News

On 1/8/22, Mr. Nicholas Polosky presented his work titled, “Machine Learning Subsystem for Autonomous Collision Avoidance on a small UAS with Embedded GPU” at IEEE International Workshop on Communication and Networking for Swarms Robotics (Robocom) 2022.

In this work, ANDRO’s Marconi-Rosenblatt AI/ML Innovation Lab introduced and demonstrated MR-iFLY, a framework that combines the strengths of traditional approaches and machine learning for autonomous collision avoidance for sUAS with minimal sensors. This is part of Navy SBIR Phase II work that is being done at ANDRO’s MR Lab.

Read the full paper here

ANDRO Computational Solutions, LLC is proud to announce that Anu Jagannath, Senior Scientist and Co-Director of the Marconi-Rosenblatt AI/ML Innovation Lab, has been elevated to an IEEE Senior Member level. This is a very significant accomplishment.  Senior Member is the highest grade for which IEEE members can apply or be nominated.  Senior Membership grade is held by only 8% of IEEE’s approximately 428,000 members.  Elevation to this grade requires extensive experience and reflects professional maturity and documented achievements of significance.

Anu Jagannath and Dr. Jithin Jagannath, leaders of the Marconi-Rosenblatt AI/ML Laboratory at ANDRO, had their article Dataset for Modulation Classification and Signal Type Classification for Multi-Task and Single Task Learning accepted to Elsevier Journal on Computer Networks.

Abstract

Wireless signal characterization is a growing area of research and an essential tool to enable spectrum monitoring, tactical signal recognition, spectrum management, signal authentication for secure communication, and so on. Recent years have witnessed several deep neural network models to perform single task signal characterization such as radio fingerprinting for emitter identification, automatic modulation classification, spectrum sharing, etc. However, with the emergence of 5G and the prospects of beyond 5G communication, there has been an increased deployment of edge devices that requires lightweight neural network models to perform signal characterization. To this end, a multi-task learning model that can perform multiple signal characterization tasks with a single neural network model has been proposed. However, due to the novel nature of multi-task learning as applied to signal characterization, there is a lack of a corresponding dataset with multiple labels for each waveform. In this paper, we openly share a synthetic wireless waveforms dataset suited for modulation recognition and wireless signal (protocol) classification tasks separately as well as jointly. The waveforms comprise radar and communication waveforms generated with GNU Radio to represent a heterogeneous wireless environment.

Read the full article here

RadarCommDataset: Radar & Communication Signal and Modulation Classification Dataset

A. Jagannath, J.Jagannath, “Dataset for Modulation Classification and Signal Type Classification for Multi-task and Single Task Learning”, Computer Networks (Elsevier), 2021.

Rome Sentinel

Principal staff members Dr. Andrew Drozd and Dr. Jithin Jagannath, of ANDRO Computational Solutions, were recently awarded honors of distinction by the Institute of Electrical and Electronics Engineers (IEEE) for their years of service, contributing to technological excellence, and advancing innovation.

Drozd is the president of ANDRO, a scientific research and development business established 1994. Jagannath is the chief scientist of technology and director of ANDRO’s Marconi-Rosenblatt Artificial Intelligence and Machine Learning Innovation Lab.

Drozd’s credentials include professional engineering certifications from the international Association of Radio and Telecommunications Engineers (iNARTE); his past presidency of the IEEE Society on Electromagnetic Compatibility (EMC-S); past chairmanship of the IEEE EMC-S Standards Development and Education Committee; current membership on the FCC’s Communications, Security, Reliability, Interoperability Council (CSRIC VIII); membership in the Alpha Gamma Omicron Chapter of Kappa Delta Pi Honor Society in Scientific Education; chairmanship of the Project Fibonacci Foundation, Inc.; and as a 2019 inductee in the Rome Academy of Sciences Hall of Fame.

Jagannath was nominated by the IEEE Mohawk Valley Section for the Region 1 Award in Technical Innovation in the Industry/Government category. The IEEE Region 1 Board of Governors recently announced Jagannath as the 2021 recipient with the citation, “For innovative contributions in machine learning techniques for the wireless domain.”

Jagannath was recognized for scientific research leading to innovative and advanced technologies developed under several highly competitive, federally-funded projects over the past several years.

“ANDRO encourages membership and active participation in all aspects of the IEEE locally, regionally, and globally and is a strong supporter of its mission and vision to benefit society through science, technology, and engineering,” stated Drozd.

Read the full story here