Multiscale Analysis of Multimodal Imagery for …...Multiscale Analysis of Multimodal Imagery for...

Multiscale Analysis of Multimodal

Imagery for Cooperative Sensing

Erik Blasch

[email protected]

PM: Dr. Frederica Darema

DDDAS PI Meeting

01 – 03 Dec 2014

Alex Aved, Guna Seetharaman – AFRL/RIE

Yu Chen, R. Wu, B. Liu, Binghamton University

Raj Ezekial - IUIP

Haibin Ling – Temple University

Collaborators: Salim Hariri – Univ. AZ

Dan Shen, G. Chen , others – Information Fusion Tech

Riad Hammoud – BAE

Zhi (Gerry) Tian – NSF, GMU, Khanh Pham (AFRL/RV)

mailto:[email protected]

Multiscale Analysis of Multimodal

Imagery for Cooperative Sensing

Erik Blasch

[email protected]

PM: Dr. Frederica Darema

DDDAS PI Meeting

01 – 03 Dec 2014

mailto:[email protected]

Blasch/– DDDAS Review 2014

OUTLINE

PI: Erik Blasch (AFRL/RIEA)

• Multi-INT Analysis

• Multi-Modal

• Multi-Scale

• Graphical Fusion

• Cooperative Sensing

• Video-Text Fusion

• Cyber Trust Info

• Cloud Applications

Scenarios

Theory

Measurements Visualizations

User

Software

Models

Data Analytics

Control

Management Interaction

Systems Level DDDAS for advanced Information Fusion

User Refinement Info Management

Metrics

Challenge

Problems

Uncertainty

Reasoning

Belief


Multi-Modal Cooperative Sensing PI: Erik Blasch (AFRL/RIEA), 2013

• Modeling: Using the DDDAS paradigm, we designed a novel method for information fusion situation awareness modeling from video and text data for improved information management of PED-cell operations and the results provide user access to different data sources in a succinct User-Defined Operating Picture (UDOP)

• Algorithms: Using the DDDAS paradigm, we innovated a Dynamic multi-modal (DMM) evidential fusion method for big data multi-intelligence surveillance application delivering high-confident and usable results over Bayesian techniques

• Sensing: Using the DDDAS paradigm, we developed a novel method of collection of image exploitation and text extraction measurements for FMV/HUMINT association to increase area coverage and activity identification accuracy

• Software: To support DDDAS environments, the project has developed new cloud-enabled software methods for data/model access and storage to support UAV/UGV applications enabling real-time performance and increased data throughput

• Meaning: Method, DDDAS paradigm, Application

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Scan number

m(C

)

Estimation of belief assignment for Cargo Type

Ground truth

Demspters rule

PCR5 rule

Bayes Rule

Blasch/– DDDAS Review 2014 5

DDDAS-AFRL

AFRL/RI – Slides – “Information Directorate overview”

http://www.wpafb.af.mil/shared/media/document/AFD-131008-023.pdf

Cyber Assurance

Sensor/Data Exploitation

Cyber Integration/OPs

Activity-Based Analysis

Information Handling

Analytical Systems

Security

Information Management

Resilient- Synchronized Systems

Advanced C2 Systems

Trusted Systems

High Performance Computing

Network Technology


Multi-Modal Cooperative Sensing PI: Erik Blasch (AFRL/RIEA), 2014

• Modeling: Using the DDDAS paradigm, we incorporated graphical information fusion modeling for activity analysis from video and text data for event detection with Narratives to support a User-Defined Operating Picture (UDOP) user-machine integration

• Algorithms: Using the DDDAS paradigm, we designed a evidential reasoning method for dynamic-data trust assessment of data for cyber operations, assurance, and downstream processing to support various

• Sensing: Using the DDDAS paradigm, we developed a novel method of collection of association of chat (text) with video tracks (ACT) measurements for multi-modal data alignment to determine event boundaries and activity identification organization for data storage and reporting

• Software: To support DDDAS environments, the project has developed new container-based virtualization (versus hypervisor VM) information management approach to partition processes against system resources for robotics applications

• Meaning: Method, DDDAS paradigm, Application


OUTLINE

• Motivation

• Multi-INT Processing for Information Fusion (Variety)

• DDDAS Concept for Video-Text Sensing and Fusion

• DDDAS match with Information Fusion (Modeling/Measurements)

• DDDAS for MultiModal/MultiScale multi-INT Fusion (Velocity)

• Information Fusion – Trust in Information

• Information Management – QoS Trust

• Evidential Reasoning for Trust Assessment (Veracity)

• DDDAS With Container-Based Cloud Software

• Software speed-up of Big Data at scale (Volume)

• Developing results over different applications (SSA, Robotics)

• Summary: DDDAS Systems-Level Applications


Multi-INT Data

8

Information Fusion Systems consists of data and machines.

