FAU Engineering & Computer Science News - March 2022

LEADING IN TECHNOLOGY & INNOVATION

The Latest News

ENGINEERING & COMPUTER SCIENCE

Spring 2022

TABLE OF CONTENTS SUBSTANCE DEVELOPED AT FAU COULD MAKE BREATHING A BREEZE IN SPACE

4

FAU TO EXPAND SOUTHEAST NATIONAL MARINE RENEWABLE ENERGY CENTER

5

FAU NAMES PHF COMPUTATIONAL BRAIN HEALTH GRADUATE FELLOWS

7

NOVEL WEARABLE ARMBAND HELPS USERS OF PROSTHETIC HANDS TO ‘GET A GRIP’

8

3D ROBOTIC SPINE ‘TWIN’ OFFERS NEW WAY TO PREVIEW SURGICAL PROCEDURES

9

FAU LANDS $1.3 MILLION NIH GRANT FOR RAPID, AUTOMATED HIV SELF-TEST

11

NOVEL FAU TECHNOLOGY FOR SELF-DRIVING CARS EARNS SECOND U.S. PATENT

12

GUNFIRE OR PLASTIC BAG POPPING? TRAINED COMPUTER KNOWS THE DIFFERENCE

13

FAU LAUNCHES NEW ‘PROFESSIONAL PH.D. PROGRAM IN COMPUTER SCIENCE’

15

SUBSTANCE DEVELOPED AT FAU COULD MAKE BREATHING A BREEZE IN SPACE At elevated levels, carbon dioxide (CO2) may act as an asphyxiant by reducing normal oxygen levels in breathing air, resulting in dizziness, difficulty breathing, elevated blood pressure and heart rate, among others. Accordingly, the United States Occupational Safety and Health Administration (OSHA) has established a permissible exposure limit for CO2 of 5,000 parts per million by volume in air over an eight-hour work shift.

Removing CO2 requires air revitalization systems, especially in space, to ensure the health and safety of astronauts on long-term exploration missions. One such air revitalization system established

by NASA is the Environmental Control and Life Support system, which provides a safe breathing atmosphere for humans in space. To create livable conditions for a human crew in enclosed environments, the CO2 exhaled during respiration is removed from the cabin air. The technology uses carbon dioxide removal assembly, which consists of two identical sets of in-series desiccant (drying agent) and adsorbent (material that captures CO2) beds.

The carbon dioxide removal assembly system treats a gaseous mixture of humid air with elevated levels of CO2, which enters the desiccant bed first where the water vapor is selectively removed. Then, the dried gas enters

the adsorbent bed where a molecular crystal material called zeolite-5A removes CO2. Once the adsorbent beds in the first set are saturated, the gas stream is directed to the second set of beds, while both adsorbent beds in the first set are switched to a regeneration mode at an elevated temperature to recover their adsorption affinity for the subsequent cycle. This system, however, requires numerous adsorption beds and a lot of power consumption to revitalize the air.

There is a need to develop alternative adsorbent materials that can efficiently integrate and intensify the air revitalization process. Researchers from Florida Atlantic University’s College of Engineering and Computer Science could have a promising solution.

In a study, published in the Chemical Engineering Journal , researchers investigated and compared the performance of two adsorbent materials: the commercially available zeolite-5A and a white powdery substance they synthesized in their lab called aminegrafted SBA-15 silica, an “aminosilica.”

applications are different. These differences are what inspired the FAU researchers to develop an adsorbent material designed specifically for air revitalization applications.

For the study, the researchers tested the ability of the aminosilica they synthesized to capture CO2 from dilute streams, including air revitalization and life support systems in enclosed environments. They systematically studied zeolite-5A and aminosilica by conducting comparative performance metrics, specifically, activation temperature and duration, equilibrium CO2 uptake, CO2 adsorption kinetics, regeneration temperature and duration and cyclic stability.

Results showed that the aminosilica material developed at FAU outperformed the zeolite-5A material by requiring lower activation and regeneration temperatures.

The aminosilica also required shorter activation and regeneration durations, and achieved faster CO2 adsorption kinetics.

