Presentation & Paper By Chris Malzone and given by Mike Mutschler, RESON. Focuses on the benefits of fusing multiple sources of acoustic data through fusion. Presentation also introduces the concept of fusion as well as looks at a Habitat Mapping Case Study in the US Virgin Islands as analysed via the Eonfusion software.
Moving beyond the 4th Dimension of Quantifying, Analyzing & Visualizing A...chrismalzone
Presentation & Paper By Chris Malzone and given by Mike Mutschler, RESON. Focuses on the benefits of fusing multiple sources of acoustic data through fusion. Presentation also introduces the concept of fusion & looking at multisource 4-dimensional data. It also brings home the point through a Habitat Mapping Case Study in the US Virgin Islands as analysed via the Eonfusion software.
The magnitude of data being stored and processed in the Cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and mobile code offloading. Concurrently, cloud services are getting more global and geographically distributed. To handle such changes in its usage scenario, the Cloud needs to transform into a completely decentralized, federated and ubiquitous environment similar to the historical transformation of the Internet. Indeed, research ideas for the transformation has already started to emerge including but not limited to Cloud Federations, Multi-Clouds, Fog Computing, Edge Computing, Cloudlets, Nano data centers, etc.
Standardization and resource management come up as the most significant issues for the realization of the distributed cloud paradigm. The focus in this thesis is the latter: efficient management of limited computing and network resources to adapt to the decentralization. Specifically, cloud services that consist of several virtual machines, dedicated network connections and databases are mapped to a multi-provider, geographically distributed and dynamic cloud infrastructure. The objective of the mapping is to improve quality of service in a cost-effective way. To that end; network latency and bandwidth as well as the cost of storage and computation are subjected to a multi-objective optimization.
The first phase of the resource mapping optimization is the topology mapping. In this phase, the virtual machines and network connections (i.e. the virtual cluster) of the cloud service are mapped to the physical cloud infrastructure. The hypothesis is that mapping the virtual cluster to a group of data centers with a similar topology would be the optimal solution.
Replication management is the second phase where the focus is on the data storage. Data objects that constitute the database are replicated and mapped to the storage as a service providers and end devices. The hypothesis for this phase is that an objective function adapted from the facility location problem optimizes the replica placement.
Detailed experiments under real-world as well as synthetic workloads prove that the hypotheses of the both phases are true.
Moving beyond the 4th Dimension of Quantifying, Analyzing & Visualizing A...chrismalzone
Presentation & Paper By Chris Malzone and given by Mike Mutschler, RESON. Focuses on the benefits of fusing multiple sources of acoustic data through fusion. Presentation also introduces the concept of fusion & looking at multisource 4-dimensional data. It also brings home the point through a Habitat Mapping Case Study in the US Virgin Islands as analysed via the Eonfusion software.
The magnitude of data being stored and processed in the Cloud is quickly increasing due to advancements in areas that rely on cloud computing, e.g. Big Data, Internet of Things and mobile code offloading. Concurrently, cloud services are getting more global and geographically distributed. To handle such changes in its usage scenario, the Cloud needs to transform into a completely decentralized, federated and ubiquitous environment similar to the historical transformation of the Internet. Indeed, research ideas for the transformation has already started to emerge including but not limited to Cloud Federations, Multi-Clouds, Fog Computing, Edge Computing, Cloudlets, Nano data centers, etc.
Standardization and resource management come up as the most significant issues for the realization of the distributed cloud paradigm. The focus in this thesis is the latter: efficient management of limited computing and network resources to adapt to the decentralization. Specifically, cloud services that consist of several virtual machines, dedicated network connections and databases are mapped to a multi-provider, geographically distributed and dynamic cloud infrastructure. The objective of the mapping is to improve quality of service in a cost-effective way. To that end; network latency and bandwidth as well as the cost of storage and computation are subjected to a multi-objective optimization.
The first phase of the resource mapping optimization is the topology mapping. In this phase, the virtual machines and network connections (i.e. the virtual cluster) of the cloud service are mapped to the physical cloud infrastructure. The hypothesis is that mapping the virtual cluster to a group of data centers with a similar topology would be the optimal solution.
