A $3 million National Science Foundation grant will help Rutgers train graduate students to develop robots for the future that integrate technology, computer science and machine learning with social and behavioral sciences like psychology, cognitive science and urban policy planning.

“The students may come from a technology background or from a social science background for this unified training of interdisciplinary skills,” said Kristin Dana, professor of electrical and computer engineering in the School of Engineering, who has assembled an interdisciplinary team of faculty spanning five departments and three schools. “We are at a point in technology where robotics may soon be part of everyday life and work but we want robots to be developed in a way that they can adapt to human needs and desires, rather than the other way around.”

The first major traineeship grant awarded to the School of Engineering is called Socially Cognizant Robotics for a Technology Enhanced Society (SOCRATES). Its mission is to converge socially aware technologists and technology aware social scientists into socially cognizant roboticists, Dana said.

The program will focus on semiautomated systems capable of performing daily life through the Robotics for Everyday Augmented Living (REAL) program, and is expecting to train over 35 graduate students initially across the academic units of engineering, computer science, behavioral sciences, and public policy. Trainees will engage in fundamental research to understand and model the social dimensions of robot deployments and advance the long-term goal of dignified living and working in a technologically enhanced society. Some 100 students will benefit from some component of the program over the next five years.

The participants will be trained in the technology available for building and controlling robots; collecting and learning from large data sets; designing socially cognizant systems; and planning for a positive societal impact while mitigating unintended consequences.

 “We plan to do experiments using our Rutgers Robotics Live Lab with key application domains like strength and mobility, recycling and trash collection, food preparation and smart buildings,” Dana said.

A multifaceted approach will be used to recruit students from various disciplines, including the student-led robotics club that focuses on a novice-to-expert (N2E) strategy used to motivate and help students who want to learn disciplined robotics but have no prior experience.

By taking the intimidation factor out of learning a new coding skill, Dana said the N2E student-led modules are welcoming to beginners and encourage participation. The NRT recruitment strategy will also include a Faculty Talk-it-up Robotics Series for recruiting underrepresented populations. The program will also coordinate internships that are aligned with changing workforce and research needs.

Dana and her team worked on their SOCRATES proposal for two years.

Meet the Core Research Team

From left to right: (top row): Jacob Feldman, Jingang Yi, Pernille Hemmer; (center): Clinton Andrews, Kristin Dana, Matthew Stone; (bottom): Aaron Mazzeo, Hal Salzman, Kostas Bekris

Kristin Dana, professor of electrical and computer engineering, School of Engineering; Kostas Bekris, associate professor, computer science department, School of Arts and Sciences;

Clinton Andrews, professor of urban planning and associate dean for research, Edward J. Bloustein School of Planning and Public Policy; Jacob Feldman, professor of psychology and cognitive science, School of Arts and Sciences; Jingang Yi, professor of mechanical and aerospace engineering, School of Engineering; Pernille Hemmer, associate professor of psychology and cognitive science, School of Arts and Sciences;

Aaron Mazzeo, associate professor of mechanical and aerospace engineering, School of Engineering; Hal Salzman, professor of planning and public policy, Edward J. Bloustein School of Planning and Public Policy; Matthew Stone, professor and department chair of computer science, School of Arts and Sciences; Kathy Haynie, director of Haynie Research and Evaluation and the team’s external evaluator on STEM education.

Story by Marisol Seda for Rutgers Today.

$3M Grant from National Science Foundation Supports Research Traineeship for Robotics of the Future Program to Improve Quality of Life. A new group of socially-cognizant roboticists will emerge from Rutgers, The State University of New Jersey thanks to a $3M National Science Foundation Research Traineeship (NRT) grant.

5G Wireless May Lead to Inaccurate Weather Forecasts

Upcoming 5G wireless networks that will provide faster cell phone service may lead to inaccurate weather forecasts, according to a Rutgers study on a controversial issue that has created anxiety among meteorologists.

“Our study – the first of its kind that quantifies the effect of 5G on weather prediction error – suggests that there is an impact on the accuracy of weather forecasts,” said senior author Narayan B. Mandayam, a Distinguished Professor at the Wireless Information Network Laboratory (WINLAB), who also chairs the Department of Electrical and Computer Engineering in the School of Engineering at Rutgers University–New Brunswick.

