Research Thrust R3 Image and Data Information Management · Leader, RPI John Proakis Co-Leader, NU Shiv Kalyanaraman, RPI Masoud Salehi, NU John Woods, RPI Gary Saulnier, RPI Greg

Research Thrust R3Image and Data Information Management

Research Thrust R3Image and Data Information Management

Diverse Problems – Similar SolutionsDiverse Problems – Similar Solutions

James ModestinoDavid Kaeli

James ModestinoDavid Kaeli

A. OverviewB. R3 Fundamental Research

R3a. Data Compression

R3b. Multimedia Networks

R3c. Scalable Computing

R3d. Image Databases and

Metadata

C. Impact and TimelineD. I-PLUS

A. OverviewB. R3 Fundamental Research

R3a. Data Compression

R3b. Multimedia Networks

R3c. Scalable Computing

R3d. Image Databases and

Metadata

C. Impact and TimelineD. I-PLUS

A. OverviewA. Overview

§ Acquisition, transmission, storage, processing, retrieval, dissemination, display and visualization of sensor data§ Development of a distributed collaborative research environment§ Provide remote Web access to I-PLUS resources

ScopeScope

SubsurfaceSensing and

Modeling

Validation Testbeds

Environmental-CivilApplications

Bio-MedicalApplications

Image and DataInformation

Management

Physics-BasedSignal Processing andImage Understanding

I–PLUS

R1R1R2R2

R3R3L1L1

L2L2

L3L3

ScopeScope

EngineeredSystemEngineeredSystem

EnablingTechnologiesEnablingTechnologies

FundamentalScienceFundamentalScience

§ Provide compression, processing, transmission and storage resources§ Provide communications/networking support infra-

structure and collaborative work environment

§ Provide compression, processing, transmission and storage resources§ Provide communications/networking support infra-

structure and collaborative work environment

§ Provide repository for tools and Solutionware§ Administer CenSSIS information management§ Provide Web access to CenSSIS resources

§ Provide repository for tools and Solutionware§ Administer CenSSIS information management§ Provide Web access to CenSSIS resources

L1L1

L2L2

L3L3

§ Develop efficient, flexible compression approaches§ Develop efficient/reliable transport schemes§ Develop new collaborative work environments§ Develop specific scalable computing approaches § Develop image database, archiving/search schemes

§ Develop efficient, flexible compression approaches§ Develop efficient/reliable transport schemes§ Develop new collaborative work environments§ Develop specific scalable computing approaches § Develop image database, archiving/search schemes

EE § Enable distributed education component§ Enhance engineering curriculum§ Enable distributed education component§ Enhance engineering curriculum

R3 GoalsR3 Goals

EngineeredSystemEngineeredSystem

EnablingTechnologiesEnablingTechnologies

FundamentalScienceFundamentalScience

EducationEducation

Focused orPulsed Probe

Focused orGated Detector

(LPM)

2D Photomosaic ofUnderwater Thermal Vent

WHOI Data Set

2D Photomosaic ofUnderwater Thermal Vent

WHOI Data Set

§ Processing/Storage/Transmission Problem§ Transmission Time 40 min @ 100 Mb/s§ Compression Reduces to < 0.5 min§ Layered Progressive Scheme Useful

§ Processing/Storage/Transmission Problem§ Transmission Time 40 min @ 100 Mb/s§ Compression Reduces to < 0.5 min§ Layered Progressive Scheme Useful

MultipleOverlapping

MultipleOverlapping

OpticalImagesOpticalImages

SensorData

SensorData

Typical Localized Probing & Mosaicing(LPM) ApplicationTypical Localized Probing & Mosaicing(LPM) Application

