CLASSIFICATION AND EVALUATION OF COHERENT SYNCHRONOUS SAMPLED DATA TELEMETRY SYSTEMS
Wave and pulse modulation for transmitting data over channels perturbed by gaussian noise
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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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A Dimensionally Aligned Signal Projection for Classification of Unintended Radiated Emissions
Characterization of unintended radiated emissions (URE) from electronic devices plays an important role in many research areas from electromagnetic interference to nonintrusive load monitoring to information system security. URE can provide insights for applications ranging from load disaggregation and energy efficiency to condition-based maintenance of equipment-based upon detected fault conditions. URE characterization often requires subject matter expertise to tailor transforms and feature extractors for the specific electrical devices of interest. We present a novel approach, named dimensionally aligned signal projection (DASP), for projecting aligned signal characteristics that are inherent to the physical implementation of many commercial electronic devices. These projections minimize the need for an intimate understanding of the underlying physical circuitry and significantly reduce the number of features required for signal classification. We present three possible DASP algorithms that leverage frequency harmonics, modulation alignments, and frequency peak spacings, along with a two-dimensional image manipulation method for statistical feature extraction. To demonstrate the ability of DASP to generate relevant features from URE, we measured the conducted URE from 14 residential electronic devices using a 2 MS/s collection system. Furthermore, a linear discriminant analysis classifier was trained using DASP generated features and was blind tested resulting in a greater than 90% classification accuracy for each of the DASP algorithms and an accuracy of 99.1% when DASP features are used in combination. Furthermore, we show that a rank reduced feature set of the combined DASP algorithms provides a 98.9% classification accuracy with only three features and outperforms a set of spectral features in terms of general classification as well as applicability across a broad number of devices.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
On the Origin of Reverse Transcriptase-Using CRISPR-Cas Systems and Their Hyperdiverse, Enigmatic Spacer Repertoires
Cas1 integrase is the key enzyme of the clustered regularly interspaced short palindromic repeat (CRISPR)-Cas adaptation module that mediates acquisition of spacers derived from foreign DNA by CRISPR arrays. In diverse bacteria, the cas1 gene is fused (or adjacent) to a gene encoding a reverse transcriptase (RT) related to group II intron RTs. An RT-Cas1 fusion protein has been recently shown to enable acquisition of CRISPR spacers from RNA. Phylogenetic analysis of the CRISPR-associated RTs demonstrates monophyly of the RT-Cas1 fusion, and coevolution of the RT and Cas1 domains. Nearly all such RTs are present within type III CRISPR-Cas loci, but their phylogeny does not parallel the CRISPR-Cas type classification, indicating that RT-Cas1 is an autonomous functional module that is disseminated by horizontal gene transfer and can function with diverse type III systems. To compare the sequence pools sampled by RT-Cas1-associated and RT-lacking CRISPR-Cas systems, we obtained samples of a commercially grown cyanobacterium— Arthrospira platensis . Sequencing of the CRISPR arrays uncovered a highly diverse population of spacers. Spacer diversity was particularly striking for the RT-Cas1-containing type III-B system, where no saturation was evident even with millions of sequences analyzed. In contrast, analysis of the RT-lacking type III-D system yielded a highly diverse pool but reached a point where fewer novel spacers were recovered as sequencing depth was increased. Matches could be identified for a small fraction of the non-RT-Cas1-associated spacers, and for only a single RT-Cas1-associated spacer. Thus, the principal source(s) of the spacers, particularly the hypervariable spacer repertoire of the RT-associated arrays, remains unknown. IMPORTANCE While the majority of CRISPR-Cas immune systems adapt to foreign genetic elements by capturing segments of invasive DNA, some systems carry reverse transcriptases (RTs) that enable adaptation to RNA molecules. From analysis of available bacterial sequence data, we find evidence that RT-based RNA adaptation machinery has been able to join with CRISPR-Cas immune systems in many, diverse bacterial species. To investigate whether the abilities to adapt to DNA and RNA molecules are utilized for defense against distinct classes of invaders in nature, we sequenced CRISPR arrays from samples of commercial-scale open-air cultures of Arthrospira platensis , a cyanobacterium that contains both RT-lacking and RT-containing CRISPR-Cas systems. We uncovered a diverse pool of naturally occurring immune memories, with the RT-lacking locus acquiring a number of segments matching known viral or bacterial genes, while the RT-containing locus has acquired spacers from a distinct sequence pool for which the source remains enigmatic. While the majority of CRISPR-Cas immune systems adapt to foreign genetic elements by capturing segments of invasive DNA, some systems carry reverse transcriptases (RTs) that enable adaptation to RNA molecules. From analysis of available bacterial sequence data, we find evidence that RT-based RNA adaptation machinery has been able to join with CRISPR-Cas immune systems in many, diverse bacterial species. To investigate whether the abilities to adapt to DNA and RNA molecules are utilized for defense against distinct classes of invaders in nature, we sequenced CRISPR arrays from samples of commercial-scale open-air cultures of Arthrospira platensis , a cyanobacterium that contains both RT-lacking and RT-containing CRISPR-Cas systems. We uncovered a diverse pool of naturally occurring immune memories, with the RT-lacking locus acquiring a number of segments matching known viral or bacterial genes, while the RT-containing locus has acquired spacers from a distinct sequence pool for which the source remains enigmatic.
