ADA: A NEW PROGRAMMING LANGUAGE.

In an attempt to control defense software costs while maintaining system reliability, the DOD (Department of Defense) has sponsored the development of a new programming language, called Ada. While Ada supports many of the features found in Fortran and Pascal, it has additional features making it easier and more reliable to use in large complex engineering software systems. Currently, the DOD is requiring that many new defense software systems be written in Ada. The author reviews the history of Ada\u27s development and examines the primary constructs of the language. The philosophy behind Ada program design is reviewed by designing and coding a simple program to add two matrices. Ada\u27s future prospects are examined as related to both defense and nondefense applications

Medial Axis Transform using Ridge Following

The intent of this investigation has been to find a robust algorithm for generation of the medial axis transform (MAT). The MAT is an invertible, object centered, shape representation defined as the collection of the centers of disks contained in the shape but not in any other such disk. Its uses include feature extraction, shape smoothing, and data compression. MAT generating algorithms include brushfire, Voronoi diagrams, and ridge following. An improved implementation of the ridge following algorithm is given. Orders of the MAT generating algorithms are compared. The effects of the number of edges in the polygonal approximation, shape area, number of holes, and number/distribution of concave vertices are shown from test results. Finally, a set of useful extensions to the ridge following algorithm are discussed

Deduction of a Functional Dependency from a Set of Functional Dependencies

This paper describes an algorithm called the Deduction Tracing Algorithm (DTA) which utilizes basic properties of functional dependencies from database systems and a modification of a tree search algorithm from artificial intelligence. The algorithm takes a set of functional dependencies, F, along with a specific functional dependency L → R as input and produces a list of functional dependencies from F that can be used to deduce L → R. The resulting algorithm is easily automated to provide relational database users with a tool for organizing their queries

Intensity Blending of Computer Image Generation-Based Displays

State-of-the-art combat simulators require a 360 degree field of view, allowing the pilot and radar intercept officer to have the same visibility in the simulator that they would experience in the aircraft. The sky/earth display must be computer - generated and displayed with a minimum of two channels to provide the most realistic display possible. The two channels of display come together in the dome, forming an equator, that must be as indiscernible to the aircrew as possible. To accomplish this, an algorithm has been developed for controlling the video output which makes the two separate channel displays appear as one continuous 360 degree display

Process Driven Software Engineering Environments

Software development organizations have begun using Software Engineering Environments (SEEs) with the goal of enhancing the productivity of software developers and improving the quality of software products. The encompassing nature of a SEE means that it is typically very tightly coupled with the way an organization does business. To be most effective, the components of a SEE must be well integrated and the SEE itself must be integrated with the organization. The challenge of tool integration increases considerably when the components of the environment come from different vendors and support varying degrees of “openness”. The challenge of integration with the organization increases in a like manner when the environment must support a variety of different organizations over a long period of time. In addition to these pressures, any SEE must perform well and must “scale” well as the size of the organization changes. This paper proposes basing the Software Engineering Environment on the software development process used in an organization in order to meet the challenges of integration, performance, and scaling. The goals and services of distributed operating systems and Software Engineering Environments are outlined in order to more clearly define their roles. The motivation for using a well defined software development process is established along with the benefits of basing the Software Engineering Environment on the software development process. Components of a SEE that could effectively support the process and provide integration, performance, and scaling benefits are introduced along with an outline of an Ada program used to model the proposed components. The conclusion provides strong support for process driven SEEs, encourages the expansion of the concept into other “environments,” and cautions against literal interpretations of “process integration” that may slow the acceptance of this powerful approach

Ethernet Performance: Design and Implementation Study

General concepts concerning local area network designs, functions and topologies will be presented. Ethernet as a multipoint bus topology local area network will be presented in detail. The Carrier Sense Multiple Access/Collision Detect (CSMA/CD) method of fairly regulating access to the shared network bus is studied. The Ethernet Network in relation to the Open Systems Interconnect (OSI) is reviewed, but only the layers pertaining to Ethernet are discussed throughout the majority of the paper. The specifications as described by Xerox, Digital and Intel are presented to help the designer understand the network\u27s physical limitations. Analytical models are used to predict performance and actual measured performance studies will be used to make performance assumptions. The performance is studied under varying load conditions. The data gathered concerns both limits imposed on the number of users by the finite bandwidth of the channel and efficient utilization of that channel. In conclusion, design specifications and performance data will be used together to formulate a design methodology for building the most efficient Ethernet network

SeMi-Supervised Adaptive Resonance Theory (SMART2)

Adaptive resonance theory (ART) algorithms represent a class of neural network architectures which self-organize stable recognition categories in response to arbitrary sequences of input patterns. The authors discuss incorporation of supervision into one of these architectures, ART2. Results of numerical experiments indicate that this new semi-supervised version of ART2 (SMART2) outperformed ART for classification problems. The results and analysis of runs on several data sets by SMART2, ART2, and backpropagation are analyzed. The test accuracy of SMART2 was similar to that of backpropagation. However, SMART2 network structures are easier to interpret than the corresponding structures produced by backpropagation

Pattern Recognition for Nondestructive Evaluation

The issues involved in automating nondestructive evaluation (NDE) techniques are outlined. Attention is given to research focused on the application of machine learning techniques to the construction and maintenance of knowledge-based systems which are capable of evaluating the readings from nondestructive tests that have been performed on aircraft components. Preliminary results obtained from this research are described. In particular, the authors discuss the application of a symbolic machine learning algorithm, ID3, to the NDE problem. ID3 has been used by Douglas Aircraft to classify defects in sets of standard NDE reference blocks. Based on the preliminary results, a need for an improved method of distinguishing features in the test waveforms is identified. The authors also outline a feature extraction approach from pattern recognition, called scale-space filtering, which can be used to preprocess data for input into a classification algorithm such as ID3

Why do models overestimate surface ozone in the Southeast United States

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx  ≡  NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°  ×  0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer

Wet scavenging of soluble gases in DC3 deep convective storms using WRF-Chem simulations and aircraft observations

We examine wet scavenging of soluble trace gases in storms observed during the Deep Convective Clouds and Chemistry (DC3) field campaign. We conduct high-resolution simulations with the Weather Research and Forecasting model with Chemistry (WRF-Chem) of a severe storm in Oklahoma. The model represents well the storm location, size, and structure as compared with Next Generation Weather Radar reflectivity, and simulated CO transport is consistent with aircraft observations. Scavenging efficiencies (SEs) between inflow and outflow of soluble species are calculated from aircraft measurements and model simulations. Using a simple wet scavenging scheme, we simulate the SE of each soluble species within the error bars of the observations. The simulated SEs of all species except nitric acid (HNO_3) are highly sensitive to the values specified for the fractions retained in ice when cloud water freezes. To reproduce the observations, we must assume zero ice retention for formaldehyde (CH_2O) and hydrogen peroxide (H_2O_2) and complete retention for methyl hydrogen peroxide (CH_3OOH) and sulfur dioxide (SO_2), likely to compensate for the lack of aqueous chemistry in the model. We then compare scavenging efficiencies among storms that formed in Alabama and northeast Colorado and the Oklahoma storm. Significant differences in SEs are seen among storms and species. More scavenging of HNO_3 and less removal of CH_3OOH are seen in storms with higher maximum flash rates, an indication of more graupel mass. Graupel is associated with mixed-phase scavenging and lightning production of nitrogen oxides (NO_x), processes that may explain the observed differences in HNO_3 and CH_3OOH scavenging