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>> Welcome to theCube's coverage of Dell Technologies
2023 from Las Vegas.
And prior to the show, we took the opportunity
to dig into a really important topic, railroad safety,
which along with rail car maintenances
been in the news lately.
And with me are Ali Biers
who's the director of product marketing
for Edge Solutions at Dell Technologies.
And Jeff Necciai
who's the Chief Technology Officer at Duos Technologies.
Really interesting company folks.
Welcome to theCUBE. Thanks so much for coming on.
>> Thank you.
>> Jeff, Duos Technologies, tell us about the company.
Really fascinating work that you're doing.
>> Sure Dave, we develop and install comprehensive
rail car inspection portals which provide a 360 degree view
of the train as it's traveling at track speed.
It could be up to about 125 miles an hour.
Now, from that, we create high resolution detailed images
that are acquired of the top the sides
and even the undercarriage of the rail car.
Now, we use artificial intelligence on those images
to automatically identify defects
and anomalies on those rail cars.
We present all of that to rail car inspectors,
which basically makes their job easier,
it makes inspection faster and more accurate.
So in a nutshell, that's what duos does
and that's what the rail car inspection portal does.
>> It's pretty amazing for the audience.
If you google this topic, Google Duos,
there's a really cool video on Facebook
and it shows the train going down the track,
like you said you have 125 miles an hour
and it's almost like it's going through
like a mini car wash that's really high with no scrubbers.
And then, so all this happens in like in instant
how does it actually happen?
What kind of technology is involved to make this work?
>> Yeah, you mentioned car wash.
Actually some of our customers refer to it as a car wash
because a lot of folks think it looks like a car wash.
And I guess in this case
it's kind of looks like a train wash.
But the rail car inspection portal
itself is assembled from the ground up.
It utilizes a custom-built trust system
and a steel canopy over the track, which gives it that look.
You have a steel building over the track.
Now, the canopy provides a controlled environment
for imaging and some protection
from outside elements like the weather.
Now, underneath the canopies where the action really happens
it's where the magic happens.
And that includes an array of line scan
and area scan cameras, precision optics
and lighting systems of lasers and track based,
linear speed sensors and AEI tag readers and more.
So there's a lot of technology packed
under that steel canopy.
Now, all of this equipment is
it's powered and monitored from a Trackside Edge data center
where all of the servers
and the power equipment are located.
So really, if you want to know how it works,
as the train passes those mini cameras and sensors
and related devices, they scan the train
and perform that high resolution imaging.
The images and data they're recorded and stored
and it's actually processed in real time at track speed.
But the process itself and the processing itself
goes far beyond simple image acquisition.
Like I said, there's a lot of technology being utilized.
We utilize lines, game cameras for high resolution.
So the cameras themselves have to be synchronized
with the speed of the moving train.
In addition to get a usable image of a rail car,
we have to undergo a process called car cutting
which it gives you a usable image
of each individual rail car,
independent of the entire train.
So there's a lot of stuff, a lot of processing
that goes on and it's all done there
under that car wash head.
So that's kind of how it happens.
>> This is unbelievable.
So there's a huge change, if I understand it.
So prior to this, you had inspectors,
so the train would have to stop slow down,
I mean stop completely for, I don't know how long,
but give us the before and the after.
What's the kind of the ultimate impact that this is having?
>> Yeah, honestly, if you're not using remote
and AI assisted rail car inspection,
what your rail car inspectors are doing
is they're walking the track.
Usually they have two or three of them,
they're walking alongside the train
and they're crawling in and under the rail car
to see the underside.
So there's a big safety aspect to it
as well as the speed aspect.
You mentioned the train being stopped.
Of course, if I'm going to inspect the train physically,
that train has to be stopped.
And that's what we call dwell time.
And so what we're trying to do for the railroads
obviously, is eliminate or limit dwell time
as much as possible.
So most of the anomalies can be detected remotely
just using visualization and thereby decrease dwell time,
increase safety, and that's the target of the solution.
So there's a big before and after.
So, yeah.
>> Very amazing.
You know, Ali, I feel like we've been looking forward to
and waiting for this day for a long time.
And you guys talk a lot about delivering realtime
insights at the edge other companies obviously do as well.
This is pretty realtime, isn't it?
What's your take on this?
Yeah, I mean unbelievable.
>> It's an amazing example of an edge use case.
I mean, I think often when we talk about the Edge
we're talking about use cases,
we're using the words business outcomes.
But what it really means
when you're delivering real time insights
is that you can go from something that used to be manual
into a more automated fashion.
What it means is you might have top
and bottom line revenue impact
and you're going to have increased accuracy
and all these other time savings, dwell time
that Jeff was just talking about.