DFIG Fusion Levels

Level 0

Level 1

Level 2

Level 4

Level 3

Level 5

Threats

Situations

Objects

Signals

Plans

Video

Text

Machine

Mission

GEOINT E. Blasch, J. Nagy, A. Aved, W. M. Pottenger, M. Schneider, R. Hammoud, E. K. Jones, A. Basharat, A. Hoogs, G. Chen, D. Shen, H. Ling, “Context aided Video-to-Text Information Fusion,” Int’l.. Conf. on Information Fusion, 2014.


DDDAS (Multimodal Cooperative Sensing)

Dynamic Data Driven Application Systems (DDDAS)

Theory

Simulations

Measurements

Sensor

Management

Forecasting, Prediction

Operational Condition

Fidelity

Filtering

Mission

Management

User

Refinement

Situation Assessment

Object ID

and Tracking

Full Motion Video

Modeling

Multi-scale

Multimodal Data

2000 2500 3000 3500 4000 45000.996

0.997

0.998

0.999

1

1.001

1.002

1.003

1.004x 10

4


Fusion Algorithm/Processes

Sensor

Model

Environment

Detect

Track

Geolocate

ID

Sensor(s) Target ATR

Decisions

Human

Decisions

Sensor

Management Registration

Environment

Model

Performance

Model

Target

Models &

Database

Adaptation

Behavior

Models

Anticipate

10

E. Blasch, G. Seetharaman, and K. Reinhardt, “Dynamic Data Driven Applications System concept for Information Fusion,” International Conference on Computational Science, 2013. (ICCS13), Procedia Computer Science, Vol. 18, Pages 1999-2007, 2013.


DDDAS and Information Fusion

Links to Information Fusion

Scenarios

Measurements

Algorithms

User

Software

Models

Data

Analytics

Control

Management Interaction

Information Fusion Levels

1

3

4 5

2

6

N

Estimation

- Tracking

- Pattern Rec.

Analytics

- Situation

Awareness

Theory

E. Blasch, G. Seetharaman, and K. Reinhardt, “Dynamic Data Driven Applications System concept for Information Fusion,” International Conference on Computational Science, 2013. (ICCS13), Procedia Computer Science, Vol. 18, Pages 1999-2007, 2013.


Information Fusion and DDDAS

• DDDAS and Information Fusion

• Environmental modeling for object assessment, situation and impact assessment over mission needs

E. P. Blasch, E. Bosse, and D. A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA, 2012.

• Information Fusion

• Processing Levels : L0 data registration, L1 object assessment,

(tracking, classification) L2 situation awareness L3 impact assessment

(threat). L4 process refinement, L5 user refinement L6 mission management

• Applications : emergency response, sensor /user control (CASA), transportation. HERE, focused on ISR tracking in imagery

E. P. Blasch, E. Bosse, and D. A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA, 2012.


OUTLINE

• Motivation




• DDDAS for MultiModal/MltiScale multi-INT Fusion (Velocity)









DISTRIBUTION A: Published Information

Multi-media INdexing playER (MINER)

List of Detected

Activities in the Last 5 Minutes

Target of Interest & Associated Chat

User-Defined Geo-Fence

Target’s Footprint on the Map

Filtering of the Video Summary by Location

& Activity/Event Type(i.e., Turn-Park)

R. I. Hammoud, C. S. Sahin, E. P. Blasch, B. J. Rhodes, and T. Wang, “Automatic Association of Chats and Video Tracks for Activity Learning and Recognition in Aerial Video Surveillance,” Sensors, 14,

19843-19860, 2014.



ACT v2.0: Example representation of:

(a) video track and

(b) a chat-message

as graphs.

Chat Message: 1 black suv travels north west along road 1 block to the left of main highway

center of screen

ACT v1.0

Track 0000 and Track

0001 are matched with

the chat message

ACT v2.0

Track 0001 is the only

one matched with the

chat message

•New Appearance Feature Increases Detection Rate.

Automatic Association of Chat Messages and Video Tracks (ACT)

• Appearance (color) attribute decreases the

false detection rate.


