Theia FAQ’s with Jon

Jon at Radenso

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We researched this before we named it and saw that traditionally it has been pronounced Thay-uh and Thee-uh For the detector, the official correct pronunciation is Thee-uh because I like it better.
 

Tb12

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It's 2020 in Ohio, is the detector shipping yet? ;)
 

Vortex

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We researched this before we named it and saw that traditionally it has been pronounced Thay-uh and Thee-uh For the detector, the official correct pronunciation is Thee-uh because I like it better.

lol, well I guess that’s that then. :p Thee-uh does roll off the tongue a bit nicer too.
 

Windstrings

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Thee-iaya = The "intelligent assistant"

Thor = Just bc it's cool, smashing the opposition...

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riseboi

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We researched this before we named it and saw that traditionally it has been pronounced Thay-uh and Thee-uh For the detector, the official correct pronunciation is Thee-uh because I like it better.

This should definitely be pinned or added to the FAQ of this thread!

 

alloy00

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Jon - thanks as always for your updates! Really looking forward to Theia, as are many of us. This whole process to develop a new product from a clean sheet of paper fascinates me. Rather than start a new thread, I figured I'd ask my question here.

It's been ~2 months since SEMA and I'm curious how development is coming along for you and your team (Note I am not asking "when will it ship?"!!!). By this I mean, for example:
1) Are there tasks & challenges that were on the 'to-do list' in November which have since been addressed?
2) Have any new tasks/challenges arisen?
3) Are more parts & pieces selected as you build your BOM?
4) Is the tooling, supply chain, assembly process developing?
5) Have you determined how PhaseView can/may be implemented and/or how may it benefit end-users?
6) Anything else we'd be interested to know? :)
 

Jon at Radenso

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Jon - thanks as always for your updates! Really looking forward to Theia, as are many of us. This whole process to develop a new product from a clean sheet of paper fascinates me. Rather than start a new thread, I figured I'd ask my question here.

It's been ~2 months since SEMA and I'm curious how development is coming along for you and your team (Note I am not asking "when will it ship?"!!!). By this I mean, for example:
1) Are there tasks & challenges that were on the 'to-do list' in November which have since been addressed?
2) Have any new tasks/challenges arisen?
3) Are more parts & pieces selected as you build your BOM?
4) Is the tooling, supply chain, assembly process developing?
5) Have you determined how PhaseView can/may be implemented and/or how may it benefit end-users?
6) Anything else we'd be interested to know? :)

1. Yes, tons of them. Development has continued at a nice clip since SEMA and every week we accomplish new objectives.

2. Of course. From a technical standpoint things have been relatively uneventful, but there are many logistical challenges related to BOM cost and lead time that are incredibly complicated to deal with. But this is just the nature of development, we've been through it before. With a project this ambitious there will always be unforeseen challenges we will have to deal with - during development, at launch, and supporting it for years after.

3. One rule of thumb to live by with us is that we are usually a step beyond what we publicly show for competitive reasons, so yes.

4. This is always a part of development

5. The ability to see phase is an integral part of the system and isn't something that really has to be "implemented" other than deciding which way we go about constructing the RF front end to get IQ samples, which was already done by SEMA. It will benefit everything automatically by virtue of having more information to sample.

6. I guess I just wish people understood the sheer complexity of undertaking a project like this from scratch and trying to deliver a simple result to the consumer. I always want to share more because the development process is so, so interesting, but I don't want to tell our competitors how to do it either. We have had many make or break moments over the last two years that could have sunk the project, but somehow we kept getting lucky and now the project is stable and in good shape. Even finding the engineers I did was pure luck; you could double my budget for them and I think it would take me years to find people like them. We found them almost immediately by pure luck.
 

cihkal

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1. Yes, tons of them. Development has continued at a nice clip since SEMA and every week we accomplish new objectives.

2. Of course. From a technical standpoint things have been relatively uneventful, but there are many logistical challenges related to BOM cost and lead time that are incredibly complicated to deal with. But this is just the nature of development, we've been through it before. With a project this ambitious there will always be unforeseen challenges we will have to deal with - during development, at launch, and supporting it for years after.

3. One rule of thumb to live by with us is that we are usually a step beyond what we publicly show for competitive reasons, so yes.

