ENGINEERING RESEARCH INSTITUTE
OVERVIEW OF RESEARCH EXPERTISE
And
FACILITIES
BY
Dr. Lonnie Sharpe, Interim Dean
Dr. Mohan J. Malkani, Associate Dean for Graduate Studies
Dr. Hinton Jones, Interim Associate Dean for Undergraduate
Programs
(615) 963-5400, mmalkani@tnstate.edu
ENGINEERING
ENGINEERINGRESEARCH
RESEARCHINSTITUTE
INSTITUTE(ERI)
(ERI)
Research
ResearchCenters
Centersand
andLaboratories
Laboratories
● Center for Battlefield Sensor Fusion – ARO (2004)
● Center for Environmental Engineering -- DOE (1996)
● Center for Neural Engineering -- ONR (1992)
● Digital Signal/ Image Processing Laboratory -- Air Force (1991)
● Intelligent Control Systems Laboratory -- NASA (1993)
● Design Methodologies Laboratory -- NASA (1993)
● Intelligent Manufacturing Laboratory -- SME,ONR (1994)
● Intelligent Health Monitoring Laboratory -- PSU/MURI-DURIP (1998)
● Computer and Information Systems Laboratory -- DOD/HP (1996,99)
● Automatic Target Recognition (ATR) Test-bed --- AFRL (2006-2008)
ERI MISSION
ERI
ERI Conducts
Conducts Research
Research in “Cutting-Edge
Technology”
Technology” Areas
Some ERI sample strengths are:









Artificial Intelligence/NN/FL/GA
Database Design&Data Mining
Parallel&Distributed Computing
Modeling, Simulation & Analysis
Speaker Recognition
Signal/ Image Processing
Intelligent Control Systems
Intelligent Health Monitoring
Robotics and Automation









Intelligent Manufacturing
Human-Machine Interfaces
Sensors and Machine Vision
CAD/CAM/CAE Tools
Wireless Communication
Automatic Target Recognition
Cyber Security
Environmental Remediation
Probabilistic Design
Methodologies
SMALL BUSINESS TECHNOLOGY TRANSFER (STTR) PROGRAM
NASA 1994 PHASE I
(PROPOSALS SELECTED FOR FUNDING OF STTR PHASE I CONTRACTS)
SMALL SCALE ROBOTICS
● MID-SOUTHE ENGINEERING, INC.
TENNESSEE STATE UNIVERSITY
Spherical Motor and Neural Controller for
Micro Precision Robot Wrist
● ROBOTICS RESEARCH CORPORATION
JET PROPULSION LABORATORY
Next Generation Controller for Redundant
Robots
● TRANSITIONS RESEARCH CORPORATION
UNIVERSITY OF SOUTHERN
CALIFORNIA
Combined Distance and Orientation Sensor
(CODOS)
● AMHERST SYSTEMS, INC.
