1) The document describes the Universal Transverse Mercator (UTM) projection system used for geospatial referencing, which divides the globe into zones and uses a grid coordinate system of eastings and northings.
2) It provides instructions for configuring the Pathloss software to read Planet terrain database files using UTM projection, which involves setting the datum, selecting the Odyssey UTM reader, specifying the file directory and index, and testing with sample sites.
3) Issues that could cause problems reading the terrain database correctly include incorrect UTM zone specification, file directory, index details, or byte order settings compared to the actual database format.
The Aiguille du Midi is a 3,842m mountain in the French Alps. In 1955, the Téléphérique de l'Aiguille du Midi cable car was built to reach its summit, holding the record as the highest vertical ascent cable car in the world. It takes passengers from Chamonix to the summit in 20 minutes, offering panoramic views from its observation platform. The mountain provides access to skiing routes and trails leading to Mont Blanc.
Mobile trends for 2010 and beyond include:
1) Capacitive touchscreens becoming cheaper and more common in mobile devices.
2) Increasing processing power and higher resolution displays in mobile phones.
3) The continued popularity of feature phones alongside more advanced smartphones.
1. The document presents research on using artificial neural networks to model and predict the mechanical properties of structural steels after heat treatment.
2. Over 125 types of non-alloy and alloy structural steels were examined, including properties like strength, impact resistance, and hardness.
3. Neural networks were trained using input parameters like chemical composition, heat treatment type and parameters, and geometry to predict mechanical properties. Results showed the neural networks could accurately predict properties within the given input ranges.
El documento presenta los objetivos y metas de Harrison Gutiérrez Castillo para su portafolio digital en el año 2007, incluyendo aprender mucho sobre computación y cumplir con las metas propuestas de manera responsable. También contiene varias notas de amor dirigidas a una persona no identificada, expresando sentimientos de aprecio y promesas de amor eterno.
El documento describe las principales capas de la Tierra: la geosfera (incluyendo la corteza, el manto y el núcleo), la atmósfera, la hidrosfera y la biosfera. También explica los procesos geológicos de placas tectónicas, volcanes, terremotos, erosión y su relación con la dinámica interna de la Tierra.
The Aiguille du Midi is a 3,842m mountain in the French Alps. In 1955, the Téléphérique de l'Aiguille du Midi cable car was built to reach its summit, holding the record as the highest vertical ascent cable car in the world. It takes passengers from Chamonix to the summit in 20 minutes, offering panoramic views from its observation platform. The mountain provides access to skiing routes and trails leading to Mont Blanc.
Mobile trends for 2010 and beyond include:
1) Capacitive touchscreens becoming cheaper and more common in mobile devices.
2) Increasing processing power and higher resolution displays in mobile phones.
3) The continued popularity of feature phones alongside more advanced smartphones.
1. The document presents research on using artificial neural networks to model and predict the mechanical properties of structural steels after heat treatment.
2. Over 125 types of non-alloy and alloy structural steels were examined, including properties like strength, impact resistance, and hardness.
3. Neural networks were trained using input parameters like chemical composition, heat treatment type and parameters, and geometry to predict mechanical properties. Results showed the neural networks could accurately predict properties within the given input ranges.
El documento presenta los objetivos y metas de Harrison Gutiérrez Castillo para su portafolio digital en el año 2007, incluyendo aprender mucho sobre computación y cumplir con las metas propuestas de manera responsable. También contiene varias notas de amor dirigidas a una persona no identificada, expresando sentimientos de aprecio y promesas de amor eterno.
El documento describe las principales capas de la Tierra: la geosfera (incluyendo la corteza, el manto y el núcleo), la atmósfera, la hidrosfera y la biosfera. También explica los procesos geológicos de placas tectónicas, volcanes, terremotos, erosión y su relación con la dinámica interna de la Tierra.
The document discusses major sources of freely available digital elevation model (DEM) data, including the National Elevation Dataset (NED) for the US, Shuttle Radar Topography Mission (SRTM) data globally, and the Global Multi-resolution Terrain Elevation Data (GMTED2010) set. It provides details on the spatial resolution and geographic coverage of each DEM data source, as well as how to access and download the data through websites like the USGS Earth Explorer.
