Management System
Resume:
Mokhtar M. Sadok, Ph.D. Phone:
*** ******** ****, *********, *******
*******.*****@*****.***
SUMMARY
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Electrical and computer engineering professional with eighteen years of R&D
experience and excellent academic qualifications. Recipient of 10 patents
and external funding solicitation awards. Solid experience in interacting
with academia, small businesses, and outside organizations to drive
innovation and maintain technical excellence. Seeking leadership position
in the area of advanced technology development of sensors and integrate
systems.
Managed Projects/Areas:
. Passive wireless sensors
. RFID and Wireless Networks
. IP Landscape & Innovation Management
. Digital Signal/Image Processing
. Wavelets and Joint Time-Frequency Analysis
. AI, Neural Networks, and Fuzzy Logic
. Stochastic process analysis and systems control
. Monitoring, Detection, Diagnosis & Prognosis
. Data Fusion, Reduction, Mining & Analysis
. Pattern Recognition & Smart Sensors
. Health and Usage Monitoring Systems (HUMS)
. Acoustic, IR, and Optical sensing
PROFESSIONAL EXPERIENCE
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Goodrich, Sensors and Integrated Systems, Advanced Technology 1998-Present
Recruited to adopt advanced concepts in developing state-of-the-art
solutions for fuel quantity measurement, computer vision, nondestructive
testing, health and usage management systems, and passive wireless sensing.
Knowledge & Communication Systems- Technical Lead (2007-Present
Secured corporate funds to develop battery-free wireless sensors for harsh
environment deployment. Led a cross-functional team of engineers designing,
implementing, testing, and validating the proposed approach. Led other
initiatives in parallel to maintain the company competitive edge.
. Successfully driven the project ahead of schedule within budget and built
a TRL5 prototype for a totally passive wireless latch sensing and
indication system in the aircraft engine nacelle. Under FCC radiation
limits at the microwave range, the prototype was able to wirelessly power
latch sensors at 3.2 meters.
. As a technology authority, represented the company in AVSI (i.e.
Aerospace Vehicle Systems Institute- a cross business consortium that
includes major aerospace US companies in addition to the FAA, NASA, and
DOD) to study the prospects of certifying RFID in commercial airplanes.
. Initiated a continuous improvement event to apply advanced signal
processing techniques in sensor calibration which resulted in increasing
sensor accuracy by more than 20%.
. Successfully applied a new knowledge management tool (Goldfire) to 3
different projects and provided training on the tool to the technology
team of engineers and company managers.
. Spearheaded company efforts to contract outside IP management firms (from
India) to address the need of various programs to identify with the
competition and general IP landscape which resulted in saving the company
bottom line $600k of licensing fees.
Advanced Technology- Technical Lead (2005-2007
Led several advanced technology initiatives focused on Condition Based
Maintenance for HUMS Systems and Wireless Sensing Technology. Led the
testing process of the wireless HUMS project that included writing test
procedures for data collection, planning and executing environmental tests
to successfully meet Safety of Flight criteria, gathering and analyzing
data, and generating test reports. Put together a 70-page strategic white
paper on RFID with its active, passive, semi-passive, and Surface Acoustic
Wave (SAW) types detailing the opportunities and risks of this technology
to various product lines of the company. Led a trade study of various
wireless technologies including Bluetooth, WiFi, ultrawideband, and Zigbee
for a selected set of new and legacy systems in commercial and military
aircraft.
. Developed a set of algorithms for Transmit Power Control (TPC) for
wireless HUMS which resulted in extending the battery life more than 4
folds
. Secured internal funding for the development of an advanced set of
algorithms based on decision trees and fuzzy logic implemented for the
case of generator compartment of UH60L helicopters.
. After being tapped by another company division to develop health
prognosis algorithms for an air probe sensor, recommended changing the
data collection method which provided apt explanation of observed
phenomena and led to the identification of resourceful data for most
efficient prognosis algorithms.
Advanced Technology-Principal Investigator (2004-2005
Submitted a winning proposal in response to an open Broad Agency
Announcement (BAA) of the FAA and secured government funds to build a hand
held device for Non Destructive Testing (NDT) of wire defects in aircraft.
Led a diverse team of engineers and external scientists to develop state-of-
the-art wire diagnostics technology. Developed, planned, and executed
procedures to collect and analyze field data aboard a B737 airplane. Teamed
with NASA to apply a novel time-frequency analysis technique to detect
transient signatures for better detection and isolation of defects in aging
wires.
. Completed such a complex project in time within budget with high customer
satisfaction through technical leadership, adopting advanced simulation,
rapid prototyping, and validation methodologies.
. Introduced a patented technique to the NDT technical community based on
TFDR (Time-Frequency Domain Reflectometry) in contrast to classical TDR
and FDR techniques for wire diagnostics.
. Developed wire health management algorithms that resulted in detecting
and isolating insulation defects in coaxial wires and opens and shorts in
single wires (never done before).
Advanced Technology- Algorithms Development Lead (2002-2004
Led the algorithm development task of an $8M computer-vision program for
early and accurate fire detection in cargo bays of Airbus A340 aircraft.