But in the case of Duos,
not only are they improving this rail car
and employee safety and enhancing the accuracy
of some of their measurements
but this can also help them avoid accidents
and ultimately potentially save lives.
And it really just doesn't get more important than that.
>> So this makes sense of course,
'cause the industry you got a challenging environment.
It's critical infrastructure and it's harsh,
but like what's inside, I mean, you got compute,
you got storage, you got sensors, you got cameras.
Paint a picture for us, Jeff, if you would.
>> Yeah, well obviously Dell has been instrumental
in partnering with us for the last three years.
I'm sorry, for the last five years of the solution.
And most recently we've kind of changed up the solution
to have faster kinds of servers and storage mechanisms.
But we have an array of servers
and array of networking equipment
that we have in there for the past, like I said
for the past three years.
We recently upgraded so that we can do 125 miles an hour.
Now what that represents, just so you know,
for two tracks trains moving roughly 120 miles an hour,
that's a little bit of over 80 gigabytes per second,
which is an enormous amount of data to be able to store
and process.
So we certainly rely on those the compute power
and the storage mechanisms
that Dell helped us bring to fruition
and design the mechanisms that we need
to be able to to do that kind of speed.
>> Yeah, and it's not just across this industry.
I mean I think this particular use case
really highlights the paramount importance of safety
and security but for computer vision,
it really does have an impact across multiple industries.
If you think about a retail store, grocery store,
when something falls off the shelf,
somebody can be alerted and before a slip and fall accident
can occur by somebody in the store,
computer vision can be used to spot that.
Same thing for manufacturing.
Somebody can be spotted when they're working too close
to a robotic arm or a machine and it can help spot
and identify those to make it a far better
and safer experience.
So I think just technology at the Edge done right
can help deliver incredibly powerful insight
to help you run your business better
but also to deliver these incredible outcomes
and real innovation.
So it's exciting to work with customers
from across all industries
to make these innovations possible.
>> Yeah, I'll bet.
I mean, I'm curious as to what's a Trackside data center?
I don't think I've ever, well,
I guess some of the race car drivers,
they pump data into a Trackside data center.
But what does a railroad Trackside data center look like?
>> So the Trackside data center, for us,
we call it the Edge data center because again,
all of the processing and storage
is being done at the Edge in real time
because the results are required in real time.
If I'm going to inspect a train
and inspect rail cars for safety issues,
I want them as quickly as possible.
So preferably while that train is moving, right?
So we do everything in real time or close to real time.
The Edge data center, which resides Trackside
is absolutely necessary to do all that processing on site.
So inside the edge data center,
which some of our customers refer to as a bungalow
but really it's a separate data center.
It's a separate server room, right.
And so what we have is racks
and racks of servers some dedicated to storage
and some dedicated to monitoring and control
and some just hosting our software that the customers use.
And so that array of servers and storage technology
is right there.
And obviously it's all environmentally controlled
and secured the way it should be.
So we don't have to worry about back hauling information
to be used immediately back to a cloud data center
because obviously the the size of the images
and the size of the data that we're back hauling
would be way too large to get it in a timely fashion.
>> Yeah, Ali, I'm interested in sort of Dell's role
and how you think about it
because when you first go back to the beginning
of Dell saying, "Hey, we should do something at the Edge."
You had a choice, you could think about,
okay, we're going to apply horizontal technologies,
but you know, at the end of the day
it's going to be industry specific and I think you chose
to go obviously they made the right call
with horizontal technologies,
but here's a perfect example of you're applying it
within a specific in industry.
So I'm curious as to sort of how you think about helping,
various industries and how applicable this is horizontally.
>> Yeah, there are so many industries now
that are requiring immediate access to the data
and insights and that does often require processing
the data right there at the Edge.
And when you begin to think about doing this at scale
with many locations or distributed geographic footprint
across multiple locations, this can get incredibly complex.
And so while we've been working with customers
at the Edge for about 20 years
and we have a very sizable business
that we've been helping customers with for a long time,
we're looking to continue to grow that
to make it easier for customers.
And what we see from customers is really three common things
that they're generally trying to reduce.
The things are still too complex
so reduce the complexity that they're seeing.
So gain more speed, be able to do it faster and better
and less manual.
The second one is to do it in a more secure way.
And then the third is really to be able to do it at scale.
So if you think about why this is required
and how our strategy came about
it's because the Edge is the exact opposite
of the controlled environment that you would find
in a data center or cloud.
The Edge is geographically distributed
and it doesn't always have a qualified IT person
sitting right there.
So as you're standing that all up,
you need to really design it with the end in mind,
otherwise you're going to grow use case by use case
and stack by stack, and you're going to end up
with a lot of snowflake solutions
that are really difficult to manage and secure in mass.