19843-19860, 2014.


Pattern Learning of Activity Models

• Pattern Learning and Segmentation

• Track initiation, maintenance, and association for event detection

• Ramer-Douglas-Peucker (RDP) algorithm for track segmentation for activity modeling



19843-19860, 2014.


Contextual Analysis

Computer Vision

Context Channels

Computational

Segmentation

Detection

Classification

Sensor/Object

Models

Sensor

Networking

Physical

Geometry

Features

Tracks

Location

Causes

Ambient Intelligence

Contextual Analysis

Photogrammetric

Natural Language

Processing Generation

Evaluation

Channel Configuration

Environment

Network Analysis

Background

Models

Determination

Situation

Assessment Awareness Understanding

Cognitive

Networking

Entities Course of Action

Model

Association

Social

Networking

Images Multimedia Images and Terrain

Syntactic

Tracks

Content Based

Image Retrieval

Anticipatory

Autonomy

Cues

E. Blasch, “Book Review: 3C Vision: Cues, Context, and Channels,” IEEE Aerospace and Electronic Systems Magazine, Vol. 28, No. 2, Feb. 2013.


E. P. Blasch, S. K. Rogers, H. Holloway, J. Tierno, E. K. Jones, R. I. Hammoud, “QuEST for Information

Fusion in Multimedia Reports,” International Journal of Monitoring and Surveillance Technologies Research

(IJMSTR), 2014.

Video Event Segmentation by Text (VEST)


Fusion of Event Boundaries

• Determines the boundaries errors with sum of Gaussians

– Fusion across uncertainty

0 50 100 150 200 250 300 3500

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1GAuss Sum

Video

Chat

All

• Corresponding activities of interest as shown between 50-80 and 140-180 seconds

• Lag of commentary against video events

• After 200 seconds, commentary timestamps do not provide sufficient information to segment the video


E. P. Blasch, S. K. Rogers, H. Holloway, J. Tierno, E. K. Jones, R. I. Hammoud, “QuEST for Information Fusion in

Multimedia Reports,” International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 2014.

http://www.google.com/imgres?imgurl=http://www.toledoblade.com/image/2013/09/11/800x_b1_cCM_z/Mexico-US-Soccer-DONOVAN.jpg&imgrefurl=http://www.toledoblade.com/sports/2013/09/11/US-clinches-World-Cup-berth-beats-Mexico-2-0.html&h=512&w=800&tbnid=tbC_UM28RoCA3M:&zoom=1&docid=kBfOb_mUJ4WBaM&ei=qBa3U8q-MYKsyATI64CoCA&tbm=isch&ved=0CGYQMyg_MD8&iact=rc&uact=3&dur=792&page=7&start=58&ndsp=10


Call-Out Narratives

Potential Cohesive Narratives

Exploited Video

External Narratives

Team A wins Game

Report

Game

Sports Narrative

• Graphical Fusion

Dribbles Ball

In-bound Ball

Receives Pass

Ready to Shoot

Players in Paint

Blocks Shot

Steals Ball

Passes Ball

Play Resumes

Beats Defender Dunks Ball

Team

Player

Time

Space

Location

Activity

Intent

Graph1, 2. .., n

Player No. 19 moves ball forward in midcourt

US

19

10:21

Open Court

Midcourt

Dribble

Offense

Graph


Narratives as Combined Activities E. P. Blasch, S. K. Rogers, H. Holloway, J. Tierno, E. K. Jones, R. I. Hammoud, “QuEST for Information Fusion in

Multimedia Reports,” International Journal of Monitoring and Surveillance Technologies Research (IJMSTR), 2014.

http://www.google.com/imgres?imgurl=http://www.toledoblade.com/image/2013/09/11/800x_b1_cCM_z/Mexico-US-Soccer-DONOVAN.jpg&imgrefurl=http://www.toledoblade.com/sports/2013/09/11/US-clinches-World-Cup-berth-beats-Mexico-2-0.html&h=512&w=800&tbnid=tbC_UM28RoCA3M:&zoom=1&docid=kBfOb_mUJ4WBaM&ei=qBa3U8q-MYKsyATI64CoCA&tbm=isch&ved=0CGYQMyg_MD8&iact=rc&uact=3&dur=792&page=7&start=58&ndsp=10


