Tag Archives: Raspberry Pi

Spinach and plant nanobionics

Who knew that spinach leaves could be turned into electronic devices? The answer is: engineers at the Massachusetts Institute of Technology, according to an Oct. 31, 2016 news item on phys.org,

Spinach is no longer just a superfood: By embedding leaves with carbon nanotubes, MIT engineers have transformed spinach plants into sensors that can detect explosives and wirelessly relay that information to a handheld device similar to a smartphone.

This is one of the first demonstrations of engineering electronic systems into plants, an approach that the researchers call “plant nanobionics.”

An Oct. 31, 2016 MIT news release (also on EurekAlert), which originated the news item, describes the research further (Note: Links have been removed),

“The goal of plant nanobionics is to introduce nanoparticles into the plant to give it non-native functions,” says Michael Strano, the Carbon P. Dubbs Professor of Chemical Engineering at MIT and the leader of the research team.

In this case, the plants were designed to detect chemical compounds known as nitroaromatics, which are often used in landmines and other explosives. When one of these chemicals is present in the groundwater sampled naturally by the plant, carbon nanotubes embedded in the plant leaves emit a fluorescent signal that can be read with an infrared camera. The camera can be attached to a small computer similar to a smartphone, which then sends an email to the user.

“This is a novel demonstration of how we have overcome the plant/human communication barrier,” says Strano, who believes plant power could also be harnessed to warn of pollutants and environmental conditions such as drought.

Strano is the senior author of a paper describing the nanobionic plants in the Oct. 31 [2016] issue of Nature Materials. The paper’s lead authors are Min Hao Wong, an MIT graduate student who has started a company called Plantea to further develop this technology, and Juan Pablo Giraldo, a former MIT postdoc who is now an assistant professor at the University of California at Riverside.

Environmental monitoring

Two years ago, in the first demonstration of plant nanobionics, Strano and former MIT postdoc Juan Pablo Giraldo used nanoparticles to enhance plants’ photosynthesis ability and to turn them into sensors for nitric oxide, a pollutant produced by combustion.

Plants are ideally suited for monitoring the environment because they already take in a lot of information from their surroundings, Strano says.

“Plants are very good analytical chemists,” he says. “They have an extensive root network in the soil, are constantly sampling groundwater, and have a way to self-power the transport of that water up into the leaves.”

Strano’s lab has previously developed carbon nanotubes that can be used as sensors to detect a wide range of molecules, including hydrogen peroxide, the explosive TNT, and the nerve gas sarin. When the target molecule binds to a polymer wrapped around the nanotube, it alters the tube’s fluorescence.

In the new study, the researchers embedded sensors for nitroaromatic compounds into the leaves of spinach plants. Using a technique called vascular infusion, which involves applying a solution of nanoparticles to the underside of the leaf, they placed the sensors into a leaf layer known as the mesophyll, which is where most photosynthesis takes place.

They also embedded carbon nanotubes that emit a constant fluorescent signal that serves as a reference. This allows the researchers to compare the two fluorescent signals, making it easier to determine if the explosive sensor has detected anything. If there are any explosive molecules in the groundwater, it takes about 10 minutes for the plant to draw them up into the leaves, where they encounter the detector.

To read the signal, the researchers shine a laser onto the leaf, prompting the nanotubes in the leaf to emit near-infrared fluorescent light. This can be detected with a small infrared camera connected to a Raspberry Pi, a $35 credit-card-sized computer similar to the computer inside a smartphone. The signal could also be detected with a smartphone by removing the infrared filter that most camera phones have, the researchers say.

“This setup could be replaced by a cell phone and the right kind of camera,” Strano says. “It’s just the infrared filter that would stop you from using your cell phone.”

Using this setup, the researchers can pick up a signal from about 1 meter away from the plant, and they are now working on increasing that distance.

Michael McAlpine, an associate professor of mechanical engineering at the University of Minnesota, says this approach holds great potential for engineering not only sensors but many other kinds of bionic plants that might receive radio signals or change color.

“When you have manmade materials infiltrated into a living organism, you can have plants do things that plants don’t ordinarily do,” says McAlpine, who was not involved in the research. “Once you start to think of living organisms like plants as biomaterials that can be combined with electronic materials, this is all possible.”