104 1K x 1K 24 30 GBytes

Number ofImages

Number ofImages

Image SizeIn Pixels

Image SizeIn Pixels

Number ofBits/PixelsNumber ofBits/Pixels

Total SizeOf Data SetTotal Size

Of Data Set

Single Quadrature Microscope

View of Mouse EggMultiple Views perSlice and Multiple

Slices per ReconstructionMultiple Reconstructions

for Moving ObjectsNU/MGH Data Set

Single Quadrature Microscope

View of Mouse EggMultiple Views perSlice and Multiple

Slices per ReconstructionMultiple Reconstructions

for Moving ObjectsNU/MGH Data Set

MultipleViews

MultipleViews

PossibleTemporal

Sequences

PossibleTemporal

Sequences SensorData

SensorData

Object

Detectors

Sources

(MVT)

§ Transmission Time 4 min @ 100 Mb/s§ For 100 Reconstructions is 400 min§ Compression Reduces to < 1 min§ Utilize 3-D Volumetric Compression§ Multiresolution Layered Scheme Useful

§ Transmission Time 4 min @ 100 Mb/s§ For 100 Reconstructions is 400 min§ Compression Reduces to < 1 min§ Utilize 3-D Volumetric Compression§ Multiresolution Layered Scheme Useful

Number ofViews/Slices

10

Number ofViews/Slices

10

Image SizeIn Pixels/View

1K x 1K


1K x 1K

Number ofBits/Pixels

24


24

Total SizeOf Data Set3 GBytes

Total SizeOf Data Set3 GBytes

Number ofSlices

100

Number ofSlices

100

Typical Multi-View Tomography (MVT) ApplicationTypical Multi-View Tomography (MVT) Application

Remote HyperspectralSensing of Coastal Regions

UPRM Data Set

Remote HyperspectralSensing of Coastal Regions

UPRM Data Set

MultipleWavelengths

MultipleWavelengths

MultipleImagesMultipleImages

SensorData

SensorData

(MSD)

Wide BandProbe

Narrow BandDetectors

backgroundagua1agua-2agua4basuracultivo1cultivo2cultivo-recortadomontanascultivo4cutlivo-costarenazona-urbanaagua-5cultivo6agua6techo-almacencultivo-corto-Nagua-costa-Nvegetacionrio

Number ofImages/

Wavelength103

Number ofImages/

Wavelength103


1K x 1K


1K x 1K


16


16

Total SizeOf Data Set20 TBytes

Total SizeOf Data Set20 TBytes

Number ofWavelengths

103

Number ofWavelengths

103

§ Transmission Time 56 hr @ 100 Mb/s§ Compression Reduces to < 30 min§ Exploit Correlation Across Wavelengths§ Layered Progressive Scheme Useful

§ Transmission Time 56 hr @ 100 Mb/s§ Compression Reduces to < 30 min§ Exploit Correlation Across Wavelengths§ Layered Progressive Scheme Useful

Typical Multi-Spectral Discrimination (MSD) ApplicationTypical Multi-Spectral Discrimination (MSD) Application

Focused orPulsed Probe

Focused orGated Detector

(LPM) (MSD)

Wide BandProbe

Narrow BandDetectors

Photomosaic of the Curved Human Retina

RPI Data Set

Photomosaic of the Curved Human Retina

RPI Data Set

MultipleOverlapping

MultipleOverlapping

Temporal Sequence of

Multi-SpectralImages

Temporal Sequence of

Multi-SpectralImages Sensor

DataSensor

Data

A: Retinal Surface (visible channel)B: Subsurface (near infrared)

A: Retinal Surface (visible channel)B: Subsurface (near infrared)

§ Real-Time Transmission Requires 720Mb/s § Compression Reduces to < 6 Mb/s§ Transmission Time 6.6 hr @ 100 Mb/s§ Layered Transport Scheme Useful

§ Real-Time Transmission Requires 720Mb/s § Compression Reduces to < 6 Mb/s§ Transmission Time 6.6 hr @ 100 Mb/s§ Layered Transport Scheme Useful

Image SizeIn Pixels1K x 1K

Image SizeIn Pixels1K x 1K


12


12

Total SizeOf Data Set

300-600 GBytes

Total SizeOf Data Set

300-600 GBytes

Length of Procedure

1-2 hrs

Length of Procedure

1-2 hrs

Number ofChannels

2

Number ofChannels

2Frame Rate

30/secFrame Rate

30/sec

Typical LPM/MSD ApplicationTypical LPM/MSD Application

How Can We Provide Adequate Communications/Networking Infrastructure?How Can We Provide Adequate Communications/Networking Infrastructure?

