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ALERT Phase 2 Year 4 Annual Report Available Online! September 27, 2017

ALERT is proud to announce that the Phase 2 Year 4 Annual Report is now available for download online. This report details the continued research in ALERT’s four thrusts:

  • R1 Characterization & Elimination of Illicit Explosives
  • R2 Trace & Vapor Sensors
  • R3 Bulk Sensors & Sensor Systems
  • R4 Video Analytics & Signature Analysis

A full bibliography of publications and presentations conducted under ALERT support follows the individual project reports. Comprehensive descriptions of the Year 4 activities that took place in our Research and Transition, Education, Strategic Studies, Safety, and Information Protection Programs, as well as the ALERT Phase 2 Overview and Year 4 Highlights, Infrastructure and Evaluation, and Industrial/Practitioner and Government Partnerships can also be accessed in the Annual Report.

DEADLINE EXTENDED TO 10/20: ALERT Professional Development Award September 22, 2017

ALERT Students, the deadline for the ALERT Professional Development Award has been extended to Friday, October 20, 2017. This award provides up to three students with financial support to attend a conference or visit an ALERT or DHS affiliated lab as part of their research experience.

Apply Today!

Postdoctoral Position Available at Tufts University September 19, 2017

Applications are invited for a postdoctoral position in the Tufts Information and Networked Systems (TINS) lab in collaboration with the recently established Center for Applied Brain and Cognitive Sciences (CABCS) at Tufts University and the U.S. Army Natick Soldier Systems Center at Natick, MA. This appointment would be for 12-18 months with an estimated start date of October 2017.

The primary project is entitled “Real time prediction of individual and team performance metric from neurophysiological measurements and team interaction data.” Under this project, the fellow will work with Tufts ECE faculty, Dr. Shuchin Aeron and Dr. Eric Miller, as well as CABCS scientists to develop supervised and semi-supervised machine learning algorithms that are capable of predicting cognitive state (e.g., stress level and alertness) and task performance metrics (e.g., target identification and marksmanship) from a wide assortment of physiological sensor data (both labeled and unlabeled) including information collected continuously as a function of time (EEG, FNIRS, Heart Rate) as well as data at a relatively few points in time before, during, and after a specific task (saliva and urine samples).  In addition to assessing individuals, data will be collected to support the characterization of team and intergroup dynamics. We anticipate the effort will require the use of several classical as well as recent developments in machine learning and in particular recursive neural networks, manifold learning, and social network analysis.

While previous experience in theoretical and applied machine learning would be ideal, we welcome applicants with significant experience in related fields including information theory, statistical signal processing, sparse signal or image processing, compressive sensing, and distributed convex optimization.

Interested applicants should send a cover letter detailing their research interests and career goals, CV, and names and contact information of 3 references to Dr. Shuchin Aeron ([email protected]).

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New Video Analytics Dataset available for use August 25, 2017

ALERT Airport Re-Identification Dataset

As part of the ALERT video analytics effort, researchers at Northeastern University and Rensselaer Polytechnic Institute developed an annotated dataset that accurately reflects the real-world person re-identification problem. The dataset was constructed using video data from the six cameras installed post central security checkpoint at an active commercial airport within the United States. (No NDA required)

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Follow ALERT on Facebook August 24, 2017

ALERT recently joined Facebook! Follow us online and keep up to date on ALERT’s research and education programs, as well as upcoming events and opportunities. Search @alertcoe on Facebook for ALERT updates.
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Five Questions with Srikrishna Karanam (RPI, MS ’14, PhD ’17) July 28, 2017

Former ALERT student researcher, Srikrishna Karanam, reflects on his time with ALERT and how it prepared him for working in the Homeland Security Enterprise.

Srikrishna joined ALERT in 2013 as a graduate student working with Prof. Richard J. Radke at Rensselaer Polytechnic Institute (RPI) on video analytics problems in camera networks. At RPI, he earned his MS in Electrical Engineering and his Ph.D. in Computer and Systems Engineering. Srikrishna is now working as a Research Scientist at Siemens Corporate Research, focusing on computer vision and machine learning.

What professional development opportunities, aside from research experience, benefitted you during your time as an ALERT student?

SK:  During my time as an ALERT student, I attended several major conferences in Computer Vision – CVPR 2015 in Boston, MA, BMVC 2015 in Swansea, UK and ICCV 2015 in Santiago, Chile.  Going to these conferences allowed me to discuss open problems and establish connections with several researchers in my field. Furthermore, I participated in several ALERT events – ASPIRE, ADSA, and ALERT annual meetings – where I got opportunities to present my work to several stakeholders in the security and surveillance industry.

These ALERT events were crucial in that they helped me focus my algorithmic and systems research on operational aspects from an end-user’s perspective – I believe these are critical issues as we transition laboratory research into working prototypes in the real world. 

We understand that you were working under the supervision of former ALERT student, Ziyan Wu (RPI, PhD ‘14) during an internship with Siemens Corporate Research (Princeton, NJ) last year. What were some highlights from that experience?

