On the Frontlines of UAP Detection | John and Gerry Tedesco

Added: 2026-05-12

[00:00:03][music] Greetings everybody. Uh well, here we are. Session seven. Uh we're the day is going by very quick, but boy has this been really exciting. This session provides a practitioner's perspective on field-based UAP observations and detection operations. Jerry and do John Tedesco will discuss operational workflows, deployment challenges, lessons learned from real world observation programs. Topics include instrumentation constraints, environmental co confounders, data handling under field conditions, and the importance of discipline methodologies.

[00:00:49]The session offers grounded insights into how detection efforts perform outside laboratory settings and what is required to improve reliability and credibility. Uh in fact we are so on the online in the front line that both Jerry and John are actually sitting in their mobile unit as we speak. Let me introduce Jerry. Uh Jerry Tedesco is a multi-disiplinary technologist, a researcher with a career spanning avionics, defense systems, forensic laboratory science, and applying scientific investigation. He currently works as an avionic specialist at GE Aerospace, supporting advanced military aerospace systems, and previously spent over a decade at DRS Technologies working on a reconnaissance and surveillance system for the United States and allied defense organizations.

[00:01:37]He's also a scientific researcher affiliate with Harvard's uh university's Galileo project. Uh John is a veteran electrical and uh and electronics engineer with over 40 years of experience in laboratory and engineering and instrumentation and product safety and applied research. He holds degrees in electrical engineering, electronics technology and technical education as a patent inventor uh in advanced optical systems. and his current research interests include instrumentation, optical technologies, and the scientific study of UFOs or UAP. He's affiliated also with the Harvard University project. And I just want to say that I'm I'm proud of having both of these gentlemen being able to share with us what they have done and I think you're going to get a lot out of this. So, John, Jerry, I'll turn it over to you.

[00:02:28]>> Okay. Thank you, Rich. Uh, good afternoon everyone. uh and welcome to UAP detection and tracking summit. This is uh session seven on the front lines of uh UAP detection and uh what I will uh set up here is a uh PowerPoint presentation on the [clears throat] uh last five years of our research uh study on coastal long.

[00:02:55]So let's get that loaded up. Okay. Um again my name is John Tedesco and I'll be one of the presenters for the session along my brother and colleague uh Jerry Tedesco. Together we'll be sharing uh what we've learned through a five-year uh field-based UAP research study conducted along the coast of Long Island uh where instruments, data, and real world operational constraints define what can and cannot be claimed. Um I can tell you upfront that this work has not been without challenges. Uh we've had quite a few especially when operating outside of the uh laboratory control conditions uh environmental variability uh sensor limitations uh calibration discipline and and data integrity all become critical issues when you're working in the field. But it's precisely these constraints that make the results meaningful because they reflect how detection actually occurs in the real world.

[00:03:55]And um uh for decades you know the uh the public conversation around UAP has been driven uh by you know uh stories, speculation, isolated observations uh often compelling but in many cases rarely testable. Uh curiosity and interest alone is not sufficient uh I don't believe without infrastructure. Uh the laboratory mobile laboratory is that infrastructure. um the ability to measure uh constrain and independently verify what is being observed in the real world. This session is grounded in a simple premise that underpins the entire summit. Data drives disclosure, not belief, not narrative and not authority, but credible calibrated cross validated data.

[00:04:45]Today we'll walk through uh what looks like uh uh when detection systems are are deployed on the front lines where theory meets instrumentation and where uncertainty is addressed through measurement rather than assumption.

[00:05:05]So in this slide uh introduces our crossborder uh detection framework which is built on the principle that no single sensor can fully characterize complex or anomalous events. Instead of relying on one instrument or one part of the electromagnetic spectrum, we examine the same event through multiple independent physical interactions. So this slide introduces our crossmodal detection framework uh which is built on the principle that no single sensor can fully characterize complex anomalous events. Instead of relying on one instrument or one part of the electromagnetic spectrum, we examine the same event through multiple independent physical interactions. By fusing data across radar, optical, infrared, ultraviolet and other electromagnetic domains, we reduce the risk that any one sensor artifacted environmental effect or interpretive assumption can drive the conclusion. The strength of this approach comes from having more sense not doesn't come from having more senses but but from correlating independent measurements so that confidence emerges from physical convergence rather than from one single viewpoint.

[00:06:11]Next slide.

[00:06:13]Um this slide describes our multi-ensor fusion framework through crossmodal validation. It functions as a forensic model for analysis rather than a descriptive anecdotal one. In this framework, each sensor contributes an independent physical constraint and those constraints collectively narrow the range of plausible explanations.