Replication management is the second phase where the focus is on the data storage. Data objects that constitute the database are replicated and mapped to the storage as a service providers and end devices. The hypothesis for this phase is that an objective function adapted from the facility location problem optimizes the replica placement.
Detailed experiments under real-world as well as synthetic workloads prove that the hypotheses of the both phases are true.
Feature Extraction from the Satellite Image Gray Color and Knowledge Discove...IJMER
Satellite take images of the Earth in selected spectral bands that are in both the visible and
the infrared portions of the electromagnetic spectrum. Many Satellites provide three types of Satellite
Images. These Images are Visible Satellite Image, Infrared Satellite Image, and Water Vapor Satellite
Image. These images are important for different reasons, and, in some cases, all three are needed to
accurately interpret atmospheric conditions. These Satellite images contain different types of cloud. This
paper shows cloud feature extraction using Histogram. A table that shows cloud existence in different
image is created, called Association table in which Y represents cloud is exist and N represent not exist.
Association rule mining is applied to this table to make relations between different clouds and discover
the knowledge about cloud existence.
FME Around the World (FME Trek Part 1): Ken Bragg - Safe Software FME World T...IMGS
Aim: "To seek out innovative FME users
throughout the galaxy, sharing
their stories and ideas to inspire
you to take your data where no
data has gone before."
Digital Heritage Documentation Via TLS And Photogrammetry Case Studytheijes
In the last decade, several manual tradition measurement techniques were used to document the heritage buildings around the word; however, some of these techniques take a long time, often lack completeness, and may sometimes give unreliable information. In contrast, terrestrial laser scanning “TLS” surveys and Photogrammetry have already been undertaken in several heritage sites in the United Kingdom and other countries of Europe as a new method of documenting heritagesites. This paper focuses on using the TLS and Photogrammetry methods to document one of the important houses in Historic Jeddah, Saudi Arabia, which is Nasif Historical House, as an example of Digital Heritage Documentation (DHD).
Extend Your Journey: Considering Signal Strength and Fluctuation in Location-...Chih-Chuan Cheng
Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without impacting the application’s semantics adversely. To solve the fundamental problem, we propose a dynamic programming algorithm and prove its optimality in terms of energy savings. Then, we perform postoptimal analysis to explore the tolerance of the algorithm to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues.We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging and demonstrate the applicability of the proposed algorithm towards signal strength fluctuations.
With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one.
The resolution merge or pan sharpening is the technique used to obtain high resolution multi-spectral images. The color information is collected from the coarse resolution satellite data and the intensity from the high resolution satellite data.
The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset.
The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Feature Extraction from the Satellite Image Gray Color and Knowledge Discove...IJMER
Satellite take images of the Earth in selected spectral bands that are in both the visible and
the infrared portions of the electromagnetic spectrum. Many Satellites provide three types of Satellite
Images. These Images are Visible Satellite Image, Infrared Satellite Image, and Water Vapor Satellite
Image. These images are important for different reasons, and, in some cases, all three are needed to
accurately interpret atmospheric conditions. These Satellite images contain different types of cloud. This
paper shows cloud feature extraction using Histogram. A table that shows cloud existence in different
image is created, called Association table in which Y represents cloud is exist and N represent not exist.
Association rule mining is applied to this table to make relations between different clouds and discover
the knowledge about cloud existence.
FME Around the World (FME Trek Part 1): Ken Bragg - Safe Software FME World T...IMGS
Aim: "To seek out innovative FME users
throughout the galaxy, sharing
their stories and ideas to inspire
you to take your data where no
data has gone before."
Digital Heritage Documentation Via TLS And Photogrammetry Case Studytheijes
In the last decade, several manual tradition measurement techniques were used to document the heritage buildings around the word; however, some of these techniques take a long time, often lack completeness, and may sometimes give unreliable information. In contrast, terrestrial laser scanning “TLS” surveys and Photogrammetry have already been undertaken in several heritage sites in the United Kingdom and other countries of Europe as a new method of documenting heritagesites. This paper focuses on using the TLS and Photogrammetry methods to document one of the important houses in Historic Jeddah, Saudi Arabia, which is Nasif Historical House, as an example of Digital Heritage Documentation (DHD).
