Author Archives: feq12001

How UConn Researchers are Teaching Robots to Think Like Humans

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

Zhang and His Students Look To Advance Power Systems Into The Future

Peng with the members of his Power and Energy Systems Lab. (UConn Photo/Christopher Larosa)

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. 

(UConn Photo/Christopher Larosa)

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. 

Remembering Charles Knapp, an Engineering Icon

Professor Emeritus Charles Harris Knapp, 86, passed away at

Robert (left) and Charles ’53, ’62 Knapp. (Photo courtesy of The UConn Foundation)

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

ECE Seminar Series: Quantum Dot Channel FETs and Nonvolatile Memories: Fabrication and Modeling

ECE title

ECE Seminar Series Fall 2017

Wednesday November 15th 2:30pm-3:30 PM, GENT 103

Quantum Dot Channel FETs and Nonvolatile Memories: Fabrication and Modeling

Dr. Jun Kondo

Abstract: This talk presents modeling and fabrication of quantum dot channel field effect transistors (QDC-FETs) using cladded Ge quantum dots on poly-Si thin films grown on silicon-on-insulator (SOI) substrtes. HfAlO2 high-k dielectric layers are used for the gate dielectric. QDC-FETs exhibit multi-state I-V characteristics which enable two-bit processing, and reduce FET count and power dissipation, and are expected to make a significant impact on the digital circuit design. Quntum dot channel FETs are also configured as floating gate quatnum dot nonvolatile memories (QDC-QDNVMs). In NVMs, we use floating gate comprising of GeOx-Ge quantum dots. QD nonvolatile memories (QD-NVMs) are fabricated on polysilicon thin films usingn SOI substrates. HfAlO2 high-k insulator laeyrs are used for both tunnel gate oxide as well as conhtrol gate dielectric. QDC-NVMs provide not only significantly higher ID current flow, but also significantly higher threshold voltage shifts which improve the threshold voltage variation, and show the potential for fabricating multi-bit nonvolatile memories.

Portable Microscope Makes Field Diagnosis Possible

Siddharth Rawat, left, a Ph.D. student, and Bahram Javidi, Board of Trustees Distinguished Professor of Electrical and Computer Engineering, operate a prototype device to examine blood samples for diseases. The portable holographic field microscope offers medical professionals a fast and reliable tool for the identification of diseased cells. (Peter Morenus/UConn Photo)


A portable holographic field microscope developed by UConn optical engineers could provide medical professionals with a fast and reliable new tool for the identification of diseased cells and other biological specimens.

The device, featured in a recent paper published by Applied Optics, uses the latest in digital camera sensor technology, advanced optical engineering, computational algorithms, and statistical analysis to provide rapid automated identification of diseased cells.

One potential field application for the microscope is helping medical workers identify patients with malaria in remote areas of Africa and Asia where the disease is endemic.

Quick and accurate detection of malaria is critical when it comes to treating patients and preventing outbreaks of the mosquito-borne disease, which infected more than 200 million people worldwide in 2015, according to the Centers for Disease Control. Laboratory analysis of a blood sample remains the gold standard for confirming a malaria diagnosis.  Yet access to trained technicians and necessary equipment can be difficult and unreliable in those regions.

The microscope’s potential applications go far beyond the field diagnosis of malaria. The detailed holograms generated by the instrument also can be used in hospitals and other clinical settings for rapid analysis of cell morphology and cell physiology associated with cancer, hepatitis, HIV, sickle cell disease, heart disease, and other illnesses, the developers say.

In checking for the presence of disease, most hospitals currently rely on dedicated laboratories that conduct various tests for cell analysis and identification. But that approach is time consuming, expensive, and labor intensive. It also has to be done by skilled technicians working with the right equipment.

“Our optical instrument cuts down the time it takes to process this information from days to minutes,” says Bahram Javidi, Board of Trustees Distinguished Professor in the Department of Electrical and Computer Engineering and the microscope’s senior developer. “And people running the tests don’t have to be experts, because the algorithms will determine if a result is positive or negative.”

The research team consulted with hematologists, and the algorithms used with the instrument are able to compare a sample against the known features of healthy cells and the known features of diseased cells in order to make proper identification. “It’s all done very quickly,” Javidi says.