“A wealth of information”

In the 2014 plant nanobionics study, Strano’s lab worked with a common laboratory plant known as Arabidopsis thaliana. However, the researchers wanted to use common spinach plants for the latest study, to demonstrate the versatility of this technique. “You can apply these techniques with any living plant,” Strano says.

So far, the researchers have also engineered spinach plants that can detect dopamine, which influences plant root growth, and they are now working on additional sensors, including some that track the chemicals plants use to convey information within their own tissues.

“Plants are very environmentally responsive,” Strano says. “They know that there is going to be a drought long before we do. They can detect small changes in the properties of soil and water potential. If we tap into those chemical signaling pathways, there is a wealth of information to access.”

These sensors could also help botanists learn more about the inner workings of plants, monitor plant health, and maximize the yield of rare compounds synthesized by plants such as the Madagascar periwinkle, which produces drugs used to treat cancer.

“These sensors give real-time information from the plant. It is almost like having the plant talk to us about the environment they are in,” Wong says. “In the case of precision agriculture, having such information can directly affect yield and margins.”

Once getting over the excitement, questions spring to mind. How could this be implemented? Is somebody  going to plant a field of spinach and then embed the leaves so they can detect landmines? How will anyone know where to plant the spinach? And on a different track, is this spinach edible? I suspect that if spinach can be successfully used as a sensor, it might not be for explosives but for pollution as the researchers suggest.

Here’s a link to and a citation for the paper,

Nitroaromatic detection and infrared communication from wild-type plants using plant nanobionics by Min Hao Wong, Juan P. Giraldo, Seon-Yeong Kwak, Volodymyr B. Koman, Rosalie Sinclair, Tedrick Thomas Salim Lew, Gili Bisker, Pingwei Liu, & Michael S. Strano. Nature Materials (2016) doi:10.1038/nmat4771 Published online 31 October 2016

This paper is behind a paywall.

The last posting here which featured Strano’s research is in an Aug. 25, 2015 piece about carbon nanotubes and medical sensors.

Beating tactical experts in combat simulation—AI with the processing power of a Raspberry Pi

It looks like one day combat may come down to who has the best artificial intelligence (AI) if a June 27, 2016 University of Cincinnati news release (also on EurekAlert) by M. B. Reilly is to be believed (Note: Links have been removed),

Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee — who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise — in a high-fidelity air combat simulator.

The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is “the most aggressive, responsive, dynamic and credible AI I’ve seen to date.”

Details on ALPHA – a significant breakthrough in the application of what’s called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes.

The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors.

The news release goes on to provide a overview of ALPHA’s air combat fighting and strategy skills,

ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment. In its earliest iterations, ALPHA consistently outperformed a baseline computer program previously used by the Air Force Research Lab for research.  In other words, it defeated other AI opponents.

In fact, it was only after early iterations of ALPHA bested other computer program opponents that Lee then took to manual controls against a more mature version of ALPHA last October. Not only was Lee not able to score a kill against ALPHA after repeated attempts, he was shot out of the air every time during protracted engagements in the simulator.

Since that first human vs. ALPHA encounter in the simulator, this AI has repeatedly bested other experts as well, and is even able to win out against these human experts when its (the ALPHA-controlled) aircraft are deliberately handicapped in terms of speed, turning, missile capability and sensors.

Lee, who has been flying in simulators against AI opponents since the early 1980s, said of that first encounter against ALPHA, “I was surprised at how aware and reactive it was. It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment. It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed.”

He added that with most AIs, “an experienced pilot can beat up on it (the AI) if you know what you’re doing. Sure, you might have gotten shot down once in a while by an AI program when you, as a pilot, were trying something new, but, until now, an AI opponent simply could not keep up with anything like the real pressure and pace of combat-like scenarios.”

But, now, it’s been Lee, who has trained with thousands of U.S. Air Force pilots, flown in several fighter aircraft and graduated from the U.S. Fighter Weapons School (the equivalent of earning an advanced degree in air combat tactics and strategy), as well as other pilots who have been feeling pressured by ALPHA.

And, anymore [sic], when Lee flies against ALPHA in hours-long sessions that mimic real missions, “I go home feeling washed out. I’m tired, drained and mentally exhausted. This may be artificial intelligence, but it represents a real challenge.”