OUTLINE

• Motivation













Trust In Information Fusion H

um

an

- M

ac

hin

e In

terfa

ce

Systems Design Machine Human

High-Level

Information Fusion

Info

rmati

on

/Reso

urc

e

M

an

ag

em

en

t

Situ

atio

n A

na

lys

is

Evalu

atio

n

Low-Level Information Fusion

Perception

Object

Observable

Situation

Scenario

Sensation

Comprehension

Projection

Assessment Awareness

22

TRUST

E. P. Blasch, E. Bosse, and D. A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA, 2012


Trust in Action

Trust (Multidiscipline)

- Users

- Systems

23

Jin-Hee Cho, Member, IEEE, Ananthram Swami, Fellow, IEEE, and Ing-Ray Chen, Member, IEEE, “A Survey on Trust Management for Mobile Ad Hoc Networks,” IEEE COMMUNICATIONS

SURVEYS & TUTORIALS, VOL. 13, NO. 4, FOURTH QUARTER 2011


Information Management Model HLIF Figure 5.1

24

Trust



Information Registration Types, Schemas, Metadata

Info Discovery Registered, Available,

Active

Info Exchange Pub, Sub, Query, Broker, Deliver

Information Persistence Store and Retrieve Information

Administrative Controls Consoles, Monitors, Configuration

En

terp

ris

e S

erv

ice

Sec

uri

ty

Inte

gra

tio

n

for

Au

thN

an

d A

uth

Z

Info

Acce

ss

Co

ntr

ol

Po

licy-B

ased

Typ

e a

nd

Co

nte

nt

Access C

on

tro

l

Information Management Control Figure 5.5

25 E. P. Blasch, E. Bosse, and D. A. Lambert, High-Level Information Fusion Management and Systems Design, Artech House, Norwood, MA, 2012


Trust Stack

Authentication and Authorization

Behavior Analysis Analysis (Situation Awareness)

Collecting Raw Metrics

Domain Trust Authority

Policies Enforcement

Secure Communication Security

Workflow

QoS

Brokerage

E. Blasch, Y Al-Nashif, and S. Hariri, “Static versus Dynamic Data Information Fusion analysis using DDDAS for Cyber Trust,” International Conference on Computational

Science, Procedia Computer Science, 2014.


Co

nte

xt-

Ba

se

d

Fil

teri

ng

Tru

st

Eva

lua

tio

n

ATM Functionalities

Entity

Environment

User

Context

Mo

nit

ori

ng

Vulnerability Analysis

Anomaly Analysis

Security Policy Analysis

Qu

an

tifi

ca

tio

n

Trust Database

Self-Trust Evaluation

Peer-Trust Evaluation

Directly

Collected

Trust

Metrics Collected

Information

Trust Metrics between analysis and filtering

Activity

Data Decision Dissemination

Situation Analysis

Machine-Trust Evaluation

E. Blasch, Y Al-Nashif, and S. Hariri, “Static versus Dynamic Data Information Fusion analysis using DDDAS for Cyber Trust,” International Conference on Computational

Science, Procedia Computer Science, 2014.


TrustFlow

I/O

Software

TRUST Network

Hardware

User

Application

Machines

Memory

Read/Write

# of Cores

Paging

CPU

Utilization

Bandwidth

Packet Rate

Number of

Connection

OS

Location

Middleware

Firmware

Logs

Machine

Manager

Protection

Apps

Developer

Manager

Logs

Correct

Signature

Logs

Behavior

Privilege

Level

Voltage

Fan

Frequency Temperature

E. Blasch, Y Al-Nashif, and S. Hariri, “Static versus Dynamic Data Information Fusion analysis using DDDAS for

Cyber Trust,” International Conference on Computational Science, Procedia Computer Science, 2014.


Policy Issues

Text

Video


• Example

Text

Video

Fusion

Fusion

PAP

Polices

PRP

PEP PDP

PDP

PIP

Not Trust

Trust

Fusion

PAP PRP PEP

PIP

Video (HUMINT)

Video (OSINT)

Text (HUMINT)

Not Fused

Bad text

report




Trust

The trust of an entity is a function of its CIA:

Confidentiality, Integrity, and Availability:

And since trust metrics are used to determine the values of

the CIA components, we can use a function h that will map the

trust metrics to the CIA components to get the trust:

T (E) = f (Confidentiality, Integrity,Availability)

T (E) = f (h(Trust Metrics))

Directly collected

Trust Measured outputs




DSMT Fusion: Basics

Adapted from DMST Tutorial : Jean Dezert, 2008

Decision Level

Conflict

Assessment

(PCR5)

