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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Efficient Merge and Insert Operations for Binary Heaps and Trees

Binary heaps and binary search trees merge efficiently. We introduce a new amortized analysis that allows us to prove the cost of merging either binary heaps or balanced binary trees is O(l), in the amortized sense. The standard set of other operations (create, insert, delete, extract minimum, in the case of binary heaps, and balanced binary trees, as well as a search operation for balanced binary trees) remain with a cost of O(log n). For binary heaps implemented as arrays, we show a new merge algorithm that has a single operation cost for merging two heaps, a and b, of O(absolute value of a + min(log absolute value of b log log absolute value of b. log absolute value of a log absolute value of b). This is an improvement over O(absolute value of a + log absolute value of a log absolute value of b). The cost of the new merge is so low that it can be used in a new structure which we call shadow heaps. to implement the insert operation to a tunable efficiency. Shadow heaps support the insert operation for simple priority queues in an amortized time of O(f(n)) and other operations in time O((log n log log n)/f (n)), where 1 less than or equal to f (n) less than or equal to log log n. More generally, the results here show that any data structure with operations that change its size by at most one, with the exception of a merge (aka meld) operation, can efficiently amortize the cost of the merge under conditions that are true for most implementations of binary heaps and search trees.

Kuszmaul, Christopher Lee↗
DOE OSTI2025

DEDUPKV: A Space-Efficient and High-Performance Key-Value Store via Fine-Grained Deduplication

Log-Structured Merge Tree (LSM-tree) based key-value stores excel in write-intensive environments but suffer from data duplication, consuming up to 49% of storage space in LSM-tree-based key-value store deployments. Traditional solutions like compression and coarse-grained file system-level deduplication introduce overhead or have limited effectiveness. In this study, we propose DedupKV, a fine-grained deduplication framework tailored for LSM-tree, maximizing data reduction efficiency while minimizing write stalls and read overheads. DedupKV features three key innovations: (1) FLUSH-integrated inline deduplication, which removes duplicates during memory-to-storage writes; (2) WAL file-based offline deduplication, repurposing write-ahead logs to avoid double writes; and (3) elastic execution, dynamically balancing inline and offline deduplication based on memory pressure and workload intensity. Additionally, dynamic granularity management reduces deduplication metadata overhead. We implemented these four ideas in RocksDB for the first time and conducted experiments in a Linux environment. Our evaluation shows that WAL file-based offline deduplication and DedupKV outperform BlobDB by 33% and 23%, respectively, in write-heavy workloads, while reducing write amplification by 1.2 ×, 2 ×, and 1.6 × for real KV datasets.

Jamil, Safdar [Sogang University]↗
NASA NTRS1993

Entropy reduction via simplified image contourization

The process of contourization is presented which converts a raster image into a set of plateaux or contours. These contours can be grouped into a hierarchical structure, defining total spatial inclusion, called a contour tree. A contour coder has been developed which fully describes these contours in a compact and efficient manner and is the basis for an image compression method. Simplification of the contour tree has been undertaken by merging contour tree nodes thus lowering the contour tree's entropy. This can be exploited by the contour coder to increase the image compression ratio. By applying general and simple rules derived from physiological experiments on the human vision system, lossy image compression can be achieved which minimizes noticeable artifacts in the simplified image.

Turner, Martin J.↗
NASA NTRS1992

An improved classification tree analysis of high cost modules based upon an axiomatic definition of complexity

Identification of high cost modules has been viewed as one mechanism to improve overall system reliability, since such modules tend to produce more than their share of problems. A decision tree model was used to identify such modules. In this current paper, a previously developed axiomatic model of program complexity is merged with the previously developed decision tree process for an improvement in the ability to identify such modules. This improvement was tested using data from the NASA Software Engineering Laboratory.

Tian, Jianhui↗
NASA NTRS1980

An automated approach to the design of decision tree classifiers

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.