This document provides instructions for calculating vegetation indices from Landsat 5 TM and Landsat 7 ETM+ data using ArcGIS. It describes a multi-step process to: 1) reclassify Landsat digital number data to exclude null values, 2) convert Landsat 5 TM data to the Landsat 7 ETM+ format, 3) calculate radiance values, 4) calculate reflectance values using sun elevation angles and earth-sun distances, and 5) enforce positive reflectance values by setting negatives to zero. This allows vegetation indices to be accurately calculated from the reflectance data.
Data Processing Techniques for 3D Surface Morphologytheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation.
Topics:
1. Introduction to GIS
2. Components of GIS
3. Types of Data
4. Spatial Data
5. Non-Spatial Data
6. GIS Operations
7. Coordinate Systems
8. Datum
9. Map Projections
10. Raster Data Compression Techniques
11. GIS Software
12. Free GIS Data Resources
The document describes a system that uses data mining techniques and GPS to guide ships based on weather conditions. GPS is used to determine a ship's location, which is then compared to a weather report database using classification models. A decision tree is generated from the training data to predict weather conditions and determine if it is safe for the ship to continue its course. When the ship's location is received via GPS, the decision tree is used to analyze weather data for that area and send guidance to the ship about navigating safely.
The document discusses experiments conducted with national digital elevation models (DEMs) like the National Elevation Dataset (NED) and Shuttle Radar Topography Mission (SRTM) data. It analyzes the accuracy of NED and SRTM when compared to GPS data collected at a university golf course. The experiments show that SRTM has slightly better accuracy than NED, but may not accurately represent terrain in forested areas. The study also demonstrates that NED and SRTM can be used to create acceptable orthophotographs at a scale of 1:10,000 based on industry standards.
Raster data consists of a grid of cells where each cell contains a value representing information like elevation, imagery, or categories. Raster data is useful for representing continuous surfaces and for spatial analysis. It has advantages like simple data structure and ability to perform overlays, though large rasters require significant storage space. Characteristics include cell values, size, and representation of phenomena as surfaces divided into equal cells.
The document provides an overview of the HDF-EOS5 file format including:
- HDF-EOS5 files contain coremetadata, archivemetadata, and StructMetadata global attributes that provide information on the file structure and contents.
- Files can contain Grid, Swath, Point, Zonal Average, and Profile data structures with no size limits.
- Swath data is organized by time or track with irregular spacing, storing geolocation, time, and data in arrays. Grid data is organized by regular geographic spacing specified by projection parameters with data and geolocation information separated. Point data specifies locations but with no organization of the data.
Geographic Information Systems (GIS) are computer systems for capturing, storing, analyzing and displaying spatially-referenced data related to positions on Earth. GIS uses maps to show relationships within places and between places. It allows users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. The key components of a GIS are types of data (spatial and non-spatial), operations, coordinate systems, and software. Common data types include raster (grid cells), vector (points, lines and polygons), and attributes (descriptive data). Compression techniques help reduce large raster file sizes. Popular open source and commercial GIS software packages and free online
The document describes the general structure of HDF-EOS5 files. HDF-EOS5 files can contain gridded data, swath data, point measurements, and other objects. Key aspects include required and optional metadata like coremetadata and archivemetadata, along with data structures like grids, swaths, and points. Grids contain projection information and dimensions to geolocate data fields. Swaths contain geolocation, data, and profile fields along with dimension maps. Points contain scattered records linked by a common field. Compression is supported at the field level within data structures.
This document discusses the use of remote sensing data in GIS. It describes how remote sensing data needs to be geometrically corrected to match the projection system of the GIS database. It also discusses how classified remote sensing data like land cover maps can be overlaid with other geographic data in GIS. Image data from remote sensing can be analyzed with other data like elevation maps to improve classification accuracy. Orthophoto maps can be created by correcting distortions in aerial photos and satellite imagery using digital elevation models and ground control points. Remote sensing raster data is usually easier to import into a GIS than vector data derived from image classification, which requires more processing to extract boundaries and polygons.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). It is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives of the course are to introduce students to the basic concepts of GIS and provide an understanding of spatial data structures, management processes, and analysis tools.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). The course is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives are to introduce GIS fundamentals and processes of data management, analysis, and output. Students will learn about spatial data structures, data quality standards, and tools for data input, analysis, and management. The course aims to provide knowledge of GIS concepts and techniques.