The lead author is Mohammad Yousefvand, a Rutgers electrical engineering doctoral student. Co-authors include Professor Chung-Tse Michael Wu in the Department of Electrical and Computer Engineering, Professor Ruo-Qian (Roger) Wang in the Department of Civil and Environmental Engineering and Joseph Brodie, director of atmospheric research in the Rutgers Center for Ocean Observing Leadership.

The Rutgers study used computer modeling to examine the impact of 5G “leakage” – unintended radiation from a transmitter into an adjacent frequency band or channel – on forecasting the deadly 2008 Super Tuesday Tornado Outbreak in the South and Midwest.

The signals from the 5G frequency bands potentially could leak into the band used by weather sensors on satellites that measure the amount of water vapor in the atmosphere and affect weather forecasting and predictions. Meteorologists rely on satellites for the data needed to forecast weather.

“It can be argued that the magnitude of error found in our study is insignificant or significant, depending on whether you represent the 5G community or the meteorological community, respectively,” Mandayam said. “One of our takeaways is that if we want leakage to be at levels preferred by the 5G community, we need to work on more detailed models as well as antenna technology, dynamic reallocation of spectrum resources and improved weather forecasting algorithms that can take into account 5G leakage.”

Read the complete article at  https://www.rutgers.edu/news/5g-wireless-may-lead-inaccurate-weather-forecasts

ECE Associate Professor Dario Pompili is the recipient of a new NSF award for the project titled “xl_NGRAN–Navigating Spectral Utilization, LTE/WiFi Coexistence, and Cost Tradeoffs in Next Gen Radio Access Networks through Cross-Layer Design." This is a 3-year $450K grant from the Spectrum and Wireless Innovation enabled by Future Technologies (SWIFT) program.

In this project, Dr. Pompili and his team will design the  xl_NGRAN framework for 5G (virtualized) cellular networks that enables optimized cross-layer decisions for on-demand resource allocation and in-network content caching, and navigates the tradeoffs among radio resources, system cost, LTE/WiFi technology coexistence, and caching service. The rapid growth of mobile multimedia applications and the Internet of Things (IoTs) have placed severe demands on wireless network infrastructures such as ultra-low latency, user experience continuity, and high reliability. Mobile devices are nowadays the predominant medium of access to Internet services due to an increase in their computation and communication capabilities. However, enabling applications that require real-time, in-the-field data collection and processing using mobile devices is still challenging due to (1) the insufficient computing capabilities and unavailability of aggregated/global data on individual mobile devices and (2) the communication cost and response time involved in offloading data to remote computing resources for centralized computation. In light of these limitations, the Mobile Edge Computing (MEC) concept has emerged, which aims at uniting telco, IT, and cloud computing to deliver cloud services directly from the network edge.  With a cloud-based framework, and specifically via NG-RAN virtualization, network resources including physical infrastructure and spectrum are abstracted in such a way as to provide a developing platform to support various services, thus maximizing resource utilization. The framework performance will be assessed via three research tasks and one validation/assessment plan considering Augmented Reality (AR)-based applications in a smart-device context. In Task 1, resource-allocation solutions will be designed, while considering LTE/WiFi coexistence requirements, to minimize the power consumption at both the cell sites and the Central Unit (CU) pool by dynamically adapting the Distributed Unit (DU) density and size of the Virtual Machines (VMs) hosting the DU pool based on traffic fluctuations. In Task 2, functional splitting will be enabled through cross-layer design; a novel dynamic radio-resource allocation and flexible functional split will be introduced to optimize the accumulated data rate and network power consumption in NG-RANs. In Task 3, the joint problem of service caching and task-offloading assignment will be studied in a dense network where each user can exploit the degrees of freedom in offloading different portions of its computation task to nearby DUs.  

A team of ECE researchers led by Distinguished Professor Athina Petropulu (PI) have received a new NSF award titled “MIMO Radar With Sparse Linear Arrays - Theory, Implementation and Applications." This project includes Professor Yingying Chen and Assistant Professor Chung-Tse Michael Wu as co-PIs.