Integrity Level

Sources Level

Set Assessment

(DSmC)

Conjunctive Consensus on

hyper-power set D

Integrity Constraints on

D

Proportional Conflict Redistribution

Evidential Reasoning

m1() … mk()

Quantitative bba

q1() … qk()

Qualitative bba

Z1() … Zk()

Conditional Probabilities

Subjective Objective

DSmT Dempster

Shafer

Bayes




Evidential Reasoning Trust Results

0 20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Scan number

Tru

st

Trust in Decision

Demspters rule

PCR5 rule

Bayes Rule

0 20 40 60 80 100 120-1

-0.5

0

0.5

1

1.5

2

2.5

3

Scan number

Perf

Im

pro

vm

ent

Trust in Decision

Ground truth

Demspters rule

PCR5 rule

• Inputs: Cyber Comm Results of Measurements

• Determine: Trust level based on reliability

• CONCLUSION: Need PCR5/6 to deal with Dynamic Decision Making

• NOTE: Bayes can’t change beliefs quickly with changing intrusion




OUTLINE

• Motivation













Cloud Multi-INT Tracking and ID

Current – predefined scheme, tasks

New – cloud computing has quick response,

high flexibility with VMs

Hadoop MapReduce scheduler

B. Liu, E. Blasch, Y. Chen, A. J. Aved, A. Hadiks, D. Shen, G. Chen, “Information Fusion in a Cloud Computing Era: A Systems-Level Perspective,” IEEE Aerospace and Electronic Systems Magazine, Vol. 29, No. 10, pp. 16 –

24, Oct. 2014.


Hardware/Software Layered

Architecture Comparison

B. Liu, E. Blasch, Y. Chen, A. J. Aved, A. Hadiks, D. Shen, G. Chen, “Information Fusion in a Cloud Computing Era: A Systems-Level Perspective,” IEEE Aerospace and Electronic

Systems Magazine, Vol. 29, No. 10, pp. 16 – 24, Oct. 2014.


Testbed Evaluation

• Experimental prototype

A cloud-enabled distributed robotic network

A UAV drone tracking three mobile robots

A cloud testbed in our datacenter

B. Liu, E. Blasch, Y. Chen, A. J. Aved, A. Hadiks, D. Shen, G. Chen, “Information Fusion in a Cloud

Computing Era: A Systems-Level Perspective,” IEEE Aerospace and Electronic Systems Magazine, Vol.

29, No. 10, pp. 16 – 24, Oct. 2014.


A Cloud-enabled Robotic System

Faster robotic applications development

Easier to get started

More efficient robot resources usage

B. Liu, E. Blasch, Y. Chen, A. J. Aved, A. Hadiks, D. Shen, G. Chen, “Information Fusion in a Cloud

Computing Era: A Systems-Level Perspective,” IEEE Aerospace and Electronic Systems Magazine,

Vol. 29, No. 10, pp. 16 – 24, Oct. 2014.


Virtualization Technologies

Hypervisor-based Virtualization

Hypervisor – “a piece of computer software, firmware or hardware that

creates and runs virtual machines.” [1]

Virtual machine – “a software implementation of a machine (for example,

a computer) that executes programs like a physical machine.” [2]

Container-based Virtualization (OS-level)

Container – “an isolated entity which performs and executes exactly like a

stand-alone server.” [3]

[1] http://en.wikipedia.org/wiki/Hypervisor

[2] http://en.wikipedia.org/wiki/Virtual_machine

[3] http://openvz.org/Container

Full Virtualization: Hardware Emulation (Unmodified OS)

Hypervisor: translate/execute privileged instructions on-the-fly

Para-virtualization

Hypervisor-aware, modified operating systems

Operating System-Level Virtualization (Does not rely on hypervisor)

Native Virtualization (Hardware-Assisted Virtualization)

R. Wu, Y. Chen, E. Blasch, B. Liu, G. Chen, D. Shen, “A Container-based Elastic Cloud Architecture for Real-Time Full-Motion Video (FMV) Target Tracking,” IEEE Applied Imagery Pattern Recognition Workshop, 2014. (Best Student Poster)


Container- vs. Hypervisor-based

Virtualization Technologies

M. G. Xavier, M. V. Neves, F. D. Rossi, T. C. Ferreto, T. Lange, and C. A. F De Rose, "Performance evaluation of container-based virtualization for high performance computing environments." in Parallel, Distributed and Network-Based Processing (PDP), 2013 21st Euromicro International Conference on, pp. 233-240. IEEE, 2013.


