^{Illustration of network concept. A UConn researcher at the National Institute for Undersea Vehicle Technology is developing a ‘smart sensor network’ that is both energy-efficient and resilient, to track targets such as enemy submarines. (Getty Images)}

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A team of UConn engineers is developing an energy-efficient “smart sensor network” to track targets of interest, such as the proximity of enemy submarines or ships to Navy vessels.

The U.S. Navy currently uses underwater Intelligence, Surveillance, & Reconnaissance (ISR) sensor networks that run on full power, which can be a problem for long-term operations. The more accurate the sensor, the more power they consume.

The sensor networks currently being used could consist of several multi-modal sensor nodes, called sensor buoys, where each node acts independently and contains a diverse sensor suite, a data-processing unit, a transmitter and receiver, and a GPS device. The sensor suite can be composed of different types of sensors to detect and track targets, such as underwater microphones and active sonars.

Traditionally, these sensor nodes operate on full power, running all devices simultaneously, but the batteries that power them typically burn out within a few days of operation,  just as cell phones suck up more power when running multiple operations. This causes sensing failures which, in turn, leads to holes in coverage and affects tracking performance.

This poses a challenge to the Navy, since it deploys thousands of acoustic sensor networks throughout the ocean, where battery replacement can be time-consuming or impossible.

To address the challenge, Shalabh Gupta, a UConn engineer and researcher at the National Institute for Undersea Vehicle Technology, devised the concept of a “smart sensor network” that is energy-efficient as well as resilient to failures.

 ^{Intelligent Energy-efficient Sensor Network. (Illustration by Hayley Joyal ’18 (SFA))}

In a smart sensor network, sensor nodes adapt their sensing modalities based on the information about the targets’ whereabouts. Thus, the nodes around the target, such as a ship or submarine, activate their high-power sensing devices to track the target accurately, pinpointing its location, velocity, and trajectory.

On the other hand, the nodes that are located farther away from the target cycle between low-power sensing and sleep states to minimize energy consumption while still remaining aware.

Thus, if a low-power sensor detects a target, the node switches to high-power sensing to track it. Similarly, the high-power sensing devices that are tracking the target predict the target’s trajectory and alert other sensors within range of the target’s path, so that they switch to high power. Once the target has passed outside of a sensor’s range, it reverts to low-power mode.

The smart sensor networks also provide resilience. If a few nodes in the network fail, then the nodes surrounding the hole in coverage formed by the failed nodes jointly optimize to expand their sensing ranges to cover the gap.

“These networks have to contain built-in, distributed intelligence,” says Gupta, an assistant professor of electrical and computer engineering.

His first research paper on the algorithm, coauthored by graduate student James Hare, was published online in IEEE Transactions on Cybernetics in August 2017.

With this advance, crews on ships and submarines will be able to track enemy watercraft with batteries that last about 60 to 90 percent longer, Gupta says.

Gupta’s lab has prototypes of the sensors for ground use, and has been talking with Navy personnel about using them for the underwater acoustic sensor network.  He is currently seeking funding to build underwater sensors.

There’s a great scene in the movie “Iron Man” where Robert Downey Jr.’s character Tony Stark (aka Iron Man) is crawling across his lab, desperately trying to reach the small arc reactor he needs to keep his heart beating and stay alive.

Weakened by a run-in with arch villain Obadiah Stane, Stark can’t reach the gizmo where it sits on a tabletop. Defeated, he rolls onto his back, exhausted and pondering his inevitable doom.

But the very moment that we think our intrepid hero’s a goner, a metallic hand appears at Stark’s shoulder, holding the lifesaving device. “Good boy,” Stark says weakly as he takes the device from his robot assistant, Dum-E.

And just like that, our hero is saved.

From the dutiful shuffling of C-3PO to the terrorizing menace of The Terminator, Hollywood has made millions tantalizing audiences with far-out robot technology. Scenes like the one in “Iron Man” make for good entertainment, but they also are based, to some degree, in reality.

Dum-E’s interaction with Stark is called collaborative robotics, where robots with advanced artificial intelligence, or A.I., not only work alongside us humans but also are able to anticipate our actions and even grasp what we need.

