Sensors, Collections & Phenomenology
Sensor technology is developing rapidly. New technologies and new applications are occurring frequently in the commercial sector and are being applied, matured or adapted for mission specific programs on the defense side too.
Talking us through how Leidos is involved in this arena is Cayley Rice, Enterprise Lead for Sensors, Collections & Phenomenology at Leidos.
With the influx of products and tools available commercially, and a growing trend of commercial sensors for common items like your doorbell, your smartphone or your thermostat, for example, how and where does Leidos fit into the picture?
What custom sensors is Leidos building and how are these being integrated into systems that can be rapidly deployed into some of the most demanding and mission critical kinds of environments?
Cayley provides a fascinating insight into the challenges and requirements that we have for this technology, as well as the need to speed up the timeframe for adapting sensor systems to commercial speed.
“We're basically adapting technology from the commercial world that got us smartphones, and using that same kind of process to advance micro-electronics for sensors and signal processing.”
On today’s podcast:
- Sensors, Collections & Phenomenology
- The capabilities Leidos has in this arena
- The opportunities with sensors for government agencies today
- What distinguishes Leidos from the commercial space
- The challenge of cycle time to new innovations
Cayley Rice (00:00): So we really have quite a wide array of different kinds of sensor capabilities that spans pretty much all the places where humans operate in any way, even deep sea and space.
Bridget Bell (00:18): Welcome to MindSET, a Leidos podcast. I'm your host, Bridget Bell.
Meghan Good (00:22): And I'm your host, Meghan Good. Join us as we talk with pioneers in science, engineering, and technology, to understand their creative mindset and share their stories of innovation.
Meghan Good (00:37): On today's MindSET. We spoke with Cayley Rice, who leads Leidos' capabilities in sensors, collections and phenomenology. She starts with her background in mathematics and was actually a high school math teacher for a year and describes how she came to Leidos and focused on microelectronics. And then, being a good mathematician, she gives us definitions to start the conversation on what are sensors, collections and phenomenology.
Meghan Good (01:05): We discuss trends with commercial sensors, and then how we're also building custom sensors at Leidos and working together to integrate them into systems that could be rapidly deployed into some of the most demanding and mission critical kinds of environments.
Bridget Bell (01:22): And she gave us examples of how we're using sensors and even what sensors look like in a post-COVID world.
Meghan Good (01:30): With that, we also talked about how we can improve processing, as well as privacy, and think through some of the requirements and limitations. She talked a lot about how we need to speed up the timeframe for adapting these sensor systems at commercial speed and some practical systems engineering kind of ways of how to do it.
Bridget Bell (01:48): So let's get started with the conversation.
Bridget Bell (01:58): Welcome to MindSET. Today, we're speaking with Cayley Rice who leads Leidos' capabilities on sensors, collections, and phenomenology.
Bridget Bell (02:06): Welcome, Cayley.
Cayley Rice (02:08): Hi, it's nice to be here.
Meghan Good (02:10): So let's start with you telling us a little bit about your background and role with Leidos.
Cayley Rice (02:15): So actually I started my career, in terms of a real job, teaching high school math, but I only lasted a year. And I have great respect for anyone who stays in that longer. I actually went back to school to earn a PhD in theoretical math at UC San Diego and become a professor, but that was just before 2008, and the economy collapsed. So after about two years in a small town in Michigan, I moved back to San Diego and started working at SAIC. And I've been with the company ever since.
Cayley Rice (02:49): So looking back, I'm actually really grateful that the division manager at the time, who was a theoretical physicist, was willing to hire a theoretical mathematician to do engineering and radar and electronic warfare. It's turned out really well. I've been working in the electronic warfare division for a while now, specifically doing sensors, signal processing, modeling, system engineering, and most recently working on development of microelectronics for those kinds of applications.
Cayley Rice (03:19): We're basically adapting technology from the commercial world that got us smartphones, and using that same kind of process to advance microelectronics for sensors and signal processing.