4. This is always a part of development

5. The ability to see phase is an integral part of the system and isn't something that really has to be "implemented" other than deciding which way we go about constructing the RF front end to get IQ samples, which was already done by SEMA. It will benefit everything automatically by virtue of having more information to sample.

6. I guess I just wish people understood the sheer complexity of undertaking a project like this from scratch and trying to deliver a simple result to the consumer. I always want to share more because the development process is so, so interesting, but I don't want to tell our competitors how to do it either. We have had many make or break moments over the last two years that could have sunk the project, but somehow we kept getting lucky and now the project is stable and in good shape. Even finding the engineers I did was pure luck; you could double my budget for them and I think it would take me years to find people like them. We found them almost immediately by pure luck.
Thanks for the great feedback, hugely appreciated!
 

Windstrings

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Thanks for the great feedback, hugely appreciated!
Ditto! Talent attracts talent!

You guys must be having a blast doing the Star Trek thing and "going where no one has gone before"!

Who wouldn't want to be included?

Trouble is finding character, gifting & vision....

Congrats to you guys!

Thx for giving us a peek!

I know it must be terrible not being able to share everything!

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MachineLearner

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I'm not really a countermeasures enthusiast (yet), but I came across this project the other day as a suggested youtube video. Really cool stuff you are doing here in SW Ohio, Jon and Co.!

A thought I had; you could use similar technology for lidar jamming. Specifically, while you've used a CNN to train for classification, it would be possible to use something like NEAT (NeuroEvolution of Augmenting Topologies) on top of a MLP to train a lidar jamming strategy. So you have a CNN classifier on the receive end, and for the response, all you'd have to do is pipe the error state of the gun back into the "response" network as an input for training. While a conventional NN takes advantage of gradient descent on a fixed topology, which limits solutions to local minimums, NEAT allows both the weights and topology to evolve. Theoretically, you could create a network which evolves the most optimized solution to defeat any given lidar. The only catch is that you need the error state of the gun, basically in real-time, which means you need any gun you'd want to train for. Since there is a response involved, and the lidar's reaction to that response is the desired outcome, you couldn't rely on recoded data. Although this has to be true for any lidar jammer manufacturer, they're just likely manually experimenting with responses/jamming patterns until they find something that works, with perhaps some degree of reverse-engineering the internals. As you've mentioned in videos, the main benefit would be the faster time to market with better, more effective jamming strategies than those found through manual experimentation. You could probably even do this with a vanilla network, but I have to assume that lidar manufacturers are using some pretty sophisticated anti-jamming, and if not today, will be, and a genetic algorithm like NEAT could likely brute force an optimized solution for any current or future anti-jamming strategy. It's all just a beam of light, after all.

Just thinking out loud here. Carry on. :)
 

Windstrings

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Dang, I could have swore Google had a translate to English function on here.. Can't find it! [emoji28]

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MachineLearner

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Dang, I could have swore Google had a translate to English function on here.. Can't find it! [emoji28]

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Haha, yeah, sorry, I was using a bit of the engineering jargon.

TL;DR: You could leverage similar technologies to those used in Theia for better, more effective laser jamming with a faster-to-market release for new laser guns.
 

2FST4U

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1. Yes, tons of them. Development has continued at a nice clip since SEMA and every week we accomplish new objectives.

2. Of course. From a technical standpoint things have been relatively uneventful, but there are many logistical challenges related to BOM cost and lead time that are incredibly complicated to deal with. But this is just the nature of development, we've been through it before. With a project this ambitious there will always be unforeseen challenges we will have to deal with - during development, at launch, and supporting it for years after.

3. One rule of thumb to live by with us is that we are usually a step beyond what we publicly show for competitive reasons, so yes.

4. This is always a part of development

5. The ability to see phase is an integral part of the system and isn't something that really has to be "implemented" other than deciding which way we go about constructing the RF front end to get IQ samples, which was already done by SEMA. It will benefit everything automatically by virtue of having more information to sample.

6. I guess I just wish people understood the sheer complexity of undertaking a project like this from scratch and trying to deliver a simple result to the consumer. I always want to share more because the development process is so, so interesting, but I don't want to tell our competitors how to do it either. We have had many make or break moments over the last two years that could have sunk the project, but somehow we kept getting lucky and now the project is stable and in good shape. Even finding the engineers I did was pure luck; you could double my budget for them and I think it would take me years to find people like them. We found them almost immediately by pure luck.

