ECE Associate Professor Laleh Najafizadeh is the recipient of an exploratory EAGER NSF award for the project "Adapting Multi-Modal BCI-Based Assistive Technologies for Patients with High Spinal Cord Injury." This is a 1 year $120,000 award from the NSF.

In this project, Laleh will advance brain computer interface (BCI) technology by bringing the technology closer to the needs of patients with high spinal cord injury (SCI). While BCI-based assistive technologies hold a great promise to improve the quality of life of patients with SCI by enabling them to independently perform simple activities of their daily life, several factors have prevented their successful application in practical and clinical settings. A major contributing factor to this problem is related to the secondary health conditions (e.g. chronic pain) that are developed as a result of SCI, but have been largely ignored in BCI research. The presence of pain can negatively impact the control signals acquired from BCIs, and consequently, compromise the operation of the assistive device, thereby, adding to stress, discomfort, and depression for the patient. Using innovative approaches and experiments, this research aims to investigate how pain impacts the operation of multi-modal BCIs. The outcome of this work is expected to provide guidelines for developing effective methods that aid in realizing robust and high-performance BCI-based assistive technologies for SCI patients.

The project details can be found here:
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1841087&HistoricalAwards=false

Congratulations, Laleh!

ECE Professor Yingying Chen is a recipient of a new National Science Foundation (NSF) award for the project titled "Software Hardware Architecture Co-design for Low-power Heterogeneous Edge Devices" This is a three-year project of $500,000 in collaboration with Binghamton University. The Rutgers share of the award is $320,000.

In this project, Yingying and her team aim to remove the barrier that keeps deep learning techniques away from pervasive low-power mobile edge computing devices and enable high-accuracy, low-latency applications in future mobile edge computing. This research plans to systematically investigate the fundamental and challenging issues targeting to significantly reduce the cost of deep learning inference process in mobile edge devices with guaranteed performance. The team will build a software accelerator that enables the broad deployment of heavy-cost deep learning models into resource-constrained, heterogeneous mobile edge devices (e.g., low-cost sensing platforms and IoT devices). They will develop deep-learning resource management algorithms that can adjust structures of different deep learning models according to hardware constraints of heterogeneous edge devices. More specifically, this research analyzes distinct deep learning behaviors on mobile edge devices and designs different strategies to improve the efficiency of multiple deep-learning-based inference models. Furthermore, this research develops algorithms that can adjust the complexity of different deep learning models to reduce their energy and memory consumption on mobile edge devices. In addition, this project designs power-centric resource reallocation algorithms to verify and deploy the mobile-friendly deep learning models.

You can find more details on the project at the NSF page here:

 https://nsf.gov/awardsearch/showAward?AWD_ID=1909963&HistoricalAwards=false

Congratulations, Yingying!

ECE Assistant Professor Anand Sarwate (PI) and ECE Associate Professor Waheed Bajwa(co-PI) are the recipients of a new NSF award for the project titled "ESTRELLA: Exploiting Structure in Tensors for Representation, Estimation, and Limits of Learning Algorithms." This is a three-year $499,976 award.

In this project, Anand and Waheed will  pursue a comprehensive theory for tensor data by focusing on a family of structured statistical models in which the number of parameters can be controlled in a principled manner.  Tensors are multidimensional mathematical objects that generalize vectors (one dimensional) and matrices (two dimensional) to higher dimensions. They are ubiquitous in applications involving complex-structured data and can be used to model data samples (e.g. video) or higher-order correlations between statistical variables. Although tensors have been used for decades in a variety of disciplines, statistical and signal processing methods using structured models for tensor data are less mature than their vector and matrix counterparts. This project draws on the tight connection between tensor decompositions and structured matrix models in order to formulate the estimation of structured tensor models as nonconvex optimization problems over highly structured spaces of matrices. The work will quantify the number of data samples needed to obtain a given parametrized structured tensor model and developing efficient algorithms for estimating the associated parameters. The resulting methods will simplify the measurement, storage, and statistical modeling of tensor-structured data. The outcomes of this project should impact many areas in which tensor data are being used, such as medical imaging, climate science, machine learning, computer vision, text and speech processing, and radar systems. Because of the wide-ranging uses of tensor data, this project also facilitates interactions between multiple research communities from statistics, engineering, and basic sciences. 
 