Cayley Rice (03:31): I also am lucky to lead our technical core competency in sensors, collections, and phenomenology, which means interacting across the company and getting kind of a taste of what the whole company does in that realm and how it all fits together.
Bridget Bell (03:48): So since you are a mathematician, and we've learned through MindSET that with mathematicians, we have to define first principles first and have definitions upfront. And so with that, what are sensors, collection, and phenomenology?
Cayley Rice (04:07): So phenomenology actually comes from a Greek word meaning anything that can be observed. And so phenomenology is, in this context, really things like radio frequency infrared, optical, radiological, biological, so things like bioterrorism or COVID-19. The sensor is the device that actually does the observing. And so we're really familiar with a lot of different kinds of sensors in our everyday lives. We have biological sensors like seeing and hearing where eyes or ears kind of gather information from the outside world, and then our brains process it to turn that into real information and make decisions. And so a sensor is really just that same kind of system. There's some kind of hardware that is going to collect data from the outside world. And then there's some kind of processing that occurs with it that turns it into information for a decision maker. So whether that's a human that's making a decision or an AI/ML system that is making the decision, the sensor is the thing that can get you the information.
Bridget Bell (05:17): So it sounds like we come across sensors and collections and phenomenology in all aspects of our daily lives. But what capabilities does Leidos have in this area?
Cayley Rice (05:30): Leidos actually works across an enormous array of different phenomenologies. So I mentioned a few, but we work in the electromagnetic spectrum very heavily. So radar, RF, electro-optical, infrared, magnetic. We also have a lot of acoustic work, either underwater or ambient, and things like ground sensors and seismic detectors. We work in radiological, chemical, biological detection. And really, between all of those different things, we design and build different kinds of sensors or signal processing that goes on platforms all the way from the deep sea, through the surface of the earth, airplanes, and satellites in space. So we really have quite a wide array of different kinds of sensor capabilities that spans pretty much all the places where humans operate in any way, even deep sea and space.
Cayley Rice (06:33): And that really starts from the really fundamental basics of the physics and the math of how these things work, how do radar waves propagate through space? How do they bounce off of things? Builds all the way up through rapid prototyping, which Dynetics really was a huge addition to the company to shore up a lot of the rapid prototyping capabilities with their facilities and expertise, all the way through operational systems and maintenance that occurs on systems around the world.
Meghan Good (07:04): Wow. So that really is a broad range of different kinds of sensors used for different purposes that we work with. Now, I'm wondering, in today's world, commercially, we have so many different kinds of sensors available from our smartphones to doorbells, to cameras, to you name it, lots of different small sensors that we can purchase really easily. What's different about the kinds of sensors that we build or work with at Leidos?
Cayley Rice (07:30): The kinds of sensors that we focus on are really customized for very specific and difficult environments. So the requirements are very stringent and either the space is very regulated, mission critical. I mean, if you're working on a military installation, you're going to have a lot of safety and security measures that are aren't really needed for your doorbell.
Cayley Rice (08:00): The other key feature is very precise environmental requirements. So I mentioned deep sea, there's tremendous pressure, deep sea. Ocean water is very corrosive and difficult to maintain electronics. So there's a lot of environmental constraints on that problem that require a very customized or tailored solution for the very specific mission of the customer.
Cayley Rice (08:30): In space, you have radiological concerns, which also bring up a whole slew of problems related to reliability. And you also can't just go up and connect up to a satellite and recharge the batteries the same way you can with your cell phone. So you need to make sure that you're designing something that's going to live in space without any kind of power renewal or updates to certain components. The widespread technology that gets used for all of our consumer goods doesn't have those same kinds of really robustness, reliability, safety, and/or security components that are really integral to the kind of sensors work that Leidos is doing.
Bridget Bell (09:17): So for these like highly regulated environments and stringent requirements, I've got to guess there's going to be several challenges or threats that you might not see in a commercial arena. So what are the biggest challenges that you see for government agencies?