[00:06:32]Uh so what is meant uh in here when we have a part of here that talks about internally self-consistent. What we mean by that is um it's a system or an argument or a story that doesn't contradict itself. Each data point supports the others. And to decide whether a data point for a set of observations is resistant for a single source of failure or bias, we are essentially looking for a robustness through triangulation. In data science and logic, this means that even one sensor or perspective fails or is compromised, the conclusion remains stable because it is supported by other independent pillars of evidence.

[00:07:10]Okay. So um before we get into u you know uh the instrumentation and and the technological payload uh if you look at the image you'll see that u you know we have two uh we have a mobile platform and a secondary platform uh which is uh the newest platform we have which is for uso um research uh which we hope uh we'll we'll get be able to get into this year.

[00:07:35]Um so phase three expands a night crawler into um a coordinated land sea uh sensing architecture. The land-based mobile laboratory uh and a dedicated maritime vessel uh function as a set of complimentary sensor sensor nodes which uh enables cross domain cross modal validation of UAP USO activity rather than functioning as a a standalone vehicle. and serves as a landbased center node within a coordinated multi-dommain detection architecture which is a mouthful there. Um for phase three, this architecture is expanded to include a maritime research vessel uh creating a coupled land sea observational system designed to investigate both u unidentified aerial phenomena as well as um unidentified submersible objects USOs.

[00:08:30]Uh so this u this uh uh the advantage of this mobile research uh system allows our researchers to to conduct research directly at the work site even in remote and challenging locations. Uh Robert Moses uh was was a bit challenging in the beginning um learn to overcome at least some of that. This uh this is a real-time uh data collection platform equipped with advanced instruments um the lab advanced instruments at the labyrus uh which is a full scale sensor integration uh the ergonomics of that integration and efficiency which is which uh reduces our setup times. This enables uh real-time data collection and analysis uh while on the move. Uh the enhanced mobility supports efficient responses uh to the environmental uh to the environment environmental events and um and urgent uh field investigations which we uh we do come across from time to time. Uh this increases our research and effectiveness. [snorts] Um and in the image uh it's a smaller one. The next slide we should be able to show it with a little bit more detail.

[00:09:42]Okay. So um if you look at the slide uh this is the nighter's uh telemetry cockpit we call it the mobile sensor fusion and forensic analysis center is designed for real-time crossodal validation of UAP and USO events. Um in the center there you'll see there's a central monitor um which is displaying a long uh wave infrared uh camera system.

[00:10:05]Uh it's marine base. Um it's the fleer um the fleer uh M3 364C.

[00:10:13]Uh we also have um uh we have a uh two uh two radar uh Xband system. One is horizontal scan, one is vertical scan and um uh along with that we have various different um uh active charts going. One is ADSB, the other one is uh maritime uh shipping travel uh out there. We want to know what's going on on the water and um in order to uh you know uh qualify or disqualify an observation.

[00:10:50]So our research uses the electromagnetic spectrum as the foundational model for crossmodal analysis because every credible observation of an aerial or transmium optic is is at its core an interaction with the electromagnetic spectrum. Whether we are measuring reflected radar energy or emitted thermal radiation, optical luminosity or the absence of cooperative RF signals, we are observing how an object interacts with the M spectrum in different ways and at different scales. No region of the spectrum is sufficient on its own.

[00:11:20]Radar provides precise range, velocity, acceleration, but it tells us little about the material properties of the or the thermal behavior of the objects.

[00:11:29]Visible imagery offers morphology in motion context yet is highly susceptible to atmospheric effects and misinterpretation.

[00:11:36]Infrared bands can reveal thermodynamic behavior, a propulsion signature, and physical properties of the object and atmospheric temperature inversions, but only when properly differentiated across wavelength regimes, each portion of the spectrum answers a different physical question about the same object. By treating the M spectrum as a unifying measurement uh framework, crossmodal analysis becomes a constraintbased process rather than a narrative.

[00:12:04]Reflected versus emitted energy can be distinguished. Aspect dependent effects can be separated from intrinsic behavior. Apparent anomalies in one band must reconcile with the observations in others or they fail the analytic validation. This layered spectral approach dramatically reduces mclassifications and prevents any single sensor artifact in driving the conclusions. In short, we do not use electromagnetic spectrum because it is exotic or speculative. We use it because it is the only physics-based framework capable of supporting independent corabboration, forensic reconstruction and defensible analysis. Crossmodal fusion across the EM spectrum is what allows observations to mature into evidence and evidence to support transparency, public trust, and informed decision-making.

[00:12:52]So the question is why look at the electromagnetic spectrum when studying UAPs? We look at the EM spectrum because every credible observation of a UAP is ultimately an electromagnetic interaction. Either energy that is reflected, emitted, absorbed, or conspicuous conspicuously absent. Radar returns, optical imagery, infrared heat signatures, RF emissions, and ADSB signals are not separate phenomena. They are different expressions of the same physical object interacting with the different regions of the AMP spectrum.