Although aminosilicas are among the most extensively studied adsorbents for removing CO2 from different gas streams such as biogas, landfill gas and ambient air, their use for air revitalization Read more at eng.fau.edu

Page 04

FAU TO EXPAND SOUTHEAST NATIONAL MARINE RENEWABLE ENERGY CENTER Designated by the United States Department of Energy (DOE) as one of four national marine renewable energy centers, Florida Atlantic University’s Southeast National Marine Renewable Energy Center (SNMREC) has announced expansion plans later this year that will include new university partnerships, capabilities and opportunities.

With significant untapped marine energy resources in the U.S., skyrocketing gas prices and U.S. reliance on oil imports, opportunities exist to safely capture and convert energy into clean, renewable electricity for communities and other emerging uses in the Blue Economy.

In 2021, the DOE estimated the technical potential of marine energy resources, which include wave, tidal, ocean current, ocean thermal, and riverine in all 50 states to be 2,300 terawatt hours per year – equivalent to 57 percent of all U.S. electricity generated in 2019. Utilizing just one-tenth of these resources is enough energy to power 22 million homes.

Established in 2010, SNMREC focuses on accelerating the responsible commercialization of marine energy technologies. The center provides technology assistance, workforce development, regulatory and policy support, environmental measurement, ocean current and ocean thermal resource measurements, on and offshore testing infrastructure, stakeholder outreach, international standards and certification, and support for other key aspects of this emerging clean power sector.

SNMREC has leveraged more than $22 million in state, federal, and private funding to advance projects worldwide to harness the vast untapped energy from the ocean. From securing the country’s first offshore lease for marine energy activities to a demonstration that generated uninterrupted power from the Gulf Stream for a continuous 24-hour period in 2020, SNMREC has established itself as a global leader in the sector.

“We have helped to establish a strong foundation for the offshore energy sector to grow,” said Gabriel Alsenas, SNMREC director. “However, there is much left to realize for the vision of harnessing our oceans to power our communities. To hasten commercialization efforts, we have begun discussions with several university partners to expand the center’s footprint to address the evolving needs of the marine energy sector, especially for projects along the entire U.S. East Coast, not just the southeast.”

Historically, SNMREC has focused on harnessing ocean currents like the Gulf Stream. However, many of the center’s turbine technology developer partners also target rivers and tidal flows. FAU’s new university partners will leverage and expand their research capabilities to better support the sector’s commercial project ambitions in New England and elsewhere.

“We look forward to expanding the University of Massachusetts Amherst’s previous work in the offshore energy

and Blue Economy sectors, especially with respect to solving technical and socio-economic challenges that energy conversion systems face,” said Krish Sharman, Ph.D., professor and endowed chair in renewable energy, Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst.

Wave energy available on the U.S. East Coast also can provide significant power to communities, yet few commercial projects have been proposed. FAU’s new partners will provide significant simulation, testing and research capabilities to help attract and support a wave energy community.

According to Richard Kimball, Ph.D., Presidential Professor of Ocean Engineering and Energy, Department of Mechanical Engineering at the University of Maine, the university’s Alfond W2 Wind Wave Basin, a scaled ocean field test site in Castine and a full-scale demonstration site in Mohegan are available to move wave energy systems from concept to commercial maturity.

“Our work in marine policy, fisheries and regulatory domains in support of offshore energy development has been key for several commercial tidal and river projects in the U.S., as well,” said Kimball. “A healthy industry requires a robust ecosystem of suppliers, service providers and stakeholders working together locally and regionally. This also includes a better understanding of local and regional stakeholder expectations as projects are proposed.”

Read more at eng.fau.edu

Page 05

Page 06

FAU NAMES PHF COMPUTATIONAL BRAIN HEALTH GRADUATE FELLOWS A $1 million gift last fall from Palm Health Foundation (PHF) has launched the Florida Atlantic University StilesNicholson Brain Institute’s “Computational Brain Science and Health Graduate Fellowships.” The gift, awarded through PHF’s Brain Health Innovation Fund, supports new technologies, treatments, resources and educational tools to advance brain health in the community.