STATE UNIVERSITY OF NEW YORK
AT BUFFALO
Foveal Sensor and Image Processor Prototype
● AEROMOVER SYSTEMS CORPORATION
● ENDOROBOTICS CORPORATION
THE UNIVERSITY OF MICHIGAN
UNIV OF CALIFORNIA BERKELEY
Differentiated Universal End Effector
Milli-Robots for Surgical Teleoperation
● VISUAL INSPECTION TECHNOLOGIES,
INC., SRI INTERNATIONAL
Mobile Magnetic Robots for Inspection of
Steel Structures
Center for Neural Engineering - Funded by ONR
1992 - 2004
AFRL
Health Monitoring
Of NASP
Hypersonic
Structures
1991-1995
VA
DOE
Localization Of
Ventricular
Arrhythmogenic
Foci
Chaos Control
In
A Fluidized
Bed
1996-1999
Lockheed
Martin
Predictive
Maintenance
1995-Present
1994-1996
CALTECH
Intelligent fault
diagnosis
tools
1996-1998
1996-1999
1998-2003
Virtual Tandem
Vehicles- Mobility
Controller For
Mobile Robots
Signal / Noise
Separation
& Blind
Deconvolution
1994
1997-1998
1994-1995
NASA/JPL
Visual Telerobotic
Task Planning Of
Cooperative Robots
Using Soft Computing
1998-2000
2003-2005
Applied Research Lab - Penn. State University
CBM of bearing
and data
acquisition
Integrated predictive
diagnosis for
helicopter
gear box
1997-1998
1997-1998
Physics- based
modeling of
bearing
1998-1999
NASA
Robust Integrated
Neurocontroller
For Complex
Dynamic Systems
1992-Present
NSF ARMY TACOM
Towards
Telepresence
Using Mobile
Robots
STTR In
Robotics
Nn
Classification
For Digital
Communication
Biologically
Motivated NN
(PCNN) & Mobile
Robots
2000-2003
NSA
NASA
ONR
Embodiment of
Intelligent
Behaviors on Mobile
Robots
Helicopter
Control
using
NN, FL, GA
NASA
Intelligent
ControlWind Power
Analysis
NASA ARC
ONR
1995-Present
NSF
DOE
1993-1996
NASA
Boeing
Intelligent
Aircraft
Controller
1995-1996
Boeing
Air To Air Campaign
Thunder Model
Using Genetic
Algorithms
1998-1999
Measure of
effectiveness &
performance for AI
based monitoring
systems
Simulation
based design using
PDM, FEA & SM
1998-1999
1999-2000
April 2005
Design Methodology Laboratory
• Umbilical Retract Mechanism
• Design of a Cockpit Crew Station
• Base Drive Unit for a Reconfigurable
Tooling Device
• Genetic Algorithm Methodology for
Battlefield Allocation
Conceptualize, Design and Fabricate a
Prototype D5 Umbilical Retract
Mechanism (Navy)
Design of a Hardware and Software of
a Base Drive Unit for a Reconfigurable
Tooling Device (Boeing)
Genetic Algorithm Methodology for Battlefield Allocation
Best
Allocation
Allocation
Input
File
Population
of
Allocation
Genetic
Algorithm
THUNDER
War
Results
Genetic Algorithm Methodology for
Battlefield Allocation (Boeing)
Design of Anthropometric
Accommodations in Crew Station
Cockpit (Boeing)
Intelligent Manufacturing Research Laboratory
TSU Machinery Condition
Monitoring Laboratory
 State-of-the-art Experimental Test
beds for Machinery Condition
Monitoring.
 State-of-the-art Data Acquisition
Systems with Supporting Software &
Hardware for Active and Passive
Machinery Fault Diagnosis and
Prognosis.
Tennessee State University
CONDITION BASED MAINTENANCE
Signal Processing
Data Acquisition
40
40
0.2
0
0.05
0.1
-0.2
0
0.1
0.2
0.3
0.4
0.5
Time (seconds)
10
0
15
20
15
25
0.6
0.7
20
0.8
-0.2
-0.3
-0.1
-0.4
-0.15
-0.5
0
-0.2
-0.6
0
0
-0.1
-0.05
-0.1
-0.4
20
10
0.1
Filtered Signal
0.1
0.3
0.2
Filtered Signal
60
60
0.2
0.4
Filtered Signal
80
80
Acceleration waveform at Left Bearing Vertical(No Load)
0.3
0.6
-0.3
-0.4
-0.2
0
-0.5
0
0.1
0.1
0.2
0.1
0.2
0.3
0.4
0.5
Time (seconds)
0.6
0.7
0.3 0.4 0.5 0.6
Time (seconds)
0.2
0.7
0.8
0.3 0.4 0.5 0.6
Time (seconds)
0.7
0.8
0.8
Critical System Components
60
Features
Extraction
Feature Vector Set
Selection for Fault
Diagnosis
50
40
30
20
Neural-Network
Selection for Fault
Diagnosis
10
0
Novelty Faults
Banks of Neural Networks






Fault Pattern Recognition
Fault Pattern Classification)
Diagnostics & Prognostics
Rule-Based
Fault Reasoning
Diagnostic
Data Fusion
Causality
Reasoning
Intelligent Manufacturing Research Laboratory
TSU Integrated
Manufacturing
Laboratory
 Established Since 1996
 Funded by:
 Office of Naval Research
 Society of Manufacturing
Engineering
 TSU College of Engineering
 Project Sponsors:
 Office of Naval Research
 SME
 Industry
Tennessee State University
Intelligent Manufacturing Research Laboratory
Tennessee State University
TSU ROBOTIC-INTEGRATED MANUFACTURING LABORATORY
Robotic Machine Vision
System for Inspection
and Quality Control of
Manufactured Products.