[00:13:22]Relying on a single part of the spectrum invites misinterpretation. Visible light can be misleading due to atmospheric effects, range ambiguity, or perceptual bias. Radar can confirm motion and kinematics, but cannot alone involve material properties or thermodynamics.

[00:13:39]Infrared sensing reveals heat and energy dis dissipation, but only when interpreted across multiple IR bands. By examining patterns across the spectrum rather than isolated signals, we dramatically reduce the risk that artifacts, sensor limitations or assumptions drive the conclusions.

[00:13:56]Using EM spectrum as the organizing framework enables crossmodal validation.

[00:14:01]Reflected energy can be distinguished from emitted energy. Apparent motion can be tested against kinematic constraints and thermal behavior can be compared against known propulsion models. So um so here's a typical uh linear graphic representation of the EM spectrum uh showing lower to higher energy range from left to right. Uh this widely recognized chart was designed by Henry D. Hubard of the US National Bureau of Standards. The graph shows both wavelength and frequency. In the world of automotive and aerospace engineering, it's all about where the heavy stuff goes relative to the front and back or nose to tail. I just thought we'd throw that in.

[00:14:46]Okay, so uh here's our representation of the EM spectrum and I hope everybody could hear me.

[00:14:52]Uh what we have is a an updated chart uh that we've we've developed uh to show more accurate representation of the spectrum and uh it also lays out the frequency wavelengths and it also uh lists the night crawler sensor payload uh in the uh in the fourth column and in the last column we have the acoustic spectrum. U so this shows us the ranges that the our equipment operates in in parametrically.

[00:15:20]Yeah. and and something like this too.

[00:15:22]This is uh this model basically and we're not going to go through all the frequencies and the wavelengths, that sort of thing, but it serves as a model for any other researchers that are uh incorporating this kind of equipment in a in a uh in a uh a field research study uh like the one we've done, you know, over the the past few years. Um and we just felt that it was an effective model for the kind of equipment that we use.

[00:15:49]Okay. So um uh the the one thing I do want to talk about and and it has to do with the the instrumentation deployed uh primary means of detection uh which in our cases uh you know we're using uh several different active radar systems there monostatic radar uh I know there's a lot of talk about passive radar which definitely has some benefits as well um but in our case it's it's uh it's active radar systems using uh two geometries um you uh uh horizontal and vertical x and y axises. And um uh and the reason why we do that is to give us a better cross-sectional uh viewpoint of of an object that is uh that is either uh crossing our uh our you know our point uh you know in in a in a uh you know a north to south or an east to west direction. uh we can be able we be able to get a uh if there's a fuselage there even at 80,000 ft you know we can get we can get a a pretty decent reflection. Um we also use millimeter wave radar uh which is the uh newest radar systems we're using which is a very very uh narrow beam. Um uh it's it's it's very good for small objects. Um it's the K and KA band. Uh very similar to what uh you know the police use the speed guns.

[00:17:13]Um and this is a doppler kind uh kinematics. Uh but it also has it's linear and rotational radial and axial velocity. So if this object is rotating, which is um you know sometimes we'll observe those we'll observe objects that are in some kind of rotation we can determine what that speed is um and of course that would distinguish or differentiate between uh something that is flying in a in a linear way across the sky versus something that that is showing that's showing some kind of rotation. I should also add that that rotation also gives us an identifying feature.

[00:17:48]Say if we're looking at a UAS system, the larger the drone is, this the the slower the rotation of the rotors so that we could determine if it's a basically the general size of the of the uh the drone, whether it's a large industrial size or or something small, >> right? It's been it's been very helpful in the in the in the drone space as well.

[00:18:11]So um uh the other thing we want to look at is the you know we use spectrum analyzers for everything when we're we're um interfacing uh any kind of a dynamic uh transducer uh we prefer to use um these spectrum analyzers um uh to to to compare those signals whether they're uh significant uh signals or large signals or small signals uh on the kind of display that a spectrum analyzer gives It's a ratio based on decibb logarithmic scale. So, so we can equally see a small signal as well as a large signal on the same scale and be able to make some kind of determination or comparison between the two. Um, and this is something that's done in RF work quite a bit. Um, and we're looking at something that's either residing close or near the noise floor or even in the noise floor. um the um uh uh you know instrumentation amplifiers that are employed within the spectrum analyzer uh helps to pull that out and condition that noise get that common no common mode noise out of it. Uh very important especially when we're using ultrasonics.

[00:19:23]Um this is a um uh this is an image from uh one of our uh case studies. It's a it's an ultrasonic representation of something that we pulled out. Uh this happened in March of 2023 off the coast.

