Speaker: Dr. Shirin Jalali, Nokia Bell Labs
Title: Compression Codes for Efficient Data Acquisition
The 2018 ECE Research Day, held on November 14, was a great success. This event was a great opportunity for ECE students to present their research projects, share their creative ideas, and network with their peers. 60 posters were presented by graduate and undergraduate students, covering a diverse range of research topics. The event was well received by faculty, students and industry representatives.
The special thanks to Prof. Laleh Najafizadeh for coordinating this important event that showcases the exciting research in our department!
Rutgers engineering student Michael Edwin won first place for his undergraduate research project that applied electromagnetic waves in the detection of human vital signs and human tracking, during the 6th Annual Black Doctoral Network Conference in Charlotte in October. Edwin, who is a senior majoring in electrical and computer engineering (ECE), conducts research alongside his mentor ECE assistant professor Chung-Tse Michael Wu in the Microwave Research Lab. The Black Doctoral Network provides opportunities for collaboration, support and resource sharing between scholars across university lines by encouraging intellectual curiosity and transformative research.
Using a low-cost metamaterial Leaky Wave Antenna (LWA) and a 2D frequency scanning array, Edwin expanded on existing LWA research by demonstrating LWA is capable of reading and measuring the heart rate and respiratory rates of different individuals simultaneously. The research also demonstrated an ability to simultaneously track a human and a metallic object’s position and location through the emission of radiation waves, which is reflected off the human or object back to the LWA. This research will have applications in the enhancement of security surveillance, radar sensing, indoor monitoring, and motion sensing.
Edwin is a 2018 Ronald E. McNair Post-Baccalaureate Achievement scholar which is a national program supported by the U.S. Department of Education to increase the attainment of doctoral degrees among underrepresented populations. He is a member of the National Society of Black Engineers, serving as the research chair of the Rutgers chapter; a Camp UKnight leader for new incoming students; a leadership ambassador for the Leadership and Experiential Learning Department; and serves as a student consultant for the Office of Information Technology at Rutgers.
A team of high-school students – Caroline Abel, Ray Chen, James Gallicchio, Grace Zhang, and Kathryn Zhou – who participated in the 2018 New Jersey Governor’s School of Engineering & Technology (GSET) at Rutgers University and worked in Professor Dario Pompili’s Cyber-Physical Systems Laboratory (CPS Lab), have won the Best Paper Award at the 2018 IEEE MIT Undergraduate Research Technology Conference (URTC). The conference, which was hosted on the MIT campus in Cambridge, MA, on October 5-7, 2018, brought together undergraduates from around the world to present, discuss, and develop solutions to advance technology for humanity. As an IEEE official conference, undergraduates may publish papers of their school projects, research, innovations, or case studies. The GSET at Rutgers University is an intensive residential summer program that brings together some of New Jersey’s most talented and motivated high school students. Free of grades and official credit, students spend part of the summer following their junior year studying on the campus of the Rutgers University School of Engineering at no cost to their families. During their summer research, under the supervision of Rutgers/ECE PhD student Mehdi Rahmati and MS student Adam Gurney, the five high-school students explored underwater video signal transmission techniques to enable a wide range of applications in the underwater environment that require real-time multimedia acquisition and classification. The paper, titled “Adaptive Feedback Protocol for Underwater Vehicles via Software-Defined Acoustic Modems,” is available here.
Congratulations to the Rutgers/SoE GSET high-school student team on this recognition!
Abstract: Private information retrieval (PIR) is a canonical problem to study the privacy of users as they download content from publicly accessible databases. In PIR, a user (retriever) wishes to download data from one or more databases in such a way that no individual database can tell which data has been retrieved. PIR has originated in the computer science literature, and has recently been revisited by the information theory community.
Fourth generation wireless, better known as 4G, turned mobile phones into movie-streaming platforms, but the next wireless revolution promises more than speedy downloads. It could pave the way for surgeons operating remotely on patients, cars that rarely crash, and events that can be vividly experienced from thousands of miles away.
To realize this vision of the future, the National Science Foundation (NSF) and an industry consortiumare investing $100 million in the next seven years to build a set of wireless networks for U.S. researchers to test new ways of boosting Internet speeds to support data-intensive applications in robotics, immersive virtual reality and traffic safety. New York and Salt Lake City are the first cities to receive funding under the NSFPlatforms for Advanced Wireless Research (PAWR) initiative, with New York set to receive $22.5 million.
