded in favor of more important areas. Thus limited resources for data storage and transmission are allocated to the most significant images.

Eberlein, Susan↗