horizontal gene transfer↗
Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images
Electroluminescence (EL) imaging of photovoltiac (PV) modules offers high-speed, high-resolution information about device performance, affording opportunities for greater insight and efficiency in module characterization across manufacturing, research and development, and power plant operations and management. Predicting module electrical properties from EL image features is a critical step toward these applications. In this article, we demonstrate quantification of both generalized and performance mechanism-specific EL image features, using pixel intensity-based and machine learning classification algorithms. From EL image features, we build predictive models for PV module power and series resistance, using time-series current-voltage (I-V) and EL data obtained stepwise on five brands of modules spanning three Si cell types through two accelerated exposures: damp heat (DH) (85°C/85% RH) and thermal cycling (TC) (IEC 61215). Overall, 195 pairs of EL images and I-V characteristics were analyzed, yielding 11700 individual PV cell images. A convolutional neural network was built to classify cells by the severity of busbar corrosion with high accuracy (95%). Generalized power predictive models estimated the maximum power of PV modules from EL images with high confidence and an adjusted-R 2 of 0.88, across all module brands and cell types in extended DH and TC exposures. Mechanistic degradation prediction was demonstrated by quantification of busbar corrosion in EL images of three module brands in DH, and subsequent modeling of series resistance using these mechanism-specific EL image features. For modules exhibiting busbar corrosion, we demonstrated series resistance predictive models with adjusted-R 2 of up to 0.73.
14 SOLAR ENERGY↗
Computer aided detection of surgical retained foreign object for prevention
Purpose: Surgical retained foreign objects (RFOs) have significant morbidity and mortality. They are associated with approximately $1.5 × 10{sup 9} annually in preventable medical costs. The detection accuracy of radiographs for RFOs is a mediocre 59%. The authors address the RFO problem with two complementary technologies: a three-dimensional (3D) gossypiboma micro tag, the μTag that improves the visibility of RFOs on radiographs, and a computer aided detection (CAD) system that detects the μTag. It is desirable for the CAD system to operate in a high specificity mode in the operating room (OR) and function as a first reader for the surgeon. This allows for fast point of care results and seamless workflow integration. The CAD system can also operate in a high sensitivity mode as a second reader for the radiologist to ensure the highest possible detection accuracy. Methods: The 3D geometry of the μTag produces a similar two dimensional (2D) depiction on radiographs regardless of its orientation in the human body and ensures accurate detection by a radiologist and the CAD. The authors created a data set of 1800 cadaver images with the 3D μTag and other common man-made surgical objects positioned randomly. A total of 1061 cadaver images contained a single μTag and the remaining 739 were without μTag. A radiologist marked the location of the μTag using an in-house developed graphical user interface. The data set was partitioned into three independent subsets: a training set, a validation set, and a test set, consisting of 540, 560, and 700 images, respectively. A CAD system with modules that included preprocessing μTag enhancement, labeling, segmentation, feature analysis, classification, and detection was developed. The CAD system was developed using the training and the validation sets. Results: On the training set, the CAD achieved 81.5% sensitivity with 0.014 false positives (FPs) per image in a high specificity mode for the surgeons in the OR and 96.1% sensitivity with 0.81 FPs per image in a high sensitivity mode for the radiologists. On the independent test set, the CAD achieved 79.5% sensitivity with 0.003 FPs per image in a high specificity mode for the surgeons and 90.2% sensitivity with 0.23 FPs per image in a high sensitivity mode for the radiologists. Conclusions: To the best of the authors’ knowledge, this is the first time a 3D μTag is used to produce a recognizable, substantially similar 2D projection on radiographs regardless of orientation in space. It is the first time a CAD system is used to search for man-made objects over anatomic background. The CAD system for the μTags achieved reasonable performance in both the high specificity and the high sensitivity modes.