The full abstract is available here:  https://www.nsf.gov/awardsearch/showAward?AWD_ID=1910110  

Congratulations, Anand and Waheed!
 

ECE Associate Professor Saman Zonouz is the recipient of  a new Department of Energy (DOE) award for the project titled "Deep Cyber-Physical Situational Awareness for Energy Systems:  A Secure Foundation for Next-Generation Energy Management." This is a three-year $2.7M collaborative project between Rutgers, Texas A&M University, University of Illinois at Urbana-Champaign, Pacific Northwest National Labs and Sandia National Labs. Rutgers' share of this award is $500,000. 

Saman and the team will enhance the reliability and resilience of our nation’s critical energy infrastructure by designing and building a secure end-to-end system for managing the energy system, communications, security, and modeling and analytics. Their design of the next generation energy management system will take a data-fusion approach that reenvisions the requirements of the system modeling together with all of the real-time cyber and physical sources that are critical to the management and trustworthiness of the data pipeline. The research conducted will answer the following questions: (i): “Can we redesign a next-generation energy management system to be intrinsically cyber-physical and secure, such that the system can detect malicious and abnormal events through the fusion of cyber and physical data?” (ii) “How can this new system facilitate online and automated control actions that couple cyber and physical control spaces?” (iii) “How will this new world differ from how energy management systems in the energy sector are designed, deployed, and operated today?” 

Congratulations, Saman!

ECE Associate Professors Saman Zonouz (PI) and Mehdi Javanmard (co-PI) are the recipients of a new NSF award for the project titled "Srch3D: Efficient 3D Model Search via Online Manufacturing-specific Object Recognition and Automated Deep Learning-Based Design Classification." This is a three-year $1.2M collaborative award led by Rutgers with Georgia Tech. Rutgers' share of this award is $600,000.

Saman and his team will develop algorithms for advanced 3D model analysis, indexing and search algorithms that can identify designs of interest within a large number of proven design files accurately in runtime. The research will involve development of algorithms for automated design search via 3D object detection with adaptive resolutions. They will build on top of state-of-the-art computer vision techniques, namely histogram of gradients (HOG), and extend them to three-dimensional spaces for the manufacturing design files. Additionally, the project will research algorithms for runtime 3D object classification and labeling via data-driven modeling. The solutions will use deep neural networks to search and identify objects of interest from a large design repository. The use of relatively high-level data-driven models, along with the detailed HOG-based solutions, will enable our online 3D model search engine to accept a different variety of input object formats from the users, such as sketches or photos of the objects of interest, their (partial) G-Code, computer-aided design design files, or English descriptions and keywords. The framework will be accessible via a public cloud-based 3D model search service. In the vein of google.com and virustotal.com for document and malware search, respectively, the proposed framework will realize the aforementioned modules as a cloud-based search engine service that allows anyone to search for their design of interest using different input formats. 

Congratulations, Saman and Mehdi!

Rutgers Electrical Engineering Alumnus Shares his Journey into Space

Robert J. Cenker, a Rutgers-New Brunswick electrical engineering alumnus, was a crew member on the 1986 space shuttle Columbia, where he changed the face of cable TV across the United States. During his six-day mission, which began Jan. 12, 1986, he observed the deployment of an RCA satellite and conducted an experiment on an infrared imaging camera. In total, Cenker traveled more than 2.1 million miles in 96 Earth orbits and logged more than 146 hours in space. The mission was the final flight before the Challenger disaster, which killed seven crewmembers, including teacher Christa McAuliffe, who trained with him. As a result, Cenker's Columbia mission was called "the end of innocence" for the Shuttle program.