How the Device Works

When it comes to identifying patients with malaria, here’s how the device works: A thin smear from a patient’s blood sample is placed on a glass side, which is put under the microscope for analysis. The sample is exposed to a monochromatic light beam generated by a laser diode or other light source. Special components and optical technologies inside the microscope split the light beam into two beams in order to record a digital hologram of the red blood cells in the sample. An image sensor, such as a digital webcam or cell phone camera, connected to the 3-D microscope captures the hologram.  From there, the captured data can be transferred to a laptop computer or offsite laboratory database via the internet. Loaded with dedicated algorithms, the computer or mobile device hardware reconstructs a 3-D profile of the cell and measures the interaction of light with the cell under inspection. Any diseased cells are identified using computer pattern recognition software and statistical analysis.

Quantitative phase profiles of healthy red blood cells (top row) and malaria infected cells (bottom row). (Holographic microscope image courtesy of Bahram Javidi)

Red blood cells infected with the malaria-causing Plasmodium parasite exhibit different properties than healthy blood cells when light passes through them, Javidi says.

“Light behaves differently when it passes through a healthy cell compared to when it passes through a diseased cell,” Javidi says. “Today’s advanced sensors can detect those subtle differences, and it is those nanoscale variations that we are able to measure with this microscope.”

Conventional light microscopes only record the projected image intensity of an object, and have limited capability for visualizing the detailed quantitative characterizations of cells. The digital holograms acquired by UConn’s 3-D microscope, on the other hand, capture unique micro and nanoscale structural features of individual cells with great detail and clarity. Those enhanced images allow medical professionals and researchers to measure an individual cell’s thickness, volume, surface, and dry mass, as well as other structural and physiological changes in a cell or groups of cells over time – all of which can assist in disease identification, treatment, and research. For instance, the device could help researchers see whether new drugs impact cells positively or negatively during clinical trials.

The techniques associated with the holographic microscope also are non-invasive, highlighting its potential use for long-term quantitative analysis of living cells.

Conventional methods of testing blood samples for disease frequently involve labeling, which means the sample is treated with a chemical agent to assist with identification. In the case of malaria, red blood cells are usually treated with a Giemsa stain that reacts to proteins produced by malaria-carrying parasites and thus identifies them. But introducing a chemical into a live cell can change its behavior or damage it.

“If you’re doing an in vitro inspection of stem cells, for instance, and you introduce a chemical agent, you risk damaging those cells. And you can’t do that, because you may want to introduce those cells into the human body at some point,” Javidi says. “Our instrument doesn’t rely on labeling, and therefore avoids that problem.” 

Ph.D. students Tim O’Connor ’17 (ENG), left, Siddharth Rawat, and Adam Markman ’11 (ENG) operate a prototype device to examine blood samples for diseases at the Javidi lab in the Information Technologies Engineering Building. (Peter Morenus/UConn Photo)

The holographic microscope was developed in UConn’s new Multidimensional Optical Sensing & Imaging Systems or MOSIS lab, where Javidi serves as director. The MOSIS lab integrates optics, photonics, and computational algorithms and systems to advance the science and engineering of imaging from nano to macro scales.

A comprehensive report on the MOSIS lab’s work with 3-D optical imaging for medical diagnostics was published last year in Proccedings of the IEEE, the top-ranked journal for electrical and electronics engineering. Joining Javidi in this research are graduate students Adam Markman, Siddharth Rawat, Satoru Komatsu, and Tim O’Connor from UConn; and Arun Anand, an applied optics specialist with Maharaja Sayajirao University of Baroda in Vadodara, India.

The microscope research is supported by Nikon and the National Science Foundation (ECCS 1545687). Students are supported by the U.S. Department of Education, GE, and Canon fellowships. Other sponsors that have supported Javidi’s broader research work and the MOSIS lab over the years include the Defense Advanced Research Projects Agency or DARPA, the U.S. Airforce Research Lab, the U.S. Army, the Office of Naval Research, Samsung, Honeywell, and Lockheed Martin. He has collaborated with colleagues from numerous universities and industries around the world during his time at UConn, including research facilities in Japan, Korea, China, India, Germany, England, Italy, Switzerland, and Spain, among other countries.

Javidi is working with colleagues at UConn Health, including medical oncology and hematology specialist Dr. Biree Andemariam and her staff, for other medical applications. UConn’s tech commercialization office has been involved in discussing potential marketing opportunities for the portable digital microscope. A prototype of the microscope used for initial tests was assembled using 3-D printing technologies, lowering its production costs.

 

Original from UConn Today, Colin Poitras.

Navy Using New UConn Software to Improve Navigation

The Navy is using new software developed by UConn engineering professor Krishna Pattipati to vastly improve the ability to route ships through unpredictable situations.