60 APPLIED LIFE SCIENCES↗
Vegetation canopy discrimination and biomass assessment using multipolarized airborne SAR
Multipolarized airborne Synthetic Aperture Radar (SAR) data were acquired over a largely agricultural test site near Macomb, Illinois, in conjunction with the Shuttle Imaging Radar (SIR-B) experiment in October 1984. The NASA/JPL L-band SAR operating at 1.225 GHz made a series of daily overflights with azimuth view angles both parallel and orthogonal to those of SIR-B. The SAR data was digitally recorded in the quadpolarization configuration. An extensive set of ground measurements were obtained throughout the test site and include biophysical and soil measurements of approximately 400 agricultural fields. Preliminary evaluation of some of the airborne SAR imagery indicates a great potential for crop discrimination and assessment of canopy condition. False color composites constructed from the combination of three linear polarizations (HH, VV, and HV) were found to be clearly superior to any single polarization for purposes of crop classification. In addition, an image constructed using the HH return to modulate intensity and the phase difference between HH and VV returns to modulate chroma indicates a clear capability for assessment of canopy height and/or biomass. In particular, corn fields heavily damaged by infestations of corn borer are readily distinguished from noninfested fields.
Ulaby, F. T.↗
Learned adaptive properties for mitigation of weight perturbations in embedded spiking networks
Recent years have seen an increased importance of neural network inference in edge-based scenarios, which impose size and power constraints requiring novel computing devices. These same edge scenarios may require operating over long periods of time, or exposure to extreme environments, resulting in a drift of neural network weights that cause degraded performance. In searching for ways to develop neural network approaches that perform robustly under these conditions, we propose a biologically-inspired mechanism for the dynamic adaptation of within-neuron parameters that is guided by a global context signal carrying information about perturbations and variability in incoming stimuli. Specifically, we demonstrate that adaptive voltage thresholds or neuronal time constants, when informed by a global context signal, can enable network-level mechanisms to recover from perturbed synaptic weights. Consistent with prior literature, the context-modulated approach is effective for recurrent, but not feedforward networks, by modulating network level dynamics. We demonstrate this approach successfully recovers performance in image classification tasks and spatiotemporal tracking tasks under idealized and Gaussian noise as well as for realistic perturbations from a memristive device when exposed to ionizing radiation. Finally, we discuss how this approach enables the design of robust and energy-efficient neuromorphic systems that perform well, even in resource-constrained scenarios with extreme environments such as edge processing.
context modulation↗
Analysis of the Long-Term Performance Degradation of Crystalline Silicon Photovoltaic Modules in Tropical Climates
For this study, the long-term performance degradation of five different types of commercial crystalline silicon (c-Si) photovoltaic (PV) modules (one sample for each type) in the tropics, or more specifically the tropical rainforest climate 'Af' by the Koppen climate classification (Singapore), is investigated over a period of seven years, based on outdoor I-V measurements at 10-s intervals. The degradation rates of these c-Si PV modules, based on the temperature-corrected performance ratio, range from -0.03 to -0.47%/year, which satisfy the products' performance warranty. Further analysis of the short-circuit current (I sc), open-circuit voltage, and fill factor for these modules shows that the performance degradation is mainly attributed to the l sc loss. Lastly, the possible causes of the module performance deterioration are also discussed.
14 SOLAR ENERGY↗
Dual host specificity of phage SP6 is facilitated by tailspike rotation
Bacteriophage SP6 exhibits dual-host adsorption specificity. The SP6 tailspikes are recognized as important in host range determination but the mechanisms underlying dual host specificity are unknown. Cryo-electron tomography and sub-tomogram classification were used to analyze the SP6 virion with a particular focus on the interaction of tailspikes with host membranes. The SP6 tail is surrounded by six V-shaped structures that interconnect in forming a hand-over-hand hexameric garland. Each V-shaped structure consists of two trimeric tailspike proteins: gp46 and gp47, connected through the adaptor protein gp37. SP6 infection of Salmonella enterica serovars Typhimurium and Newport results in distinguishable changes in tailspike orientation, providing the first direct demonstration how tailspikes can confer dual host adsorption specificity. SP6 also infects S. Typhimurium strains lacking O antigen; in these infections tailspikes have no apparent specific role and the phage tail must therefore interact with a distinct host receptor to allow infection. - Highlights: •Cryo-electron tomography reveals the structural basis for dual host specificity. •Sub-tomogram classification reveals distinct orientations of the tailspikes during infection of different hosts. •Tailspike-adaptor modules rotate as they bind different O antigens. •In the absence of any O antigen, tailspikes bind weakly and without specificity to LPS. •Interaction of the phage tail with LPS is essential for infection.