Developed, tested, and implemented more than 18 algorithms for image
enhancement to contrast fire and smoke areas, automatic data registration
for 5 video streams, image segmentation for hotspot tracking, and fuzzy
logic-based decision making algorithms for early and accurate fire
detection using video streams and 1-D temperature and relative humidity
signals.
. Detected all tested fires according to the European standards ahead of
the competition system- on board A340 aircraft. In one of typical
standard fire tests (smoldering wood -i.e. TF2) the system detected the
fire and alarmed 5 minutes ahead of the competition. In another type of
fire tests (ethanol- i.e. TF 6) the system detected fire in a few seconds
compared to the competition system was unable to detect the fire at all.
. The developed system never alarmed in all tested false alarms made of
dust and fog in contrast to the competition system that falsely alarmed
in all of the tested cases.
. To the highest customer satisfaction acknowledgment, the system presented
the information to the cabinet crew using a patented image enhancement
technique that selects the best camera view and overlays smoke and fire
information over cargo bay background.
Advanced Technology- Algorithm Development (1998-2002
Developed state of the art algorithms to modernize fuel quantity
measurement technology in aircraft. Led a series of seminars, training,
and tutorials across the company that show the benefits of various
nonlinear techniques for signal and image processing including neural
networks, fuzzy logic, and wavelets. Developed and tested algorithms based
on a combination of stereo-image processing and structural laser light
identification techniques to estimate fuel height for optical fuel gauging.
. Conducted a comprehensive trade study on accuracy and sensitivity
analysis of Neural Network-based fuel quantity measurement systems as
compared to classical linear-based ones. The study assured executive
management and engineers to adopt neural networks as a new tool for fuel
quantity measurement through verification and validation of system
stability and improved system accuracy by an order of magnitude.
. Built a compelling simulation tool in Matlab/Simulink that showed the
efficacy of Neural Networks as it allowed the elimination of the
densitometer- most expensive part (i.e. $3,400) of the classical A321
fuel quantity system- while maintaining the same accuracy as the onboard
linear system.
. Implemented a real-time stable and accurate Best Linear Unbiased
Estimator (BLUE) to correct for pressure data dropouts during flight
testing which allowed the provision of a continuous stream of data- a
necessary prerequisite for the main data fusion algorithm of fuel
quantity computation.
. Developed a patented technique that permitted unique identification of
ultrasonic signatures which solved a critical problem of discriminating
between ultrasonic echoes inside the fuel tank.
. Tapped by executive management to lead the data analysis effort to honor
a request by NTSB following TWA flight 800 plane crash. The analysis was
instrumental in understanding the crash cause which provided and early
lead to the company to design a device and later capture the lion chair
of a new market for transient suppression devices following an
anticipated FAA regulation to limit transients in fuel tanks.
Graduate Research Assistant, Dept. of ECE Eng., Tennessee Tech University---
Led advanced research in the area of artificial intelligence, Neural
Networks, Fuzzy Logic, Control Systems, and System Monitoring.
Developed and implemented a patented neuro-fuzzy system for Tennessee
Valley Authority (TVA) to detect and locate boiler tube leaks in power
plants.
Built an optical expert system based on approximate reasoning of fuzzy
logic for handwritten numeral recognition with application to machine-based
zip code reading. The system (made of more than 20 routines) was so
successful in identifying various handwritings making it the talk of the
graduate students and professors of the department for weeks as they wanted
to challenge its recognition capability.
Used adaptive filtering principles to show the stability of decimated
systems of long impulse response. Applied to real-time echo-cancellation,
the system converged twice faster than typical filters.
Advanced a novel multiscale modeling approach based on wavelets and neural
networks for image segmentation and interpretation applied to human face
recognition
. Successfully implemented a Hidden Markov Model (HMM) for speech
recognition.
Electrical Engineering Instructor, Vocational Institute for Electronics
Taught courses in basic electronics and digital circuit design (in French).
Supervised several projects for the graduating students of the institute
. Strongly recommended by the administration for enrollment on a graduate
school program in USA
Software Engineer, National School of Engineering,
Took part of the software architecture team for the graphics module of
CASBA- a European software for structural engineering
. Led the coding task for the "zoom" function of the graphics module of
CASBA
SKILLS
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Computer
. Languages: Matlab, C/C++, Visual Basic, DELPHI, LABVIEW, FORTRAN, PASCAL,
ASSEMBLY
. Packages: MS-Office, MS Project, CDTM, RTM, Goldfire
Others
. Active reviewer in Optical Engineering and Photonic Technology (OEPT) and
Prognostics and Health Management (PHM) societies
. Member of the HKN honor society, IEEE, and SPIE
. Fluent in English, French, and Arabic
EDUCATION
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Ph.D. Electrical and Computer Engineering from Tennessee Technological
University (GPA 3.93)
Dissertation: Wavelets and Neural Networks-based Multiscale Modeling:
Application to Human Face Recognition.
M.S. Signal Processing and Digital System Analysis (high honors),
Dissertation: Reduced Adaptive Modeling: Case of Long Impulse Response
Channels for Echo Cancellation in Amphitheatres
B.S. Electronics, 1991
PATENTS/PUBLICATIONS
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. Recipient of ten patents in the area sensors, integrated systems, and
data fusion
. Author of numerous journal and conference articles.
Contact this candidate