Major research discoveries generate news headlines. But a research undertaking by one University of Connecticut engineering lab seeks to forestall some headlines of a different kind.

The loss of life because of weather events, as happened on Oct. 1, 2015 when cargo ship El Faro sank with its 33-member crew in Hurricane Joaquin, is one example. Transcripts released by the National Transportation Safety Board showed an increasingly anxious and panicked crew as the 790-foot vessel sailed into the raging storm two years ago.

Software developed by Krishna Pattipati, UTC Professor in Systems Engineering at UConn and his research team, in collaboration with the U.S. Naval Research Laboratory-Monterey, may go a long way toward avoiding such tragedies.

The prototype, named TMPLAR (Tool for Multi-objective Planning and Asset Routing), is now being used by the Navy to vastly improve the ability of ships to reroute through unpredictable weather. It is the type of technology transition that the new National Institute for Undersea Vehicle Technology based at UConn Avery Point, is now able to foster.

Screenshot of a requested ship transit from San Diego, California, toward Alaska. The black line is the suggested route the Navy navigator is given to accept or reject and send on as directions to a ship’s captain. The numbered red circles are ‘waypoints’ along the route, with the starting point labeled ‘0’. These waypoints divide up a possibly long voyage and keep the ship’s path in check.

Created by Pattipati and electrical and computer engineering graduate students David Sidoti, Vinod Avvari, Adam Bienkowski, and Lingyi Zhang, and undergraduate students Matthew Macesker and Michelle Voong, TMPLAR is still in development, but it has already been fully integrated with the Navy’s meteorology and oceanographic weather forecasts.

Members of the UConn team meet weekly with Navy officials, via teleconference, to discuss project updates and receive  feedback.

“Their progress is fast,” says Sidoti. “Frankly, it’s kept us on our toes as we try to manage both our academic responsibilities here at UConn while enhancing and updating the software.”

TMPLAR is like a much more complex version of Google Maps, because it will be applied to ships and submarines, where there is no underlying network of roadways to navigate.

In Google Maps, a user typically seeks to maximize the average speed of travel between start and end locations to get to a destination in the shortest amount of time, hence the route may favor highways instead of back roads.

Pattipati’s team is now approaching problems with upwards of 17 or more objectives, which may change depending on the vehicle and the conditions.

The algorithms take into account obstacles such as ocean depth, undersea pipelines, cables, oil rigs, for example. And they factor in multiple user objectives, whether to traverse to an area to minimize travel time, maximize fuel efficiency given the predicted weather, accomplish training objectives, or maximize operational endurance.

“The tool guarantees safe travel from any point in the ocean, above, on, or below its surface, while making choices en route that optimize fuel consumption and cater to any set of objectives of the operator,” says Sidoti. “Using special clustering techniques, the tool’s algorithms have even been applied to finding low-risk routes that avoid storms or hurricanes.”

The next step for TMPLAR is programming the tool for use by aircraft, such as drones.

Last month, Pattipati and Sidoti traveled to San Diego to demonstrate the capabilities of the software to the Space and Naval Warfare Systems Center Pacific. Their algorithim is now going to be integrated with a tool for aircraft carrier strike group planning.

The lab first published details about the software last year in the journal IEEE, the world’s largest professional organization for the advancement of technology. Avvari, one of the graduate students, will detail some of the enhancements that have been made since then at an upcoming professional conference.

And, as the software transitions to operational settings, the team is looking to speed up the capabilities to output smart weather-informed route recommendations in less than a second. Adding neural network modules to TMPLAR is another new horizon; artificial intelligence would help condense solutions so it is less overwhelming to a user, says Sidoti.

When he reviewed the factors faced by the crew of El Faro using TMPLAR software, Sidoti was able to find safe routes for the ship that involved waiting at waypoints and varying the ship’s speed in order to avoid unsafe environmental conditions, while also reducing costs of the route.

The Coast Guard’s report on the tragedy – released just a month ago – said the captain misjudged the strength of Hurricane Joaquin and should have changed the El Faro’s course.

Sidoti found up to eight possible safe routes using TMPLAR. That’s the sort of information he hopes other captains will have.

Recently, the team received notification that the software was demo’ed to onboard ship navigators who were interested to the point that they requested the ability to use it in order to plan and test it on a real-world deployment.

Funding for this research is supported by the U.S. Office of Naval Research under contracts #N00014-16-1-2036 and #N00014-12-1-0238; by the Naval Research Laboratory under contract #N00173-16-1-G905; and by the Department of Defense High Performance Computing Modernization Program under subproject contract #HPCM034125HQU.