Cayley Rice (09:36): Yeah. So radar is a great example here because radar has been working really since World War II. The British had radar set up on their coast to be able to detect early warning signals of incoming airplanes. And it's worked ever since, but the problem is that you can defeat a radar with a jammer. So as long as there is a sensor, like a radar, that's requiring certain information and that is widely available technology, people create other technology to counteract it, which means even more development has to happen to overcome that sort of threat, like in this case, the jammer.
Cayley Rice (10:22): The same is true in the infrared spectrum, electro-optical. So in all of these situations, that cycle time on developing new threats has been getting shorter and shorter because of the rapid expanse in commercial electronics and development of the commercial side. That's a real problem for defense because the DOD isn't known for moving quickly. There's a lot of testing, a lot of very rigorous testing that has to go into all of these components to make sure that they all work, that they are robust, they are reliable, they're secure in a way that even our cell phones that have all of our personal information don't necessarily need that level of security because they're not making life or death sorts of decisions.
Cayley Rice (11:13): So for the government, they need to find a way to move at the same pace as commercial technology, or at least be able to update and respond to a threat that's evolving at that kind of pace, which is extremely difficult. So I think that that's where most of the biggest problems are going to come from. And that might mean advanced sensors or advanced techniques. It might mean cyber security kinds of threats and keeping up with that. But I think that that's probably the underlying factor across many of the biggest concerns to DOD in particular.
Meghan Good (11:48): So with that, it sounds like there probably are a lot of opportunities since certainly over the last 20 years or so we've seen technology advancing very quickly. And there's other measures that have happened on the commercial side. What kinds of opportunities do you see about bringing that sort of speed up into the sensor work that government agencies are doing today?
Cayley Rice (12:13): Well, so it's, in some ways, the flip side of the same coin. Those same commercial technologies are available to government programs and sensors. And we've been seeing that more and more in a lot of work coming out of DARPA in particular, but really all of the DOD agencies, and a lot of the parts of Leidos as well, of adapting the new commercial technologies and finding ways to integrate them with either custom components that add the security, the reliability in the really harsh environments, or signal processing that integrates different pieces together and just does it better. So we need to be able to, instead of building necessarily the coolest systems from scratch, be able to integrate systems that are probably very similar in a lot of their components to our competitors, our foreign countries, but that do it smarter, that integrate them in more effective ways to get better performance, to get better reliability and protection against those evolving threats. So there is a lot of work going on in that realm right now. And I think that that's probably a huge opportunity. There could be a lot more going on there as well.
Meghan Good (13:31): So it sounds like an ultimate systems engineering and improvement kind of challenge and solution right now that there's these components that are ever changing, and you're trying to integrate them into these systems for different missions. But then at the same time, you have to do that even better as you're trying to speed up the process of delivering those systems.
Cayley Rice (13:53): Yeah. I think that that sums it up really well. And I think that you also need to bear in mind the fact that you'll have to update it even faster. So as you're trying to design everything even better to start with, you need to design in scalability and flexibility to enable upgrades rapidly in the future. And in a lot of ways that relies on heavy processing so that you're making upgrades to the software and not upgrades to the actual radar antenna system. Those are much slower to implement.
Meghan Good (14:28): Now, I'm curious, Cayley, in this COVID-19 pandemic world that we're in, what do you think about temperature scanning and the kinds of sensors needed for that?
Cayley Rice (14:38): I think that the sensors themselves are things we already have. I mean, we all have a thermometer in our house. Again, you want to move to something that is frictionless so that you could do it very quickly in a situation like an airport or a school, but even that, we have the basically infrared based sensors that can do that. The interesting, the new part, is what do you do with that information? Do you track it? If you track it, what are the security implications of that, the personal information and identity sort of privacy issues around that? And those are the kinds of things that Leidos is actually looking at on a broader scale.
Cayley Rice (15:21): But I think that the interesting problem there is not necessarily the sensor. It's really, what do you do with that information?