Extend Your Journey: Considering Signal Strength and Fluctuation in Location-...Chih-Chuan Cheng
Reducing the communication energy is essential to facilitate the growth of emerging mobile applications. In this paper, we introduce signal strength into location-based applications to reduce the energy consumption of mobile devices for data reception. First, we model the problem of data fetch scheduling, with the objective of minimizing the energy required to fetch location-based information without impacting the application’s semantics adversely. To solve the fundamental problem, we propose a dynamic programming algorithm and prove its optimality in terms of energy savings. Then, we perform postoptimal analysis to explore the tolerance of the algorithm to signal strength fluctuations. Finally, based on the algorithm, we consider implementation issues.We have also developed a virtual tour system integrated with existing web applications to validate the practicability of the proposed concept. The results of experiments conducted based on real-world case studies are very encouraging and demonstrate the applicability of the proposed algorithm towards signal strength fluctuations.
With increasing use of remote sensing, the need for crispier, accurate and enhanced precision has deemed to the improvement in the spectral and spatial resolution of remotely sensed imagery. For most of the systems, panchromatic images typically have higher resolution, while multispectral images offer information in several spectral channels. Resolution merge (also called pan-sharpening) allows us to combine advantages of both kinds of images by merging them into one.
The resolution merge or pan sharpening is the technique used to obtain high resolution multi-spectral images. The color information is collected from the coarse resolution satellite data and the intensity from the high resolution satellite data.
The main constraint is to preserve the spectral information for aspects like land use. Saving theimage from distortion of the spectral characteristics is important in the merged dataset.
The most common techniques for spatial enhancement of low-resolution imagery combining high and low resolution data can be used are: Intensity-Hue-Saturation, Principal Component, Multiplicative and Brovey Transform.
THIS PRESENTATION IS TO HELP YOU PERFORM THE TASK STEP BY STEP.
Gaining insight to Acoustic Measurements through the fusion of multisource data
1. Mike Mutschler RESON Inc Chris Malzone Myriax Inc Gaining insight to Acoustic Measurements through the fusion of multisource data
2. Requirements for Gaining Insight Clarity This Requires Clean Acoustics Clean Hardware Resolution Confidence Is what you see the same thing every time?! Seabat 7101 Seabat 8101
3. System AdvancementsWith Advancements in System Architecture Clarity and Resolution Are Obtained S/V Minotaur SeaTronics Ltd 7125 SB Outfall, Line0008 S/V Minotaur SeaTronics Ltd 7125 SB Outfall, Line0008 “Spoking” or Coherent Noise due to poor grounding logic System Improvements and Data Visualization 20th Century 21st Century Multipath Very Clean Acoustic with no multipath or coherent noise! Image from Clarke, et al CHC 2006 Presentation Image from JH Clarke (Univ of New Brunswick), et al CHC 2006 Presentation “The latest generation of multibeamechosounders shows significant improvement in the sonar’s acoustical architecture and hence better signal-to-noise ratios.” - Brian Calder, UNH
4. But Gaining Confidence…. A Multibeam, for example, only provides a single source of multiple streams of data For Habitat Mapping, this data provides a means to calculate derived values of the Seafloor Including Rugosity – A measure of Surface Roughness or an indicator of the seafloor composition Bathymetric Positioning Index - a measure of where a referenced location is relative to the locations surrounding it. Slope – How steep the terrain is These are all used to determine habitat types Does this data reflect what is really present on the seafloor?
5. Means to Verify By integrating multisource data, we can verify the results of measurements through either: Visual Ground Truthing Calibration and/or comparison of “like instruments” (e.g. split beam vsmultibeamsonars)
6. Integrating Observations Problem: Typically Temporal, Visual and Analytical Components of Data Integration Requires a Multi-Pronged Approach in order to gain inferences Solution is DATA FUSION!