Collaborative robotics represents the frontier of robotics and A.I. research today. And it’s happening at UConn.

Three thousand miles away from the klieg lights of Hollywood, Ashwin Dani, director of UConn’s Robotics and Controls Lab, or RCL, stands in the stark fluorescent light of his Storrs office staring at a whiteboard covered in hastily scrawled diagrams and mathematical equations.

Here, in the seemingly unintelligible mishmash of numbers and figures, are the underlying mathematical processes that are the lifeblood of collaborative robotics.

If robots are going to interact safely and appropriately with humans in homes and factories across the country, they need to learn how to adapt to the constantly changing world around them, says Dani, a member of UConn’s electrical and computer engineering faculty.

“We’re trying to move toward human intelligence. We’re still far from where we want to be, but we’re definitely making robots smarter,” he explains.

All of the subconscious observations and moves we humans take for granted when we interact with others and travel through the world have to be taught to a robotic machine.

When you think about it, simply getting a robot to pick up a cup of water (without crushing it) and move it to another location (without spilling its contents or knocking things over) is an extraordinarily complex task. It requires visual acuity, a knowledge of physics, fine motor skills, and a basic understanding of what a cup looks like and how it is used.

“We’re teaching robots concepts about very specific situations,” says Harish Ravichandar, the senior Ph.D. student in Dani’s lab and a specialist in human-robot collaboration. “Say you’re teaching a robot to move a cup. Moving it once is easy. But what if the cup is shifted, say, 12 inches to the left? If you ask the robot to pick up the cup and the robot simply repeats its initial movement, the cup is no longer there.”

Repetitive programs that work so well for assembly-line robots are old school. A collaborative robot has to be able to constantly process new information coming in through its sensors and quickly determine what it needs to do to safely and efficiently complete a task. If that robot is part of an assembly line, the line has to shut down and the robot has to be reprogrammed to account for the change, an inefficient process that costs manufacturers money. Hence the thinking robot this team is trying to create.

While the internet is filled with mesmerizing videos of robots doing backflips, jumping over obstacles, and even making paper airplanes, the UConn team’s effort at controlling robots through advanced artificial intelligence is far less flashy but potentially far more important.

Every move the UConn team wants its test robot to make starts here, says Dani, with control theory, engineering, whiteboards, and math.

“We’re writing algorithms and applying different aspects of control theory to take robot intelligence to a higher level,” says Ravichandar. “Rather than programming the robot to make one single movement, we are teaching the robot that it has an objective — reaching for and grabbing the cup. If we succeed, the robot should be able to make whatever movements are necessary to complete that task no matter where the cup is. When it can do that, now the robot has learned the task of picking something up and moving it somewhere else. That’s a very big step.”

While most of us are familiar with the robots of science fiction, actual robots have existed for centuries. Leonardo da Vinci wowed friends at a Milan pageant in 1495 when he unveiled a robotic knight that could sit, stand, lift its visor, and move its arms. It was a marvel of advanced engineering, using an elaborate pulley and cable system and a controller in its chest to manipulate and power its movements.

But it wasn’t until Connecticut’s own Joseph Engelberger introduced the first industrial robotic arm, the 2,700-pound Unimate #001, in 1961 that robots became a staple in modern manufacturing.

Unimates were first called into service in the automobile industry, and today, automobile manufacturers like BMW continue to be progressive leaders using robots on the factory floor. At a BMW plant in Spartanburg, South Carolina, for example, collaborative robots help glue down insulation and water barriers on vehicle doors while their human counterparts hold the material in place.

The advent of high-end sensors, better microprocessors, and cheaper and easily programmable industrial robots is transforming industry today, with many mid-size and smaller companies considering automation and the use of collaborative robots.

Worldwide use of industrial robots is expected to increase from about 1.8 million units at the end of 2016 to 3 million units by 2020, according to the International Federation of Robotics. China, South Korea, and Japan use the most industrial robots, followed by the United States and Germany.

Anticipating further growth in industrial robotics, the Obama administration created the national Advanced Robotics Manufacturing Institute, bringing together the resources of private industry, academia, and government to spark innovations and new technologies in the fields of robotics and artificial intelligence. UConn’s Robotics and Controls Lab is a member of that initiative, along with the United Technologies Research Center, UTC Aerospace Systems, and ABB US Corporate Research in Connecticut.

