Pre-Proposal Webinar
Transcript of Pre-Proposal Webinar
Pre-Proposal WebinarOffice of Naval Research
Announcement #W911SR-14-2-0001, RPP-1912
Agenda & Outcomes
Meeting Agenda
• Welcome (5 minutes)
• Teaming Agreements (10 minutes)
• Sensor Example (30 minutes)
• Announcement Overview (15 minutes)
• Questions (15 minutes)
W911SR-14-2-0001, RPP-1912www.msrdconsortium.org/onr2019rrpp
Teaming Agreements
Proposals from a team of university investigators are warranted when the necessary expertise in addressing the multiple facets of the topics may reside in different universities, or in different departments in the same university.
One institution shall be the primary awardee for the purpose of award execution. The PI shall come from the primary institution. The relationship among participating institutions and their respective roles, as well as the apportionment of funds including sub-awards, if any, shall be described in the proposal text
W911SR-14-2-0001, RPP-1912www.msrdconsortium.org/onr2019rrpp
Collaborative Research
A copy of the Teaming Agreement will be posted on MSRDC's website after the webinar. A link will be included in the follow-up email.
Pre-Proposal Webinar Webinarwith MSRDC Members
8 July 2019Mike Wardlaw
Maritime Sensing 321MS
WarfighterSupremacy
UnderseaBattlespace &Maritime DomainAccess
Aviation, Force Projection & Integrated Defense
Mission Capable,Persistent &SurvivableSea Platforms
Information, Cyber & Spectrum Superiority
Amphibious Expeditionary Maneuver
NRE FrameworkAddendum
Sensing &Sense-Making
ScalableLethality
Operational Endurance
Integrated &Distributed Forces
Augmented Warfighter
R & D Priorities
Distribution Statement A: Approved for public release 2
Five Framework Prioritiesthat are Strategic andWarfighter-Focused…
…Translates to SixTechnology-Focused
Integrated Research Portfolios
Basic Research$547M
Applied Research$956M
Advanced TechnologyDevelopment
$879MDemonstrationand Validation
$37M
The Portfolio Investment Relative to Navy Budget
MILPERS$46.0B
PROCUREMENT$44.3B
O&M$50.5B
MILCON$2.2B
RDT&E$17.9B
FY17 DoN BUDGET$160.9B
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Ocean Battlespace Sensing DepartmentResearch Areas
NRE Framework Priorities
• Augmented Warfighter• Operational Endurance
• Sensing & Sense-Making
• Scalable Lethality
SpaceMarine
Meteorology
Physical Oceanography
OceanAcoustics
NNR
Arctic & Global Prediction
Marine Mammals & Biology
MIW
Ocean Engineering & Marine Systems
Unmanned Systems Technology & Autonomy
ASW
Research Facilities
4Distribution Statement A: Approved for public release; distribution is unlimited.
Undersea Signal Processing
Team Structure & Research Objectives
MaritimeSensing
Active SensingTraweek
Increase Directivity
Decrease Noise/Clutter
Passive SensingBlackmon / Wardlaw
Increase Spatial Aperture
Increase Sensitivity
Non-Acoustic SensingM. Wardlaw / Blackmon
Increase Spatial Aperture
Improve Logistical & Installation Options
Information Theoretic Sensing
Dynamic Resource Allocation
Standoff Material Characterization
New Photonic Components
ResearchObjectives
6.2Tech.Base
Development Approach
6.1 Basic Research
6.3 Transition to Targeted Applications6.4 Demonstration and Validation
TechnologyTechnologyTechnologyTechnologyTechnologyTechnology
Building “Capacities”
Building “Capabilities”
DiscoveryCycle(D&I)
InnovationCycle
(INP/FNC)
DiscoveryCycle(D&I)
InnovationCycle
(INP/FNC)
Time & Money
Why should make sensors "smarter"
• It's becoming increasingly difficult to justify the huge foundry investments required to maintain Moore's Law.
• Unique opportunity to create more "elegant" sensing design options providing "intelligent" feedback.
• Allow the sensor to dynamically learn how best to allocate its sampling, processing, and communication resources
• Capability based sensing using capacity based sensors: adapted by both external objectives and internal conditions.
• Embedding deep learning machines into sensor designs could help mitigate Moore's Law's possible demise.
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What’s the Problem?
• We are quite literally drowning in data!• Traditionally, we’ve relyed on Moore’s Law to “Brute
Force” throughput– Increased Communication BW (Telecommunications)– Increased Computational Power (Personal
Computing)• Constraints
– Information Coherence (Inherent, Cognitive)– Size, Weight & Power (SWAP)– Legal Restriction and Regulation
• Smart Sensing makes AI inherent to the sensing process– Optimizes Throughput – Addresses Constraints
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Canonical Sensor “with DL”
Data
Information
SamplingPlatforms
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What do I mean by “Smart Sensing?• Optimize Sensor Throughput
– Use DL to train adaptive feedback loops to minimize Shannon entropy (the measure of uncertainty) against noise and clutter, resulting in increased subsystem information content
• Minimize Constraints– Use DL to discover space-time coherence relationships
that can then be exploited– Use DL to discover and overcome cognitive biases– Reduce Instantaneous Dynamic Range (IDR) requirements– Reduced IDR reduces SWAP requirements– Reallocating sensor resources to minimize unauthorized data
collects, reducing the opportunity for misuse
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Capacity and Capability
• I assert that capacity and capability are inherently different.
• Capacity is from the Latin word capacitatemmeaning “breadth” and in the simplest sense means “ability”.
• Capability exist inside a capacity as “an ability”. If you have the ability (the capacity), it means you know how to do something. If you have the capability, it means you have the actual power to do something.
Without Capacity, Capabilities cannot be realized.