Robotic Assembly System
For Intelligent Manipulation
and Assembly of
Manufacturing Parts.
Sensor-Based
Automated Guided
Vehicle For
Intelligent
Navigation Within
Manufacturing
Environment.
Tennessee State University
Department of Electrical and Computer Engineering
Intelligent Control Systems (ICS) Lab
(Dr. Saleh Zein-Sabatto, mzein@tnstate.edu)
Students,
Infrastructure and
Space
Department of Electrical and Computer Engineering
3500 John A. Merritt Blvd
Nashville, TN 37309
Tele: (615) 963-5369
Fax: (615) 963-2165
email: mzein@tnstate.edu
Tennessee State University
Department of Electrical and Computer Engineering
Intelligent Control Systems (ICS) Lab
(Dr. Saleh Zein-Sabatto, mzein@tnstate.edu)
Intelligent Control Systems
Research Work
“Control and Coordination of
Multiple Unmanned Areal
Vehicles (UAVs)”
Testing & Simulation
Design & Modeling
Hardware Prototyping
Tennessee State University
Department of Electrical and Computer Engineering
Intelligent Control Systems (ICS) Lab
(Dr. Saleh Zein-Sabatto, mzein@tnstate.edu)
Intelligent Mobile Robotics
Research Work
Funded by Office of Naval Research (ONR)
“Development of Robots
Intelligent Behaviors and MultiRobots Coordination”
Multiple Robots Coordination
Robots Intelligent Behaviors
Students Robotics Design & Competitions
Penn State - DARPA - MURI Project
Autonomous Surveillance Perspectives
Speech Recognition by Mobile Robots
TSU will develop mapping that will be used by consortium partners
Soldiers Recognize
Commands and act
TSU
TSURobotics
Robotics
Lab
Lab
Kinematic and
Dynamic Module
Communication
Protocols
Scheduling and
Synchronization
Schemes
Man-Machine
Interface
Static/Dynamic
Parameters
Neural-Network
Terrain Learning
Module
Physical
Environment
Wireless
Communication
Module
Signal/Image
Processing
Schemes
Sensory Info
Acquisition &
Fusion
Integrated
Mobility
Supervisory
Controller
Distributed
FMCell
Simulation
Environment
Fuzzy-Logic
Motion Controller
Module
Behavior-based
Cooperative
Tactical Strategies
Algorithmic
Supportive
Tools
Three small mobile robots communicate
and follow the commander
Genetic-Algorithm
Tactical Formation
Module
Behavior-based
Navigation
Module
World Perception
Modeling
Module
Diagnostic and
Conflicts
Handling Module
ROBOTIC
COMMUNICATION
The Evolution of Cyber-Security at TSU
Spring
Summer
Design of a Firewall for a Wireless
Network
2004
2005
2006
Fall
The Design of a Network
Security Procedure to
Secure a Manufacturing
Process
Design of a Manufacturing
Facility with Network
Operations Which Produces
Row Carts: Computer
Network
Investigation of Detection
and Prevention Methods
for War Driving
The Design of an Adaptive
Architecture for Aircraft
Communications
The Implementation of TCP/IP to
Develop a New Protocol for
Wireless Networks to Aid In
Intrusion Detection and Location
Tracking of Nodes
Development of a Security Model
for Detecting Malicious Hosts in
Mobile Agent Technology in a
Mobile Data Access System
(MAMDAS)
Design of an Indoor Wireless
User Localization and Tracking