[00:19:38]And um it was an ultrasonic signal, very low in amplitude um but uh very unique um there was some um outliers in there um frequencies approaching one megahertz in a cone pattern in a very very um well-defined cone pattern. You can see that in the water waterfall display uh below the upper graph. Um the upper the upper graph is the the actual uh magnitude and um frequency of the of the signals.

[00:20:08]Below is a is a is a time indicator. Um you know it's it's time and amplitude together. So we can see we can definitely see some kind of a pattern that is equally spaced that we we recorded these ultrasonic signals both on the off the southshore and the north shore of of coastal long island but only out to sea not inland nothing in >> right so if we rotated that transducer away from the water we lose these signals immediately they they attenuate down >> these correlated but the targets 78 on them >> exactly >> so Um uh the other major part of our research too is using a non-iming environmental sensors or detection devices. Um very importantly you know in years past we used to use standard detectors didn't have a lot of gain uh had uh noise issues uh reliability uh cost was was great but but uh you know it didn't really help to resolve uh uh you know a signal to noise ratio issue or or have enough uh gain or quantum gain in order to look at those signals completely and especially over long distances. Um what we use is we use novel detectors. These novel detectors are are costly. Um but they they're they are are quantum uh they're quantum components um interface with instrumentation amplifiers and they can you know uh basically pull these these small signals out of the noise floor very easily. Uh we also use um various different sensors. Um we have um uh near infrared, shortwave infrared, uh UVA, B and C um and ionizing radiation which uh you know of course the the the standard detector in a in a geiga counter would be a mullet tube. Um very similarly you know uh is the UVC detector which uses a photo multiplier tube which works principle is basically the same. Um very sensitive extremely sensitive to UVC or um or what they call solar blind signals. Um, we've actually tested this particular detector which is available available from Hamamasu and uh it'll detect a a 5 mm electrical discharge at over 600 ft.

[00:22:33]Um, and this is helpful especially if we're looking for objects that people report as plasmoids or pl object or some kind of plasma enclosure.

[00:22:45]So you know again extending the you know the the um uh the solar blind u uh measurement you know we're using detectors uh you know we listed here um they're um uh there's there's a company called Bernie that makes the A and B uh detectors um they what they start out uh BU is black is basically black light UVA is black light 350 to 400 nanometers um UVB There's the of course where you get your sunburns uh 280 to 3 250 nanometers and then we have the UBC uh where we use the Hamomatu detector which is the absent solar blind that's basically uh sun rays which is blocked by the u the atmosphere by the ozone layer. So very little of it uh gets or penetrates through to the ground. This is uh this is great when we're trying to find some kind of a background signal and we're looking for a source. Well, that source can't come from the sun or the chances of it coming from the sun is is marginal. It's a very slim slim chance. So if we see a detector going off, uh normally what happens is it's a it's a flame somewhere uh creating a UV a UV signal within the 185 nanometer um uh wavelength. Uh an electrical arc or a corona discharge.

[00:24:09]We've had that with telephone poles with high voltage transformers during a rainy or a humid day. You'd see uh you actually hear the electrical discharge, but you can't you couldn't see it. It's invisible. Well, the Hamomatu detector will pick that up right away. Um, a rocket plume detection, great for that.

[00:24:27]Anomalous atmospherical plasma studies.

[00:24:29]Now, we have had one case where we observed what we believe was some kind of a a spheroid or a plasmoid uh as it's sometimes called uh which triggered our UVC detector, a hamomacher detector from a distance of over a quarter mile and it was triggered multiple times. and we'll you know hopefully we'll be able to show you that video uh towards the end of the presentation.

[00:24:59]So um again uh uh you know we believe that the UBC detection to be one of the best anomalies to detect due to the solar blind condition of the sun through the atmosphere. UB the UBC emissions uh implies local energy release generated locally um and uh due to the fact that the earth's atmosphere of course is blocking the sun's rays that the ozone layer um at grounds of sea level solar UVC radiation is essentially zero. Uh unlike visible IR radar or RF there is no ambient or illumination floor. Uh this creates what is effectively a a naturally noisefree uh spectral window.

[00:25:38]UBC detection therefore s uh signals active physical processes not passive reflection.

[00:25:47]Um with the solar blind detection uh visible spheroid plasmas can be detected quite easily. Uh we believe that we prove that uh the hamomamasu hamomatu photo multiplier tubes are excellent choice for the solar blind uh solar blind studies. uh due to their sensitivity of their photocathodes.

[00:26:10]Um we had very little success with UV enhanced photo dodes. Um the microch plates on the detectors are very very sensitive and it's a narrow band uh interference filter which is the glass itself that that uh that acts to block the IR and visible light. Companies like Hamomat who manufacture well-known solar blind photo tubes used in long range fire and arc detection systems. Uh it's also used on planes to detect fire forest fires from from uh from incredible heights.