CenSSISCenSSIS


ClustersClusters CollaborationCollaboration

ConferencingConferencing

Web AccessWeb Access

DatabasesDatabases

Multimedia NetworkInternet 2

Multimedia NetworkInternet 2

TestbedsTestbeds

B. Fundamental ResearchB. Fundamental Research

Goal: Develop information management concepts, tools and infrastructure to support basic CenSSIS scientific mission

Goal: Develop information management concepts, tools and infrastructure to support basic CenSSIS scientific mission

§ Distribution

§ Transport

§ Collaboration

§ Web Access

§ Distribution

§ Transport

§ Collaboration

§ Web Access

§ Representation

§ Compression

§ Transmission

§ Storage

§ Representation

§ Compression

§ Transmission

§ Storage

§ Computing

§ Storage

§ Scalability

§ Solutionware

§ Computing

§ Storage

§ Scalability

§ Solutionware

§ Archiving

§ Image Formatting

§ Image Browsing

§ Data Migration

§ Archiving

§ Image Formatting

§ Image Browsing

§ Data Migration

Components of R3 Research ThrustComponents of R3 Research Thrust

R3Image and Data

InformationManagement

R3Image and Data


R3dImage Databases

and Metadata

R3dImage Databases

and Metadata

R3cScalable

Computation

R3cScalable

Computation

R3bMultimedia Networks


R3aData

Compression

R3aData

Compression

R3a/b Data Compression/

Multimedia Networks

R3a/b Data Compression/

Multimedia Networks

R3c/d Scalable Computation/Database and Metadata

R3c/d Scalable Computation/Database and Metadata

James ModestinoLeader, RPI

John ProakisCo-Leader, NU

Shiv Kalyanaraman, RPIMasoud Salehi, NUJohn Woods, RPIGary Saulnier, RPIGreg Suski, LLNL

David KaeliLeader, NU

Robert FutrelleCo-Leader, NU

Jose Cruz-Rivera, UPRMRoscoe Giles, BUClaudio Rebbi, BUElias Manolakos, NUWaleed Meleis, NU

The R3 Research TeamThe R3 Research Team

Image/VideoSensor DataAcquisition

Image/VideoSensor DataAcquisition

PreprocessingRepresentationCompression

EC Coding

PreprocessingRepresentationCompression

EC Coding

User Applications

Render/EnhanceDisplay/Playback

User Applications

Render/EnhanceDisplay/Playback

EC DecodingDecompressionReconstructionPostprocessing

EC DecodingDecompressionReconstructionPostprocessing

Storage/ArchivingIndexingRetrieval

Storage/ArchivingIndexingRetrieval

ModulationTransmissionNetworkingDistribution

Demodulation

ModulationTransmissionNetworkingDistribution

Demodulation

Issues and Requirements Depend on Sensor Modality Type and Communications/Storage Infrastructure

Issues and Requirements Depend on Sensor Modality Type and Communications/Storage Infrastructure

TransmissionChannel

TransmissionChannel

StorageChannelStorageChannel

Object

MSDMSD

LPMLPM MVTMVT

R3a/b Image and Data Management IssuesR3a/b Image and Data Management Issues

§ Multi-resolution, or layered, coding approaches§ Model-based compression schemes§ Feature-based compression schemes§ Multi-objective compression schemes§ Data fusion in compressed domain§ Joint Source-Channel Coding (JSCC) schemes§ Applications built on enhanced TCP/IP networking

protocols§ Utilize existing lower-layer networking infrastructure

§ Multi-resolution, or layered, coding approaches§ Model-based compression schemes§ Feature-based compression schemes§ Multi-objective compression schemes§ Data fusion in compressed domain§ Joint Source-Channel Coding (JSCC) schemes§ Applications built on enhanced TCP/IP networking

protocols§ Utilize existing lower-layer networking infrastructure

R3a/b Compression, Transmission and StorageR3a/b Compression, Transmission and Storage