Raster data is represented by a grid of cells, where each cell contains numeric or qualitative values. Raster data comes from sources like images, maps, and satellite imagery. Common analyses of raster data include buffering, reclassification, hillshades, interpolation, and surface calculation. Buffering assigns "in" and "out" values to cells based on their distance from a feature. Reclassification reassigns cell values. Hillshades create shaded relief maps from elevation data. Interpolation estimates values between known data points. Surface calculation performs cell-by-cell mathematical functions on rasters.
This document discusses using LiDAR data to map hydrologic features on timberlands owned by Plum Creek Timber Company in Essex County, Vermont. The goals are to locate hydrologic features like streams more accurately than the company's current data by processing LiDAR point cloud data. Key steps discussed include acquiring LiDAR data for the study area, performing QA/QC on the data, creating terrain and hydrologic digital elevation models (DEMs) from the LiDAR point clouds, and comparing the results to the company's existing hydrology data to improve accuracy.
Efficient Query Evaluation of Probabilistic Top-k Queries in Wireless Sensor ...ijceronline
The document summarizes research on efficient query evaluation methods for probabilistic top-k queries in wireless sensor networks. It proposes three algorithms (SSB, NSB, BB) that use the concepts of sufficient and necessary sets to prune data and reduce transmissions between clusters and the base station. It also develops an adaptive algorithm that dynamically switches between the three based on estimated costs. Experimental results show the algorithms outperform baselines and the adaptive approach achieves near-optimal performance under different conditions.
For a new better version of this tutorial see my Google Slides with embedded videos.
https://docs.google.com/presentation/d/1MftEOT3uvYpCVwUaLMhsesm5Que-Kr7GQRV4pKZ2SNQ/edit?usp=sharing
This is a 2019 tutorial on how to do watershed delineation using ArcMap 10. It is an open education resource. Please let me know if you find it useful or see something that could be improved. Feel free to use it for teaching Geographic Information Science.
This document discusses organizing raw data from a study on employee commute distances into a grouped frequency distribution. The raw data consists of the number of miles each employee traveled to work each day. To draw useful conclusions, the data is organized into a table with class intervals, tallies of data points within each interval, and cumulative frequencies. The document provides guidelines for determining class limits and widths to properly categorize the data while maintaining mutually exclusive and exhaustive classes of equal size. Organizing data into a frequency distribution in this way facilitates analysis and presentation of the distribution's shape.
This document provides information about data editing in a geographic information system (GIS). It discusses detecting and correcting errors in GIS data from various sources. It also describes reprojecting, transforming, and generalizing data to a common reference system. Matching boundaries between adjacent map sheets and rubber sheeting techniques are also summarized. Vector and raster data models, structures, and file formats used in GIS are briefly explained.
GPS works by using a network of satellites that transmit radio signals to receivers on Earth. These signals allow GPS receivers to calculate their position by determining distance from multiple satellites using trilateration. The document discusses factors that can impact accuracy, such as satellite geometry and signal interference, and provides best practices for using GPS to collect data for mapping trails.
The document describes a radio network design program with several modules for different design tasks. The main modules are: Network, which provides an interface for designing radio links between sites; Terrain Data, which creates terrain profiles between sites; Antenna Heights, which determines antenna heights to satisfy clearance criteria; Microwave/VHF-UHF Worksheets, which perform transmission analyses and reliability calculations for different frequency ranges; and Area Coverage, which models signal coverage for a network. The program handles tasks like importing site data, analyzing interference, and generating reports. It models aspects like terrain, antennas, and propagation to design radio networks.
The Pathloss installation program before August 1999 did not properly set the Windows registry entry to load Pathloss files with a .pl4 extension when double clicked. The document provides steps to edit the registry and add the missing %1 portion to the "ab" icon entry, which specifies the Pathloss program installation directory so that double clicking .pl4 files will now start the program and load the file.