Prof. Dipankar Raychaudhuri and Ivan Seskar (WINLAB/ECE) are leading the NSF-funded Rutgers/Columbia/NYU “COSMOS” project aimed at real-world deployment of advanced wireless platforms in New York City.
Led by researchers at Rutgers, Columbia and NYU, and in partnership with New York City, Silicon Harlem, City College of New York, University of Arizona, and IBM, the platform in New York, called COSMOS, will be a proving ground for a new generation of wireless technologies and applications. The COSMOS testbed will cover one square mile in West Harlem, with City College to the north, Columbia University’s Morningside Heights campus to the south, the Hudson River to the west, and Apollo Theater to the east. This vibrant, densely populated neighborhood is seen as an ideal place to push the bandwidth and latency limits of 4G, and even fifth-generation wireless technology, or 5G, which carriers are starting to roll out in some cities now.
By 2020, the number of Internet-connected devices is expected to grow to 20 billion, creating an urgent need in the U.S. and abroad for infrastructure that can rapidly process all that data. To improve networking speeds, the New York City COSMOS network will tap previously unused radio spectrum bands and integrate optical fibers underground with radio antennas and other equipment on city rooftops and light poles.
The high-bandwidth, low-latency network is expected to allow applications to transmit data faster than one gigabit per second and reduce response times to a few milliseconds, improving performance 10-fold over current wireless networks. To achieve this high level of performance, data-processing will be handled by on-site “edge cloud” servers rather than in far-off data centers.
The open-access COSMOS platform will allow researchers from anywhere in the country to log in and try out their ideas for improving networkperformanceand creating city-focused applications, from augmented-reality navigation for the blind to “smart” traffic lights.
“COSMOS is an outdoor laboratory that will allow us to test entirely new classes of wireless applications such as smart intersections that can process massive data in real-time,” said principal investigator Dipankar Raychaudhuri, an engineering professor at Rutgers University-New Brunswick, and director of its Wireless Information Network Laboratory, or WINLAB.
The technologies underpinning the experiments will include:
- mm-Wave Radio Bands: The use of new millimeter-wave bands, from 20 GHz to 200 GHz, will make it possible to extract more capacity from the radio spectrum, but one drawback is that mmWave signals don’t travel as far. To overcome this, researchers will use the network to test new radio and antenna designs and techniques for aiming radio waves directly at mobile devices.
- Software-Defined Radios: Processing signals with software rather than hardware increases network flexibility and allows researchers to experiment with a wide range of frequency bands. The radios will be used to test new algorithms to support mmWave and flexible use of frequencies across various bands, a feature known as dynamic spectrum access.Edge Cloud: By shifting data-processing from cloud-based data centers to servers integrated into the wireless access network, researchers can speed up processing time. This is especially critical for applications involving Internet-connected devices that require fast response time.
- Advanced Optical Networking: To use edge-cloud infrastructure effectively, a fast front-haul network with high bandwidth and low-delay connectivity is needed to tie together computing clusters and the wireless access network. COSMOS will offer this connectivity with state-of-the-art wavelength division multiplexed optical technology.
New York’s tech sector is now the nation’s third largest, after Texas and California, with most of those jobs concentrated in New York City, according to a recent New York State Comptroller report. The City has embraced the COSMOS project for its potential to create far-ranging public benefits. These include bringing startups to the neighborhood that can build smart-city applications that make cities safer and more resilient. Applications to come out of COSMOS could reduce the number of crashes that injure and kill drivers and pedestrians, improve accessibility for people with disabilities, and make next-generation 911 systems more secure.
“We are eager for the opportunity to accelerate the development of new products and services based on advanced wireless technology, and shrink their time to market in New York City, benefitting millions of residents and visitors,”said Chief Technology Officer Miguel Gamiño, Jr.
The project will also provide hands-on STEM training for students and West Harlem residents who will be among the first to see and touch technologies that are still years away from appearing on the market. Silicon Harlem will involve K-12 students from the community and City College will partner with researchers to involve its engineering students and support the testbed installation.
One key piece of radioequipment to be piloted will be the millimeter-wave wireless antennas and radio front-ends that will be unique to COSMOS. These mmWave radios will operate at 28 GHZ, a frequency recently made available by the U.S. Federal Communications Commission.
