Θ Washington's UAS Sensing by yourDragonXi Δ 8th of May 2018 Ω 3:17 PM

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~ INSITU ~ Boeing ~ University of Washington	~ Ballard ~ Microsoft ~ Sagetech	~ sense for Ξ ~ sense for Ξ ~ sense for Ξ
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«UAS Sensing in U.S.	Θ	Θ
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~ INSITU

»INSITU
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~ Boeing

»Boeing
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~ University of Washington

»University of Washington
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~ Ballard

»Ballard Technology

To: Support@ballardtech.com
cc: Mike.OBrien@ballardtech.com
From: sec@yourdragonxi.com
Subject: Thanks to John at Ballard Technology from Paivi of S&S 22 May 2014

Thanks for your answer John!

Please, email me the URL for LE1553-5 card specifications.
Our technical guys will take take a close look whether it could be used in Windows 8.1 workstation for software development.

Windows 8.1 as an OS might have potential in software development for devices such as base station
to process sensored big data at field and for mobile devices at field - what do you think?

Mobile software development might also be done also under - at least it looks even in the "land of Linux"
that guys from Seattle and Finland may - and they have to - invent something new!
This is the reason I asked about the card fitting into PCI-E bus.

Yes, you are absolutely right; other Ballard Technology products may have more potential for UAS system devices
such as UAVs, base stations, wireless sensors and mobile devices, which S&S designs both to fit and last
at remote and demanding theaters.

Which avionics busses are you dealing with ?
Any UAV bus ?

Sorry, I can't name any individual program S&S is currently bidding.

However, I can tell the following:

1) Instead of devices such as UAVs, S&S focuses on
network-centric unmanned and autonomous systems (UAS)
for military, patrol and business operations
at remote and demanding theaters.

2) Piloted air vehicles can be used to rapidly deploy UAVs and base stations
from different platforms, aircraft carriers included

3) Due to the current situation in the United States, S&S bids also in the EU and countries,
which sem to be years ahead of USA in applying UAS systems for business & patrol operations
- soon also in UAS hardware and software technology due to wiser legislation and export financing!

S&S shares information with partners as follows:

1) http://www.dragonxi.com/
~ the site is based on Plone content management system >> http://plone.org/
and Zope >> http://www.zope.org/

2) https://www.yourdragonxi.org/
~ php site to share information such as UAS prospects
>> http://yourdragonxi.org/sense/uas/index-uas-xi2.html

3) https://www.yourdragonxi.com/
~ mobile collaboration

Looking forward
to hear from You!
Paivi

From: Support@ballardtech.com
cc: Mike.OBrien@ballardtech.com
To: sec@yourdragonxi.com

Hello Paivi,

Our LE1553-5 card will work in a x16 PCIe slot.

Other products might be more appropriate. Typically UAS systems are concerned with size, weight, and power, in addition to ruggedization. An XMC module might be more appropriate for an airborne platform, as opposed to a PCIe card with an edge connector.

> Is there API & SDK to emulate other device such as UAV, sensor, base station ?

We only deal with digital avionics busses. RF and aircraft dynamics simulation software would need to come from other companies.

Can you share the name of the program you are bidding on?

Thanks
John Lopez
Application Support Manager
425-339-0281 x120
-----Original Message-----
From: sec@yourdragonxi.com [mailto:sec@yourdragonxi.com]
Sent: Wednesday, May 21, 2014 9:38 AM
To: Ballard Technology Support
Cc: sec@yourdragonxi.com
Subject: Web Tech Support Request - Paivi MayHill
Importance: High

This email was generated from the Ballard Technology Website Tech Support Form

-------------------- Contact Information --------------------
Name: Paivi MayHill
Email: sec@yourdragonxi.com
Phone: 831-646-0951
Company: Small & Smart
Country: United States
Contact Preference: Email

------------------------- Hardware --------------------------
Product: Cables And Accessories
Serial Number:
Other: LE1553-5 PCI Express 1553 interface card

------------------------- Software --------------------------
Product: API Libraries
Serial Number:
Other: SDK

-------------------------- System ---------------------------
Operating System: Windows
OS Version: 8.1 Enterprise
OS Service Pack:
OS Other: Evaluation version

---------------------- Message / Notes ---------------------- Dear Sirs,

Small&Smart (S&S) is a Califonia company developing unmanned and autonomous systems (UAS)
for network-centric operations at remote and demanding theaters.

S&S is looking for partners to develop UAS systems for Navy.