New goals have been set for ALPHA according to the news release,

Explained Ernest, “ALPHA is already a deadly opponent to face in these simulated environments. The goal is to continue developing ALPHA, to push and extend its capabilities, and perform additional testing against other trained pilots. Fidelity also needs to be increased, which will come in the form of even more realistic aerodynamic and sensor models. ALPHA is fully able to accommodate these additions, and we at Psibernetix look forward to continuing development.”

In the long term, teaming artificial intelligence with U.S. air capabilities will represent a revolutionary leap. Air combat as it is performed today by human pilots is a highly dynamic application of aerospace physics, skill, art, and intuition to maneuver a fighter aircraft and missiles against adversaries, all moving at very high speeds. After all, today’s fighters close in on each other at speeds in excess of 1,500 miles per hour while flying at altitudes above 40,000 feet. Microseconds matter, and the cost for a mistake is very high.

Eventually, ALPHA aims to lessen the likelihood of mistakes since its operations already occur significantly faster than do those of other language-based consumer product programming. In fact, ALPHA can take in the entirety of sensor data, organize it, create a complete mapping of a combat scenario and make or change combat decisions for a flight of four fighter aircraft in less than a millisecond. Basically, the AI is so fast that it could consider and coordinate the best tactical plan and precise responses, within a dynamic environment, over 250 times faster than ALPHA’s human opponents could blink.

So it’s likely that future air combat, requiring reaction times that surpass human capabilities, will integrate AI wingmen – Unmanned Combat Aerial Vehicles (UCAVs) – capable of performing air combat and teamed with manned aircraft wherein an onboard battle management system would be able to process situational awareness, determine reactions, select tactics, manage weapons use and more. So, AI like ALPHA could simultaneously evade dozens of hostile missiles, take accurate shots at multiple targets, coordinate actions of squad mates, and record and learn from observations of enemy tactics and capabilities.

UC’s Cohen added, “ALPHA would be an extremely easy AI to cooperate with and have as a teammate. ALPHA could continuously determine the optimal ways to perform tasks commanded by its manned wingman, as well as provide tactical and situational advice to the rest of its flight.”

Happily, insight is provided into the technical aspects (from the news release),

It would normally be expected that an artificial intelligence with the learning and performance capabilities of ALPHA, applicable to incredibly complex problems, would require a super computer in order to operate.

However, ALPHA and its algorithms require no more than the computing power available in a low-budget PC in order to run in real time and quickly react and respond to uncertainty and random events or scenarios.

According to a lead engineer for autonomy at AFRL, “ALPHA shows incredible potential, with a combination of high performance and low computational cost that is a critical enabling capability for complex coordinated operations by teams of unmanned aircraft.”

Ernest began working with UC engineering faculty member Cohen to resolve that computing-power challenge about three years ago while a doctoral student. (Ernest also earned his UC undergraduate degree in aerospace engineering and engineering mechanics in 2011 and his UC master’s, also in aerospace engineering and engineering mechanics, in 2012.)

They tackled the problem using language-based control (vs. numeric based) and using what’s called a “Genetic Fuzzy Tree” (GFT) system, a subtype of what’s known as fuzzy logic algorithms.

States UC’s Cohen, “Genetic fuzzy systems have been shown to have high performance, and a problem with four or five inputs can be solved handily. However, boost that to a hundred inputs, and no computing system on planet Earth could currently solve the processing challenge involved – unless that challenge and all those inputs are broken down into a cascade of sub decisions.”

That’s where the Genetic Fuzzy Tree system and Cohen and Ernest’s years’ worth of work come in.

According to Ernest, “The easiest way I can describe the Genetic Fuzzy Tree system is that it’s more like how humans approach problems.  Take for example a football receiver evaluating how to adjust what he does based upon the cornerback covering him. The receiver doesn’t think to himself: ‘During this season, this cornerback covering me has had three interceptions, 12 average return yards after interceptions, two forced fumbles, a 4.35 second 40-yard dash, 73 tackles, 14 assisted tackles, only one pass interference, and five passes defended, is 28 years old, and it’s currently 12 minutes into the third quarter, and he has seen exactly 8 minutes and 25.3 seconds of playtime.’”

That receiver – rather than standing still on the line of scrimmage before the play trying to remember all of the different specific statistics and what they mean individually and combined to how he should change his performance – would just consider the cornerback as ‘really good.’

The cornerback’s historic capability wouldn’t be the only variable. Specifically, his relative height and relative speed should likely be considered as well. So, the receiver’s control decision might be as fast and simple as: ‘This cornerback is really good, a lot taller than me, but I am faster.’