The COSMOS research team is led by Raychaudhuri and Ivan Seskar at Rutgers, and Gil Zussman and Sundeep Rangan, electrical engineering professors atColumbia Engineering and New York University’s Tandon School of Engineering respectively.
The Rutgers team at WINLAB will build on extensive research experience with wireless testbeds, software-defined radio technology, and mobile Internet architecture. WINLAB’s open-access, NSF-funded ORBIT wireless-testbed is currently used by researchers nationally to run controlled experiments at scale. Other COSMOS team members include electrical engineering professors Marco Gruteser and Narayan Mandayam, and computer science professor Thu Nguyen.
Engineers are essential to our health. From water purification and flood mitigation to surgical equipment and chemotherapy, they contribute across many specialties.
In countries around the world, severe resource limitations can get in the way of health and health care delivery—and the role engineers play can be even more crucial. A pace-setter in this evolving field is Umer Hassan, a new faculty member in the School of Engineering with a joint appointment at Rutgers Global Health Institute. He is pursuing an interdisciplinary set of research initiatives at Rutgers that share a common goal: saving lives, particularly in vulnerable communities.
Engineering Meets Medicine Meets Global Health
Hassan, an assistant professor, is working with colleagues universitywide to apply his electrical and computer engineering expertise—which incorporates personalized medicine, predictive prognostic systems, and infectious disease—to solve pressing global health challenges. One such challenge: sepsis, a life-threatening condition caused by the body’s response to infection. Any kind of infection can potentially lead to sepsis—urinary tract infection, strep throat, influenza—and even routine surgeries create a risk. The human body’s complex response in sepsis can cause tissue damage, organ failure, and death. Sepsis survivors have described their pain as feeling like they were going to die.
In 2017, the World Health Organization identified as a global health priority the urgent need to improve the prevention, diagnosis, and management of sepsis. Hassan is designing a medical device that would bring to a patient’s bedside the capacity to diagnose sepsis—and do so quickly, accurately, inexpensively, and with minimal training required for health care providers.
Inexpensive Device Detects Sepsis Quickly and Accurately
WHO estimates that sepsis causes 6 million deaths worldwide every year, most of which are preventable. In lower- and middle-income countries, there may not even be a trained clinician available to draw a blood sample to test for sepsis, let alone a physician specialist capable of managing such a dynamic, life-threatening condition.
Intervention, by way of innovation, is desperately needed.
Hassan is actively engaged in the global effort to combat sepsis. “We are building an automated device that would cost less than $10 a test and is simple to operate. Many countries’ health systems cannot support large equipment and expensive technologies that require advanced training and knowledge to use,” he says.
Time and accurate diagnoses are critical factors in managing sepsis. Hassan is working across disciplines at Rutgers and with industry partners to identify new biomarkers and create machine-learning algorithms—essentially, artificial intelligence systems—that will “dramatically improve clinicians’ abilities to diagnose as well as predict sepsis,” he says. “Not only in resource-limited settings, but everywhere.”
Hassan’s findings were recently published in the journal Nature Communications, in a co-authored paper titled “A point-of-care microfluidic biochip for quantification of CD64 expression from whole blood for sepsis stratification.” This spring, he will teach an engineering course in which students will learn to develop applications for global health settings.
Story by Lori Riley for Rutgers Global Health Institute
Photography by Nick Romanenko
By Ariana Tantillo
The framework could revolutionize drug design by supporting accurate and rapid calculations of how strongly compounds bind to target molecules
Solutions to many real-world scientific and engineering problems—from improving weather models and designing new energy materials to understanding how the universe formed—require applications that can scale to a very large size and high performance. Each year, through its International Scalable Computing Challenge (SCALE), the Institute of Electrical and Electronics Engineers (IEEE) recognizes a project that advances application development and supporting infrastructure to enable the large-scale, high-performance computing needed to solve such problems.
This year’s winner, “Enabling Trade-off Between Accuracy and Computational Cost: Adaptive Algorithms to Reduce Time to Clinical Insight,” is the result of a collaboration between chemists and computational and computer scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Rutgers University, and University College London. The team members were honored at the 18th IEEE/Association for Computing Machinery (ACM) International Symposium on Cluster, Cloud and Grid Computing held in Washington, DC, from May 1 to 4. “We developed a numerical computation methodology for accurately and rapidly evaluating the efficacy of different drug candidates,” said team member Shantenu Jha, Associate Professor at Rutgers ECE and Chair of the Center for Data Driven Discovery, part of Brookhaven Lab’s Computational Science Initiative. “Though we have not yet applied this methodology to design a new drug, we demonstrated that it could work at the large scales involved in the drug discovery process.”