 

Original from UConn Today, Kristen Cole.

Award-winning Paper Questions ECG As Secure Biometric

A paper from UConn fourth year PhD student Nima Karimian has won the best student paper award at the recent IJCB 2017 conference in Denver.

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.

ECE Seminar Series: Energy Harvesting (EH) Opportunities in 5G

ECE title

ECE Seminar Series Fall 2017

Monday October 16th 11:00am-12:00 PM, ITE 401

Energy Harvesting (EH) Opportunities in 5G

Brian Zahnstecher

PowerRox

Abstract: We have all seen plenty of the marketing hype for what will eventually become 5G in the ~2020 production deployment timeframe. At PowerRox, we have spent the last year trying to shed light on the most critical aspects of the network (from architecture to utilization), which are the paradigm shifts in power electronics and power utilization required to enable 5G. Now, we can investigate further into one of the most interesting, yet highly underappreciated, opportunities in 5G power…energy harvesting (EH). Everyone likes the thought of free, ambient energy, but most think this technology neither produces a usable amount of power nor has a production ecosystem mature enough for a telecom deployment. This talk will not only help to dispel these perceptions, but also help open the eyes of attendees to applications that they might not have otherwise thought were possible and/or applicable to telco applications (with a special focus on 5G).

Energy harvesting (EH) presents a host of interesting and useful applications that can be utilized today as well as provides a roadmap for enhancing/increasing application use cases moving forward. From mW to MW, there are scalable EH technologies to take advantage of nearly every energy source physics affords us (i.e. – kinetic, thermal, RF, photovoltaic, piezoelectric, vibrational, etc.). The major shift from macro towers to heterogeneous networks (HetNets) of many small cells makes 5G an ideal candidate for EH applications. Battery mitigation is a key goal of EH technology initially by supplementing battery power to extend battery life and eventually disposing of them altogether. Even security at many network points from the base station to the grid-level can benefit from EH by achieving grid independence and/or inhibiting undesired network penetration.

This talk will provide attendees with a wealth of knowledge and thought-provoking insight on how EH can be applied to 5G and creative applications beyond. First, we will provide a quick overview of EH sources/technologies, and review the transducers, power management ICs (PMICs)/topologies, energy storage, and test/measurement solutions that make up the production ecosystem. Then, we will deepdive on the implementation of these constituents into practical power electronics solutions and see how we can scale (even μW) to more usable power levels. Finally, we will close with a number of quick case studies on how to apply EH to 5G (and related) applications at all levels of the network from data center to edge. Additionally, we will look at some more unique applications (i.e. – Security) within 5G that EH is a key enabler for.


Short Bio
: Brian Zahnstecher is a Sr. Member of the IEEE, Chair of the IEEE SF Bay Area Power Electronics Society (PELS), and the Principal of PowerRox, where he focuses on power design, integration, system applications, OEM market penetration, and private seminars for power electronics. He has successfully handled assignments in system design/architecting, AC/DC front-end power, EMC/EMI design/debug, embedded solutions, processor power, and digital power solutions for a variety of clients. He previously held positions in power electronics with industry leaders Emerson Network Power, Cisco, and Hewlett-Packard, where he advised on best practices, oversaw product development, managed international teams, created/enhanced optimal workflows and test procedures, and designed and optimized voltage regulators. He has been a regular contributor to the industry as an invited speaker, author, workshop participant, session host, roundtable moderator, and volunteer. He has over 13 years of industry experience and holds Master of Engineering and Bachelor of Science degrees from Worcester Polytechnic Institute.

PowerRox is a firm dedicated to solving power problems for those seeking to establish or enhance their position in the enterprise and consumer power electronics marketplace. We specialize in improving efficiency, increasing reliability, achieving cost reduction through hands-on support and training/seminars/workshops. We can solve problems in power supply design, power system development, system debug and test, cost/performance analysis, marketing, and re-design. We are committed to meeting all deadlines, performing on-budget, debugging/testing solutions to required levels, and doing the highest quality work possible.