The document discusses major sources of freely available digital elevation model (DEM) data, including the National Elevation Dataset (NED) for the US, Shuttle Radar Topography Mission (SRTM) data globally, and the Global Multi-resolution Terrain Elevation Data (GMTED2010) set. It provides details on the spatial resolution and geographic coverage of each DEM data source, as well as how to access and download the data through websites like the USGS Earth Explorer.
This document provides instructions for calculating vegetation indices from Landsat 5 TM and Landsat 7 ETM+ data using ArcGIS. It describes a multi-step process to: 1) reclassify Landsat digital number data to exclude null values, 2) convert Landsat 5 TM data to the Landsat 7 ETM+ format, 3) calculate radiance values, 4) calculate reflectance values using sun elevation angles and earth-sun distances, and 5) enforce positive reflectance values by setting negatives to zero. This allows vegetation indices to be accurately calculated from the reflectance data.
Data Processing Techniques for 3D Surface Morphologytheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation.
Topics:
1. Introduction to GIS
2. Components of GIS
3. Types of Data
4. Spatial Data
5. Non-Spatial Data
6. GIS Operations
7. Coordinate Systems
8. Datum
9. Map Projections
10. Raster Data Compression Techniques
11. GIS Software
12. Free GIS Data Resources
The document describes a system that uses data mining techniques and GPS to guide ships based on weather conditions. GPS is used to determine a ship's location, which is then compared to a weather report database using classification models. A decision tree is generated from the training data to predict weather conditions and determine if it is safe for the ship to continue its course. When the ship's location is received via GPS, the decision tree is used to analyze weather data for that area and send guidance to the ship about navigating safely.
The document discusses experiments conducted with national digital elevation models (DEMs) like the National Elevation Dataset (NED) and Shuttle Radar Topography Mission (SRTM) data. It analyzes the accuracy of NED and SRTM when compared to GPS data collected at a university golf course. The experiments show that SRTM has slightly better accuracy than NED, but may not accurately represent terrain in forested areas. The study also demonstrates that NED and SRTM can be used to create acceptable orthophotographs at a scale of 1:10,000 based on industry standards.
Raster data consists of a grid of cells where each cell contains a value representing information like elevation, imagery, or categories. Raster data is useful for representing continuous surfaces and for spatial analysis. It has advantages like simple data structure and ability to perform overlays, though large rasters require significant storage space. Characteristics include cell values, size, and representation of phenomena as surfaces divided into equal cells.
The document provides an overview of the HDF-EOS5 file format including:
- HDF-EOS5 files contain coremetadata, archivemetadata, and StructMetadata global attributes that provide information on the file structure and contents.
- Files can contain Grid, Swath, Point, Zonal Average, and Profile data structures with no size limits.
- Swath data is organized by time or track with irregular spacing, storing geolocation, time, and data in arrays. Grid data is organized by regular geographic spacing specified by projection parameters with data and geolocation information separated. Point data specifies locations but with no organization of the data.
Geographic Information Systems (GIS) are computer systems for capturing, storing, analyzing and displaying spatially-referenced data related to positions on Earth. GIS uses maps to show relationships within places and between places. It allows users to create interactive queries (user-created searches), analyze spatial information, edit data in maps, and present the results of all these operations. The key components of a GIS are types of data (spatial and non-spatial), operations, coordinate systems, and software. Common data types include raster (grid cells), vector (points, lines and polygons), and attributes (descriptive data). Compression techniques help reduce large raster file sizes. Popular open source and commercial GIS software packages and free online
The document describes the general structure of HDF-EOS5 files. HDF-EOS5 files can contain gridded data, swath data, point measurements, and other objects. Key aspects include required and optional metadata like coremetadata and archivemetadata, along with data structures like grids, swaths, and points. Grids contain projection information and dimensions to geolocate data fields. Swaths contain geolocation, data, and profile fields along with dimension maps. Points contain scattered records linked by a common field. Compression is supported at the field level within data structures.