Drug discovery is kind of like designing a key to fit a lock. In order for a drug to be effective at treating a particular disease, it must tightly bind to a molecule—usually a protein—that is associated with that disease. Only then can the drug activate or inhibit the function of the target molecule. Researchers may screen 10,000 or more molecular compounds before finding any that have the desired biological activity. But these “lead” compounds often lack the potency, selectivity, or stability needed to become a drug. By modifying the chemical structure of these leads, researchers can design compounds with the appropriate drug-like properties. The designed drug candidates then move along the development pipeline to the preclinical testing stage. Of these candidates, only a small fraction enters the clinical trial phase, and only one ends up becoming an approved drugfor patient use. Bringing a new drug to the market can take a decade or longer and cost billions of dollars.
Overcoming drug design bottlenecks through computational science
Recent advances in technology and knowledge have resulted in a new era of drug discovery—one that could significantly reduce the time and expense of the drug development process. Improvements in our understanding of the 3D crystal structures of biological molecules and increases in computing power are making it possible to use computational methods to predict drug-target interactions.
In particular, a computer simulation technique called molecular dynamics has shown promise in accurately predicting the strength with which drug molecules bind to their targets (binding affinity). Molecular dynamics simulates how atoms and molecules move as they interact in their environment. In the case of drug discovery, the simulations reveal how drug molecules interact with their target protein and change the protein’s conformation, or shape, which determines its function.
However, these prediction capabilities are not yet operating at a large-enough scale or fast-enough speed for pharmaceutical companies to adopt them in their development process. “Translating these advances in predictive accuracy to impact industrial decision making requires that on the order of 10,000 binding affinities are calculated as quickly as possible, without the loss of accuracy,” said Jha. “Producing timely insight demands a computational efficiency that is predicated on the development of new algorithms and scalable software systems, and the smart allocation of supercomputing resources.”
Jha and his collaborators at Rutgers University, where he is also a professor in the Electrical and Computer Engineering Department, and University College London designed a software framework to support the accurate and rapid calculation of binding affinities while optimizing the use of computational resources. This framework, called the High-Throughput Binding Affinity Calculator (HTBAC), builds upon the RADICAL-Cybertools project that Jha leads as principal investigator of Rutgers’ Research in Advanced Distributed Cyberinfrastructure and Applications Laboratory (RADICAL). The goal of RADICAL-Cybertools is to provide a suite of software building blocks to support the workflows of large-scale scientific applications on high-performance computing platforms, which aggregate computing power to solve large computational problems that would otherwise be unsolvable because of the time required. In computer science, workflows refer to a series of processing steps necessary to complete a task or solve a problem. Especially for scientific workflows, it is important that the workflows are flexible so that they can dynamically adapt during runtime to provide the most accurate results while making efficient use of the available computing time. Such adaptive workflows are ideal for drug discovery because only the drugs with high binding affinities should be further evaluated.
Jha’s team demonstrated how HTBAC could provide insight from drug candidate data on a short timescale by reproducing results from a collaborative study between University College London and the London-based pharmaceutical company GlaxoSmithKline to discover drug compounds that bind to the BRD4 protein. Known to play a key role in driving cancer and inflammatory diseases, the BRD4 protein is a major target of bromodomain-containing (BRD) inhibitors, a class of pharmaceutical drugs currently being evaluated in clinical trials. The researchers involved in this collaborative study are focusing on identifying promising new drugs to treat breast cancer while developing an understanding of why certain drugs fail in the presence of breast cancer gene mutations.
HTBAC not only has the potential to improve the speed and accuracy of drug discovery in the pharmaceutical industry but also to improve individual patient outcomes in clinical settings. Using target proteins based on a patient’s genetic sequence, HTBAC could predict a patient’s response to different drug treatments. This personalized assessment could replace the traditional one-size-fits-all approach to medicine. For example, such predictions could help determine which cancer patients would actually benefit from chemotherapy, avoiding unnecessary toxicity.