Meghan Good (15:28): That's a good point because that is the thing, right? Because there is the other side. It's not just the phenomenology and the sensor to measure it. You're also collecting it.
Bridget Bell (15:37): Well, and that leads to my next question, that in that collection side, and what do you do with the data? I imagine there are a lot of constraints or limitations. And so can you talk to us about some of the major constraints, whether it's on the sensor side, but really on that collection side. What have you seen for limitations there?
Cayley Rice (15:59): That depends so much on the particular example. In the temperature and COVID examples, the personal privacy would be the biggest driver in what you would do with that information. And that's driven by all sorts of laws and regulations in the health environment, which honestly isn't my area of expertise. On the defense side, it comes down to classification and whether it's a secret collection or not. We run into a lot of challenges in terms of having data sets available to develop new kinds of algorithms and technologies, just because we do run into this problem of either privacy or security and those data sets being available only for that one very narrowly defined mission, as opposed to being widely available as a source of kind of a test bed for future work. That is kind of one of the challenges. The upside of the proliferation of commercial sensors is that it really isn't that hard to go and collect kind of stand in data in a lot of cases.
Meghan Good (17:15): So in a lot of the episodes of MindSET, we've talked about data and how important the data privacy and the protection of that data is. I know recently we had an episode that interviewed Julie Rosen on data analytics and health, and she described herself, I think, as a mother tiger trying to protect health data. And so I think your response really resonates as we recognize that is it a large constraint and limitation, and we're doing what we can to make sure we're protecting it and treating it appropriately.
Cayley Rice (17:47): I love that description of mother tiger protecting data.
Bridget Bell (17:50): And I can almost see Julie doing that too. It's wonderful. But speaking of Julie and the episode that we did there, we delved a lot into her career. So I'm curious, Cayley, what interested you in a career in sensors? What would you tell those interested in exploring a similar career?
Cayley Rice (18:10): As I mentioned earlier, my decision to come and start working in sensors really had more to do with the economic situation in 2008, and less to do with the specific work. I was very motivated to move back to San Diego. And this was a job opportunity that seemed like it was probably going to be interesting. That's really been the driving force behind my career is that when people have offered me things that seem like they're going to be interesting, I've accepted them. And that's true of starting at Leidos back then, and also since in all my different roles across kind of technical management, individual contributor, working on different kinds of programs, doing different kinds of things across really the spectrum of sensors all the way through now as the TCC lead.
Cayley Rice (19:03): I think that one of the important things, if someone really wants to come work in this sort of field is I do think that my math background was incredibly valuable. So if your goal is to especially work on the signal processing side, it really boils down to math. So having that strong math underpinning is going to make a huge difference in your ability to come up with new ideas and new algorithms. So while there are certain requirements, for example, for an undergraduate degree in electrical engineering, I would try to focus on some of the more mathematically rigorous options instead of maybe some of the other things.
Cayley Rice (19:50): That said there's a lot of work in sensors that isn't signal processing, that's hardware engineering, material science. There's a lot of very interesting work there that requires more understanding of chemistry. So I guess fundamentally is there's a lot of different parts to sensors and phenomenology, and having some sense of the part that is interesting to you and making sure that whatever the foundational principles are that drives that piece, you're familiar with them. I think it all starts from first principles. And for my case, in signal processing, those first principles really are mathematical. But for some of the other pieces, it might be physics. It might be materials. It might be chemistry.
Cayley Rice (20:33): The sensors work that's going on is happening at such a rapid pace that it's very exciting to be in this field right now and to see the new technologies and the new applications that are occurring in the commercial sector and also being applied and matured or adapted for the very hard mission specific programs on the defense side as well.
Bridget Bell (20:58): Well, thank you, Cayley, for your time and for your perspective on sensors, collection, and phenomenology.
Cayley Rice (21:05): Thanks for inviting me. It's been great to talk to you guys.
Meghan Good (21:08): And thanks to our audience for listening to MindSET. If you enjoyed this episode, please share with your colleagues and visit Leidos.com/MindSET.