This document discusses the use of remote sensing data in GIS. It describes how remote sensing data needs to be geometrically corrected to match the projection system of the GIS database. It also discusses how classified remote sensing data like land cover maps can be overlaid with other geographic data in GIS. Image data from remote sensing can be analyzed with other data like elevation maps to improve classification accuracy. Orthophoto maps can be created by correcting distortions in aerial photos and satellite imagery using digital elevation models and ground control points. Remote sensing raster data is usually easier to import into a GIS than vector data derived from image classification, which requires more processing to extract boundaries and polygons.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). It is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives of the course are to introduce students to the basic concepts of GIS and provide an understanding of spatial data structures, management processes, and analysis tools.
This document outlines the syllabus for a course on Geographic Information Systems (GIS). The course is divided into 5 units that cover fundamentals of GIS, spatial data models, data input and topology, data analysis, and applications of GIS. The objectives are to introduce GIS fundamentals and processes of data management, analysis, and output. Students will learn about spatial data structures, data quality standards, and tools for data input, analysis, and management. The course aims to provide knowledge of GIS concepts and techniques.
Raster data is represented by a grid of cells, where each cell contains numeric or qualitative values. Raster data comes from sources like images, maps, and satellite imagery. Common analyses of raster data include buffering, reclassification, hillshades, interpolation, and surface calculation. Buffering assigns "in" and "out" values to cells based on their distance from a feature. Reclassification reassigns cell values. Hillshades create shaded relief maps from elevation data. Interpolation estimates values between known data points. Surface calculation performs cell-by-cell mathematical functions on rasters.
This document discusses using LiDAR data to map hydrologic features on timberlands owned by Plum Creek Timber Company in Essex County, Vermont. The goals are to locate hydrologic features like streams more accurately than the company's current data by processing LiDAR point cloud data. Key steps discussed include acquiring LiDAR data for the study area, performing QA/QC on the data, creating terrain and hydrologic digital elevation models (DEMs) from the LiDAR point clouds, and comparing the results to the company's existing hydrology data to improve accuracy.
Efficient Query Evaluation of Probabilistic Top-k Queries in Wireless Sensor ...ijceronline
The document summarizes research on efficient query evaluation methods for probabilistic top-k queries in wireless sensor networks. It proposes three algorithms (SSB, NSB, BB) that use the concepts of sufficient and necessary sets to prune data and reduce transmissions between clusters and the base station. It also develops an adaptive algorithm that dynamically switches between the three based on estimated costs. Experimental results show the algorithms outperform baselines and the adaptive approach achieves near-optimal performance under different conditions.
For a new better version of this tutorial see my Google Slides with embedded videos.
https://docs.google.com/presentation/d/1MftEOT3uvYpCVwUaLMhsesm5Que-Kr7GQRV4pKZ2SNQ/edit?usp=sharing
This is a 2019 tutorial on how to do watershed delineation using ArcMap 10. It is an open education resource. Please let me know if you find it useful or see something that could be improved. Feel free to use it for teaching Geographic Information Science.
This document discusses organizing raw data from a study on employee commute distances into a grouped frequency distribution. The raw data consists of the number of miles each employee traveled to work each day. To draw useful conclusions, the data is organized into a table with class intervals, tallies of data points within each interval, and cumulative frequencies. The document provides guidelines for determining class limits and widths to properly categorize the data while maintaining mutually exclusive and exhaustive classes of equal size. Organizing data into a frequency distribution in this way facilitates analysis and presentation of the distribution's shape.
This document provides information about data editing in a geographic information system (GIS). It discusses detecting and correcting errors in GIS data from various sources. It also describes reprojecting, transforming, and generalizing data to a common reference system. Matching boundaries between adjacent map sheets and rubber sheeting techniques are also summarized. Vector and raster data models, structures, and file formats used in GIS are briefly explained.
GPS works by using a network of satellites that transmit radio signals to receivers on Earth. These signals allow GPS receivers to calculate their position by determining distance from multiple satellites using trilateration. The document discusses factors that can impact accuracy, such as satellite geometry and signal interference, and provides best practices for using GPS to collect data for mapping trails.