FAU has selected four Ph.D. students whose work embraces computational neuroscience to receive the inaugural fellowships. They will be collaborating w i t h FAU fa c u l t y o n a d va n ce d research targeted at understanding the underpinnings and treatment of various brain disorders.

“With generous support from Palm Health Foundation, we are training talented young scientists who will contribute important knowledge and discoveries that will help to address some of our most pervasive and debilitating brain disorders,” said Randy D. Blakely, Ph.D., executive director, FAU Stiles-Nicholson Brain Institute and a professor in biomedical science of FAU’s Schmidt College of Medicine. “These outstanding Ph.D. students selected to receive our first fellowships excel in their fields of research, which target data-intensive investigations underlying Alzheimer’s disease, Parkinson’s disease, autism spectrum disorders, ADHD, depression, among others, that benefit from computational approaches.”

The recipients of the inaugural PHF Computational Brain Health Graduate Fellowships are: Jasmine Chan: Chan is a fourth-year graduate student working under the mentorship of Teresa Wilcox, Ph.D., interim dean and chair, FAU Department of Psychology, within the Charles E. Schmidt College of Science, and a member of the FAU Stiles-Nicholson Brain Institute; Terrence Barnhardt, Ph.D., an associate scientist, FAU Department of Psychology; and Behnaz Ghoraani, Ph.D., associate professor, FAU

Department of Electrical Engineering and Computer Science, within the College of Engineering and Computer Science, a fellow of the FAU Institute for Sensing and Embedded Networks Systems Engineering (I-SENSE), and a member of the FAU Stiles-Nicholson Brain Institute.

Chan will use a powerful signal processing approach and machine learning to dissect the patterns of brain activity that occur when individuals form ad hoc categories in novel situations.

When people are presented with new situations, they often have to search their knowledge to form a new category of items that are appropriate to use for the given scenario. Interestingly, these ad hoc categories are not generated as effectively among older adults and patients with Alzheimer’s disease (AD).

Findings from this project could be used as a diagnostic tool to identify cognitive changes in patients at risk for, or suffering from, AD and other cognitive disorders as biomarkers for objectively selecting and developing treatment options.

Hadi Esfandi: Esfandi is a third-year graduate student who is working under the mentorship of Ramin Pashaie, Ph.D., an associate professor, FAU Department of Electrical Engineering and Computer Science, within the College of Engineering and Computer Science, a fellow of FAU’s I-SENSE, and a member of the FAU Stiles-Nicholson Brain Institute.

Esfandi will design a computational model to understand how altered neurovascular coupling supports AD risk. Neurovascular coupling is a process by which brain cells control the flow of blood to support neural function and health. Esfandi’s research will help to better understand the pathophysiology of the disease, develop new treatments and therapies for AD, and identify neurovascular signals for early detection of AD. Approximately 580,000 people are now living with AD in Florida and this number is projected to increase to more than 720,000 by 2025.

Joseph McKinley: McKinley is a fourthyear graduate student working under the mentorship of Christopher Beetle, Ph.D., associate professor, FAU Department of Physics, within the Charles E. Schmidt College of Science; and Emmanuelle Tognoli, Ph.D., a research professor, FAU Department of Psychology and Center for Complex Systems and Brain Sciences, within the Charles E. Schmidt College of Science, and a member of the FAU Stiles-Nicholson Brain Institute.

McKinley will develop computational models to provide a theoretical foundation for the dynamic responses to neurostimulation. Neurostimulation is a health intervention that disrupts pathological states of neuronal activity, with broad applications in the treatment of many illnesses including Parkinson’s disease, chronic pain, and major depression. Such a framework will help tailor treatment protocols to the unique needs of individual patients, improving efficacy and minimizing side effects.

Yosun Yoon: Yoon is a fourth-year graduate student working under the mentorship of Sang Hong, Ph.D., an associate professor, FAU Department of Psychology within the Charles E. Schmidt College of Science, and a member of the FAU Stiles-Nicholson Brain Institute. Yoon’s research is focused on computational models, which indicate that the strength of synchronization among brain networks promotes flexible cognitive behavior. Yoon will use a non-invasive brain stimulation technique to examine and model how synchronization of the frontoparietal network alters cognitive flexibility.