Argentiero, P.↗
NASA NTRS2006

Merging IceSAT GLAS and Terra MODIS Data in Order to Derive Forest Type Specific Tree Heights in the Central Siberian Boreal Forest

Mapping of boreal forest's type, biomass, and other structural parameters are critical for understanding of the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. We believe the nature of the forest structure information available from MISR and GLAS can be used to help identify forest type, age class, and estimate above ground biomass levels beyond that now possible with MODIS alone. The ground measurements will be used to develop relationships between remote sensing observables and forest characteristics and provide new information for understanding forest changes with respect to environmental change. Lidar is a laser altimeter that determines the distance from the instrument to the physical surface by measuring the time elapsed between the pulse emission and the reflected return. Other studies have shown that the returned signal may identify multiple returns originating from trees, building and other objects and permits the calculation of their height. Studies using field data have shown that lidar data can provide estimates of structural parameters such as biomass, stand volume and leaf area index and allows remarkable differentiation between primary and secondary forest. NASA's IceSAT Geoscience Laser Altimeter System (GLAS) was launched in January 2003 and collected data during February and September of that year. This study used data acquired over our study sites in central Siberia to examine the GLAS signal as a source of forest height and other structural characteristics. The purpose of our Siberia project is to improve forest cover maps and produce above-ground biomass maps of the boreal forest in Northern Eurasia from MODIS by incorporating structural information inherent in the Terra MISR and ICESAT Geoscience Laser Altimeter System (GLAS) instruments. A number of forest cover classifications exist for the boreal forest. We believe the limiting factor in these products is the lack of structural information, particularly in the vertical dimension. The emphasis of this project is to improve upon satellite maps of boreal forest structure parameters (i.e. height and biomass) using temporal, multi-angle, and vertical profile information of GLAS data. The existing and near future lidar data is useful for demonstrating these techniques and pursuing current estimates. Future lidar missions may be several years in the future, so we will work other new data sets that may aide in biomass estimates such as ALOS PALSAR We will continue this work to produce an accurate map of current above ground forest phytomass/carbon storage possible for the study area. We plan to develop, test, and integrate remote sensing methods for extracting forest canopy structure measures. We are compiling our field measurements and will compare them with the remote sensing methods where possible. We also be able to produce a realistic error bound on the remotely sensed carbon estimates.

Ranson, K. Jon↗
NASA NTRS2012

Wide Range SET Pulse Measurement

A method for measuring a wide range of SET pulses is demonstrated. Use of dynamic logic, faster than ordinary CMOS, allows capture of short pulses. A weighted binning of SET lengths allows measurement of a wide range of pulse lengths with compact circuitry. A pulse-length-conservative pulse combiner tree routes SETs from combinational logic to the measurement circuit, allowing SET measurements in circuits that cannot easily be arranged in long chains. The method is applied to add-multiplex combinational logic, and to an array of NFET routing switches, at .35 micron. Pulses are captured in a chain of Domino Logic AND gates. Propagation through the chain is frozen on the trailing edge by dropping low the second "enable" input to the AND gates. Capacitive loading is increased in the latter stages to create an approximately logarithmic weighted binning, so that a broad range of pulse lengths can be captured with a 10 stage capture chain. Simulations show pulses can be captured which are 1/5th the length of those typically captured with leading edge triggered latch methods, and less than the length of those captured with a trailing edge latch method. After capture, the pulse pattern is transferred to an SEU protected shift register for readout. 64 instances of each of two types of logic are used as targets. One is a full adder with a 4 to 1 mux on its inputs. The other is a 4 x 4 NFET routing matrix. The outputs are passed through buffered XNOR comparators to identify pulses, which are merged in a buffered not-nand (OR) tree designed to avoid pulse absorption as much as possible. The output from each of the two test circuits are input into separate pulse measurement circuits. Test inputs were provided so that the circuit could be bench tested and calibrated. A third SET measurement circuit with no inputs was used to judge the contribution from direct hits on the measurement circuit. Heavy ions were used with an LET range from 12 to 176. At LET of 21 and below, the very small number of SETs were not significantly higher in the test over the control circuits. At higher LET the test circuit SETs are one or two orders of magnitude greater than for the control circuit. The NFET circuit produces more and slightly longer SETs as expected. But the differences do not appear to be significant enough to modify strategies now used to avoid capture of SETs in chips such as FPGAs. Complete data and graphs will be in the full paper / presentation. In the summary figure below left, NOCL is the reference circuit without any input, and number of stages triggered is plotted. Simulation at right shows the smallest pulse captured (stage 2) at about 300 ps. Our conclusion is that the method is promising, but that improvements in the merge network are desirable before applying in a deep submicron process