Manufacturers see real value in integrating collaborative robots into their production lines. The biggest concern, clearly, is safety.

There have been 39 incidents of robot-related injuries or deaths in the U.S. since 1984, according to the federal Occupational Safety and Health Administration. To be fair, none of those incidents involved collaborative robots and all of them were later attributed to human error or engineering issues.

The first human known to have been killed by a robot was Robert Williams in 1979. Williams died when he got tired of waiting for a part and climbed into a robot’s work zone in a storage area in a Ford Motor plant in Flat Rock, Michigan. He was struck on the head by the robot’s arm and died instantly. The most recent incident happened in January 2017, when an employee at a California plastics plant entered a robot’s workspace to tighten a loose hose and had his sternum fractured when the robot’s arm suddenly swung into action.

“When you have a human and a robot trying to do a joint task, the first thing you need to think about of course is safety,” says Dani. “In our lab, we use sensors that, along with our algorithms, not only allow the robot to figure out where the human is but also allow it to predict where the human might be a few seconds later.”

One way to do that is to teach robots the same assembly steps taught to their human counterparts. If the robot knows the order of the assembly process, it can anticipate its human partners’ next moves, thereby reducing the possibility of an incident, Dani says. Knowing the process would also allow robots to help humans assemble things more quickly if they can anticipate an upcoming step and prepare a part for assembly, thus improving factory efficiency.

“Humans are constantly observing and predicting each other’s movements. We do it subconsciously,” says Ravichandar. “The idea is to have robots do the same thing. If the robot sees its human partner performing one step in an assembly process, it will automatically move on to prepare for the next step.”

Which brings us back to the whiteboards. And the math.

Failure is always an option. But when the math finally works, Ravichandar says, the success is exhilarating.

“Once you have the math figured out, it’s the best feeling because you know what you want the robot to do is going to work,” Ravichandar says with an excited smile.

“Implementing it is a whole other challenge,” he adds quickly, his passion for his work undiminished. “Things never work the first time. You have to constantly debug the code. But when you finally see the robot move, it is great because you know you have translated this abstract mathematical model into reality and actually made a machine move. It doesn’t get any better than that.”

With an eye on developing collaborative robotics that will assist with manufacturing, Dani and his team spent part of the past year teaching their lab’s test robot to identify tools laid out on a table so it can differentiate between a screwdriver, for example, and a crescent wrench, even when the tools’ initial positions are rearranged. Ultimately, they hope to craft algorithms that will help the robot work closely with a human counterpart on basic assembly tasks.

Another member of the team, Ph.D. candidate Gang Yao, is developing programs that help a robot track objects it sees with its visual sensors. Again, things we humans take for granted, such as being able to tell the difference between a bird and a drone flying above the trees, a robot has to learn.

Building advanced artificial intelligence doesn’t happen overnight. Ravichandar has been working on his projects for more than three years. It is, as they say, a process. Yet the team has learned to appreciate even the smallest of advances, and late last year, he flew to California to present some of the lab’s work to an interested team at Google.

“C-3PO is a protocol droid with general artificial intelligence,” says Ravichandar. “What we are working on is known as narrow artificial intelligence. We are developing skills for the robot one task at a time and designing algorithms that guarantee that whatever obstacles or challenges the robot encounters, it will always try to figure out a safe way to complete its given task as efficiently as it can. With generalized intelligence, a robot brings many levels of specific intelligence together and can access those skills quickly on demand. We’re not at that point yet. But we are at a point where we can teach a robot a lot of small things.”

Inevitably, as robots gain more and more human characteristics, people tend to start worrying about how much influence robots may have on our future.

Robots certainly aren’t going away. Saudi Arabia recently granted a robot named Sophia citizenship. Tesla’s Elon Musk and Deep Mind’s Mustafa Suleyman are currently leading a group of scientists calling for a ban on autonomous weapons, out of concern for the eventual development of robots designed primarily to kill.

Although it doesn’t apply directly to their current research, Dani and Ravichandar say they are well aware of the ethical concerns surrounding robots with advanced artificial intelligence.