The long-term goal of the project is to build a foundation for developing a drug-free treatment for people with deficits in cognitive flexibility, such as people with autism spectrum disorders, attention-deficit/hyperactivity disorder and major depressive disorder. In 2021, the United States Centers for Disease Control and Prevention reported that approximately 1 in 44 children in the U.S. is diagnosed with an autism spectrum disorder.

Read more at eng.fau.edu

Page 07

NOVEL WEARABLE ARMBAND HELPS USERS OF PROSTHETIC HANDS TO ‘GET A GRIP’ Typing on a keyboard, pressing buttons on a remote control or braiding a child’s hair has remained elusive for prosthetic hand users. With current myoelectric prosthetic hands, users can only control one grasp function at a time even though modern artificial hands are mechanically capable of individual control of all five digits.

A first-of-its-kind study using haptic/touch sensation feedback, electromyogram (EMG) control and an innovative wearable soft robotic armband could just be a game changer for users of prosthetic hands who have long awaited advances in dexterity. Findings from the study could catalyze a paradigm shift in the way current and future artificial hands are controlled by limb-absent people.

Researchers from Florida Atlantic University’s College of Engineering and Computer Science in collaboration with FAU’s Charles E. Schmidt College of Science investigated whether people could precisely control the grip forces applied to two different objects grasped simultaneously with a dexterous artificial hand.

For the study, they also explored the role that visual feedback played in this complex multitasking model by systematically blocking visual and haptic feedback in the experimental design. In

addition, they studied the potential for time saving in a simultaneous object transportation experiment compared to a one-at-a-time approach. To accomplish these tasks, they designed a novel multichannel wearable soft robotic armband to convey artificial sensations of touch to the robotic hand users.

Results, published in Scientific Reports, showed that multiple channels of haptic feedback enabled subjects to successfully grasp and transport two objects simultaneously with the dexterous artificial hand without breaking or dropping them, even when their vision of both objects was obstructed.

In addition, the simultaneous control approach improved the time required to transport and deliver both objects compared to a one-at-a-time approach commonly used in prior studies. Of note for clinical translation, researchers did not find significant differences between the limb-absent subject and the other subjects for the key performance metrics in the tasks. Importantly, subjects qualitatively rated haptic feedback as considerably more important than visual feedback even when vision was available, because there was often little to no visually perceptible warning before grasped objects were broken or dropped.

“Our study is the first to demonstrate the feasibility of this complex simultaneous control task while integrating multiple channels of haptic feedback noninvasively,” said Erik Engeberg, Ph.D., corresponding author and a professor, FAU Department of Ocean and Mechanical Engineering , College of Engineering and Computer Science, a member of FAU’s Center for Complex Systems and Brain Sciences , Charles E. Schmidt College of Science, and a member of I-SENSE and the FAU Stiles-Nicholson Brain Institute.

“None of our study participants had significant prior use of EMG-controlled artificial hands, yet they were able to learn to harness this multitasking functionality after two short training sessions.”

To provide haptic feedback, Engeberg and the research team worked on the EMG control and design of the custom fabricated multichannel bimodal soft robotic armband with Emmanuelle Tognoli, Ph.D., co-author, a research professor, FAU Department of Psychology and Center for Complex Systems and Brain Sciences, and a member of the FAU Stiles-Nicholson Brain Institute.

Read more at eng.fau.edu

Page 08

3D ROBOTIC SPINE ‘TWIN’ OFFERS NEW WAY TO PREVIEW SURGICAL PROCEDURES Degenerative disc disease affects about 40 percent of people aged 40, increasing to about 80 percent among those aged 80 or older. The disease, which is the deterioration of one or more intervertebral discs of the spine, often is surgically treated with cervical disc implants.

In order to determine if a patient is a candidate for a cervical disc implant, surgeons have to rely primarily on the findings of diagnostic imaging studies, without any input from biomechanical data to optimize the type of prosthesis. This may occasionally lead to complications and implant failure.