7. What is “Data Fusion” Data fusion, is generally defined as the use of techniques that combine data from multiple sources and gather that information in order to achieve inferences, which will be more efficient and potentially more accurate than if they were achieved by means of a single source.
8. Fusion Must Bridge Sampling Fusion requires that linkages be created between different data sources such that attributes can be either migrated from one source to another irrespective of the source sampling rate or sampling time In short, the tool to achieve fusion must be able to integrate, fuse, link & interpolate
9. Fusion Must Bridge Data Types Seafloor information may be provided to analysts in 3 common data format types Raster - matrix of cells in continuous space. Each layer represents one attribute (although other attributes can be attached to a cell). Vector - each feature as a row in a table, and feature shapes are defined by x,y locations in space. Features can be discrete locations or events, lines, or polygons. Media – Still Images, Video or Audio
10. Example: Eonfusion A 4-Dimensional Geospatial Software Solution that provides Temporal Support: A means to easily integrate, analyze & visualize raster and vector data sources that vary in time True Data Fusion : Fuse raster and/or vector data sources to one another and quickly transfer attributes with interpolations automatically handled Visual Dataflow Model: A means to easily manage the integration of multiple data sets through an object oriented workflow model. Programming & Scripting Module: Eonfusion provides an integrated development environment that can be used to calculate spatial statistics AND/OR create Application Programming Interfaces (API’s).
11. A Broad Approach to Fusing Data Data is introduced as: irregular vector data (e.g. AUV Track Data) raster data (e.g. Multibeam Snippets Backscatter Data) media (e.g. ROV Video) Coincident visualization these data can then be viewed coincidently in 4D scenes (3D space and time) Data can be fused either: Temporally or by linking all attributes in reference to similar time stamps Spatially – By linking all attributes based on their geographic positions
14. Media Fusion Track Line (X,Y, Z+Time) Video Frames Provide a Start Time and Duration Map Frames Probe
15. Example: Buck Island, US VIFusing Media, Bathy, Backscatter, Rugosity Habitat Mapping Survey Utilizing: An Integrated Seabat Based Hydrographic System Split-Beam Scientific Echosounder Data for obtaining calibrated water column backscatter ROV to obtain video for visual ground-truthing of data Data Courtesy of Tim Battista, NOAA Biogeographic Branch
17. Data flows – Visual Dataflow ModelTo Remain Organized & Track Changes To remain organized and to track changes while integrating a wide variety of data sources, a visual dataflow model may be incorporated. Each object represents either a data source (blue objects) with metadata/projections defined*, operators to transform data (green objects) and scenes/the visualization space (orange)
19. Fusion of Raster, Vector & Media Media Fusion Operator Simple Copy Commands to Migrate Raster Attributes to the ROV Trackline Vertices Backscatter Raster Raster / Vector Fusion Via Combine Data Sets Now All Values Exist in a Common Space ROV Transect # 6 Rugosity Raster Database for Publishing Ground Truthed Values Now All Values Fused Within A Common Dataset
20. Visualizing Fused Information Since Raster information for backscatter and rugosity have been transferred over to the vector information for the ROV Track, the data can be coincidently viewed in 4D and viewed with the aid of 2D graphs
21. Visually Ground-Truth Data “Segment Probe” Allows for the user to navigate through the video using either end of the probe which will update the video accordingly. For instance, if you wish to navigate thru the video with the back probe, the video will update to the position in both space & time. Data may be visually queried as to see how the data was classified by the analyst
22.
23. Once happy the analyst can now either select from a pull-down list of classes to define that region OR create their own on the fly
24.
25. Summary Data fusion is the integration and linking of attributes between multiple-source data The process must remain simple & a visual dataflow model not only provides a means to integrate data but also a means to provide QA on methodology A visual quantification coupled with an efficient means to assimilate a final product provides insight to multi-source data collected for habitat mapping or other multi-disciplinary projects
Editor's Notes
To remain organized and to track changes while integrating a wide variety of data sources, a visual dataflow model may be incorporated. Each object represents either a data source (blue objects) with metadata/projections defined, operators to transform data (orange objects) and scenes/the visualization space (orange)