ECE Seminar Series: Resiliency and Security of the Future Power Grid

ECE title

ECE Seminar Series Fall 2017

(co-sponsor with Eversource Energy Center)

Monday October 16th 1:00-2:00 PM, LH 201

Resiliency and Security of the Future Power Grid

Chen-Ching Liu

Boeing Distinguished Professor
Director, Energy Systems and Innovation Center (ESIC)
School of Electrical Engineering & Computer Science
Washington State University

Abstract: The development of smart grid in the U.S. over the last decade significantly enhanced data acquisition capabilities on the transmission system. For the distribution network, numerous remote control devices and voltage/var control systems have been installed and millions of smart meters are now operational on the customer side. Although the level of automation has been improved, there are great challenges in the grid’s ability to withstand extreme events such as catastrophic hurricanes and earthquakes. Resiliency of the future grid can be achieved by enabling flexible reconfiguration with distributed resources, e.g., microgrid, distributed generations, as well as renewable and storage devices. Advanced and distributed operation and control will be critical for the vision. Fast increasing connectivity of the devices and systems on the power grid also led to a serious concern over the security of the complex cyber-physical system. Progress has been made in developing new technologies for cyber security of the power grid, including monitoring, vulnerability assessment, intrusion detection, and mitigation


Short Bio
: Chen-Ching Liu is Boeing Distinguished Professor at Washington State University (WSU), Pullman, WA. At WSU, Professor Liu served as Director of the Energy Systems Innovation Center. During 1983-2005, he was a Professor of Electrical Engineering at University of Washington, Seattle. Dr. Liu was Palmer Chair Professor at Iowa State University from 2006 to 2008. From 2008-2011, he served as Acting/Deputy Principal of the College of Engineering, Mathematical and Physical Sciences at University College Dublin, Ireland. Professor Liu received an IEEE Third Millennium Medal in 2000 and the Power and Energy Society Outstanding Power Engineering Educator Award in 2004. In 2013, Dr. Liu received a Doctor Honoris Causa from Polytechnic University of Bucharest, Romania. Chen-Ching chaired the IEEE Power and Energy Society Fellow Committee, Technical Committee on Power System Analysis, Computing and Economics, and Outstanding Power Engineering Educator Award Committee. He served on the U.S. National Academies Board on Global Science and Technology. Professor Liu is a Fellow of the IEEE and Member of the Washington State Academy of Sciences.

ECE Seminar Series: A New Look at Optimal Control of Wireless Networks

ECE title

ECE Seminar Series Fall 2017

Friday October 13th 2:30-3:30 PM, ITE 119

A New Look at Optimal Control of Wireless Networks

Eytan Modiano

Laboratory for Information and Decision Systems
Massachusetts Institute of Technology

Abstract: We address the problem of throughput-optimal packet dissemination in wireless networks with an arbitrary mix of unicast, broadcast, multicast and anycast traffic. We start with a review of the seminal work of Tassiulas and Ephremides on optimal scheduling and routing of unicast traffic, i.e., the famous backpressure algorithm. The backpressure algorithm maximizes network throughput, but suffers from high implementation complexity, and poor delay performance due to packets looping inside the network. Moreover, backpressure routing is limited to unicast traffic, and cannot be used for broadcast or multicast traffic. We will describe a new online dynamic policy, called Universal Max-Weight (UMW), which solves the above network flow problems simultaneously and efficiently. To the best of our knowledge, UMW is the first throughput-optimal algorithm for solving the generalized network-flow problem. When specialized to the unicast setting, the UMW policy yields a throughput-optimal, loop-free, routing and link-scheduling policy. Extensive simulation results show that the proposed UMW policy incurs substantially smaller delays as compared to backpressure.


Short Bio
: Eytan Modiano received his B.S. degree in Electrical Engineering and Computer Science from the University of Connecticut at Storrs in 1986 and his M.S. and PhD degrees, both in Electrical Engineering, from the University of Maryland, College Park, MD, in 1989 and 1992 respectively. He was a Naval Research Laboratory Fellow between 1987 and 1992 and a National Research Council Post Doctoral Fellow during 1992-1993. Between 1993 and 1999 he was with MIT Lincoln Laboratory. Since 1999 he has been on the faculty at MIT, where he is a Professor and Associate Department Head in the Department of Aeronautics and Astronautics, and Associate Director of the Laboratory for Information and Decision Systems (LIDS). His research is on communication networks and protocols with emphasis on satellite, wireless, and optical networks. He is the co-recipient of the MobiHoc 2016 best paper award, the Wiopt 2013 best paper award, and the Sigmetrics 2006 Best paper award. He is the Editor-in-Chief for IEEE/ACM Transactions on Networking, and served as Associate Editor for IEEE Transactions on Information Theory and IEEE/ACM Transactions on Networking. He was the Technical Program co-chair for IEEE Wiopt 2006, IEEE Infocom 2007, ACM MobiHoc 2007, and DRCN 2015. He is a Fellow of the IEEE and an Associate Fellow of the AIAA, and served on the IEEE Fellows committee.