Shuler, Robert L.↗
NASA NTRS2020

Shape Estimation for Elongated Deformable Object using B-spline Chained Multiple Random Matrices Model

In this paper, a B-spline chained multiple random matrix models (RMMs) representation is proposed to model geometric characteristics of an elongated deformable object. The hyper degrees of freedom structure of the elongated deformable object make its shape estimation challenging. Based on the likelihood function of the proposed B-spline chained multiple RMMs, an expectation-maximization (EM) method is derived to estimate the shape of the elongated deformable object. A split and merge method based on the Euclidean minimum spanning tree (EMST) is proposed to provide initialization for the EM algorithm. The proposed algorithm is evaluated for the shape estimation of the elongated deformable objects in scenarios, such as the static rope with various configurations (including configurations with intersection), the continuous manipulation of a rope and a plastic tube, and the assembly of two plastic tubes. The execution time is computed and the accuracy of the shape estimation results is evaluated based on the comparisons between the estimated width values and its ground-truth, and the intersection over union (IoU) metric.

Gang Yao↗
NASA NTRS1998

Efficient Encoding and Rendering of Time-Varying Volume Data

Visualization of time-varying volumetric data sets, which may be obtained from numerical simulations or sensing instruments, provides scientists insights into the detailed dynamics of the phenomenon under study. This paper describes a coherent solution based on quantization, coupled with octree and difference encoding for visualizing time-varying volumetric data. Quantization is used to attain voxel-level compression and may have a significant influence on the performance of the subsequent encoding and visualization steps. Octree encoding is used for spatial domain compression, and difference encoding for temporal domain compression. In essence, neighboring voxels may be fused into macro voxels if they have similar values, and subtrees at consecutive time steps may be merged if they are identical. The software rendering process is tailored according to the tree structures and the volume visualization process. With the tree representation, selective rendering may be performed very efficiently. Additionally, the I/O costs are reduced. With these combined savings, a higher level of user interactivity is achieved. We have studied a variety of time-varying volume datasets, performed encoding based on data statistics, and optimized the rendering calculations wherever possible. Preliminary tests on workstations have shown in many cases tremendous reduction by as high as 90% in both storage space and inter-frame delay.

CODING↗
NASA NTRS2004

Software for Checking Statecharts

HiVy is a software tool set that enables verification through model checking of designs represented as finite-state machines or statecharts. HiVy provides automated translation of (1) statecharts created by use of the MathWorks Stateflow program to (2) Promela, the input language of the Spin model checker, which can then be used to verify, or trace logical errors in, distributed software systems. HiVy can operate directly on Stateflow models, or its abstract syntax of hierarchical sequential automata (HSA) can be used independently as an intermediate format for translation to Promela. In a typical design application, HiVy parses and reformats Stateflow model file data using the programs SfParse and sf2hsa, respectively. If the parsing effort is successful, an abstract syntax tree is delivered into a file named with the extension .hsa. If the design comprises several model files, they may be merged into one .hsa file before translation into Promela. Stateflow scope is preserved, and name clashes are avoided in the merge process. The HiVy program hsa2pr translates the model from the intermediate HSA format into Promela. Additionally, HiVy provides through translation a list of all statechart model propositions that are the means for formalizing linear temporal logic (LTL) properties about the model for Spin verification.

Pingree, Paula↗
NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗
NASA NTRS2015

A High Performance Computing Approach to Tree Cover Delineation in 1-m NAIP Imagery Using a Probabilistic Learning Framework

Tree cover delineation is a useful instrument in deriving Above Ground Biomass (AGB) density estimates from Very High Resolution (VHR) airborne imagery data. Numerous algorithms have been designed to address this problem, but most of them do not scale to these datasets, which are of the order of terabytes. In this paper, we present a semi-automated probabilistic framework for the segmentation and classification of 1-m National Agriculture Imagery Program (NAIP) for tree-cover delineation for the whole of Continental United States, using a High Performance Computing Architecture. Classification is performed using a multi-layer Feedforward Backpropagation Neural Network and segmentation is performed using a Statistical Region Merging algorithm. The results from the classification and segmentation algorithms are then consolidated into a structured prediction framework using a discriminative undirected probabilistic graphical model based on Conditional Random Field, which helps in capturing the higher order contextual dependencies between neighboring pixels. Once the final probability maps are generated, the framework is updated and re-trained by relabeling misclassified image patches. This leads to a significant improvement in the true positive rates and reduction in false positive rates. The tree cover maps were generated for the whole state of California, spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles. The framework produced true positive rates of around 88% for fragmented forests and 74% for urban tree cover areas, with false positive rates lower than 2% for both landscapes. Comparative studies with the National Land Cover Data (NLCD) algorithm and the LiDAR canopy height model (CHM) showed the effectiveness of our framework for generating accurate high-resolution tree-cover maps.