The document describes a radio network design program with several modules for different design tasks. The main modules are: Network, which provides an interface for designing radio links between sites; Terrain Data, which creates terrain profiles between sites; Antenna Heights, which determines antenna heights to satisfy clearance criteria; Microwave/VHF-UHF Worksheets, which perform transmission analyses and reliability calculations for different frequency ranges; and Area Coverage, which models signal coverage for a network. The program handles tasks like importing site data, analyzing interference, and generating reports. It models aspects like terrain, antennas, and propagation to design radio networks.
The Pathloss installation program before August 1999 did not properly set the Windows registry entry to load Pathloss files with a .pl4 extension when double clicked. The document provides steps to edit the registry and add the missing %1 portion to the "ab" icon entry, which specifies the Pathloss program installation directory so that double clicking .pl4 files will now start the program and load the file.
This document proposes a standard format for digitized antenna pattern data to facilitate software development and ensure consistent usage of antenna pattern data. Key elements include an overall file format with required and optional fields to describe antenna and pattern characteristics, as well as a standard way to present pattern cut data. The format is intended to be flexible enough for different antenna types, account for various pattern geometries and polarizations, and include all necessary data for propagation prediction software.
The document discusses NADCON, which is used to transform coordinates between the North American Datum of 1927 (NAD27) and the North American Datum of 1983 (NAD83). It provides details on the specific NADCON files used for different regions of the United States and territories. Instructions are given on how to select the appropriate NADCON region and perform coordinate transformations between NAD27 and NAD83 in the Pathloss terrain modeling program.
The document describes how to load and navigate a map grid project file in Pathloss 4.0. It includes instructions on loading the example project and network files, testing clearance between sites by generating path profiles, adding elevation views, and moving sites. It also provides details on how to create a map grid project, including setting the projection and loading backdrop and elevation data sources.
The May 2002 version of the Pathloss program includes:
1) An option to not calculate tropospheric scatter loss when calculating diffraction loss, which avoids discontinuities in coverage plots.
2) Batch printing options for microwave and VHF-UHF worksheets.
3) The ability to use existing file associations when performing section operations to modify equipment parameters for multiple sites.
The document describes a new feature added to the Import link text files operation that allows for a radio and antenna cross reference. External text files cross reference identifiers in imported data to Pathloss code files, allowing interference calculations and filling in missing data. The cross reference files must be named correctly and located in the Pathloss installation directory, with each line mapping an import identifier to a Pathloss code file.
The Gauss Kruger projection is a generalized Transverse Mercator projection used to create coordinate systems for mapping countries. It defines parameters like the ellipsoid, latitude and longitude origins, false eastings and northings, and scale factor. These parameters are set differently for various countries that use the Gauss Kruger projection, such as Germany which uses 5 zones, Sweden, and Slovakia.
The document summarizes file locking and sharing features introduced in the June 2002 version of the Pathloss software. It describes how pathloss data and network files located on a shared network drive will be locked and able to be shared across multiple users. Specific details are provided around how locking and sharing of these files is implemented, including the use of additional "lck" and "glk" files to manage concurrent access and notify users of changes.
This document outlines several updates and additions to the Pathloss software. Key points include:
- New features for displaying network maps including customizable backgrounds, terrain data from various sources, and zoom functions.
- Enhancements to terrain database performance and the ability to generate elevation profiles and visibility tests between sites.
- Options to export site and link list reports in CSV format with configurable fields and formatting.
- Methods for modeling interference and correlation between desired and interfering signal fades.
- Support for ITU-R G.826 error performance objectives to estimate availability and quality of digital radio links.
- Updates to radio data files and forms to include additional parameters for modeling radio performance.
This document describes the rapid deployment feature of Pathloss 4.0 software for designing high frequency networks. It allows for automated transmission design, interference analysis under clear and rain conditions, and generation of pathloss data files. The process involves setting a high/low frequency plan, polarizations, running transmission design and interference calculations, and outputting individual pathloss files. It supports both standard and adaptive ATPC radios and can test for network stability under rain interference scenarios.
1. Pathloss 4.0 MSI PLANET Terrain Database UTM Projection
MSI PLANET Terrain Data UTM Projection
Universal Transverse Mercator (UTM) Projection
The UTM projection divides the world into 60 zones or segments. Each zone covers 6 degrees of longitude.
The first zone starts at the international date line and the numbers increase eastwards. At the center of each
zone is a central meridian, which is the measurement reference for that zone.