Barrier 3Barrier 3 Lack of Robust, Physics-Based Recognition and Sensor Fusion TechniquesLack of Robust, Physics-Based Recognition and Sensor Fusion Techniques

Barrier 6Barrier 6 Lack of Rapid Processing and Management of Large Image DatabasesLack of Rapid Processing and Management of Large Image Databases

R3a Compression Research IssuesR3a Compression Research Issues

Existing Approaches§ General Purpose§ Intended for Human

Evaluation§ Based on HVS Distortion

Criterion§ Generally Single Resolution

Lossy Compression§ Compression Efficiency

Saturated at < 100:1

Existing Approaches§ General Purpose§ Intended for Human

Evaluation§ Based on HVS Distortion

Criterion§ Generally Single Resolution

Lossy Compression§ Compression Efficiency

Saturated at < 100:1

CenSSIS Approach§ Physics-Based§ Intended for Specific

Imaging Functions§ Based on Function-Specific

Distortion Criterion§ Provides Progressive

Lossy-LosslessCompression§ Compression Efficiency

Improvements by 10-100

CenSSIS Approach§ Physics-Based§ Intended for Specific

Imaging Functions§ Based on Function-Specific

Distortion Criterion§ Provides Progressive

Lossy-LosslessCompression§ Compression Efficiency

Improvements by 10-100

§ Will Leverage Existing Approaches Where and When Possible§ Significant Improvements in Compression Efficiency

Critical to Success of CenSSIS Scientific Mission

Physics-Motivated Region-Based CodingPhysics-Motivated Region-Based Coding

Original-HealthyOriginal-Healthy

RegionsRegions

§ Macula/Fovea Region-Center§ Optical Disc-Left§ Vascular Structure § Background

§ Macula/Fovea Region-Center§ Optical Disc-Left§ Vascular Structure § Background

SegmentationSegmentation

ROI CodingROI Coding

§ Macula Region-Near Lossless§ Optical Disc-Fine§ Vascular Structure-Medium § Background-Coarse

§ Macula Region-Near Lossless§ Optical Disc-Fine§ Vascular Structure-Medium § Background-Coarse

Multiresolution ApproachMultiresolution Approach

Dyadic 3-LevelWavelet SubbandDecomposition

Dyadic 3-LevelWavelet SubbandDecomposition

FullbandFullband

HalfBandHalfBand

LowbandLowband

H-HH-H

H-LH-L

L-HL-H

LL-LLLL-LL

LH-LHLH-LHLL-LHLL-LH

LH-LLLH-LL

L - Low Frequency ComponentH - High Frequency ComponentL - Low Frequency ComponentH - High Frequency Component

SchematicRepresentationSchematicRepresentation

§ Coarse segmentation at lowest band§ Use physics-motivated segmentation§ Successive refinement with higher bands§ Differential ROI coding of regions§ Coding approach tailored to region type§ Exploit correlation across scales§ Progressive Lossy-Lossles encoding

§ Coarse segmentation at lowest band§ Use physics-motivated segmentation§ Successive refinement with higher bands§ Differential ROI coding of regions§ Coding approach tailored to region type§ Exploit correlation across scales§ Progressive Lossy-Lossles encoding

Operational UseOperational Use

Original-HealthyOriginal-Healthy Original-DiseasedOriginal-Diseased

§ Presence of Pathology Obscures Underlying Region Types § How to Reliably Extract Important Regions with Pathology§ How to Exploit the Physical Manifestations of Disease

Pathologies

§ Investigate and assess compression, transmission and storage requirements associated with initial level L2 testbeds

§ Develop preliminary modality-specific compression Approaches:§ LPM§ MVT§ MSD

§ Provide preliminary extensible collaborative work environment based on IP telephony model

§ Develop transition plan to Internet-2 and identify and formulate middleware and QoS research issues

§ Identify and demonstrate promising shared applications such as Virtual Lab

§ Investigate and assess compression, transmission and storage requirements associated with initial level L2 testbeds