[00:26:45]Uh this is a view of the uh photo detection uh tubes. They're they're uh Utron multiplier tubes. Uh two numbers that we use the R9533 which is the most sensitive, highest trans conductance, highest gain. And then there's the R2868, which is a uh tube that offers uh reasonable uh sensitivity at 5,000 on the transconductance. The uh tube to the left, the R9533 is is uh almost four times that gain.

[00:27:20]Uh here's some performance curves for the uh for the Utron Multiplier tubes.

[00:27:25]uh sunlight uh you know uh uh relatively intensity from sunlight, gas flame, hydrogen flame and electrical discharge.

[00:27:36]So in the imaging uh devices we move from um non-iming to um to imaging devices themselves. They're of course they're slower uh produce more data. Um but in a transitory or or or um uh sudden uh spike event uh there are times that we just don't have a detection. Um so the electrol optical devices uh that we uh will use is um in in our case we use the DSLR uh which is a Nikon Coolpix uh P1000 with a super zoom lens. It's uh it's 125 times optical.

[00:28:14]We apply both IR cut filters and and um um UB uh blocking filters as well. Uh so on either side of that bandwidth, we don't want any kind of bleed through. We don't want to bleed UB because the camera's uh CCD is very sensitive to both near uh UV and near IR. We want to block that completely out to isolate that and use another camera that has much better performance for UV and IR.

[00:28:45]Talking about filters, we also want to talk a little bit about polarized filters with polarized light. It's polarized light is very directional and that's something we wanted to look at with uh sightings of people uh from one vantage point seeing an object and from another vantage point not seeing the object. And this could be due to a object emitting polarized light, >> right? Yeah. So, so that so that angle the angle the incidence angle may not maybe be at a very different angle uh from somebody that might be maybe five degrees to the side or or be you know um off off to you know uh you know within within uh you know three to three to 15 degrees and um would completely block that signal. And we've had we again we've had we've had cases where that's happened.

[00:29:32]Uh the next slide, we're looking at uh midwave infrared thermal cameras. Um this is the gold standard when it comes to um thermal emissivity. Um when we're looking at, you know, thermal changes, the camera itself uh is uh extremely uh good range. Um the range um over long wave is is at least 15 times um greater.

[00:29:58]Um the camera has a a um a millime uh they call it a um a millimeter uh Kelvin range of of 0.02 uh gradients you know for each color each uh temperature change. Um the long wave um we have a a clear system uh one of the better systems and uh it it it doesn't really come as close. It's 05 Um I don't want to spend a lot of time on it, but the camera itself uh is used for gas detection. Um it's got a very u unique um uh wavelength um that uh can use be used very effectively to determine if there's um uh you know propulsion emissions and that sort of thing. uh methane gas, it'll actually be able to see that methane gas um even uh you know uh certain absorbent surfaces like water.

[00:31:01]I wanted to say one thing if you could hear me. Um I wanted to say one thing about the four ranges in IR. When we talk about near IR, uh shortwave IR, midwave IR and long long range IR, midwave and long range are thermal uh thermal emitting uh uh is thermal emissions where where uh near and shortwave IR more optical photonic emissions and we got and when we talk about shortwave IR which uh we use in the field, this is great for looking at um uh transmedium objects.

[00:31:34]um at shortwave infrared emissions uh there's a there's a higher peak absorption by water. So what you get is you can differentiate something hiding in the on the surface of the water just below the surface of the water and it gives it gives them much greater contrast and where mid and midwave IR is is got a better fidelity and a better 15 times range compared to long range IR or thermal the the forward looking infrared. So that's just the differences and why we're looking at four ranges of infrared.

[00:32:09]>> Right. And I just want to, you know, reiterate what what Jerry said is, you know, with the shortwave infrared, uh, that that absorption u wavelength they talked about that, 1400, uh, nanometers, um, uh, anything that's sitting on the water, the water itself, you know, absorbs all that energy. There's no reflective energy from it. Um, there's no emission coming from it. So, what happens is that appears black or blacker than black in a in a in a shortwave infrared uh, system. If there's something on top of the water and it has a thermal signature, it's going to show out like a sore thumb. Uh so shortwave infrared, near infrared doesn't do that.

[00:32:47]This this has got to be a shortwave camera. And a shortwave is from a,000 to 2500 nanometers. It's a more expensive camera. But in cases like this, especially USO studies, that sort of thing, I think it would be very very beneficial. Okay. So, the next camera we're we're looking at is the this is a marinebased PTZ camera that we have uh on the RV. Um it's a uh Fleer M364C.

[00:33:16]This is a longwave uh infrared camera.