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Capacity vs CapabilityHuman Eye Sensor
DataInformation
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Capacity vs CapabilityAdaptive Hardware
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LIQUID LENSES + SPATIAL LIGHT MODULATORS + MEMS MIRROR BEAM STEERING
Capacity vs CapabilityModern Car Driving Sensors
Data
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Information Data
Capacity vs CapabilitySelf-Driving Car Sensors
Data
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Data
Undersea Optical Environment
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Physics & Engineering Issues
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Major Naval Applications
LADAR
Comms
LIDAR
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In SummaryFocusing on developing the capacity to respond instead of focusing on a specific capability
provides the space & freedom necessary
to effectively deal with uncertainty,
to be creative, & generate innovative solutions.
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Opportunities for Embedded Deep Learning
Dynamically allocating internal resources!
Independently & Collectively,the system, its engineers and end
users discover, learn & adapt,
providing better information optimal capabilities.
Leads toSize, Weight & Power (SWaP) ReductionCommunication Bandwidth Reduction
Design ResilienceCost Reduction
Distribution Statement A: Approved for public release
Backup Slides
Capabilities live inside each capacity
Capabilities
Capacity:Smart phone
Text
Phone
Internet
Capabilities live inside each capacity
Capabilities
Capacity:Hybrid lidar-radar
UW Imaging
High speed
Comms Distributed Sensing
Non-acoustic
ASW
UW Proximity Detection
Hybrid LIDAR-RADAR TechnologyRadar transmission/detection in an underwater environment
Hybrid LIDAR-RADAR TechnologyRadar transmission/detection in an underwater environment
LIDARMinimize Absorption
LIDARMinimize Absorption
RADARCoherent Detection
RADARCoherent Detection
Hybrid LIDAR-RADAR TechnologyRadar transmission/detection in an underwater environment
Hybrid LIDAR-RADAR TechnologyRadar transmission/detection in an underwater environment
Hybrid LIDAR-RADAR TechnologyRadar transmission/detection in an underwater environment
Hybrid LIDAR-RADAR TechnologyRadar transmission/detection in an underwater environment
LIDARMinimize Absorption
LIDARMinimize Absorption
LIDARMinimize Absorption
LIDARMinimize Absorption
LIDARMinimize Absorption
LIDARMinimize Absorption
RADARCoherent Detection
RADARCoherent Detection
RADARCoherent Detection
RADARCoherent Detection
Capabilities live inside each capacity
Capabilities
Capacity:Autonomy
UAS
DistributedSensing
Decisions via AI/ML
Collaborative Sensing
USV/UUV
About the Announcement
The 321MS team recommends the creation of collaborative interdisciplinary solution teams to address program objective tasks for the areas indicated.• Basic Science Research Solution Team:
• Materials: Nano, Metallic, Electric, and other relevant materials• Modeling Properties and Integrated Systems
• Prototype Development Solution Team:• Prototype measurements• Prototype development• Prototype fabrication
• Validation Solution Team:• Model validation• Systems validation
Collaborative Research
July 22, 2019, 5:00pm ESTMSRDC Submission Deadline
July 24, 2019, 5:00pm ESTGovernment Submission Deadline
Important Deadlines
• State portion of the effort each member will be contributing.• Provide a schedule indicating when each member will participate in task(s).
Collaborative Research Summary
The 321MS team supports fundamental research that changes the sensor design constraint space. Traditional sensor systems typically begin as open-ended instrumentation devices, where the primary emphasis is on maximizing the amount of data that can be collected. Signal processing and communication are generally secondary considerations which are often addressed on an ad hoc, case by case basis.
Introduction & Background
W911SR-14-2-0001, RPP-1912www.msrdconsortium.org/onr2019rrpp
About the Announcement
• Task 1: Design and simulate various configurations that integrate AI into sensor systems.
• Task 2: Develop and prototype the various fixtures, hardware and software required to collect sufficient data to assess prototype sensors increased effectiveness.
• Task 3: Performance-related data shall include power consumption, space/time-bandwidth product, and effective information content.
• Task 4: Utilize, test and experiment with the prototype sensor to ascertain its effectiveness in meeting various global objectives such as detection, discrimination and tracking as compared to conventional sensor systems.
Specific Tasks to Address Program Goals
Develop sensor systems that balance the three principle sensor subsystem tasks of:• Sampling phenomenology,• Pre-processing the data sampled and• Communicating that data out.• Adding AI to the sensing process provides the
opportunity to significantly decrease entropy and increase the ratio of relevant information to raw data, its information content.
Scope & Program Goals
Read the full announcement online at
https://www.msrdconsortium.org/onr2019rrpp/
W911SR-14-2-0001, RPP-1912www.msrdconsortium.org/onr2019rrpp
The MSRDC Team
Alan A. Arnold, Ph.D.Director of Research [email protected]
Joseph Bonivel Jr., Ph.D.Research Business Development Manager
Mario Urdaneta, Ph.D.Research Business Development Manager
Susan Tsang, Ph.D.Grants and Contracts Development Manager
Kevin JacobsMembership and Marketing Manager
Research Development
Lamont HamesStrategy and Development
Business Development
Michael J. HesterChief Executive Officer
Stacey BrownDirector of Finance and Accounting [email protected]
Administration
Monique DavisProgram Director
Jorge Maciel, Ph.D.Technical Advisor
Jay Valdez, Ph.D.Technical Advisor
Technical Advisors
W911SR-14-2-0001, RPP-1912www.msrdconsortium.org/onr2019rrpp
Next Steps for SuccessReach out for an individual consultation with MSRDC.
Phone Virtual In-Person
Request an appointment online at
https://www.msrdconsortium.org/meeting/
W911SR-14-2-0001, RPP-1912www.msrdconsortium.org/onr2019rrpp