To address these problems, Florida Atlantic University’s Erik Engeberg, Ph.D., senior author of the study, and researchers from the College of Engineering and Computer Science , in collaboration with Frank Vrionis, M.D., senior author of the study and director of the Marcus Neuroscience Institute, part of Baptist Health, have created a novel robotic replica of a human spine to enable surgeons to preview the effects of surgical interventions prior to the operation.

The researchers have developed a 3D printed spine replica modified to include an artificial disc implant and outfitted with a soft magnetic sensor array. The Marcus Neuroscience Institute has its hub on Boca Raton Regional Hospital’s campus and satellite locations at Bethesda Hospital in Boynton Beach and Deerfield Beach.

The patient-specific robotic spine model was based on a CT scan of the human spine. A modified artificial disc was ‘implanted’ into the cervical spine replica and the soft magnet was embedded in the vertebra replica. A robotic arm flexed and extended the cervical spine replica while the intervertebral loads were monitored with the soft magnetic sensor array to classify the spine posture with four different machine-learning algorithms. The algorithms classified the amplitude and the locations that external loads were applied. Researchers then compared the capabilities of the algorithms to classify five different postures of the human spine robotic replica (center, mid-flexion, flexion, mid-extension and extension).

Results of the study, published in the journal Sensors , showed that the soft magnetic sensor array system had the high capability to classify the five different postures of the spine with 100 percent accuracy, which can be a predictor of different problems of the spine that people experience. These results indicate that the integration of the soft magnetic sensor array within the artificial disc ‘implanted,’ robotically actuated spine replica has the potential to generate physiologically relevant data before invasive surgeries, which could be used preoperatively to assess the suitability of a particular intervention for specific patients.

“A flexible magnetic sensor array is a new method to realize soft and stretchable magnets by mixing silicone with magnetic powder,” said Engeberg,

a professor, Department of Ocean and Mechanical Engineering within the College of Engineering and Computer Science, member of FAU’s Center for Complex Systems and Brain Sciences within the Charles E. Schmidt College of Science, and a member of the FAU Stiles-Nicholson Brain Institute. “These sensors are low-cost, highly sensitive, and easily integrated into robotic systems as the soft medium can be manipulated in many shapes and sizes.”

In addition to preoperatively assessing the suitability of a particular intervention for specific patients, this new approach could potentially assist the postoperative care of people with cervical disc implants.

Currently, postoperative instructions for patients with spine implants are qualitative (do as much as you can until the pain starts), creating fears in both the patient and the surgeon.

Questions regarding how much bending, lifting, and exercising is permissible after a cervical implant operation could be studied and correlated with biomechanical data generated by the sensorized robotic replica with individually tailored postoperative care that could be prescribed to reduce complications.

“This new approach has a powerful potential to enable surgeons to preview and compare the effects of different surgical interventions in a patient-specific manner using robotically actuated spine twins,” said Vrionis.

Read more at eng.fau.edu

Page 09

Page 10

FAU LANDS $1.3 MILLION NIH GRANT FOR RAPID, AUTOMATED HIV SELF-TEST As of 2020, there were 37.7 million people globally living with HIV and 680,000 people died from AIDS-related illnesses last year. Since the start of the epidemic, more than 36.3 million people have died from AIDS-related illnesses.

Computer Science. “Currently, there is no reliable technology that can detect HIV during the early stages of the infection or measure viral rebound in antiretroviral therapy in treated patients in self-testing format.”

Although antiretroviral therapy is effective in saving AIDS patients’ lives, implementing the therapy worldwide has been drastically hampered by the lack of treatment monitoring diagnostics and disease management. According to recent statistics by USAIDS, the coverage of antiretroviral therapy is still only 59 percent, despite being affordable or freely available in most countries. Moreover, worldwide, about 1 in 4 people who have contracted the virus are unaware of their HIV status.

One of the fundamental challenges to reduce HIV burden and its prevalence is the absence of HIV self-testing assays that are sensitive enough to detect new HIV infections during the first two-weeks, which is the acute phase, post-infection and viral rebound in virally-suppressed patients receiving antiretroviral therapy.