System
Wireless Security
Authentication Methods and
Promiscuity Detection
CURRENT RESEARCH
Localization and Tracking in
Aircraft Ground Control Utilizing
Radio Frequency Identifiers
(RFIDs)
Localization and Tracking
of a Client Process in a
given Static Indoor
Wireless Environment
Wireless Authentication,
Localization and Tracking in a
Known Wireless Network
Utilizing Radio Frequency
Identifiers (RFIDs)
COMPUTER & INFORMATION SYSTEMS(CISE)
LAB LAYOUT
Efficient Video Streaming Over Wireless Networks
Dr. Liang Hong (PI)
(Funded by NSF, 2006 - 2007)
• Objective
• Methodology
Hybrid approach dynamically
combines unequal error protection
(UEP) coding scheme and automatic
repeat request (ARQ) protocol.
• UEP explores scalability of
MPEG-4 and selectively
augments its bit stream with
error-correction bits to minimize
the loss of key symbols while
tolerating errors in visually less
sensitive details.
• Delay-aware ARQ provides a
safety net for burst errors and
maintain the strict delay
constraint.
10th frame
Original
Proposed
No
Video Frame protection Algorithm
Develop an error control scheme for
efficient video streaming over the
third generation mobile networks.
• Simulation Results
55th frame
Sensors Technology Thrust
Research-AFRL (2006-2008)
•
•
•
•
•
•
•
•
•
Automatic Target Recognition- TSU (Lead)
Electro-Optics - University of Dayton (Lead)
Radio Frequency- Louisiana State Univ. (Lead)
ATR Consortium Member Universities:
Louisiana Tech University
Michigan State University
Prairie View A & M University
North Carolina A &T State University
Chaminade University of Hawaii
● C1, C2,
C3: ● C1, C2,
C3:
CCC1390
CCC1390c
c4-H6
4-H6
●
● C1●C
●
C
● 5●C
1C
●
4●C
● C7 5
C 4
●
C3 ●C 6●C
C72
UAV fly ●C
●C 6
area
UAV fly
area
3
2
●C7:
●C7:
thermal
camera
thermal
camera
GPS
GPS
Antenna
Antenna
PTZ
PTZ
camera
UAV
camera UAV
Controller
Controller
Gyro,
Gyro,
Magnetomet
er,Magnetomet
er,
Acceleromet
erAcceleromet
er
Moving Target Detection
Subsystem Architecture
Real-Time Algorithms for Smart Airborne
Video Surveillance
Tennessee State University
Department of Electrical and Computer Engineering
Intelligent Control Systems (ICS) Lab
(Dr. Saleh Zein-Sabatto, mzein@tnstate.edu)
Image Registration
Research Work
Funded by Air Force Research
Laboratory (AFRL)
“Real-time Registration of Video
Images Captured by Cameras
Mounted on Multiple of UAVs”
Real-time Image Registration Process
Available UAV
Data Collection from a Real UAV
Students Active Participations in Research
ATR Test-Bed (ATRTB)
Electric Helicopter and 7 Cameras Surveillance System
Student Research Participations
UAV at TSU Campus
Camera
Surveillance
System at TSU
●C1
●C5
●C4
●C2
●C7
●C3 Participations
Faculty
●C6
Energy Efficient Wireless Multimedia Sensor
Networks
(Supported by AFRL-- ATR Project, 2007 - 2008)
• Objective
Develop a test-bed for energy
efficient multi-hop wireless
multimedia sensor networks to
explore design tradeoffs in crosslayer protocols, error control
schemes, mesh image/video
transmission, etc.