SK:  I was given a lot of independence in addressing existing problems the group at Siemens was tackling. This gave me an opportunity to explore several algorithmic as well as implementation and engineering components of the project I was assigned to. At the algorithmic level, I developed new algorithms and demonstrated improved performance on internal datasets. In addition, I assisted the group in integrating these algorithms as part of a large system that has been deployed for in-the-field testing.

This assignment provided me with valuable, real-world, hands-on research experience. Ziyan and others in the Vision Technologies and Solutions group were very supportive, kind, and welcoming, and I thoroughly enjoyed working there and developed great friendships along the way. 

During your time at ALERT, you collaborated with ALERT teams from RPI, Northeastern University, and Boston University. Can you tell us a little bit about these collaborations and how they have prepared you to work in industry? Have you continued these collaborations post-graduation?

SK:  I worked with the ALERT teams from RPI, Northeastern University, and Boston University on the VAST “Tag and Track” project (see related video at: https://alert.northeastern.edu/news-article/alert-101-is-back/) for over 3 years.  Each team was responsible for specific parts of the project, with the goal of deploying and testing a working prototype of the system at the Cleveland International Airport, which was successfully achieved in Summer 2015.

The “Tag and Track” project provided me with real-world research, development, and project management experience, helping develop skills that are particularly relevant to industrial research labs. At Siemens Corporate Technology, I have been working on solving vision problems with practical relevance across multiple industrial units, and my experience with ALERT has helped me transition into my current work environment seamlessly.

Because of this project, I developed close collaborations (and friendships) with several researchers from Northeastern (specifically, Mengran Gou (NU, PhD ’17) and Oliver Lehmann (NU, PhD ’15)) in addition to Ziyan Wu and Austin Li (RPI, PhD ’15) from RPI. For instance, since the winter of 2015, Mengran and I have been closely collaborating on a project where our goal is to benchmark the current state-of-the-art in person re-identification for the convenience of the larger research community – as part of this work, we have evaluated several hundreds of different algorithms on numerous public datasets. Ziyan and I have been closely working together on numerous problems for many years – initially at RPI and now at Siemens.

Can you describe your role at Siemens and the research you are conducting now?

SK:  I work as a Research Scientist in the Vision Technologies and Solutions group at Siemens Corporate Technology, where I research topics in Computer Vision and Machine Learning. I am responsible for developing algorithms to address research problems, as well as prototype systems that leverage these algorithms to solve real-world problems. My current research focuses on all aspects of image indexing, search, and retrieval with applications in object recognition and pose estimation.

Where do you see yourself in 5 years?

SK:  My past research experience at RPI and ALERT has made me realize the importance of, and challenges in, getting lab-optimized research to work effectively in the “wild” real-world. To this end, I hope to contribute towards bridging this “gap,” enabling and building systems that offer Computer Vision, Machine Learning, and Data Analytics technologies as services to solve a wide variety of real-world problems.

Summer REUs at Northeastern University and University of Puerto Rico Mayagüez July 28, 2017

July 28, 2017

This summer, ALERT is hosting three undergraduate students to participate in the 10-week Research Experience for Undergraduates (REU) program at Northeastern University. Nikhil Phatak (Computer Engineering & Computer Science ’20), Daniel Castle (Electrical Engineering ’21), and Jacob Londa (Computer Engineering ’21) are working with Prof. Carey Rappaport and graduate student mentor, Mohammad Nemati on the Advanced Imaging Technology (AIT) project. ALERT is also hosting two REU students at the University of Puerto Rico Mayagüez.

At the end of the summer, students will give a short video presentation on their research project, which will be featured on ALERT’s website. The video presentations will consist of a brief overview of each student’s research project, the project’s overall mission and activities, their specific contributions to the project, and knowledge and skills gained.

The program is hosted and sponsored by the Awareness and Localization of Explosives-Related Threats (ALERT) Department of Homeland Security Center of Excellence, and the Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems (Gordon-CenSSIS), a Graduated National Science Foundation Engineering Research Center.

ADSA16 Presentations Now Available June 20, 2017

We are pleased to announce that the presentations from The Sixteenth Advanced Development for Security Applications Workshop (ADSA16) which was held on May 2-3, 2017 at Northeastern University in Boston, MA are now available for download.

The title of the workshop was, “Addressing the Requirement for Different Stakeholders in Transportation Security.” View all slides, as well as the reports from past ADSA workshops here.

If you have any questions regarding the topics and technologies discussed at the workshop, please contact ALERT at [email protected].

ALERT Launches Video Analytics Lab at Kostas Research Institute May 30, 2017

A Better Testing Facility for Solving Real World Problems

Northeastern University’s George J. Kostas Research Institute for Homeland Security is now home to ALERT’s new Video Analytics Laboratory. Providing secure access, 1225 sq. feet of open space, controlled lighting conditions, and a fully networked and flexible camera grid, ALERT can better investigate and develop video and sensor technologies to address the needs of the Homeland Security Enterprise.