Current self-testing technologies only detect the host antibody response to HIV infection, which usually arises three to four weeks after the initial infection and it is not an indicator of therapy failure and viral rebound.

Florida Atlantic University’s College of Engineering and Computer Science, in collaboration with FAU’s Schmidt College of Medicine, has received a three-year, $1.3 million grant from the National Institutes of Health (NIH) for an automated HIV self-testing assay.

Another $1.3 million will potentially be awarded after a programmatic review of the progress made in phase I. With this grant, researchers are developing an affordable, disposable self-testing HIV-1 chip that can selectively detect HIV from whole blood samples, be highly sensitive to detect HIV during the acute infection, treatment and viral rebound; be rapid within 40 minutes; highly stable without requiring refrigeration; and fully automated providing true sample-in-answer-out ability.

“The greatest challenge to reducing HIV in developing countries that have limited resources is the absence of self-testing assays for viral load and the lack of trained technicians as well as modern laboratory infrastructure,” said Waseem Asghar, Ph.D., principal investigator and an associate professor in FAU’s Department of Electrical Engineering and Computer Science within the College of Engineering and

Stella Batalama, Ph.D., dean, College of Engineering and Computer Science.

“The technology that is being developed by professors Asghar and Caputi and our colleagues will be a game changer for helping to increase access to HIV testing and for improving treatment outcomes.”

Collaborators of the NIH grant include the University of Virginia and University of Pennsylvania.

Asghar is developing this technology with co-investigator Massimo Caputi, Ph.D., a professor of biomedical science in FAU’s Schmidt College of Medicine, who has expertise in the molecular biology of HIV-1 and the mechanisms of regulating cellular and viral splicing.

Caputi has made important contributions to the understanding of how cellular proteins modulate the replication of the HIV-1 genome.

The new technology employs an innovative microfluidic design integrated with molecular amplification that allows HIV testing without the need for skilled technicians to operate, making it suitable for self-testing format in developing countries as well as developed countries.

The researchers plan to validate the functioning microchip with blood/ plasma samples from more than 200 HIV-infected subjects.

“No molecular HIV self-testing technologies have yet been commercialized that are able to detect HIV during the early stages of acute infection or viral rebound in suppressed patients on antiretroviral therapy and there is an urgent need to develop reliable and affordable HIV self-testing technologies,” said

Page 11

NOVEL FAU TECHNOLOGY FOR SELF-DRIVING CARS EARNS SECOND U.S. PATENT A new technology for autonomous systems for self-driving cars based on machine-learning and artificial intelligence to mimic human driving behavior has earned a second competitive utility patent from the United States Patent and Trademark Office. The earlier patent, titled “Adaptive Mood Control in Semi or Fully Autonomous Vehicles,” allows an autonomous vehicle to be responsive to its passenger’s emotional state.

Developed by Florida Atlantic University’s Mehrdad Nojoumian, Ph.D., inventor, associate professor in the Department of Electrical Engineering and Computer Science , and director of the Privacy, Security and Trust in Autonomy Lab within the College of Engineering and Computer Science, “Adaptive Driving Mode in Semi or Full Autonomous Vehicles” provides a convenient, pleasant and more importantly, trustworthy experience for humans who interact with autonomous vehicles.

“Although semi or fully autonomous vehicles are becoming a reality in our life, many people still like to drive and be able to switch back and forth between self-driving and human-driving modes. In addition, people feel more comfortable if they observe that the car has a driving style similar to their own driving style or a specific person’s driving style when it is in the self-

Page 12

driving mode,” said Nojoumian. “What makes this invention so unique is the ability for a car or a set of vehicles to collaboratively learn the driving style of each individual by using machine learning and artificial intelligence and then replicating that driving behavior when it’s in the autonomous driving mode. Passengers will be able to select their own driving style or that of another person such as their partner, in the autonomous mode.”

Adaptive Driving Mode can be used in a wide range of autonomous systems, including self-driving cars, autonomous military vehicles, and autonomous public transport systems.