Heterogeneous Motes
• Wireless Multimedia Sensor Networks
Test-bed
Clients
Clients
Server
Access Point
Internet
Tier 3
802.11
Gateway
Tier 2
Low-resolution camera
Tier 1
Robot with high
resolution camera
Helicopter with
high-resolution
camera
Tier 4
802.15.4/zigbee
motes with sensors
and low-resolution cameras
Research Project for the Minority Leaders Sensors Program
(Funded by AFRL, 2007 – 2008)
Cross-Layer Design of Cognitive Networks with MIMO Technology
Objectives
Leverage MIMO technology in a
cross-layer fashion involving
network architecture, PHY, MAC,
and routing protocols to multioptimize networking performance
and maximize network life in
wireless sensor networks (WSNs)
Approach
Cross-Layer Design
MIMO-aware Hierarchical Network
Architecture with link-jumping and
head-rotation
– enabling efficient routing
– inexpensive self-reconfiguration
MultiOptimal-MAC Protocol
– a CSMA/CA based MAC with a
multi-optimizer
Efficient Routing Algorithms
– short and robust: maximize network
throughput and network lifetime
Testing and Evaluation
– Test Modeling & Test-Bed
– Performance Evaluation
MIMO Technology
Without using extra energy and
channel, a MIMO transceiver can
be used to
• Extend transmission range, or
reducing error rate at links by
using diversity gain
• Increase data rate at links by
using multiplexing gain
MIMO transceiver
T×1
R×1
T×2
R×2
T×M
R×M
MIMO sensor network
diversity gain
multiplexing gain
Research Project for the Minority Leaders Sensors Program
(Funded by AFRL, 2007 – 2008)
Cross-Layer Design of Cognitive Networks with MIMO Technology - Continue
Test and Simulation
A flat MIMO WSN with 800 nodes
Performance Evaluation
Compare the network throughput, network lifetime and
reconfiguration cost for SISO WSNs, 2×2 MIMO WSNs, and 4×4
MIMO WSNs by repeatedly broadcasting packets until the WSNs die
(C – cluster-based WSNs, F – flat (unstructured) WSNs)
Energy Consumption
Network Throughput
50000
45000
Energy (mJ)/ per packet
Number of packets per second
16
14
12
10
8
6
4
40000
35000
30000
25000
20000
15000
10000
5000
0
9.6
2
A cluster-base MIMO WSN with
800 nodes
57.6
0
9.6
19.2
Data Rate (kbps)
SISO (C)
SISO (F)
57.6
2X2 (C)
2X2 (F)
SISO (C)
SISO (F)
4X4 (C)
4X4 (F)
Time for Network Reconfiguration in
Whole Network Lifetime
2X2 (C)
2X2 (F)
4X4 (C)
4X4 (F)
Energy for Network Reconfiguration in
Whole Network Lifetime
50
70000
45
40
60000
35
Energy (mJ)
Time (second)
A MIMO WSN with
link-jumping and headrotation can live very
long: even many nodes
(yellow) died, the
remaining nodes (red)
can still form a
connected network;
19.2
Data Rate (kbps)
30
25
20
15
50000
40000
30000
20000
10
10000
5
0
0
9.6
19.2
Data Rate (kbps)
SISO (C)
SISO (F)
2X2 (C)
2X2 (F)
57.6
4X4 (C)
4X4 (F)
9.6
19.2
57.6
Data Rate (kbps)
SISO (C)
SISO (F)
2X2 (C)
2X2 (F)
4X4 (C)
4X4 (F)
CENTER OF EXCELLENCE FOR
BATTLEFIELD SENSOR FUSION (ARO)
RESEARCH FOCUS AREAS
 Systematic sensor data & information
fusion
 Sensors networking in battlefield
situations
 Multiple target identification and
tracking
 Battlefield source allocation and
management
 Networks modeling and simulation
 Network performance measurements
 Experimental testing & evaluation of
sensor network concepts.