§ Develop preliminary modality-specific compression Approaches:§ LPM§ MVT§ MSD

§ Provide preliminary extensible collaborative work environment based on IP telephony model

§ Develop transition plan to Internet-2 and identify and formulate middleware and QoS research issues

§ Identify and demonstrate promising shared applications such as Virtual Lab

Object

MSDMSD

LPMLPM MVTMVT

R3a/b First Year Anticipated ResultsR3a/b First Year Anticipated Results

§ Distribution

§ Transport

§ Collaboration

§ Web Access

§ Distribution

§ Transport

§ Collaboration

§ Web Access

§ Representation

§ Compression

§ Transmission

§ Storage

§ Representation

§ Compression

§ Transmission

§ Storage

§ Computing

§ Storage

§ Scalability

§ Solutionware

§ Computing

§ Storage

§ Scalability

§ Solutionware

§ Archiving

§ Image Formatting

§ Image Browsing

§ Data Migration

§ Archiving

§ Image Formatting

§ Image Browsing

§ Data Migration

Components of R3 Research ThrustComponents of R3 Research Thrust

R3Image and Data


R3Image and Data


R3dImage Databases

and Metadata

R3dImage Databases

and Metadata

R3cScalable

Computation

R3cScalable

Computation



R3aData

Compression

R3aData

Compression

§ 4096-node system§ Enables spatial and

spectral slicing§ UPRM will develop parallelized

versions of CenSSIS algorithms for focal plane processing§ End-to-end hardware/software

sensor design

§ 4096-node system§ Enables spatial and

spectral slicing§ UPRM will develop parallelized

versions of CenSSIS algorithms for focal plane processing§ End-to-end hardware/software

sensor design

TodayToday FutureFutureImage ResolutionImage Resolution 1,000 x 1,0001,000 x 1,000 10,000 x 10,00010,000 x 10,000Dynamic RangeDynamic Range 10 bits / pixel10 bits / pixel 12 bits / pixel12 bits / pixel

Scanning WavelengthsScanning Wavelengths 200 samples200 samples 1000 samples1000 samplesFrame rateFrame rate 30 fps30 fps 30 fps30 fps

Required BandwidthRequired Bandwidth 60 Gbits/s60 Gbits/s 36 Tbits/s36 Tbits/s

SIMD Focal Plane ArchitectureSIMD Focal Plane Architecture

Georgia Tech/UPRM SIMPil Architecture

Georgia Tech/UPRM SIMPil Architecture

ACUACU

R3c. How Do We Meet the Real-Time Processing Requirements of Hyperspectral Imaging? R3c. How Do We Meet the Real-Time Processing Requirements of Hyperspectral Imaging?

R3c. How can CenSSIS Applications Scale Up to Use Real Data Sets?R3c. How can CenSSIS Applications Scale Up to Use Real Data Sets?

§ Exploit NSF Partnerships for Advanced Computational Infrastructure (PACI) resources§ Construct Beowulf-class clusters to provide low-

cost, scalable processing (BioMed 8-node cluster and NSF MRI 32-node cluster)§ Utilize local NSF PACI/GRID computational

resources at Boston University (Mariner Center)§ Develop parallel programming toolsets around

MPI and Open/MP standards

§ Exploit NSF Partnerships for Advanced Computational Infrastructure (PACI) resources§ Construct Beowulf-class clusters to provide low-

cost, scalable processing (BioMed 8-node cluster and NSF MRI 32-node cluster)§ Utilize local NSF PACI/GRID computational

resources at Boston University (Mariner Center)§ Develop parallel programming toolsets around

MPI and Open/MP standards

CCS Center for Computational Science

SCV Scientific Computing and Visualization Group

InstrumentsInstruments

AlgorithmsAlgorithms

ImagesImages

Experi-mental

and ModelingResults

Experi-mental

and ModelingResults

How Do We Store/Organize CenSSIS Information to Enable Rapid Assessment and Dissemination?How Do We Store/Organize CenSSIS Information to Enable Rapid Assessment and Dissemination?