So our goal for our customers
is really to help them simplify their Edge.
And we do that with the best in class infrastructure
that Jeff was referencing.
We also do it though with our expertise
in vertical solutions much like you were just talking about.
So we have practice experts
that specialize in particular industries
that come to the table with a depth
and breadth of knowledge to help our customers
through particular industry specific situations.
And we actually build also validated solutions
and designs so that our customers can have a faster time
to value when they're trying to put solutions together.
And our latest evolution of that,
is that we're continuing to help customers innovate
and to do things in a less manual fashion
with Project Frontier
and the announcements that we're making at Dell Technologies
world around that.
So we're really excited with our Edge operations
software platform to help customers do some
of those things to help them simplify
their Edge in a much easier way.
>> Yeah, it's so cool to see this come to fruition
'cause we've been talking about it for a while
and to see it in real life.
Ali, it's pretty remarkable that think of what you can do
with a couple of cameras, maybe more than a couple
but still you got some sensors and it can tell you
what's happening on this multi ton railroad car.
Jeff was talking about bits I thinking
in my head bit slicing to get to the different cars
and really understand each, individual car on the train.
It's pretty mind boggling the how much technology
is sitting right here.
All at the Edge could have as an independent entity, right?
I mean you're doing all the processing there
is that correct?
>> That's true for sure.
And Jeff, you can speak to it as well,
but like I think what's really amazing about it
is that you can begin to see those data and insights
and aggregate no matter where that infrastructure
is deployed.
So of course we're talking about one specific instance
at the track side but of course,
you end up with a lot of those locations.
So providing customers with access
to their data in a real-time fashion
really creates game-changing opportunities
for them to unlock new innovation
and see trends and make decisions
that they never knew possible before.
>> Do you persist the data?
Do you send any of the data back for analysis?
Well, how's that all work?
>> So we do, I mentioned AI,
we have artificial intelligence machine learning models
that we use to detect anomalies
and any kind of defects on a rail car.
So what happens is a lot
of the results from that are verified in realtime
both by rail car inspectors
as well as by a team of data scientists
and some folks that we have back at our office here
in Jacksonville in our 24/7 data center.
So they're looking at those results in real time.
And what we do is we have a continuous learning approach
that basically we feed those results back through
the data science portion of the artificial intelligence
to make those models better over time.
So that's one of the ways that we use,
I think, the aggregated data
from all of our rail car inspection portals.
And the other way, obviously, again,
I mentioned 24/7 support and monitoring
for these critical systems.
It's done, we reach out to every single one.
We're monitoring every single one in 24/7 capacity.
And so now as far as data,
there's certain portion of the data
that we do aggregate to our data center here
but it's mostly for analytical purposes.
So the images, that's the big buckets of data
that have to remain on Trackside
simply because we don't need to aggregate them here.
They remain in those Edge data centers.
I hope to answer the question.
>> Yeah, you don't want to, I hear you.
You don't want to push all those images
through some thin pipe, right?
You'd have to put 'em on some kind of mobile device
and ship 'em somewhere.
That's what's the point.
So where is this worldwide, nationwide?
Where's it deployed today and where is it headed?
>> So we have 13 portals
and they're deployed in North America.
So both here, in domestic US as well as Canada.
And we also have a couple in Mexico as well.
So it's here, we're looking to go international.
We are talking to folks, internationally,
and so we know that it's going to grow
because there's been a lot of interest in it,
and folks are getting very, very serious about it.
So we see the benefits, I mean,
the benefits are real and they're undeniable.
>> So, al give you the last word.
How do you see the future?
I mean, obviously we have this use case
that is very specific to rail,
but it sounds like there's so much opportunity here
for Dell and your customers just
to think about in the future.
>> And I think we're just at the beginning
of really being able to unlock so much innovation.
So this is being driven by the fact that compute
is smaller and less expensive and that you can put sensors
small sensors now into so many different things.
So I feel like we're just at the beginning
of this chapter and our goal will be to help customers
to do that in a scalable way over time
where they can have the ability to manage
and secure and lifecycle all of their compute
that sits out there, whether it's Trackside
in a retail store, on a manufacturing floor,
or otherwise to help customers.
>> We've been, like I said
we've been waiting for this day for years.
We've written about and talked about
how most of the modeling, well most of the AI work today
is done in the cloud for modeling.
But, but AI inferencing at the Edge is going to explode.
And we give a bunch of examples.
This is not one that we predicted, but it's a good one.
And I think there are many more to come.
Guys, thanks so much Ali and Jeff for coming to theCUBE.
It was great to have you on.
>> Thank you.
>> Thank you so much.
>> All right, keep it right there for more coverage
from Dell Technologies World 2023 in Las Vegas.
You're watching theCUBE.
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