At the very basic level, that’s the concept involved in terms of the distributed computing power that’s the foundation of a Genetic Fuzzy Tree system wherein, otherwise, scenarios/decision making would require too high a number of rules if done by a single controller.

Added Ernest, “Only considering the relevant variables for each sub-decision is key for us to complete complex tasks as humans. So, it makes sense to have the AI do the same thing.”

In this case, the programming involved breaking up the complex challenges and problems represented in aerial fighter deployment into many sub-decisions, thereby significantly reducing the required “space” or burden for good solutions. The branches or sub divisions of this decision-making tree consists of high-level tactics, firing, evasion and defensiveness.

That’s the “tree” part of the term “Genetic Fuzzy Tree” system.

Programming that’s language based, genetic and generational

Most AI programming uses numeric-based control and provides very precise parameters for operations. In other words, there’s not a lot of leeway for any improvement or contextual decision making on the part of the programming.

The AI algorithms that Ernest and his team ultimately developed are language based, with if/then scenarios and rules able to encompass hundreds to thousands of variables. This language-based control or fuzzy logic, while much less about complex mathematics, can be verified and validated.

Another benefit of this linguistic control is the ease in which expert knowledge can be imparted to the system. For instance, Lee worked with Psibernetix to provide tactical and maneuverability advice which was directly plugged in to ALPHA. (That “plugging in” occurs via inputs into a fuzzy logic controller. Those inputs consist of defined terms, e.g., close vs. far in distance to a target; if/then rules related to the terms; and inputs of other rules or specifications.)

Finally, the ALPHA programming is generational. It can be improved from one generation to the next, from one version to the next. In fact, the current version of ALPHA is only that – the current version. Subsequent versions are expected to perform significantly better.

Again, from UC’s Cohen, “In a lot of ways, it’s no different than when air combat began in W.W. I. At first, there were a whole bunch of pilots. Those who survived to the end of the war were the aces. Only in this case, we’re talking about code.”

To reach its current performance level, ALPHA’s training has occurred on a $500 consumer-grade PC. This training process started with numerous and random versions of ALPHA. These automatically generated versions of ALPHA proved themselves against a manually tuned version of ALPHA. The successful strings of code are then “bred” with each other, favoring the stronger, or highest performance versions. In other words, only the best-performing code is used in subsequent generations. Eventually, one version of ALPHA rises to the top in terms of performance, and that’s the one that is utilized.

This is the “genetic” part of the “Genetic Fuzzy Tree” system.

Said Cohen, “All of these aspects are combined, the tree cascade, the language-based programming and the generations. In terms of emulating human reasoning, I feel this is to unmanned aerial vehicles what the IBM/Deep Blue vs. Kasparov was to chess.”

Here’s a link to and a citation for the paper,

Genetic Fuzzy based Artificial Intelligence for Unmanned Combat Aerial Vehicle Control in Simulated Air Combat Missions by Nicholas Ernest, David Carroll, Corey Schumacher, Matthew Clark, Kelly Cohen, and Gene Lee. J Def Manag [Journal of Defense Management]  6:144. doi:10.4172/2167-0374.1000144 Published: March 22, 2016

This is an open access paper.

Segue

The University of Cincinnati’s president, Santa Ono, recently accepted a job as president of the University of British Columbia (UBC), which is located in the region where I live. Nassif Ghoussoub, professor of mathematics at UBC, writes about Ono and his new appointment in a June 13, 2016 posting on his blog (Note: A link has been removed),

By the time you read this, UBC communications will already have issued the mandatory press release [the official announcement was made June 13, 2016] describing Santa Ono’s numerous qualifications for the job, including that he is a Canuck in the US, born in Vancouver, McGill PhD, a highly accomplished medical researcher, who is the President of the University of Cincinnati.

So, I shall focus here on what UBC communications may not be enclined [sic] to tell you, yet may be quite consequential for UBC’s future direction. After all, life experiences, gender, race, class, and character are what shape leadership.

President Ono seems to have had battles with mental illness, and have been courageous enough to deal with it and to publicly disclose it –as recently as May 24 [2016]– so as to destigmatize struggles that many people go through. It is interesting to note the two events that led the president to have suicidal thoughts: …

The post is well worth reading if you have any interest in Ono, UBC, and/or insight into some of the struggles even some of the most accomplished academics can encounter.