y = C(x, z) + ny = C(x, z) + n

Database (Oracle8i)Database (Oracle8i)

Metadata - eXtensible Markup Language (XML)Metadata - eXtensible Markup Language (XML)

*TBs of RAID storage

*Replication and distribution

*TBs of RAID storage

*Replication and distribution

*Image format standards

*Image format standards

§ The data about the data§ Contextual browsing§ The data about the data§ Contextual browsing

R3d. CenSSIS DatabasesR3d. CenSSIS Databases

Image IDLPM099MVT002MSD321MVT061

Image typeHyperspectral

ECGDiffusive wave

CAT scan

Image format????

SensorCompression

algorithm

ManufacturerSoftwareversion

Calibration dateRelated

Publications

Serial #HLLs

supported

§ Need for Software Engineering standards§ Standard programming interfaces (e.g., APIs)§ Version and release control§ Testing standards

§ Enable rapid prototyping of new algorithms§ Develop parallel implementations for compute-

bound algorithms and models§ Provide libraries of Solutionware toolboxes,

validated in our L2 testbeds§ Leverage PACI and industrial-partner software

development tools

y = C(x, z) + nfor (i =0; i< n, i++)

SolutionwareSolutionwareAlgorithms and ModelsAlgorithms and Models

R3d. How do we develop the subsurface imaging toolbox of the future?R3d. How do we develop the subsurface imaging toolbox of the future?

R3c/d First Year Anticipated ResultsR3c/d First Year Anticipated Results

§ Complete benchmarking of SIMPiL focal plane architecture and applications§ Set up CenSSIS web, image storage and

database servers§ Design and implement CenSSIS image database

system§ Develop CenSSIS image format standards§ Provide preliminary specification for CenSSIS

metadata databases§ Develop CenSSIS software library control system§ Complete prototype of JavaPorts programming

toolset

§ Complete benchmarking of SIMPiL focal plane architecture and applications§ Set up CenSSIS web, image storage and

database servers§ Design and implement CenSSIS image database

system§ Develop CenSSIS image format standards§ Provide preliminary specification for CenSSIS

metadata databases§ Develop CenSSIS software library control system§ Complete prototype of JavaPorts programming

toolset

C. R3 Impact and TimelineC. R3 Impact and Timeline

§ Deliver experiments and testbeds to the classroom§ Immediate practice of classroom theory

§ Remote virtual laboratory*§ New Course modules§ Digital Image and Video Processing (ECSE-6630)§ Digital Picture Processing (ECSE-6640)§ Digital Computer Architecture (ECE-3391)

§ Collaborative work environment§ Distance education and joint seminars

§ Allow students to explore using CenSSIS Solutionwareand image databases§ Develop computer-based analysis skills applied to subsurface

sensing and imaging**

§ Deliver experiments and testbeds to the classroom§ Immediate practice of classroom theory

§ Remote virtual laboratory*§ New Course modules§ Digital Image and Video Processing (ECSE-6630)§ Digital Picture Processing (ECSE-6640)§ Digital Computer Architecture (ECE-3391)

§ Collaborative work environment§ Distance education and joint seminars

§ Allow students to explore using CenSSIS Solutionwareand image databases§ Develop computer-based analysis skills applied to subsurface

sensing and imaging**

R3 Impact on EducationR3 Impact on Education

*Already being piloted in RPI Nuclear Navy program

**Already being piloted in NU GE1103 course

*Already being piloted in RPI Nuclear Navy program

**Already being piloted in NU GE1103 course

ACUACU

R3 Technology: Real-time HyperspectralImagingR3 Technology: Real-time HyperspectralImaging

Barrier 5Barrier 5 Lack of Optimal End to End Sensor Design MethodsLack of Optimal End to End Sensor Design Methods

Lack of Validated, Integrated Processing and Computation ToolsLack of Validated, Integrated Processing and Computation ToolsBarrier 7Barrier 7

SolutionwareSolutionware

Inverse ECG speedup

02468

0 5 10Nodes

Sp

eed

up

R3 Technology: Scalable Inverse SolutionsR3 Technology: Scalable Inverse Solutions

y = C(x, z) + n for (i =0; i< n, i++)