Using Video Technologies to Improve Passenger Experience

The first research project to leverage the lab is entitled Research and Development of Systems for Tracking Passengers and Divested Items at the Checkpoint. Supported by the DHS Science and Technology Directorate through the DHS Office of University Programs, this project is known by the acronym CLASP (Correlating Luggage and Specific Passengers) and leverages the technical expertise of ALERT research teams from Boston University, Marquette University, Northeastern University, Purdue University, and Rensselaer Polytechnic Institute. These teams will work towards developing an automated system capable of tracking passengers and divested items at airport security checkpoints.

CLASP will primarily focus on using video technologies to assist the Transportation Security Administration (TSA) in effectively identifying security incidents like theft of items, or bags left behind at the checkpoint. By automating and improving the technologies associated with these objectives, ALERT hopes to improve rates of detection and at the same time improve the passenger experience.

CLASP was the result of DHS’s interest in initial work done by ALERT Project Investigator Richard Radke’s lab. A video of their work can be seen below:

(Z. Wu and R.J. Radke, Real-Time Airport Security Checkpoint Surveillance Using a Camera Network. Workshop on Camera Networks and Wide Area Scene Analysis, in conjunction with CVPR 2011, June 2011.).

Government & Industry Partners Make the Difference

In order to deliver the system outlined in CLASP, the researchers working on the project require access to video data displaying real-world checkpoint security situations. Actual airport security video is generally restricted, so ALERT partnered with Massport, the Transportation Security Administration at Boston Logan International Airport, and industrial partners such as Rapiscan Systems to create an accurate representation of an airport security checkpoint in the ALERT Video Analytics Laboratory. This full-scale, mock airport security checkpoint uses the same hardware and design specifications currently used by the TSA at airports such as Logan, and gives ALERT a space to generate usable video data for this project and hopefully to the video analytics research community as a whole.

CLASP is just the beginning of work that can be done in this new laboratory and ALERT is hoping to leverage it for additional homeland security-related projects going forward. If you are interested in partnering with ALERT on future projects, please connect with us via email at [email protected].

Kurt Jaisle Selected as Finalist in IEEE AP-S Student Paper Competition May 30, 2017

May 30, 2017

ALERT student researcher and Northeastern University Scholar, Kurt Jaisle has been selected as a finalist in the 2017 IEEE Antennas and Propagation Symposium’s (AP-S) Student Paper Competition for his paper, “Ray-Based Reconstruction Algorithm for Multi-Monostatic Radar in Imaging Systems.”

Being selected as a finalist is quite an accomplishment, as each paper submitted to the IEEE AP-S Student Paper Competition undergoes three independent reviews from experts in each student’s field of study. Jaisle’s submission was selected out of 159 papers, most of which were submitted by doctoral students. Kurt is a third year undergraduate student majoring in Electrical and Computer Engineering and conducts ALERT research with Professor Carey Rappaport on the R3 Research Thrust (Bulk Sensors & Sensor Systems).

Jaisle believes that the topic of his paper is relevant to aviation security and the Homeland Security Enterprise: “Today’s airport security scanners use very computationally demanding algorithms to process sensor data into an image of a passenger. As a result, these scanners require expensive, high-performance computers to complete the algorithms in a reasonable amount of time. Yet even with these powerful machines, it can still take several seconds for a scan to be processed.” In his paper, Jaisle proposes a new algorithm that would result in significantly faster processing times resulting in shorter lines for passengers at airport security checkpoints, and a reduction in the cost of the computer hardware used in scanners, potentially making the technology more accessible for broader security applications.

Under the guidance of Professor Rappaport, Jaisle began coding the algorithm in Fall 2015. Over the course of a year, he managed to bring the algorithm from a rudimentary 2D simulation to a functional 3D simulation worthy of publication. Reflecting on his experience conducting research with Professor Rappaport, Jaisle states, “Aside from a great deal of technical knowledge, I think the most important thing I have learned from Professor Rappaport is to not leave an endeavor half-finished. Even when I was stuck on a technical challenge for weeks at a time, he would remind me that progress in research is non-linear and that it was worth seeing it through so that I could eventually share my work with the broader community.”

Jaisle’s interest in engineering was sparked during middle school, when he became involved in FIRST Robotics, a program that aims to develop young STEM leaders through robotics competitions. As time passed, he became interested in the electrical side of engineering and decided to pursue this field of study at Northeastern University. After graduation, Jaisle plans to pursue a Master’s Degree in the context of analog electronics, and is hoping that his upper level Electrical Engineering courses, co-op opportunities, and research experiences will help him choose a specific topic of study.

Jaisle will present on his selected paper at the IEEE AP-S Symposium in San Diego, California in July. Following the presentations, the Student Paper Competition Committee Chair will announce the first, second, and third place winners at the IEEE A-PS Symposium’s Annual Awards Ceremony.