60 APPLIED LIFE SCIENCES↗
The two radiative states of the Arctic atmosphere and their impacts on the surface energy budget of sea ice
The surface energy budget (SEB) is a central regulator of Arctic climate and sea ice evolution, yet its processes remain poorly constrained due to sparse observations and complex, coupled surface-atmosphere interactions. This study leverages year-long measurements from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) to provide the most comprehensive assessment to date of the central Arctic SEB and its modulation by atmospheric variability. Ship- and ice-based observations from October 2019 to September 2020 were used to directly measure or tightly constrain each term of the SEB, leading to exceptional energetic closure with the seasonal snow and ice mass balance. The analysis reveals strong seasonal transitions in atmosphere-surface energy transfer that are modulated by the atmospheric state and constrained by the ability of the surface temperature to respond. Classification of the atmosphere into its two dominant radiative states—the semi-transparent (ST) and opaque (OP)—highlights the central role of synoptic-scale variability in clouds. The ST atmospheric state dominated the long winter ice growth season, with limited cloudiness supporting persistent surface radiative cooling and ice growth. The OP state, associated with liquid-containing or thick ice clouds, became dominant in spring, with the combination of increased solar heating and cloud surface longwave warming driving ice and snow melt. Eddy covariance versus bulk approaches for deriving surface turbulent heat fluxes provide vastly different perspectives on the role of turbulence in modulating the SEB. These results establish a high-quality benchmark dataset for Arctic SEB studies and demonstrate how the balance of atmospheric radiative states exerts a first-order control on the annual evolution of the sea ice. The findings have broad implications for advancing observing technologies, understanding Arctic amplification, improving climate models, and predicting future sea ice change.
54 ENVIRONMENTAL SCIENCES↗
Application of the wavelet transform for speech processing
Speaker identification and word spotting will shortly play a key role in space applications. An approach based on the wavelet transform is presented that, in the context of the 'modulation model,' enables extraction of speech features which are used as input for the classification process.
Maes, Stephane↗
Response of pontomedullary reticulospinal neurons to vestibular stimuli in vertical planes. Role in vertical vestibulospinal reflexes of the decerebrate cat
1. To investigate the neural substrate of vestibulospinal reflexes in decerebrate cats, we studied the responses of pontomedullary reticulospinal neurons to natural stimulation of the labyrinth in vertical planes. Our principal aim was to determine whether reticulospinal neurons that terminate in, or are likely to give off collaterals to, the upper cervical segments had properties similar to those of the vestibulocollic reflex (VCR). 2. Antidromic stimulation was used to determine whether the neurons projected to the neck, lower cervical, thoracic, or lumbar levels. Dynamics of the responses of spontaneously firing neurons were studied with sinusoidal stimuli delivered at 0.05-1 Hz and aligned to the plane of body rotation, that produced maximal modulation of the neuron (response vector orientation). Each neuron was assigned a vestibular input classification of otolith, vertical canal, otolith + canal, or spatial-temporal convergence (STC). 3. We found, in agreement with previous studies, that the largest fraction of pontomedullary reticulospinal neurons projected to the lumbar cord, and that only a small number ended in the neck segments. Neurons projecting to all levels of the spinal cord had similar responses to labyrinth stimulation. 4. Reticulospinal neurons that received only vertical canal inputs were rare (1 of 67 units). Most reticulospinal neurons (48%) received predominant otolith inputs, 18% received otolith + canal input, and only 9% had STC behavior. These data are in sharp contrast to the results of our previous studies of vestibulospinal neurons. A considerable portion of vestibulospinal neurons receives vertical canal input (38%), fewer receive predominantly otolith input (22%), whereas the proportion that have otolith + canal input or STC behavior is similar to our present reticulospinal data. 5. The response vector orientations of our reticulospinal neurons, particularly those with canal inputs (canal, otolith + canal, STC) were predominantly in the roll quadrants. There was no evidence of convergence of inputs from like canals across the midline (e.g., right anterior + left anterior). 6. Two characteristics of the VCR, STC behavior and bilateral input from symmetric vertical canals (in some muscles), cannot be accounted for by the reticulospinal neurons that we studied. Because these characteristics are also not seen in vestibulocollic neurons, they are likely to be the result of the appropriate convergence of vestibular signals in the spinal cord. 7. Pontomedullary reticulospinal neurons seem particularly well suited to play a role in gravity-dependent postural reflexes of neck and limbs.