Lack of Validated, Integrated Processing and Computation ToolsLack of Validated, Integrated Processing and Computation ToolsBarrier 7Barrier 7

Linear speedupLinear speedup

CenSSIS CenSSIS

R3 Technology: Commercial Databases, Compression and Multimedia NetworksR3 Technology: Commercial Databases, Compression and Multimedia Networks

Barrier 6Barrier 6 Lack of Rapid Processing and Management of Large Image DatabasesLack of Rapid Processing and Management of Large Image Databases

MultimediaNetwork


Deliver image DB

Develop web-based query capabilities

Maintenance and upgrades to image database

Metadatadesign spec

Development/implementation of XML-based metadata database

Contextual browsing

Simulation of SIMPiL

Development of parallel hyperspectral applications

Evaluation of performance on SIMPiL hardware

Construct library system

Develop web-based rapid prototyping library system

Continued support and test of evolving Solutionware libraries

Assessment of L2 needs

New modality-specific compression approaches

Continued developmentfor new L2/L3 approaches

Assessment of L2 needs

Development of L2 support infrastructure based on

Internet-2Continued Investigation ofTransport and QoS Issues

Virtual lab applications

Extend shared applications to Internet-2 model

Continued Development Basedon Internet Evolution

Yr 5Yr 4Yr 3Yr 2Yr 1Education

Focal-plane architectures and algorithms

Image databases and metadata

Compression,Transmission and Storage

Multimedia Networking and Collaborative Work Environments

Solutionware

R3 TimelineR3 Timeline

SeaBEDSeaBED BioBEDBioBED

R1 ModelingR2 Info Extraction

R3 TechnologiesR3 Technologies

R3 TechnologiesR3 Technologies

CenSSISProductsCenSSISProducts

IndustryIndustry Academic ResearchAcademic Research

Less

ons l

earn

ed

R3 Integration into I-PLUSR3 Integration into I-PLUS

ImagesImages SW Libraries

SW Libraries SensorsSensors ToolsTools

MVT2MVT2LPM2LPM2MVT1MVT1 LPM1LPM1

D. I-PLUSD. I-PLUS

I-PLUSI-PLUS

§ I-PLUS§ Develops a unified problem-solving

platform§ Includes techniques, tools and infrastructure§ Combines the capabilities of the L2 testbeds

and Systems Integration § Allows CenSSIS to address new sets of

real-world problems§ Leveraging the combined resources, techniques

and lessons learned from multiple sensing and imaging modalities§ Provides a natural path for technology transfer

to outreach collaborators and industry partners

§ I-PLUS§ Develops a unified problem-solving

platform§ Includes techniques, tools and infrastructure§ Combines the capabilities of the L2 testbeds

and Systems Integration § Allows CenSSIS to address new sets of

real-world problems§ Leveraging the combined resources, techniques

and lessons learned from multiple sensing and imaging modalities§ Provides a natural path for technology transfer

to outreach collaborators and industry partners

CenSSIS Software Resource Center

§CenSSIS Software Engineer

§ Software libraries

§ Programming Tools and Parallel Systems Management

CenSSIS Software Resource Center

§CenSSIS Software Engineer

§ Software libraries

§ Programming Tools and Parallel Systems Management

CenSSIS Image and Data Information Systems

§CenSSIS Database Administrator

§Access to parallel processing resources

§Website Administration

CenSSIS Image and Data Information Systems

§CenSSIS Database Administrator

§Access to parallel processing resources

§Website Administration

I-PLUS - Integrated Probes to Look Under SurfacesI-PLUS - Integrated Probes to Look Under Surfaces

Systems Integration

D. Kaeli, NU

Leader

Systems Integration

D. Kaeli, NU

Leader

Documents

Research Thrust R3 Image and Data Information Management · Leader, RPI John Proakis Co-Leader, NU Shiv Kalyanaraman, RPI Masoud Salehi, NU John Woods, RPI Gary Saulnier, RPI Greg