I don’t know about anyone else but this makes feel giddy. Giddy like a little school girl! Git’em Team Radenso!!


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Windstrings

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Haha, yeah, sorry, I was using a bit of the engineering jargon.

TL;DR: You could leverage similar technologies to those used in Theia for better, more effective laser jamming with a faster-to-market release for new laser guns.
Totally understand, I'm just keeping it light.
The people out here that need to hear what your saying will understand it.

Just totally over my head... But necessary to communicate!

All fields have it.. I'm in Medical [emoji6]

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Jon at Radenso

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I'm not really a countermeasures enthusiast (yet), but I came across this project the other day as a suggested youtube video. Really cool stuff you are doing here in SW Ohio, Jon and Co.!

A thought I had; you could use similar technology for lidar jamming. Specifically, while you've used a CNN to train for classification, it would be possible to use something like NEAT (NeuroEvolution of Augmenting Topologies) on top of a MLP to train a lidar jamming strategy. So you have a CNN classifier on the receive end, and for the response, all you'd have to do is pipe the error state of the gun back into the "response" network as an input for training. While a conventional NN takes advantage of gradient descent on a fixed topology, which limits solutions to local minimums, NEAT allows both the weights and topology to evolve. Theoretically, you could create a network which evolves the most optimized solution to defeat any given lidar. The only catch is that you need the error state of the gun, basically in real-time, which means you need any gun you'd want to train for. Since there is a response involved, and the lidar's reaction to that response is the desired outcome, you couldn't rely on recoded data. Although this has to be true for any lidar jammer manufacturer, they're just likely manually experimenting with responses/jamming patterns until they find something that works, with perhaps some degree of reverse-engineering the internals. As you've mentioned in videos, the main benefit would be the faster time to market with better, more effective jamming strategies than those found through manual experimentation. You could probably even do this with a vanilla network, but I have to assume that lidar manufacturers are using some pretty sophisticated anti-jamming, and if not today, will be, and a genetic algorithm like NEAT could likely brute force an optimized solution for any current or future anti-jamming strategy. It's all just a beam of light, after all.

Just thinking out loud here. Carry on. :)

Welcome to the forum! Always great to have some local folks on the forum. Are you in the machine learning industry or just a hobbyist? Being up in Dayton I wonder if you know some of the same folks in the defense world I do?

Shoot me a pm if you're in the industry, always looking to learn more.
 

BMW4FUN

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I have a question for Jon or Radenso, please forgive me if it has been asked already as I am a Nubee on forums.
My question is, how consistent will the new Theia be from unit to unit? I've often read and seen on various radar detector tests, differences among same units (and firmware). Some units are "hotter" more sensitive to radar than other units of the same model. I was wondering if your new technology (A.I. and operating system) will eliminate this, or if not will you guys be working on solution to this?
Thanks and keep up the great work, looking forward to purchasing one when it becomes available.
 

Jon at Radenso

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I have a question for Jon or Radenso, please forgive me if it has been asked already as I am a Nubee on forums.
My question is, how consistent will the new Theia be from unit to unit? I've often read and seen on various radar detector tests, differences among same units (and firmware). Some units are "hotter" more sensitive to radar than other units of the same model. I was wondering if your new technology (A.I. and operating system) will eliminate this, or if not will you guys be working on solution to this?
Thanks and keep up the great work, looking forward to purchasing one when it becomes available.

I don't have an absolute answer to that yet since I don't have thousands of these in production yet.

I believe I have identified a specific technical reason the Escort units with LNAs are not consistent unit to unit, but I am not sure why certain Uniden units are not consistent yet (I haven't put much time into it though - the Escort one was just obvious when looking at it).

I can say that all of the differences are pretty much due to production tolerances.
 

Eloi

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Luck strikes those prepared to seize it.

Do you have ideas you found "impossible'
I have a question for Jon or Radenso, please forgive me if it has been asked already as I am a Nubee on forums.
My question is, how consistent will the new Theia be from unit to unit? I've often read and seen on various radar detector tests, differences among same units (and firmware). Some units are "hotter" more sensitive to radar than other units of the same model. I was wondering if your new technology (A.I. and operating system) will eliminate this, or if not will you guys be working on solution to this?
Thanks and keep up the great work, looking forward to purchasing one when it becomes available.
My guess is some unit will be more intelligent ...like humans.
 

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