[00:33:19]Um it's um it's got a 640x 512 microbalometer. Um very sensitive. It's um uh it's uh uh fidelity is basically basically uh 0.04 04 uh millich Kelvin. And um so uh if you're differentiating one color um temperature from another uh even subtle changes, you'll be able to see with this camera. We've compared this to other expensive cameras that have ratings of 0.1 and uh with the same resolution and uh you won't pick it up on the other cameras only on the clear. Um, it's got uh uh it's got a digital zoom which which works uh fairly well. Four times digital. Um, and it it has also has uh in the bottom um actually in the top is a is a night vision camera.

[00:34:12]That night vision camera is a it has 30 times optical zoom. It's a it's a it's a uh it's a 4K and um it has uh an extended digital zoom up to 300 times. You know, obviously we wouldn't using 300, but even at 100 and 150, it it it's it's it's pretty respectable. It it does a really good job. We've seen stuff out on the water that uh is is extremely clear and and and very stable.

[00:34:41]>> We saw things at higher altitudes, 35,000 ft, you know, incredible detail.

[00:34:45]Oh, yeah. We've actually been able to to to see these pixels of planes that were at very high altitudes which we couldn't do with other thermals.

[00:34:58]>> Okay. So, we'll talk a little bit, we're not going to get deep into this. We'll talk a little bit about the calibration standards, the importance of calibration standards. And of course, that's important to every one of us that are re, you know, researchers in this field to scientists and engineers. Um uh you want to of course you want to follow uh uh uh quality. You want to make sure it's got high quality. There's conformity. Um but I I want to get into uh there's two aspects of this. There's two schools of thought. There's accredited versus nonacredited calibration. And uh most of your manufacturers, your electronics, experimental labs, uh your analytical laboratories, whether they're clinical or chemistry, uh they they are um uh under the guide of uh accredited uh institutes of of standards. Uh there's there's a list of standards they follow.

[00:35:50]Uh there's a difference. So there's a difference between accredited and nonacredited. Most of what we're doing and probably all these groups is is nonacredited. Unless you're manufacturing something or manufact you're offering services uh clinical services or maybe uh analytical chemistry services uh everything else does is nonacredited but um that's I mean that's perfectly legal but we want you you want everybody to follow the best lab practices or what's called um uh uh good good GLP or good lab practices and uh and as long as you we suggest using the standards that out there the industry industry born standards. John and I worked in quality assurance and quality control for uh most of our career. So in what Ncroller does is we follow those industry standards those guidelines. If you if you order a piece of equipment from a manufacturer which does have accreditation uh on their on their uh their their products uh we we're asking for uh performance specs. We're looking for certificates of compliance uh to to qualify that is as uh is uh uh low risk.

[00:36:58]Um the other thing I want to mention is interrator variability. Uh this is the this is the uh the fact that uh anybody who does calibration everybody does it differently and that's that variability.

[00:37:10]So you want to like say if everybody's following standards and everybody's using best practices you could probably get everybody you know working within the same you know quality range and then you know we could trust the data that's coming out.

[00:37:22]>> Okay.

[00:37:22]>> And that also creates measurement uncertainty too. If you have two different uh methods of calibration and they they don't quite align with metrology type standards. it's uh you know that that can create an issue if we're all not on on board with certain certain uh uh calibration processes.

[00:37:43]>> So So here's a more of a list talking about uh you know using uh guided uh uh uh st uh calibration standards based on uh industry standards. Um I don't think I'm going to talk enough more about this. So we could try to share the slides and the data on this. Yeah, just as this is more or less more of a reference.

[00:38:05]This is more the same talking about accuracy, precision, reliability and uh following those standards.

[00:38:15]And in this uh in this uh um uh panel you what you see is on the bottom is industry standards. That's a list of uh of the main industry standards for electronics industry. we follow ISO 90001 and IEC 725 aerospace may follow AS900 and then you might have ISO 15189 for analytical laboratories or clinical laboratories but like I say if you use that as a guidepost that you know uh and people don't become aware that I think this side the uh the level of uh quality goes up so um in these these photos here, you have we what we we're showing you here is integrated sensor suites. Uh the ISS um uh what we've done is uh you know there's not too many offtheshelf camera systems you can buy electrol optical devices that covers the the full range of uh uh of the electromagnetic spectrum uh or at least something that you know uh that can make a real difference. Um, these cameras, they're individually camera, uh, c they're individually packaged inside this assembly, and some of these cameras are quite expensive. I mean, the camera that I'm holding my hand right now is is is well over $125,000 with equipment. Um but um you have you have uh some of the finest uh image processors um novel sensors in there and um if you're going to make a comparisons uh between um maybe uh two different types of u thermal system versus light photon cameras or light photon systems uh those comparisons uh can you yield some real real results. It's a, you know, I was talking before about, you know, being able to see something that that's at uh 35 40,000 ft. And um even a pixel, you know, a thermal pixel that can be seen can differentiate that from something else. You know, if we're seeing a heat source and then we look into our night vision, make that comparison and and the night vision has that that optical zoom range where we can clearly identify an aircraft, you know, but we also have a thermal signature to it. we can have we have something that corroborates basically you know what we're looking at. I just want to add this that this these are fully integrated systems. This is proprietary. This is something that John has designed um as an instrumentation engineer. They're fully integrated. This is actually a sensor fusion system. You have multiple sensors covering the UV to IR range and you also have a radar.