Ravichandar says the problem is known in the field as “value alignment,” where developers try to make sure the robot’s core values are aligned with those of humans. One way of doing that, Ravichandar says, is to create a safety mechanism, such as making sure the robot always understands that the best solution it can come up with for a problem might not always be the best answer.

“The time is coming when we will need to have consensus on how to regulate this,” says Ravichandar. “Like any technology, you need to have regulations. But I think it’s absolutely visionary to inject humility into robots, and that’s happening now.”

That’s good news for the rest of us, because killer robots certainly are not the droids we’re looking for.

 

originally written by Colin Poitras

Over 350 million people in the United States depend on the reliability and consistency of the 450,000 miles of high-voltage lines that form the U.S. power grid to do important daily tasks. With stronger weather events and an increasing number of cyber-attacks, reliable safeguards and technologies are needed to protect this very important utility. The Power and Energy Systems Laboratory, run by Dr. Peng Zhang, F.L. Castleman Associate Professor of Electrical and Computer Engineering and his graduate students, aims at tackling these important issues.

The lab, which focuses on smart grid technology, microgrids, and sustainable energy, has worked on several crucial projects over the past several years, including a dedicated approach to networking the grid system, determining risk assessment models for unintentional islanding of power generators, using ocean waves to generate a sustainable power source, and many other related areas of research.

The lab’s most recent research, which focuses on national infrastructure, is looking to enhance the connectedness of the fractured U.S. grid system:  

“The goal of our research is to make our nation’s energy infrastructure resilient, reliable, secure, and sustainable,” Zhang said. “One of our main areas of focus now is large systems power stability, which is important, because there are no tools available to really assess and predict the status of the system. This tool is highly needed, especially for connected systems.”

That tool, which is being developed from a $1.05 million Department of Energy grant, is being worked on by Zhang and Ph.D. student Yan Li. The idea for the grant was inspired by the learnings Zhang and his students gathered from studying the tools used by local utility company, Eversource Energy:

“The whole U.S. and Canadian grid are connected together, and it’s a huge system, so it’s  therefore very difficult to monitor, assess and control its stability ,” Zhang said. “If you look at Connecticut, companies like Eversource, for the most part, use off-line tools which run many scenarios and only look at snapshots of the system, but that kind of work is not great for analysis, it needs to be monitored and assessed in real-time.”

In particular, Li and Zhang will be forming a formal theory with mathematical rigor, which will be established for computing the bounds of all possible trajectories and estimating the stability margin for the entire system, including the integrated transmission and distribution network.

Furthermore, a new open-source tool via reachable set computations will be developed for real-time dynamic analysis and stability margin calculations. It will be applicable for not only forecasting and monitoring grid performance, but also formally verifying various resiliency enhancement strategies, such as new schemes for system integrity protection and automation to adapt to this evolution of electric networks. 

Zhang and his students are also making significant contributions to knowledge advancement in the state of Connecticut and the region, with their work at the Eversource Energy Center at UConn, which conducts research related to advancing energy technology, as well as performs significant consulting work with Eversource Energy.

Zhang and his students have specifically performed work on research related to unintentional islanding, which is a phenomenon in which a distributed generator continues to be electrified and running, even when the electrical grid surrounding it is no longer active. Traditional methods of detection can often be fooled to think that grid conditions are normal, especially when multiple power generation devices are connected to the same line. 

This kind of scenario is very dangerous to field workers, as the lack of knowledge could cause them to be electrocuted. Zhang said that the research that’s currently being done by himself and his team addresses the creation a risk assessment model to safely avoid danger:

“This kind of research is using machine learning to predict risk in the non-detection zone,” Zhang said. “By non-detection we mean that when an island occurs, there are certain scenarios where the utility company, but with this new risk assessment model, we’ll be able to accurately predict when this scenario is likely to occur.”

Most importantly though, Zhang is happy that the graduate students that he is mentoring, and providing hands-on opportunities to, are getting the necessary experience needed to launch their careers in research and academia.

Ph.D. student Taofeek Orekan, one of the members of the lab, is one of the students that will be using that experience, as he looks for post-grad opportunities in the next few months:

“This is a great lab to start off in,” Orekan said. “I know that any lab that I launch during my career in academia will absolutely be an extension of this lab.”