Which Ballard products could be used in PCI-EX16 bus to develop software under Windows 8.1 ?
Is the Ballard LE1553-5 PCI Express
the best one ?
Is there API & SDK to emulate other device such as UAV, sensor, base station ?

Thanks in advance
Paivi MayHill
Secretary of S&S / military apps

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~ Microsoft

------------------------------------------------------------------------
DJI And Microsoft Partner To Bring Advanced Drone Technology To The Enterprise
------------------------------------------------------------------------
DJI, the world�s leader in civilian drones and aerial imaging technology, and Microsoft
have announced a strategic partnership to bring advanced AI and
machine learning capabilities to DJI drones,
helping businesses harness the power of commercial drone technology and edge cloud computing.

Through this partnership, DJI is releasing a Software Development Kit (SDK) for Windows
that extends the power of commercial drone technology to the largest enterprise developer community in the world.
Using applications written for Windows 10 PCs,
DJI drones can be customized and controlled for a wide variety of industrial uses,
with full flight control and real-time data transfer capabilities,
making drone technology accessible to Windows 10 customers numbering nearly 700 million globally.

DJI has also selected Microsoft Azure
as its preferred cloud computing partner,
taking advantage of Azure�s industry leading AI and machine learning capabilities
to help turn vast quantities of aerial imagery and video data
into actionable insights for thousands of businesses across the globe.

With this partnership, DJI will have access to the Azure IP Advantage program,
which provides industry protection for intellectual property risks in the cloud.

�As computing becomes ubiquitous, the intelligent edge is emerging as the next technology frontier,�
said Scott Guthrie, EVP for Microsoft�s Cloud and Enterprise group.
�DJI is the leader in commercial drone technology, and
Microsoft Azure is the preferred cloud for commercial businesses.
Together, we are bringing unparalleled intelligent cloud and
Azure IoT capabilities to devices on the edge,
creating the potential to change the game for
multiple industries spanning agriculture, public safety, construction and more.�

DJI�s new SDK for Windows empowers developers to
build native Windows applications
that can remotely control DJI drones including autonomous flight and real-time data streaming.

The SDK will also allow the Windows developer community
to integrate and control third-party payloads like
multispectral sensors,
robotic components like custom actuators, and more,
exponentially increasing the ways drones can be used in the enterprise.

�DJI is excited to form this unique partnership with Microsoft
to bring the power of DJI aerial platforms to the Microsoft developer ecosystem,�
said Roger Luo, President at DJI.
�Using our new SDK,
Windows developers will soon be able to employ drones,
AI and machine learning technologies to create intelligent flying robots
that will save businesses time and money, and
help make drone technology a mainstay in the workplace.�

In addition to the SDK for Windows,
Microsoft and DJI are collaborating to develop commercial drone solutions
using Azure IoT Edge and AI technologies for customers in key vertical segments such as
agriculture, construction, and public safety.

Windows developers will be able to use DJI drones
alongside Azure�s extensive cloud and IoT toolset
to build AI solutions that are trained in the cloud and
deployed down to drones in the field in real-time,
allowing businesses to quickly take advantage of learnings
at one individual site and rapidly apply them across the organization.

DJI and Microsoft are already working together to advance technology
for precision farming with Microsoft�s FarmBeats solution,
which aggregates and analyzes data from aerial and ground sensors
using AI models running on Azure IoT Edge.
Microsoft FarmBeats integrates DJI�s PC Ground Station Pro software and
mapping algorithm to create real-time heatmaps on Azure IoT Edge.
These heatmaps can be used in a variety of ways like
informing a farmer�s planting strategy by indicating
whether the soil conditions are ripe for seed germination.

------------------------------------------------------------------------
»Microsoft
------------------------------------------------------------------------

Microsoft researchers test AI-controlled soaring machine
In the searing midday heat of the Nevada desert,
a white Jeep Wrangler heads down a desolate strip of dirt road,
surrounded on either side by miles of sagebrush and sand.

As the Jeep bumps along, two members of a Microsoft research team,
Jim Piavis and Rick Rogahn, steady themselves against the roll bar,
their feet planted on the seats and their upper bodies jutting out of the open roof.
They are scanning the bright blue sky, tracking a type of glider known as a sailplane.

The delicate black, white and red sailplane swerves wildly and unevenly at first,
and then gradually begins to make wide, soaring circles.