East-west grid lines are attached to the central meridian at right angles and follow great circle paths away
from it. These are lines of constant northing and do not follow parallels of latitude, since parallels of latitude
are not great circles. Finally, north-south grid lines are draw as lines of constant easting.
UTM coordinates are relative to a particular zone number and are expressed as an easting or X coordinate
and a northing or Y coordinate. The easting is the distance from the central meridian along the east west
grid lines using a positive east negative west sign convention. To avoid negative numbers, a false easting
of 500,000 meters is assigned to the central meridian.
The northing is the distance from the equator along the north-south grid lines using a positive north - neg-
ative south sign convention. To avoid negative numbers in the southern hemisphere, a false northing of
10,000,000 meters is assigned to the equator. This false northing creates an ambiguity in this coordinate
system. Consider the UTM coordinates:
zone = 12, easting = 575 kilometers, northing = 5600 kilometers.
Using the WGS84 datum, this converts to:
latitude 50 32 49.64 N and longitude 109 56 29.12 W in the northern hemisphere
or
latitude 39 44 47.78 S and longitude 110 07 28.56 W in the southern hemisphere
To resolve this ambiguity, the Pathloss program adds the suffix N or S to the UTM zone. e.g. 12N or 12S.
This false northing introduces an additional complication when a terrain data base spans the equator. North-
ings of 20 and 9980 kilometers represent points 20 kilometers north and south of the equator respectively.
Planet Terrain Database Description
The Planet terrain database format consists of regular array of elevations. Each elevation represents the av-
erage elevation in a square cell. The file is arranged in rows running from west to east starting at either the
SW or NW corner of the file. Normally the elevations are stored as 2 byte integers. The byte order could
be INTEL (little endian) or SPARC (big endian)
The geo-referencing information is contained in two external files: an index file and a projection file.
An example of a projection file is given below. The format will vary depending on the projection. This ex-
ample denotes a UTM projection in zone 18 using the WGS84 datum.
WGS-84
18
UTM
Page 1 of 6
2. MSI PLANET Terrain Database UTM Projection Pathloss 4.0
The index file defines the edges of the terrain database and specifies the cell size. One entry is provided for
each file. An example of a line in an index file is given below.
toluca1.hgt 384000 484050 2080200 2180250 50
This is interpreted as
file name tolucal.hgt
west edge 384000 meters
east edge 48450 meters
south edge 2080200 meters
north edge 2180250 meters
cell size 50 meters
The east and west edges are the UTM eastings
and the north and south edges are the UTM
northings.
Geographic Defaults
The first step is to set the datum or ellipsoid to
correspond to the projection file. Select Config-
ure - Geographic Defaults. Set the grid coordi-
nate system (projection) to UTM.
Configuration
The Pathloss program uses a generic terrain database reader which was developed for the Logica Odyssey
file format. The Planet height files can be read directly. No file conversions are necessary. The setup pro-
cedure is described below:
Select Configure - Terrain Database and
then select Odyssey UTM.
Click the Setup Primary button to configure
the terrain database.
Set directory
Click the Set Directory button and point to
the directory (folder) containing the Planet data files.
Page 2 of 6
3. Pathloss 4.0 MSI PLANET Terrain Database UTM Projection
UTM zone methods
In many cases, terrain data is supplied in a
single file. The east and west edges may ex-
tend outside of the standard UTM zone bou-
daries. In these cases, the UTM zone must
be specified by either of the following two
methods:
Check the "Use specified index file" option
and then select the file from the dropdown
list. The UTM zone associated with the se-
lected file will be used and only the selected
file will be used even if the index contains
multiple files.
Check the “Use specified UTM zone” and enter the zone number suffixed by N or S. If N or S is not spec-
ified the zone will default to the northern hemisphere. The S suffix must be specified for the southern hem-
isphere
In all other cases use the “Use standard UTM zones in index” option.
Embedded building data
Normally an elevation is interpolated from the nearest four elevations to the point of interest. If the terrain
data contains embedded building or canopy data, check the “Embedded building data” option. The cell el-
evations will be used in this case.