NASA Discipline Neuroscience↗
LCOE reduction through proactively optimized monitoring of PV Systems (Final Technical Report)
The project demonstrates the value proposition for a high-resolution monitoring system (HRMS) with diagnostic-prognostic capability and determine its impact on LCOE. The HRMS differs from conventional monitoring systems in a number of ways. First it will include the capability to automatically measure IV curves at the string and module levels. This provides a much richer view into the DC performance of the PV system and allows classification of many typical failure and degradation modes. Second, it will incorporate software capable of quantifying power and energy losses in the field as well as define the location and mechanism of the power and energy loss. Moreover, we aimed to deliver a prognostic system that is capable of predicting certain failures before they occur and giving system operators the opportunity to more efficiently plan operations and maintenance (O&M) activity in order to lower costs and increase yield over the life of the system. The research identifies cost targets required for different monitoring stages, including at the string combiner, at the individual string, and at the module level, to lower the levelized cost of energy (LCOE). Finally, a comprehensive guide determines the value PV monitoring brings to PV field operations. The guide assists plant operators in maximizing value from existing plants and identify the trade-offs of different monitoring solutions for future plants depending on the size, location, and expected system lifetime.
14 SOLAR ENERGY↗
Cyote-attack Chain Estimator
Attack Chain Estimator (ACE) Application Overview The Attack Chain Estimator (ACE) Application is a sophisticated tool designed for the ingestion, classification, sequencing, and enrichment of cybersecurity threat reports. This application leverages advanced machine learning models and extensive historical data to provide comprehensive insights into cyber threats, specifically targeting Industrial Control Systems (ICS). Purpose The primary functions of the ACE Application include: Ingestion of Cybersecurity Threat Reporting: Capable of ingesting text-based threat reports in markdown or text file format. Supports ingestion of structured data from other sources in STIX/JSON format. Classification of Report’s Text-Based Events: Utilizes a DeBERTa classifier, specifically trained on cybersecurity data, to map the events to MITRE ATT&CK for ICS Tactics and Techniques. Classification is performed using multiple Jupyter notebooks and machine learning workflows hosted as FastAPI microservices: regex_data deberta_base_35_train_hft_classifier_mlflow.ipynb hft_regex_classifier_mlflow.ipynb param_train_hft_classifier_mlflow.ipynb regex_tactic_tech.ipynb Ordering of Tactics, Techniques, and Observable Events: Sequences the identified tactics, techniques, and events to form a coherent attack chain. Enrichment with Historical Attack Chain Details: Enhances the attack chain with details from historical attacks using a Markov model developed from CyOTE Precursor Analysis Report data. The Markov model is available as a FastAPI endpoint for seamless integration. Enrichment with Adversary Emulation Capabilities Data: Integrates adversary emulation capabilities data using MITRE Caldera for OT adversary abilities UUIDs. Export of Output Files: Provides options to export the enriched attack chain in JSON or CSV formats. Routing of Output to Other Applications: Facilitates routing of output to various platforms and applications, including: Threat Intelligence Platforms COREII Scout for Threat Intelligence Analysis COREII Modeling and Simulation for Adversary Emulation Technical Description The ACE Application is an advanced cybersecurity tool designed to provide detailed threat analysis and sequence generation. It is built on a robust architecture that integrates natural language processing, machine learning, and historical data modeling. Key Components: Data Ingestion Module: Handles the input of threat reports and data from various formats, ensuring flexibility in data sources. Classification Engine: Employs DeBERTa-based classifiers hosted as FastAPI microservices to analyze and classify threat report events in accordance with the MITRE ATT&CK framework for ICS. Sequence Generator: Orders the classified events into a logical attack chain, providing clear insight into the sequence of tactics and techniques used in the threat. Enrichment Engine: Integrates historical data and adversary emulation capabilities to enhance the attack chain with valuable context and additional details. The historical data enrichment is powered by a Markov model, which is available as a FastAPI endpoint. Export and Routing Module: Facilitates the export of the enriched attack chain in multiple formats and routes the output to designated applications for further analysis or emulation.