A hawk appears next to it, following the same circular pattern.
�We�ve got a friend up there with us,� says Piavis, head of mission readiness.
�That�s a good sign.�

The 16 � -foot, 12 �- pound sailplane has found a thermal,
or an invisible column of air that rises due to heat.
Soon, it is soaring through the sky,
the Jeep reaching speeds up to 30 miles per hour
as it flies down the dusty dirt road in hot pursuit of what the team is calling the infinite soaring machine.

Microsoft researchers have created a system that uses artificial intelligence
to keep the sailplane in the air without using a motor,
by autonomously finding and catching rides on naturally occurring thermals,
similar to how many birds stay aloft.

�Birds do this seamlessly, and all they�re doing is harnessing nature.
And they do it with a peanut-sized brain,�
says Ashish Kapoor, a principal researcher at Microsoft.

The birds do it naturally.
For a machine to do it requires a complex set of AI algorithms
that can identify things like air temperature, wind direction and
areas where it is not allowed to fly.

Then, the system must use other AI methods to take that information and
make real-time predictions about where it might find its next ride on a thermal.

Taken together, that�s much more complex than most AI systems people are using today
for individual tasks like recognizing a face in a photo or the words in a conversation.

Kapoor says it�s probably one of the few AI systems operating in the real world
that�s not only making predictions but also taking action based on those predictions.

It�s still a work in progress, but Kapoor says the infinite soaring machine
could eventually be used for all sorts of practical tasks,
such as monitoring crops in rural areas or
providing mobile Internet service in a place where there�s no easy way to get needed connectivity.

�These can be your cellular towers someday,� Kapoor says.
�You don�t need any ground infrastructure.�

Eventually, the team says, the sailplane could even use solar or wind power to gather energy,
theoretically making it possible for it to stay aloft indefinitely.

The autonomous sailplane is useful in and of itself.
But Andrey Kolobov, the Microsoft researcher in charge of the project�s research and engineering efforts,
says they also expect the work to apply to plenty of other increasingly sophisticated systems
that rely on AI and will operate in real, unpredictable environments.

�For us, the sailplane is a testbed for technologies at the core of anything
that will be considered intelligent in the next 10 years,� he says.

For people to depend on AI to help them with things like driving their cars,
keeping their homes secure or managing their busy daily schedules,
these systems will need to reliably make complex decisions on the spot,
based on variables such as traffic, noise, weather, other objects and even human emotions.
What�s more, they won�t be able to make many mistakes � that would be costly and potentially dangerous.

�AI in the real world will have very little room for error, like our sailplane,� Kolobov says.

In research terms, this kind of ability is called sequential decision making under uncertainty.

�It�s really the question of, �How do you plan for the future, several steps ahead?�� Kapoor says.
�Computationally, that�s a very hard problem.�

The sailplane they are testing in Nevada relies on a battery
to run onboard computational equipment and controls such as the rudder,
plus radios to communicate with the ground.
It also has a motor so that a pilot can take over manual operation when necessary.
But once it�s up in the air, it�s being designed to operate on its own,
finding and using thermals to travel without the aid of the motor or a person.

�What we�re trying to do is make sure the sailplane is fully autonomous and
smart enough to change its course of action,� Kolobov says.

To design the system, the team of researchers began with a framework
for thinking about the problem called the partially observable Markov decision process.

Kolobov, who co-authored a book on Markov decision process,
says that�s a model for making planning decisions in an environment in which you can�t know everything.
With the sailplane, the team combined that model with another AI approach,
called Bayesian reinforcement learning,
to create a way for the system to learn what it needs to know about its environment
as quickly as possible, in order to make the right decisions.

The team also is using what�s called Monte Carlo tree search,
which is a way for AI to look for the most promising course of action.

The sailplane�s AI system is divided into two parts:
The high-level and the low-level planner.

The high-level planner takes all the factors of the environment into account and
tries to create a policy for where the sailplane should go to look for thermals.
It gets better at making those predictions as time goes on,
based on the information the sailplane collects each time it goes up in the air.

�For the high-level planner, experience matters,� Kolobov says.
�The system will perform better on Friday than on Thursday
because it incorporates information based on past flights.�

The low-level planner is the part that is using Bayesian reinforcement learning
to detect and latch onto thermals in real time,
based on data from the sailplane�s sensors.
Think of that as learning by doing.

To build those AI algorithms required months of work in offices at Microsoft�s Redmond, Washington, campus.

Once the weather turned warm and the thermals got better,
the team conducted limited test flights on a farm near the company�s headquarters.
But it wasn�t until they arrived at the tiny airfield in Hawthorne, Nevada, in mid-August
that they really got a chance to see how all their theories would play out in the real world.