[00:40:51]There's also a portable radar system in this. So they're uh they're full, like I said, they're fully integrated systems that you can input into a uh into a uh display.

[00:41:03]Okay. So, what we'd like to show you is is some of the case studies that we've had. Um this is using um this is an object that we saw off the coast of Robert Moses State Park on November 13th in 2022. It occurred at 12:31 in the morning and uh we used a solar blind detector uh out on the beach. Uh we had uh one researcher that was on the uh the east side of the beach. The other one was on the west side with the detector um in in a in a way of uh we were trying to triangulate this object as it was as it was uh uh translating itself across from west to east across our field of view. Um I'm going to play the video.

[00:41:45]Hopefully you can guys see it and it's and it's clear.

[00:41:50]Um, let me just see if I get the start.

[00:41:53]>> Let me just mention let me just mention one thing. This object made don't sound it it appeared to it appeared to be a plasma or at least demonstrate properties of a plasma uh very low in elevation uh very close close to the coastline and it it just drifted well intered actually was uh it took a course from west to east along the coastline.

[00:42:14]So, we had we had two uh IR cameras on it and uh and this UV detector.

[00:42:21]And there something else too. We also had a radar interference coming back off this object which is um a plasmas will cause uh radar interference and and and reflectivity. This is uh this is the plasmoid. Uh that's the location off the coast. Uh we had ADSB data on it. uh you know on the right side there you see in yellow you see we had a solar blind detection UBC the cloud B base based altitude that night was uh was 5,000 ft.

[00:42:51]Uh that's something we carefully check because if these objects are above 5,000 ft we have a cloud layer. You know chances are we're not going to see them.

[00:42:59]Uh so at least we know we have a general idea of what that altitude might be. Uh the wind speed that night was between 10 and 15 knots. Wind direction was northnorthwest. temperature was 31 degrees and um there was no maritime traffic um that was in that location at all. The closest maritime flight traffic was 13 miles out and 26 miles out. Uh larger ships.

[00:43:24]I just want to mention that uh that cloud-based uh altitude was important because this object is well below it. Uh it was 5,000 ft. The object you could clear could clearly see on the video if we were able to show the video that it was well below that. Um the aircraft that that was to the west of us uh we actually saw that uh with our our hyperspectral cameras and radar uh and we tracked our ADSB. They were not in the vicinity and we well will track each one of them. Each one of those were at an altitude of at least 25,000 ft to 30,000 ft uh well to uh too high in altitude to uh account for the object we had seen. All right. So uh you guys see the screen you see uh three uh spheres of light and and another sphere of light lower to the right of that as you guys can see that. Okay. So this was uh again low over the water. It was approximately uh maybe maybe uh 200 feet above the ocean surface. 236 236 >> 236 The uh the objects were only seen in uh in the infrared cameras were not seen in visibly uh extremely luminous. Um uh when we went to the coastline, these objects uh hovered out there for about 45 minutes. Um uh this other object appeared to the right and uh and uh made made its way west to right of the field of view. Left to uh from west to east of the field of view. Uh that's right to left. Um it at one point it it came closer to the shoreline, dipped under the water, came out and continued to go towards the east and then disappear. Um these objects at the like I said lasted about 45 minutes. They were fixed in 35 mile per hour winds. No movement. Um no boats who could account to account for this. They uh at the 45 minute mark they all dissipated. There was a large red flash and they were all gone.

[00:45:21]And there was no no radar targets either uh for for shipping traffic or um uh any any kind of aerial objects as at all any any kind of flights in the in the air.

[00:45:32]ADSB had nothing at the time and maritime traffic. There was no maritime traffic that that would have accounted for that.

[00:45:41]This is a um a black triangle that we observed on August 29th um 2025. This this was uh occurred, believe it or not, uh not too far from where I live. And um uh we had um I was uh setting up a spectrum analyzer in the uh mobile laboratory at the time and I had um I had ground plane interference, very powerful, strong uh ground plane um um uh signals and I it couldn't account for it. Um we also had a radar target um and it was a a darker cloud layer. the radar um uh reflected some activity off that.

[00:46:22]Uh but we saw motion. We saw something emerging from the cloud and that's what the radar was tracking. Um we watched this for uh roughly uh uh uh 10 minutes and uh what we saw was uh basically an object that was uh triangular in shape.