Paul, Tony [Idaho National Laboratory (INL), Idaho↗
A Background-Subtracted Search for Annual Modulation in CDMS II
The identification of dark matter is one of the outstanding problems of our time. Cos- mological and astrophysical clues such as structure formation and relic densities, anomalous galaxy rotation curves, and mass density profiles provide ample evidence of an undetected mass component of the universe. Meanwhile, recent advancements in particle physics point toward extensions to the Standard Model, many of which posit candidates for new particles and new physics at the weak scale and beyond. If, as expected, the confluence of the hints of new physics in particle physics, astrophysics, and cosmology at the weak scale is more than coincidence, the detection of dark matter will pave the way for a paradigm shift in our fundamental understanding of the universe.Weakly interacting massive particles (WIMPs), in particular, are a well-motivated class of candidates for particle dark matter. Naturally predicted in supersymmetry (SUSY), WIMPs are stable, weakly-interacting, and produced in sufficient abundance to comprise the quantity of missing mass in a number of simple cases. Over the last two decades of experimentation, significant areas of the parameter space defined by the simplest SUSY theories have been ruled out, but WIMPs and related particles remain compelling candidates for dark matter searches.Several avenues for the detection and identification of dark matter are currently being pursued. The present work is a description of the search for a direct detection of super- symmetric dark matter via scattering from standard model particles. The Cryogenic Dark Matter Search (CDMS) Experiment uses ionization and athermal phonon sensor technolo- gies to achieve event-by-event discrimination of electron and nuclear recoils in cryogenic germanium crystal detectors. At low energies, where the the ability to discriminate individ- ual nuclear recoil events from background is reduced, a periodic variation of the rate and crossover signatures in the energy spectrum can aid the identification of a WIMP signature in the presence of significant backgrounds. In general, the direct detection of dark matter is the first step toward the identification and classification of dark matter in the universe.This work describes a background-subtracted search for annual modulation in the WIMP- search data acquired in the Cryogenic Dark Matter Search II (CDMS II) Experiment, which was the second implementation of the highly successful CDMS technology. We observe no significant modulation in the 2.7 keVnr to 11.9 keVnr (nuclear-recoil-equivalent) energy range selected for this analysis. These results are not compatible with a WIMP dark matter interpretation of the signals reported by the DAMA/LIBRA and CoGeNT experiments, and provide complementary support to earlier CDMS low-threshold germanium analyses.
79 ASTRONOMY AND ASTROPHYSICS↗
Test and evaluation of a solar-heating system
Report documents results of evaluation tests performed on components of commerical solar heating and hot water system. Subsystems tested include flat plate solar collector, energy transport module, and control panel. Tests conducted include snow and wind loads, flame spread, and smoke classification as well as solar heating operation.
Source record↗
Instructional image processing on a university mainframe: The Kansas system
An interactive digital image processing program package was developed that runs on the University of Kansas central computer, a Honeywell Level 66 multi-processor system. The module form of the package allows easy and rapid upgrades and extensions of the system and is used in remote sensing courses in the Department of Geography, in regional five-day short courses for academics and professionals, and also in remote sensing projects and research. The package comprises three self-contained modules of processing functions: Subimage extraction and rectification; image enhancement, preprocessing and data reduction; and classification. Its use in a typical course setting is described. Availability and costs are considered.
Williams, T. H. L.↗
Issues in providing a reliable multicast facility
Issues involved in point-to-multipoint communication are presented and the literature for proposed solutions and approaches surveyed. Particular attention is focused on the ideas and implementations that align with the requirements of the environment of interest. The attributes of multicast receiver groups that might lead to useful classifications, what the functionality of a management scheme should be, and how the group management module can be implemented are examined. The services that multicasting facilities can offer are presented, followed by mechanisms within the communications protocol that implements these services. The metrics of interest when evaluating a reliable multicast facility are identified and applied to four transport layer protocols that incorporate reliable multicast.
Dempsey, Bert J.↗