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


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

3Distribution Statement A: Approved for public release

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.

Distribution Statement A: Approved for public release

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


Canonical Sensor “with DL”

Data

Information

SamplingPlatforms


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


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.


Capacity vs CapabilityHuman Eye Sensor

DataInformation


Capacity vs CapabilityAdaptive Hardware


LIQUID LENSES + SPATIAL LIGHT MODULATORS + MEMS MIRROR BEAM STEERING

Capacity vs CapabilityModern Car Driving Sensors

Data


Information Data

Capacity vs CapabilitySelf-Driving Car Sensors

Data


Data

Undersea Optical Environment


Physics & Engineering Issues


Major Naval Applications

LADAR

Comms

LIDAR



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.


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


Backup Slides

Capabilities live inside each capacity

Capabilities

Capacity:Smart phone

Mail

Text

Facebook

Phone

Internet


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


LIDARMinimize Absorption


RADARCoherent Detection

















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


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/


https://www.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

[email protected]

Mario Urdaneta, Ph.D.Research Business Development Manager

[email protected]

Susan Tsang, Ph.D.Grants and Contracts Development Manager

[email protected]

Kevin JacobsMembership and Marketing Manager

[email protected]

Research Development

Lamont HamesStrategy and Development

[email protected]

Business Development

Michael J. HesterChief Executive Officer

[email protected]

Stacey BrownDirector of Finance and Accounting [email protected]

Administration

Monique DavisProgram Director

[email protected]

Jorge Maciel, Ph.D.Technical Advisor

[email protected]

Jay Valdez, Ph.D.Technical Advisor

[email protected]

Technical Advisors


mailto:[email protected]











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/


https://www.msrdconsortium.org/meeting/

pre-proposal webinar

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