Segments↗
NASA NTRS1984

Pinacate-gran Desierto Region, Mexico: SIR-A Data Analysis

Radar images (SIR-A) from the Columbia space shuttle were used to assess the radar returns of terrain shaped by volcanic, aeolian, and fluvial processes in northwest Sonora. Field studies and photointerpretation show that sand dunes are poorly imaged by SIR-A, in contrast to SEASAT, evidently a consequence of the greater SIR-A incidence angle; star dunes are visible only as small bright spots representing merging arms at dune apices which may act as corner reflectors. Desert grasses and bushes (approx. 2 m high) have little effect on radar brightness. Only larger trees with woody trunks approx. 0.5 m across are effective radar reflectors; their presence contributes to radar bright zones along some arroyos. The radar brightness of lava flows decreases with surface roughness and presence of mantling windblown sediments and weathering products; however, old uplifted (faulted) flows are of equal brightness to fresh, unmantled aa flows. Maar craters display circular patterns of varying radar brightness which represent a combination of geometry, slope, and distribution of surface materials. Some radar bright rings in the Pinacates resemble craters on radar but are observed to be playas encircled by trees.

Christensen, P.↗
NASA NTRS1994

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complete database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful, and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications, and has produced real, published results.

Djorgovski, S. George↗
NASA NTRS1994

Multivariate Statistical Analysis Software Technologies for Astrophysical Research Involving Large Data Bases

We developed a package to process and analyze the data from the digital version of the Second Palomar Sky Survey. This system, called SKICAT, incorporates the latest in machine learning and expert systems software technology, in order to classify the detected objects objectively and uniformly, and facilitate handling of the enormous data sets from digital sky surveys and other sources. The system provides a powerful, integrated environment for the manipulation and scientific investigation of catalogs from virtually any source. It serves three principal functions: image catalog construction, catalog management, and catalog analysis. Through use of the GID3* Decision Tree artificial induction software, SKICAT automates the process of classifying objects within CCD and digitized plate images. To exploit these catalogs, the system also provides tools to merge them into a large, complex database which may be easily queried and modified when new data or better methods of calibrating or classifying become available. The most innovative feature of SKICAT is the facility it provides to experiment with and apply the latest in machine learning technology to the tasks of catalog construction and analysis. SKICAT provides a unique environment for implementing these tools for any number of future scientific purposes. Initial scientific verification and performance tests have been made using galaxy counts and measurements of galaxy clustering from small subsets of the survey data, and a search for very high redshift quasars. All of the tests were successful and produced new and interesting scientific results. Attachments to this report give detailed accounts of the technical aspects of the SKICAT system, and of some of the scientific results achieved to date. We also developed a user-friendly package for multivariate statistical analysis of small and moderate-size data sets, called STATPROG. The package was tested extensively on a number of real scientific applications and has produced real, published results.

Djorgovski, S. G.↗
NASA NTRS1991

Video Pipeline Tree For Scan Conversion Of Triangles

Scenes containing many polygons generated in real time. Video pipeline subsystem having branched structure performs scan conversion of polygons in images generated by computers. New subsystem divides polygons into triangles, each of which processed rapidly in parallel, modular fashion and merged into image.

Robinett, Warren↗
NASA NTRS2007

Program Merges SAR Data on Terrain and Vegetation Heights

X/P Merge is a computer program that estimates ground-surface elevations and vegetation heights from multiple sets of data acquired by the GeoSAR instrument [a terrain-mapping synthetic-aperture radar (SAR) system that operates in the X and bands]. X/P Merge software combines data from X- and P-band digital elevation models, SAR backscatter magnitudes, and interferometric correlation magnitudes into a simplified set of output topographical maps of ground-surface elevation and tree height.

Siqueira, Paul↗