Tejashri Kuber has been selected as the recipient of the IEEE Communications Society Phoenix ISS Award for academic year 2020-2021. The IEEE Communications Society Phoenix ISS Award was established to encourage engineering student to participate in professional activities. Awards are to be given to full-time or part-time students to cover expenses for students to attend the International Switching Symposium, or other IEEE Communications Society Conferences.

Congratulations Tejashri !

The title and abstract of Tejashri's paper follows:

Traffic Prediction by Augmenting Cellular Data with Non-Cellular Attributes

Abstract—Prediction of user traffic in cellular networks is one of the promising ways to improve resource utilization among base stations. In this study, we employ deep learning techniques, specifically a long-short-term memory module to forecast cellular traffic. We consider traffic from neighboring cells and other non- cellular traffic-related attributes such as weather, busy period data from open-source API as features to augment the cellular traffic data and improve prediction. Specifically, we augment cellular traffic data from the City of Milan and its surroundings and we perform two types of analyses: (i) a one-step prediction or a point-by-point forecast of traffic and (ii) a trend analysis which is the forecast of traffic over an extended period. We compare the results with existing statistical methods such as auto- regression integrated moving averages (ARIMA) and exponential smoothing and observe gains in the trend analysis by providing the augmented data, whereas the one-step prediction is not much impacted.

Rutgers engineers can turn “dumb” headphones into smart ones by turning them into sensors

How do you turn “dumb” headphones into smart ones? Rutgers engineers have invented a cheap and easy way by transforming headphones into sensors that can be plugged into smartphones, identify their users, monitor their heart rates and perform other services.

Their invention, called HeadFi, is based on a small plug-in headphone adapter that turns a regular headphone into a sensing device. Unlike smart headphones, regular headphones lack sensors. HeadFi would allow users to avoid having to buy a new pair of smart headphones with embedded sensors to enjoy sensing features.

“HeadFi could turn hundreds of millions of existing, regular headphones worldwide into intelligent ones with a simple upgrade,” said Xiaoran Fan, a HeadFi primary inventor. He is a recent Rutgers doctoral graduate who completed the research during his final year at the university and now works at Samsung Artificial Intelligence Center.

A peer-reviewed Rutgers-led paper on the invention, which results in “earable intelligence,” will be formally published in October at MobiCom 2021, the top international conference on mobile computing and mobile and wireless networking.

“Dumb” headphones can be plugged into a HeadFi device that connects to a cellphone, turning them into intelligent headphones. Engineers are working on a smaller version of the device. Image: Siddharth Rupavatharam

Headphones are among the most popular wearable devices worldwide and they continue to become more intelligent as new functions appear, such as touch-based gesture control, the paper notes. Such functions usually rely on auxiliary sensors, such as accelerometers, gyroscopes and microphones that are available on many smart headphones.

HeadFi turns the two drivers already inside all headphones into a versatile sensor, and it works by connecting headphones to a pairing device, such as a smartphone. It does not require adding auxiliary sensors and avoids changes to headphone hardware or the need to customize headphones, both of which may increase their weight and bulk. By plugging into HeadFi, a converted headphone can perform sensing tasks and play music at the same time.

The engineers conducted experiments with 53 volunteers using 54 pairs of headphones with estimated prices ranging from $2.99 to $15,000. HeadFi can achieve 97.2 percent to 99.5 percent accuracy on user identification, 96.8 percent to 99.2 percent on heart rate monitoring and 97.7 percent to 99.3 percent on gesture recognition.

Rutgers co-authors include Siddharth Rupavatharam, an electrical and computer engineering doctoral student, and Research Professor Richard E. Howard, the senior author and co-primary inventor at Rutgers' Wireless Information Network Laboratory (WINLAB), a research center in the School of Engineering. Engineers at the University of Science and Technology of China, University of Massachusetts Amherst, Microsoft and Alibaba Group contributed to the paper. A patent is pending.

Story by Todd Bates for Rutgers Today.

ECE Assistant Professor Umer Hassan is the recipient of an award from National Science Foundation (NSF) for the project “An Electronic-Sensing & Magnetic-Modulation (ESMM) Biosensor for Phagocytosis Quantification for Personalized Stratification in Pathogenic Infections”. This is a three-year project awarded at $360,000.

The project will enable the development of a next generation in-vitro diagnostic platform equipped with Electronic-Sensing & Magnetic-Modulation (ESMM) modules integrated in a microfluidic chip to quantify the human blood cells ability to kill pathogens. The heterogeneity of the immune system activation in response to pathogenic infections is critical to strategize the correct clinical response to treat patients. Quantifying blood cells natural ability to kill pathogens i.e., phagocytosis is critical to demonstrate the effectiveness of an individual’s response in combating pathogens. This project aims to develop a novel personalized biosensor capable of quantifying the phagocytic ability to kill the pathogens. The biosensor is equipped with microfluidics, microelectrodes for electronic sensing, and quadrupole magnetic configuration to modulate the blood cells behavior on-chip. Blood cells will interact with antibody conjugated magnetic particles and will perform phagocytosis on-chip. Furthermore, the proposed biosensor will be equipped with real-time data analysis using machine learning to improve the sensor performance. The proposed sensor will enable stratification of immune response of infected patients requiring only a drop of whole blood with a rapid time to result (TOR). Sensors will be benchmarked with patient clinical samples. Sensor will have the capability to be used at the point-of-care at multiple health-care settings. More details on the project can be found at the NSF page here.

Congratulations Umer!