The patent utilizes sensors and electronic devices to learn the driving styles of the drivers when the car is in the semiautonomous mode or human-driving mode. Additionally, the adaptive driving mode system contains real-time machine-learning mechanisms that can continue to learn the driver’s driving style over time and even exchange this information with other vehicles that a specific person drives. The profiles of the driving styles can then be used in the car allowing the vehicle to mimic different driving styles when the car is in the semi or fully autonomous mode.

“This important discovery by professor

Nojoumian highlights the necessity and urgency of developing new technologies by which a semi or fully autonomous vehicle first learns and then mimics the way that a specific driver controls the same vehicle in a non-autonomous mode,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science. “This cutting-edge technology can lead to a convenient, pleasant and trustworthy experience, when it is in the self-driving mode, for the driver and passengers who get used to that specific driving style and addresses many issues and concerns related to semi or fully autonomous vehicles.”

GUNFIRE OR PLASTIC BAG POPPING? TRAINED COMPUTER KNOWS THE DIFFERENCE According to the Gun Violence Archive, there have been 296 mass shootings in the United States this year. Sadly, 2021 is on pace to be America’s deadliest year of gun violence in the last two decades.

Discerning between a dangerous audio event like a gun firing and a non-lifethreatening event, such as a plastic bag bursting, can mean the difference between life and death. Additionally, it also can determine whether or not to deploy public safety workers. Humans, as well as computers, often confuse the sounds of a plastic bag popping and real gunshot sounds.

Over the past few years, there has been a degree of hesitation over the implementation of some of the wellknown available acoustic gunshot detector systems since they can be costly and often unreliable.

In an experimental study, researchers from Florida Atlantic University’s College of Engineering and Computer Science focused on addressing the reliability of these detection systems as it relates to the false positive rate. The ability of a model to correctly discern sounds, even in the subtlest of scenarios, will differentiate a well-trained model from one that is not very efficient.

With the daunting task of accounting for all sounds that are similar to a gunshot sound, the researchers created a new dataset comprised of audio recordings of plastic bag explosions collected over a variety of environments and conditions, such as plastic bag

size and distance from the recording microphones. Recordings from the audio clips ranged from 400 to 600 milliseconds in duration.

Researchers also developed a classification algorithm based on a convolutional neural network (CNN), as a baseline, to illustrate the relevance of this data collection effort. The data was then used, together with a gunshot sound dataset, to train a classification model based on a CNN to differentiate lifethreatening gunshot events from nonlife-threatening plastic bag explosion events.

Results of the study, published in the journal Sensors, demonstrate how fake gunshot sounds can easily confuse a gunshot sound detection system.

Seventy-five percent of the plastic bag pop sounds were misclassified as gunshot sounds. The deep learningbased classification model trained with a popular urban sound dataset containing gunshot sounds could not distinguish plastic bag pop sounds from gunshot sounds. However, once the plastic bag pop sounds were injected into model training, researchers discovered that the CNN classification model performed well in distinguishing actual gunshot sounds from plastic bag sounds.

“As humans, we use additional sensory inputs and past experiences to identify sounds. Computers, on the other hand, are trained to decipher information that is often irrelevant or imperceptible to human ears,” said Hanqi Zhuang, Ph.D., senior author, professor and chair, Department of Electrical Engineering

and Computer Science, College of Engineering and Computer Science.

“Similar to how bats swoop around objects as they transmit high-pitched sound waves that will bounce back to them at different time intervals, we used different environments to give the machine learning algorithm a better perception sense of the differentiation of the closely related sounds.”

For the study, gunshot-like sounds were recorded in locations where there was a likelihood of guns being fired, which included a total of eight indoor and outdoor locations. The data collection process started with experimentation of various types of bags, with trash can liners selected as the most suitable. Most of the audio clips were captured using six recording devices. To check on the extent of which a sound classification model could be confused by fake gunshots, researchers trained the model without exposing it to plastic bag pop sounds.