The total award amount for the three-year project is $450,000. The project aims to design Multiple-input multiple-output (MIMO) radars that have several advantages as compared to traditional phased arrays. They can achieve higher resolution with the same number of antennas. They can also achieve a wide field of view, illuminating multiple targets at the same time, which translates to faster detection time. Reduction of the number of active antennas without hurting the radar performance would reduce the cost of the radar. As such, a low-cost, high resolution radar would advance the state-of-art of smart environment, smart home, and IoT sensing, thereby enabling applications such as smart patient care, elderly monitoring, fitness assistant, etc., that rely on sensing. In an era where COVID-19 forced home isolation with limited supervision of vulnerable segments of the population, a radar device could provide information on vital signs, or detect falls without invading people's privacy in the way surveillance cameras would. MIMO radar using specially designed Sparse Linear Arrays (SLAs) can enjoy reduced hardware cost without losing the MIMO radar advantages. An SLA can be thought of as a uniform linear array with only a small number of active antennas. By careful selection of the active antennas and optimal design of transmit waveforms, one can maintain a radar performance close to that of the fully populated array. However, finding an optimal sparse array geometry in terms of the fewest antennas is a difficult combinatorial problem. The proposed project will advance the state-of-art of SLA based MIMO radar as a cost-effective imaging radar by (i) providing a novel framework for antenna selection, (ii) developing an SLA MIMO radar prototype based on frequency-scanning metamaterial (MTM) antennas, and (iii) developing real-time activity monitoring and user identification schemes that leverage the high resolution and wide field of view of MIMO SLA radar.

The link of the award is https://www.nsf.gov/awardsearch/showAward?AWD_ID=2033433&HistoricalAward...

Congratulations to Athina, Yingying and Michael!

ECE Associate Professor Mehdi Javanmard (co-PI) is part of a Rutgers/Princeton team of researchers that received an award from Defense Advanced Research Projects Agency (DARPA) for the project entitled "Modular Platform for Dynamic Biochemical Sensing of Acute Skin Wounds." This one-year, $455,649 seedling grant project aims to develop smart bandages for wound healing and is a collaborative effort with Rutgers researchers, Professor Francois Berthiaume (PI) from BME and Associate Professor Aaron Mazzeo (Co-PI) from MAE.

Congratulations, Mehdi!

A team of ECE researchers led by Distinguished Professor Athina Petropulu (PI) have received a new NSF award titled “MIMO Radar With Sparse Linear Arrays - Theory, Implementation and Applications." This project includes Professor Yingying Chen and Assistant Professor Chung-Tse Michael Wu as co-PIs. The total award amount for the three-year project is $450,000.

The project aims to design Multiple-input multiple-output (MIMO) radars that have several advantages as compared to traditional phased arrays. They can achieve higher resolution with the same number of antennas. They can also achieve a wide field of view, illuminating multiple targets at the same time, which translates to faster detection time. Reduction of the number of active antennas without hurting the radar performance would reduce the cost of the radar. As such, a low-cost, high resolution radar would advance the state-of-art of smart environment, smart home, and IoT sensing, thereby enabling applications such as smart patient care, elderly monitoring, fitness assistant, etc., that rely on sensing. In an era where COVID-19 forced home isolation with limited supervision of vulnerable segments of the population, a radar device could provide information on vital signs, or detect falls without invading people's privacy in the way surveillance cameras would. MIMO radar using specially designed Sparse Linear Arrays (SLAs) can enjoy reduced hardware cost without losing the MIMO radar advantages. An SLA can be thought of as a uniform linear array with only a small number of active antennas. By careful selection of the active antennas and optimal design of transmit waveforms, one can maintain a radar performance close to that of the fully populated array. However, finding an optimal sparse array geometry in terms of the fewest antennas is a difficult combinatorial problem. The proposed project will advance the state-of-art of SLA based MIMO radar as a cost-effective imaging radar by (i) providing a novel framework for antenna selection, (ii) developing an SLA MIMO radar prototype based on frequency-scanning metamaterial (MTM) antennas, and (iii) developing real-time activity monitoring and user identification schemes that leverage the high resolution and wide field of view of MIMO SLA radar.