ECE Research Day 2018

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!

ECE student Michael Edwin wins Undergraduate Research Competition at 6th Annual BDN Conference

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.

Rutgers SOE/GSET Team wins Best Paper Award at the 2018 IEEE MIT Undergraduate Research Technology Conference

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!

ECE Colloquium - Dr. Sennur Ulukus, University of Maryland

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.

COSMOS City Scale Testbed


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.  



Engineering Meets Medicine Meets Global Health

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

Story posted on

Software Framework Designed to Accelerate Drug Discovery

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.



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