`
Tennessee State University
Department of Electrical and Computer Engineering
Intelligent Control Systems (ICS) Lab
(Dr. Saleh Zein-Sabatto, mzein@tnstate.edu)
Wireless Sensor Networks
Research Work
Funded by Army Research office
“Large-scale Sensor
Deployment, Localization and
Processing”
Wireless Sensor Deployments
Wireless Sensor Localization
Vehicle Identifications & Classifications
Research Projects for the Center of Excellence in Battle Field Sensor
Fusion (Funded by ARO, 2005 - 2008)
Control/Communication Scheme for Mobile Sensor Networks
Objectives
Requirements
A cluster-based Control Architecture and Communication Scheme
(CACS) had been developed in the same project for a stationary
and low mobility sensor networks in the same project. It needs to
be generalized and enriched:
• Multi-mobility: nodes can be stationary, or mobile with low or
high mobility
• Multi-optimality: the scheme should maximize network
throughput, network lifetime and ensure QoS
Design of Communication Backbone
high-mobility nodes
high-mobility nodes
(1)The Stationary and low-mobility
nodes form a cluster-based
reconfigurable communication
backbone.
(2) High-mobility nodes join the
nearest cluster as members. Their
joining or leaving do not change the
backbone; therefore, do not cause
significant problems for network
reconfiguration.
• High-mobility nodes can have two different
behaviors:
(1) as end-users of receiving services; and
(2) as nodes of the sensor network providing data
and support fusion.
• The joining and leaving of the high-mobility
nodes should be time and energy efficient..
head
cluster
Sensor Network with
nodes of high mobility
Communication Backbone (black
edges) with high mobility nodes (blue)
Research Projects for the Center of Excellence in Battle Field Sensor
Fusion (Funded by ARO, 2005 - 2008)
Real Time Task and Resource Management for large Sensor Networks
Objectives
Approaches
Develop a Task and Resource Management System
(TRMS) for optimally allocating resources in real
time on a large sensor network. The goal is to
achieve both high QoS and long network lifetime.
Global TRMS at the base station:
•
Break down a user task into
elemental tasks
•
Determine the resource and
price needed for each
elemental task based on the
current network status and user
requirements.
Cluster TRMS at each cluster head:
Select member nodes for each
elemental task based on the
available resource at each
node.
Node TRMS at each member node:
Autonomously adjust the resource at
the node for QoS and energy
saving.
• Decentralize the Task and Sensor Management Approach
(developed in PSU) and embed it into the hierarchical Control
and Communication Scheme (developed in TSU) for a clusterbased sensor network.
• Experimental approaches will be used for integration.
Concept Design
Global TRMS
Future Work
Base Station
sink
backbone
ClusterTRMS
Node TRMS
Cluster
Cluster-based Sensor Network
• Implement the TRMSs.
Global TRMS and Cluster
TRMS are centralized
systems. Node TRMS is an
autonomous system.
• Embed the TRMSs into the
Control and Communication
Scheme (developed in TSU)
so that the TRMSs can
dynamically allocate the
resource based on the current
network and resource status.
Research Projects for the Center of Excellence in Battle Field Sensor
Fusion (Funded by ARO, 2005 - 2008)
Data Query & Collection Protocols on Mobile Sensor Networks
Objectives
Approaches
Investigate routing protocols for data
query and data collection for a highly
dynamic mobile sensor network.
• A Depth-First-Order (DFO) routing
protocol had been developed in the same
project. It is not robust: one node/edge
failure may stop the whole routing.
• The routing protocol for a mobile sensor
network must be robust, and time and
energy efficient.
Data Query A query request is delivered
in a top-down manner on the backbone.
When a node receives a request, it relays
the request at its pre-assigned timeslot.