In celebration of the 50th anniversary of the Apollo 11 moon landing, Cenker will join students from the New Jersey Governor’s School of Engineering and Technology, TARGET and EOF on July 19 to discuss his journey into space and offer a glimpse of what it takes to become an astronaut. He will describe how the political climate has changed since Neil Armstrong set foot on the moon in 1969 and how the country needs to band together to return to the moon – and perhaps reach Mars.

In honor of the 50th anniversary of a man landing on the moon, the NJ Space Grant Consortium and SoE Undergraduate Education – Office of Student Services, will be hosting Rutgers alumnus, Bob Cenker, to share:

• His career trajectory
• Stories from his space flight,
• and 1-2 intriguing technologies that he worked on

Please join us on Friday, July 19th at 5pm in Richard Weeks Hall 105.

Artificial intelligence and biosensors can rapidly detect if live cancer cells remain after treatment. Rutgers researchers have created a device that can determine whether targeted chemotherapy drugs are working on individual cancer patients. The portable device, which uses artificial intelligence and biosensors, is up to 95.9 percent accurate in counting live cancer cells when they pass through electrodes, according to a study in the journal Microsystems & Nanoengineering.

“We built a portable platform that can predict whether patients will respond positively to targeted cancer therapy,” said senior author Mehdi Javanmard, an assistant professor in the Department of Electrical and Computer Engineering in the School of Engineering at Rutgers University–New Brunswick.“ Our technology combines artificial intelligence and sophisticated biosensors that handle tiny amounts of fluids to see if cancer cells are sensitive or resistant to chemotherapy drugs.”

Read the full article at Rutgers Today https://news.rutgers.edu/cancer-device-created-rutgers-see-if-targeted-c...

ECE Associate Professor Saman Zonouz has been awarded the 2019 Presidential Early Career Award for Scientists and Engineers (PECASE) Award. The PECASE is the highest honor bestowed by the United States Government to outstanding scientists and engineers who are beginning their independent research careers and who show exceptional promise for leadership in science and technology. Established in 1996, the PECASE acknowledges the contributions scientists and engineers have made to the advancement of science, technology, engineering, and mathematics (STEM) education and to community service as demonstrated by scientific leadership, public education, and community outreach. The White House Office of Science and Technology Policy coordinates the PECASE with participating departments and agencies. Please see the announcement from the White House here.

Saman was recognized at a ceremony at the White House on July 25 for his research related to his NSF CAREER Award on the project "Trustworthy and Adaptive Intrusion Tolerance Capabilities in Cyber-Physical Critical Infrastructures." In this project Saman will design secure mechanisms for cyber-physical critical infrastructures that integrate networks of computational and physical processes to provide the society with essential services. The power grid, in particular, is a vast and interconnected cyber-physical network for delivering electricity from generation plants to end-point consumers. Protecting power grid critical infrastructures is a vital necessity because the failure of these systems would have a debilitating impact on economic security and public health and safety. However, several recent large-scale outages and the significant increase in the number of major attacks over the past four years confirm the insufficiency of the current protection solutions for these systems. Existing tedious manual tolerance procedures cannot protect those grids against sophisticated attacks. Additionally, the use of purely-cyber security solutions for power grid resiliency is not sufficient because they ignore the cyber-physical interdependencies, power-side sensor measurements, and the possibility of countermeasures in power infrastructures. The objective of this research is to investigate fundamental problems in cyber-physical tolerance and develop an integrated set of mathematically rigorous and real-world deployable capabilities, resulting in a system that can model, analyze, predict, and tolerate complex security incidents in computing, physical, or communication assets in a near-real-time manner. The proposed research will provide system administrators and power grid operators with scalable and online integrated cyber-physical monitoring and incident response capabilities through keeping track of cyber-physical infrastructures' dynamic evolution caused by distributed security incidents, optimal proactive response and recovery countermeasures and adaptive preparation for potential future security incidents.

Congratulations on this outstanding achievement Saman!