[00:46:40]It had three um uh three darker pads on the bottom of it uh which we really couldn't identify. Um and the interesting thing about this, we had uh two cameras trained on it um in focus um and um we had an airliner. This object we believe was about uh 4,000 ft. The airliner uh at the time was at uh almost 10,000 ft. the airliner. We could actually read the writing, the name of the airlines and so forth on the airliner. When we were looking at this object, it was it was extremely diffused and distorted um and uh seemed to change shape. But when we're wondering if that's some kind of artifact uh distortion artifact of uh of the reflected light to mention if you look at the two images, the one to the right is the object a little further away. You see it's it's triangular shape was very black and we we had looked at these with what's a vortex binocular set which has really good optics and you can see this was it was it was flat black. Uh it it it actually changed its orientation in flight. The the image to the left is the object uh magnified and you can see it's got a really weird geometry. It's got these uh spherical uh end points with something that resembles a nozzle at the bottom. Um it match matched the radar track that we have above the radar tracks. It actually one radar track show splitting into two objects. We believed it was it was the objects were close together and it we had as a merge plot and when they separated off in distance we saw the two objects. We also saw the the one object to the this one object moving slowly and there was another one that passed it just like it but at at a they looked like at a hypersonic speed.

[00:48:29]The speed of this one object that was slower moving that we were able to capture here was approximately 220 miles per hour. And like I said we have we have uh anomalous uh RF signals that we picked up. We have uh uh radar track and we have the uh the two cameras that picked it up.

[00:48:53]Okay. And this one is a is a uh a fireball like object that we uh that we tracked uh in uh in uh uh Seldon, Long Island. It was in the more central area of Long Island. Uh it was actually during the 4th of July and uh uh it was it was bizarre. There was two fireworks displays uh within 38 39 to 40 miles apart. This object seemed to show up at one fireplane head towards the other one and disappear from from two different towns. We gauged the distance and we could do we calculated the speed to be about 120 miles per hour was moving. Um we first thought about could have been a sky lantern that was released but the wind speed was uh 0 to 5 miles per hour.

[00:49:38]This object was moving at 120. Uh at when we uh zoomed in on the object there was no enclosure. There was a a vapor trail being uh released that being uh trailing it as as it was moving towards the northeast. Um it looked like it looked like a plasma or a fireball.

[00:49:58]>> We got three minutes, gentlemen.

[00:50:00]>> Okay.

[00:50:03]Can you guys see this one at all?

[00:50:04]>> We see it.

[00:50:06]>> Oh, beautiful wind.

[00:50:08]>> So, this is this was an early morning um uh object that we saw. It looked like a a a cylinder that passed an airliner. Um and um the both the cylinder and the uh the airliner were in focus. Um and we were able to gauge it with the camera.

[00:50:28]Um the distance-wise uh we were able to determine, you know, who the airliner was. Um and uh we knew exactly where it flight plan. It was basically coming into JFK. Uh, but this object seemed to cross its flight path.

[00:50:47]And here's a slow more slow motion.

[00:50:50]>> And it was highly illuminated. No wings, no tail section. It had wobble to it.

[00:51:01]>> This Did I get to the other video before?

[00:51:04]>> Yeah. This is using a P100 camera.

[00:51:08]>> Okay. Uh oh. Maybe this one will start too. Let's see.

[00:51:14]Can you guys see this video?

[00:51:16]>> Yes.

[00:51:16]>> Oh, beautiful. Okay.

[00:51:18]>> All right. Two for two. All right. So, this is an object that was on the water 4:00 in the morning. Robert Moses State Park. It was actually elevated 15 ft from the water surface and cast a reflection on the water as it was rotating.

[00:51:35]And we were able to catch this in the uh infrared optics.

[00:51:48]We believe the object was less than a quarter mile up of of our obser observation.

[00:51:57]Now I want to mention that this was the there were several of these objects.

[00:52:01]This one was visible. There were other ones like it that seemed to drift into the IR range and it disappeared from the visible range. Well, we captured on on multiple spectral cameras. We also have a radar. We also have a a target correlation with radar.

[00:52:15]>> Yeah, we have radar tracks on this as well.

[00:52:16]>> So, this these objects were uh from uh 300 ft to 1500 feet out from the shoreline and they appeared as a wage of objects in front of us that just appeared in front of us.

[00:52:28]>> Well, that's we're going to have to end it here. sadly. [laughter] >> God, do we appreciate this. This could go on forever, you know. Just want you to thank you all for what you've done.

[00:52:40]You know, by the way, >> thank you, Rich. We appreciate it.

[00:52:43]>> You're definitely appreing to hear from you and this I have been one of those wanting to hear everything you guys have to say. So, I appreciate it. Thank you again.

[00:52:53]>> Thank you, Rich. Very [music] much appreciate it.

[00:53:03]>> [music] [music]