Data Collection Data are collected in a
bottom-up manner on the backbone.
When a node receives the data from all
children, it adds its own data together and
transmits the data to its parent at its preassigned timeslot.
data gathering
1 2 3 1 3 2 4
timeslots
Testing and Evaluation
Accomplishment
Rounds to be awake for CFF
Rounds to be awake for DFO
rounds by CFF Broadcast
rounds by DFO Broadcast
600
rounds to be awake
600
number of rounds
Collision-Free-Flooding (CFF) protocol:
• multi-routing: routes are self-formed
and use TDM to avoid collision.
• Time-energy efficient: routes are short
scheduled on the communication
backbone and each node needs to be
awake only for a short time.
• Robust: even node/link failures happen,
data query/collection still continue on
other routes.
data query timeslots
2
1
2
500
400
300
200
500
400
300
200
100
100
0
0
1
2
3
4
number of nodes
Fig. 1 Time (rounds) for a CFF
broadcast and DFO broadcast
5
100
1
200
2
300
3
400
4
500
5
number of nodes
Fig. 2 Energy (rounds a node
awake) for a CFF broadcast and
a DFO broadcast
Soft Computing Techniques For Robust
UXO Classification and Decision Fusion
Tyndale Air Force Base (AFRL)
PARTNERSHIPS / CONTRACTS
TACOM
BRC
Bevilacqua Research Corporation
2007-2008 ERI Funded Research projects
1. Center for Excellence in Battlefield Sensor Fusion, funded by Army Research Office
(ARO) $2,331,255.(2004-2009)-Dr. Shirkhodaie (PI)
2. Multi-Mode UAV Sensor Technologies (Multi-MUST), funded by WPAFB, Air Force
Research laboratory (AFRL)$137,000(2006-2007)-Dr. Shirkhodaie (PI)
3. Surface and Partially Buried UXO Identification, discrimination, and Localization
Based on Cognitive Imagery Techniques, funded by Tyndall, AFRL $160,000 (20062007)-Dr. Shirkhodaie (PI)
4. Sensors Technology Thrust Research—(Automatic Target Recognition)-AFRL $780,000
(2006-2008)Dr. Mohan Malkani (PI)
5. Neural-Fuzzy Modeling in Model-Based Fault Detection, Isolation, Control
Adaptation and Reconfiguration in Turbine Engines—funded by Propulsion Directorate
(AFRL)---$ 288,520 (2007-2010)—Dr. Zein-Sabatto (PI)
6. Visual Telerobotic Task Planning of Cooperative Robots based on Soft Computing,
funded by NASA/JPL $300,000 (2004-2007)-Dr. Shirkhodaie (PI)
7.Human Systems Integration (Seating Comfort)—Boeing--$750,000( 2007-2010)- Dr.
Onyebueke (PI)
8..Cybersecurity-ORNL/BWXT Y-12 ---$538,149 Dr. Decatur B. Rogers (PI) (2207-2010)
9. Intelligent Cognitive inspection System for Manufacturing Process Automated Reasoning and
Decision Making—funded by Rolls Royce--$90,000 (200-2007)-Dr. Shirkhodaie (PI)
10. Failure Mode and Criticality Analysis of Maintenance Issue --funded by Aerospace Testing Alliance
$50,000—Dr. Devgan (PI)
STRONG POINTS OF OUR RESEARCH
CAPABILITIES













Suite of Digital Signal Processing Tools
Advanced Intelligence Tools: Neural Networks, Fuzzy Logic
and Genetic Algorithms
Fault Detection and Health Monitoring
Intelligent Control System: Aircraft, Helicopter
Modeling, Simulation and Analysis
Systems Engineering
Mobile Robot Navigation
Sensor Fusion
Data Mining
Computer Integrated Manufacturing
Integrated Design Methodologies - FEM, SM and PDM
Patents and Technology Transfer
Information Technologies