There were 374 gunshot samples initially used to train the model, which were obtained from the urban sound database. Researchers used 10 classes from the database (gun shot, dog barking, children playing, car horn, air conditioner, street music, siren, engine idling, jackhammer, and drilling). After training, the model was then used to test its ability to reject plastic bag pop sounds as true gunshot sounds.

Read more at eng.fau.edu

Page 13

Page 14

FAU LAUNCHES NEW ‘PROFESSIONAL PH.D. PROGRAM IN COMPUTER SCIENCE’ Florida Atlantic University’s College of Engineering and Computer Science recently launched a new “Professional Ph.D. Program in Computer Science,” designed specifically for working professionals.

This is the first-of-its-kind program in Florida that offers courses in a flexible format that includes evenings, weekends and online material using FAU’s learning management system. FAU utilizes the latest distance-learning technologies for off-campus student communication including online lectures, homework, projects and exams, which allows participants to pursue their academic goals while remaining employed.

“We are excited to offer this one-ofkind advanced degree program in a field that is in such high demand in Florida as well as nationwide,” said Stella Batalama, Ph.D., dean, College of Engineering and Computer Science.

“Our accelerated program is ideal for busy working professionals as it offers convenience, flexibility and a highquality graduate computer science curriculum that is delivered by faculty experts who are leaders in research and technology.”

The Professional Ph.D. Program in Computer Science takes approximately three years to complete and is fully online. The program offers students the advanced technical expertise they need to be successful in research and technical leadership positions within industry and the public sector.

Typical paths include senior positions in computer science-related fields, careers as research scientists at hightech companies and research labs and academic careers in higher education.

Topics of study include software engineering, artificial intelligence, cybersecurity and cryptography, machine learning and data analytics, spatial database and data mining, computer networking, cloud computing, Internet of Things, smart health, bioinformatics and more.

“Our new Ph.D. Program in Computer Science will enable students to solve complex problems, think critically and serve as leaders in decision making,” said Hanqi Zhuang, Ph.D., chair and professor, Department of Electrical Engineering and Computer Science.

“Graduates will advance their careers in a growing field to serve in leadership positions within industry, academia and public and private institutions.”

Full-service and all-inclusive, the Professional Ph.D. Program in Computer Science fees cover all program costs, including tuition, meals for on-campus, Saturday classes, course materials and graduation activities. The cost is $800 per credit hour (total 42 credit hours).

advanced programming methodologies, software engineering, database systems, cybersecurity, Internet of Things and smart systems, cloud computing and healthcare informatics.

The Professional M.S. in Computer Science Program requires a minimum of 30 credits. The cost is $800 per credit hour (total 30 credit hours). The program offers a flexible payment plan and financial aid payment methods are available.

For more information about these programs, please visit eng.fau.edu/ professional or contact Wendy Burrion at 561.297.3549, email wburrion@fau. edu.

“Employment of computer and information research scientists is projected to grow 22 percent from 2020 to 2030, much faster than the average for all occupations,” said Mihaela Cardei, Ph.D., associate dean for graduate studies and a professor in the Department of Electrical Engineering and Computer Science. “Obtaining a Ph.D. in this ever-evolving field will provide our graduates with a leading-edge to qualify for top leadership roles at technology companies and other organizations. In addition, earning a Ph.D. is typically the best path for those who want to enter academia to fill roles such as researcher or professor.”

The College of Engineering and Computer Science also offers a “Professional M.S. in Computer Science Program,” designed specifically for working professionals who want to advance their careers with an accelerated graduate program while continuing to work. The program can be completed on campus or fully online.

Courses are offered in the evenings, weekends and online using Canvas.

Students can complete the program in one year.

The Professional M.S. in Computer Science Program offers the latest advances in various areas such as deep learning, artificial intelligence, data science,

Page 15

The FAU College of Engineering & Computer Science Newsletter is produced by Adlai Moss, Asst. Director, Graphic Design and Marketing.

Contributors: Gisele Galoustian, Media Relations Director, Research; Alex Dolce, University Photographer.

Florida Atlantic University College of Engineering and Computer Science 777 Glades Road EE 308 Boca Raton, FL 33431-0991 561.297.3400 | info@eng.fau.edu eng.fau.edu