Container-based Virtualization

Containers are similar to virtual machines with a some

key distinctions:

Framework based on OpenVZ to improve performance of

FMV target tracking application

Parallelize application into containers

Allocate resources to balance unequal container workload

Dynamically allocate resources to improve efficiency

Advantages Disadvantages

• Less overhead than

hypervisor-based VMs

• Allows for reallocation of

resources to live containers

• Guests must share host

kernel



Why Container-based Virtualization?

B. Liu, Y. Chen, D. Shen, G. Chen, K. Pham, E. Blasch, and B. Rubin, “An Adaptive Process based Cloud Infrastructure for Space Situational Awareness Applications,” Proc. SPIE, 2014.



Space Situational Awareness Cloud system

B. Jia, K. Pham, E. Blasch, D. Shen, Z. Wang, G. Chen, “Cooperative Space Object Tracking using the SBV Sensors via Consensus-based Filters,” Int’l.. Conf. on Information Fusion, 2014.

B. Liu, Y. Chen, D. Shen, G. Chen, K. Pham, E. Blasch, and B. Rubin, “An Adaptive Process based Cloud Infrastructure for Space Situational Awareness Applications,” Proc. SPIE, 2014.


Making Use of Containers

CT 1

CT 2

CT 3

CT 4

Step 1:

Assign video

frames to

containers

Step 2:

Containers process

individual frames

Step 3:

Container recombines

processed frames into

video



Results: Container-based Framework

49

45

41 39

35 32

30 29

28

49 48

44 42

40 37

35 33 32

46 47

45

42

39 37

35 33

31

0

10

20

30

40

50

60

2 3 4 5 6 7 8 9 10

Fram

era

te (

FPS)

Number of Containers

Framerate Comparison for Allocation Methods

Equal

Optimal

Optimal+Dynamic


• Compared to sequential frame processing, the parallel container-based system improved output frame rate by up to 2.5 times.

• Employing more containers for the same job increases overhead, which can be mitigated by adaptive container resources adjustment.

• Dynamic CPU share allocation is able to achieve similar results to predetermined static allocation


Dynamic Resource Allocation

Dynamically allocate CPU time to frame processing containers

Scale – percentage of base allocation that represents

maximum increase/decrease in resource allocation


Evenly distributing CPU resources among all containers resulted in divergence in container frame processing rate. Reallocating CPU share during application execution in response to container frame rate reduced frame production spread.


Four V’s of Big Data

Volume (Scale of Data)

Variety (Types of Data)

Velocity (Speed of Data)

Veracity (Uncertainty of Data)

Sig

nific

ance

Text Video

Call-out

Events

http://www.ibmbigdatahub.com/infographic/four-vs-big-data

http://www.google.com/imgres?imgurl=http://spie.org/Images/Graphics/Newsroom/Imported-2013/004888/004888_10_fig2.jpg&imgrefurl=http://spie.org/x94023.xml&h=248&w=1050&tbnid=3FBwWp1Yx6z3_M:&zoom=1&docid=gofM_uUIHHC2AM&ei=URA9U5edD-WA2wXJ1ICgAg&tbm=isch&ved=0CIMBEIQcMBA&iact=rc&dur=614&page=2&start=8&ndsp=12


Accomplishments Yr2

• Four Journals highlighting DDDAS Applications

• Measurements: DDDAS Cloud-enabled robotics

• Modeling (track&ID), Software (Cloud), Application (ISR)

• Hosted of 2 faculty members, 6 students

• Invited DDDAS 4 presentations to RI of AFOSR PIs

• Submitted 3 Patents

• Awards

• IEEE: Three best of session, one top 5 paper (Communications)

• IEEE: Nominated for best paper (Video to Text Fusion)

• IEEE: Best student Poster (Ryan Wu)

• Joseph Mignogna Data Fusion Award (Military Sensing Society) 2014 for contributions to data, sensor, and information fusion


Special Journal

TOPIC Submission to Journal of Signal Processing Systems

http://www.springer.com/engineering/signals/journal/11265

Special Issue: Dynamic Data Driven Application Systems (DDDAS) Concepts in Signal Processing

Dr. Erik Blasch ([email protected])

Dr. Young-Jun Son ([email protected])

Dr. Shashi Phoha ([email protected])


OUTLINE

• Motivation




• DDDAS for MultiModal/MultiScale multi-INT Fusion (Velocity)








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