For more information on the lab, visit http://power.engr.uconn.edu. 

Professor Emeritus Charles Harris Knapp, 86, passed away at

home on Thanksgiving Day

surrounded by his family and loving wife of 62 years, Charleen Gaudet Knapp. Over the course of his 40-year career at UConn, Dr. Knapp enriched and shaped the

lives of innumerable students and inspired an imperishable legacy, The Charles H. Knapp Associate Professorship in Electrical Engineering.

Charlie (or “Red” or “Harris,” as he was variously known) was so eminent in his field, that a handwritten note recommending a student to a graduate program at another well-regarded university signed only “Charlie” was sufficient; no letterhead needed. Colleagues and former students remembering Charlie reiterate his traits as an educator, mentor, and researcher calling him “a true gentleman,” showing people “how to treat others,” “the best teacher,” and never seeing “him lose his cool.”

A lifelong Yankees fan, Charlie was born in New York City, but moved with his family to Coventry, CT at an early age. As an Electrical Engineering undergraduate at UConn, he was the first University Scholar from the School of Engineering and member of Tau Beta Phi, Eta Kappa Nu, and Sigma Xi engineering honor societies. Upon graduation in 1953, Charlie served two years of active duty in the U.S. Air Force before earning his Master’s degree from Yale University and working for RCA and IBM. He returned to UConn in 1958 and became the first candidate awarded a Ph.D. in Electrical Engineering.   

Charlie had many interests outside of academia. He began running in his late 20s, was an avid gardener, active member of the Storrs Congregational Church serving in many capacities including his favorite, singing in the choir for more than 40 years, and spending time with his four children and their families.

In 2012, the Knapp children honored their father with a generous donation establishing The Charles H. Knapp Associate Professorship in Electrical Engineering. Explaining the decision to create an associate professorship, typically given to newer, upcoming faculty, Charlie said, “The younger professors have the freshest education and ideas and are looking for places where they can grow. This professorship will give its holder an edge and will be very helpful in retaining the best associate professors. It is important for the university and the college if we can keep them here, and it’s good for local industry, as well.”

During his lifetime, Professor Knapp enriched the lives of countless students and colleagues, family and friends. Through their lives and his eponymous professorship, his legacy and memory will endure for generations.

 

Donations in his memory may be made to: The Charles H. Knapp Associate Professorship in Electrical Engineering, c/o The UConn Foundation, 2390 Alumni Drive., Unit 3206, Storrs, CT, 06269-3206

The Conference

The International Joint Conference on Biometrics (IJCB 2017) combines two major biometrics research annual conferences, the Biometrics Theory, Applications and Systems (BTAS) conference and the International Conference on Biometrics (ICB). The blending of these two conferences in 2017 is through special agreement between the IEEE Biometrics Council and the IAPR TC-4, and presents an exciting event for the entire worldwide biometrics research community.

The Paper

The paper, “On the Vulnerability of ECG Verification to Online Presentation Attacks,” examined the use of Electrocardiogram (ECG) as a secure biometric modality. ECG has long been regarded as a biometric modality which is impractical to copy, clone, or spoof. However, it was recently shown that an ECG signal can be replayed from arbitrary waveform generators, computer sound cards, or off-the-shelf audio players. The award-winning paper is one of the first in the field to seriously question the security of ECG verification, and goes a long way towards debunking the assumption of its security.

The paper developed a novel presentation attack where a short template of the victim’s ECG is captured by an attacker and used to map the attacker’s ECG into the victim’s, which can then be provided to the sensor using one of the above sources. The authors’ approach involved exploiting ECG models, characterizing the differences between ECG signals, and developing mapping functions that transform any ECG into one that closely matches an authentic user’s ECG. Their proposed approach, which can operate online or on-the-fly, is compared with a more ideal offline scenario where the attacker has more time and resources. In the experiments, the offline approach achieved average success rates of 97.43% and 94.17% for non-fiducial and fiducial based ECG authentication. In the online scenario, the performance is degraded by 5.65% for non-fiducial based authentication, but is nearly unaffected for fiducial authentication.

The work was supported by US Army Research Office (ARO) under award number W911NF16-1-0321.