On the day that they spot the hawk, the team has been out on this stretch of dirt road for four days,
battling dusty conditions and the blazing sun as they launch their sailplanes again and again.

Behind the Jeep, an extra-long burgundy Ford Expedition has been set up as a makeshift office,
crammed with multiple computers, tools for field repairs and four other members of the research team.
The blasting AC does little to combat the heat from the desert and the large amounts of computer equipment.

As the sailplane flies above them, research interns Iain Guilliard and Sangwoo Moon
� both of whom wrote core algorithms that help power the system �
use four laptops to track its progress and monitor how it is taking advantage of conditions including thermals.

Every few seconds, Guilliard calls out the sailplane�s flight parameters,
while those in the Jeep scan the air for physical proof of the sailplane�s position.
Kolobov and Debadeepta Dey, a Microsoft researcher who built the sailplane�s thermal prediction module,
take turns driving the movable office after the Jeep and the sailplane.

In addition to catching thermals, the system needs to plan its actions to avoid certain obstacles,
such as nearby mountains, a large lake and scores of munitions
that the U.S. Army stores in the area near the test flight site.

The messy real-world environment, with all the quirks and obstacles
that would be impossible to predict in the office, is providing the perfect testing ground for the system.

Many of the problems they encounter, and have to solve, have nothing to do with AI at all.
A glitch in the communications system creates problems with the largest sailplane they are testing, and
the team suspects that magnetic particles in the dust on the road are responsible for causing damage to an electronic component.

During one test, when they try to launch a sailplane
using a bungee cord attached to the Jeep after a motor failure, the bungee cord breaks.

With each setback, the team patiently sets to work to find a solution:
Fixing the motor, repairing the bungee cord, finding a new battery,
pulling out another sailplane when one is taken out of commission.

Kolobov says these real-world problems are exactly
what drew many people to this project.
It�s also why they aren�t too discouraged by each unexpected thing they encounter.

�This is why reality is different from simulation,� Kolobov says.
�And that�s what we came here for.
We came here to learn, and it�s not necessarily the things we expected to learn.�

It�s late on a Friday afternoon when the team gathers at the tiny Hawthorne airport for one last debrief.

About an hour earlier, on the team�s last flight of the trip,
the algorithms had performed exactly how they�d anticipated,
sending the sailplane soaring through the air � before the battery unexpectedly died,
the sailplane nose-diving toward the sand and sagebrush with alarming speed.

Rogahn, whose job was to be the human pilot � providing a backup to the AI and manoeuvring it to safety when necessary �
was able to regain control at the very last moment, preventing a crash by perhaps inches.
�We�ve reached the point this summer where it�s actually doing better than me,� he says.
�The algorithm is doing better than me as a sailplane pilot.�

~[Σ] ~[Ω] ~[Δ]!

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~ Sagetech

»Sagetech

Unmanned Aircraft Systems Operations in Civil Airspace
Sagetech Corporation proved small Unmanned Aircraft Systems (UAS) can safely fly
in controlled airspace using the same technology that has helped maintain flight safety for decades.

A PAE ISR Resolute Eagle UAS,
equipped with a Sagetech XPC-TR-50 Mode C transponder,
successfully completed a mission in civil airspace on November 8, 2017.
The flight took place at the Pan-Pacific UAS Test Range located at Pendleton Airport in Oregon
while operating under an FAA Certificate of Waiver or Authorization (COA).

Resolute Eagle is a fixed wing tactical UAS
that offers advanced performance for use in civil or military airspace.
Without a transponder, the aircraft�s small size makes it virtually invisible to Air Traffic Control (ATC).
Sagetech�s military-certified XP transponder eliminates this issue,
allowing the Resolute Eagle unmanned airplane to operate safely while sharing airspace with manned aircraft.

Sagetech�s technology has been a key enabler of small UAS military operations
for over 15 years and PAE ISR�s flight from the Pendleton, OR airport
marks another step as our products transition from military to civil use.

UAS operations are projected to increase sharply in civil airspace in the coming decade.
This event signifies PAE ISR�s ability to offer a dynamic platform to a broader customer set;
successfully fulfilling U.S. commercial and civilian requirements in addition to U.S. military missions.

Aviation transponders are a key part of the system
air traffic controllers use to maintain safe separation between aircraft.
Moreover, small UAS are not detectable passively by ATC radars because of their small size.
Sagetech�s transponders allow even these naturally stealthy aircraft to integrate seamlessly with existing ATC radar systems.

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Small & Smart Inc reserves rights to change this document without any notice
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