Index
Click the Index button
to access the digital
map index. Data can be
entered manually. Se-
lect Edit- Add on the
Index menu bar and en-
ter the data as shown on
the next page. This information is available from the Planet index files. When the data entry is complete
Click OK
Page 3 of 6
4. MSI PLANET Terrain Database UTM Projection Pathloss 4.0
Two additional fields must be set directly on the Grid data entry
form. There are:
Bottom up This field determines if the file starts in the south
west corner or the north west corner. Double click
on this field to change the setting to true of false.
If the network background or terrain view is up-
side down, then change this field.
Byte order The is equivalent to "big and little endian" Dou-
ble click on this field to change the field to
SPARC (big endian) or INTEL (little endian)
Import Index
A generalized text import feature can be custom-
ized to read Planet index files. Select Files - In-
dex to bring up the field definition dialog box.
Click the Planet button to obtain the default set-
tings for Planet indexes.
Note that the “Byte order” and “Bottom up” op-
tions may have to be experimentally determined.
Enter the UTM zone N or S suffix and check
“Zone number”. A Planet index does not include
the UTM zone number. If the UTM zone is in-
cluded in the index file, enter the field number
for the UTM zone and check the “Field number”
option.
The “Bytes per pixel setting is always 2 and the
Edge units are always meters for Planet
Click OK and load the Planet index file.
Page 4 of 6
5. Pathloss 4.0 MSI PLANET Terrain Database UTM Projection
Testing the Terrain Database Settings
This is best carried out in the Network mod-
ule. Create two sites at the south west and
north east corners of the extents of the terrain
database files. Refer to the index for the UTM
coordinates. Select Files - New to clear the
display. Select Site Data - Site List and then
select Edit - Add.
Enter the easting, northing and UTM zones.
The latitude will be automatically determined
When both sites are on the display, select Site
Data - Create background.
Select Site Data - Color 16° 20'
Legend and verify that the
NE
elevation range is reasona-
ble.
25'
30'
35'
40'
SW
16° 45'
68° 25' 20' 15' 10' 5' 68°
Possible Problems
The background is not generated
• The file names in the index file are not the same as the actual database files. The extensions may be
missing or different.
• The UTM zones in the index file are not correct or the N-S suffix is missing
• The UTM method is wrong. The zone is incorrectly specified or the N-S suffix is missing
• The SPARC - INTEL setting is wrong
• The map file directory is incorrectly set
Page 5 of 6
6. MSI PLANET Terrain Database UTM Projection Pathloss 4.0
• The file contains “no data” elevations. These values are usually 9999 and the program will ignore
these.
The elevations are not correct or the display is incomplete
• The SPARC - INTEL setting is wrong
The display is upside down. This will be very apparent if the database has several vertical tiles
• The bottom - up setting in the index is wrong
Equatorial Considerations +20,000 10,020,000
Terrain databases which span the equator require
special consideration. Two index files are shown be-
20_1 20_2
low. Both of these consists of four terrain data files
covering an area of 20 kilometers by 20 kilometers.
Two of these files extend 20 kilometers above the
equator and the other two files extend 20 kilometers 0 Equator 10,000,000
below the equator.
The first index is referenced to the northern hemi- 20_3 20_4
sphere. The map edge northings are +20,000 meters,
0 and -20,000 meters. In this case, a northern hemi-
sphere UTM zone must be specified in both the in- -20,000 9,980,000
dex and the UTM zone method
Northern Hemisphere Reference
20_1.height 565000 585000 0 20000 20
20_2.height 585000 605000 0 20000 20
20_3.height 565000 585000 -20000 0 20
20_4.height 585000 605000 -20000 0 20
The second index is referenced to the southern hemisphere. The northing of the equator is 10,000,000 me-
ters.The map edge northings are 9,980,000 meters, 10,000,000 meters and 10,020,000 meters. In this case,
a southern hemisphere UTM zone must be specified in both the index and the UTM zone method
Southern Hemisphere Reference
20_1.height 565000 585000 10000000 10020000 20
20_2.height 585000 605000 10000000 10020000 20
20_3.height 565000 585000 9980000 10000000 20
20_4.height 585000 605000 9980000 10000000 20
Page 6 of 6