Communications Signal Processing Networking

Welcome

Welcome to the CoSiNe Lab at Clarkson University. 

Our work involves the use of machine learning and signal processing in a variety of applications, including biometrics, localization, and STEM Education.  

Learn more about our work and our team at the links below. A full list of publications (updated semi-frequently) is available here.    

Our Research

NEW!

Demo on password hardening based on our research in behavioral biometrics

Demo on fluid detection in ultrasound  images

Analysis and Uses of PPG Signals 

PPGs are a non-invasive measure of blood volume changes, typically measured at the fingertip using infrared or RGB sensors. We are using PPGs from multiple sources for a variety of applications. You can read about our work with PPGs here. 

Rural Emergency Medical Services (EMS)

We are working to integrate advanced artificial intelligence (AI) and machine learning (ML) models with both national-level and regional EMS data. Our models will collect and analyze data to optimize clinical and operational performance, offering agency-specific, evidence-based recommendations and peer comparisons. We are looking at operational efficiencies (for example, improved scene time) and technology (fluid detection in ultrasound images) to improve rural EMS. 

Ordinal UNLOC 

A major challenge in indoor localization arises from the lack of reliable distance measures due to the presence of walls, floors, furniture, and other dynamically changing conditions such as the movement of people and goods, varying temperature, and air flows.

In this work, instead of direct use of unreliably measured distances, we consider ordinal data that are obtained from pairwise distance comparisons. Such ordinal data are generally more reliable than direct distance measures. We develop a computational framework, Ordinal UNLOC, to estimate the location of targets based on ordinal data.

Behavioral Biometrics

We are investigating novel behavioral biometric modalities that involve using a user’s fine-grained application usage behavior to identify him/her and protect online eCommerce and other critical infrastructures, even in the presence of identity theft and insider threats. We will use tools from machine learning and pattern recognition, including transfer learning, longitudinal methods, and deep learning to create user profiles, and perform classification tests for detection and identification.

STEM Education and the Reflections App

The “Reflections” app is audio-based and utilizes echolocation to perform ranging. A signal is generated and transmitted through the speaker of the Android device. The signal travels through the environment, strikes objects, and reflects back towards the device. The sent and received signals are used to calculate the time needed for the signal to be sent and received, and a distance estimate is calculated.

The app can be downloaded from https://play.google.com/store/apps/details?id=edu.clarkson.reflectionapp

Documentation for the App. 

RAPID Project on COVID-19 Hotspot Detection

In order to open up the economy in light of the reality of COVID-19, a suite of solutions is needed to minimize the spread of COVID-19 which includes providing tools for businesses to minimize the risk for their employees and customers. It is important to detect transmission hotspots where the contact between infected and uninfected persons is higher than average. This project will provide information to assess precisely the size, density, and locations of COVID-19 hotspots and enable issuing well-informed advisories based on data-driven continuous risk assessment. Every step will be taken to ensure privacy and network security and specific algorithms will be developed for secure access and information transfer. The project will access databases at CDC, Johns Hopkins, and the WHO, and create a comprehensive website to disseminate real-time localized COVID-19 hotspot data while maintaining privacy. The project will create new algorithms and embed them in iOS and Android apps that will continuously interact with databases. The software for mobile devices as well as central hubs will be made publicly available through APIs for use by the broader community. 

More details at:

https://sites.google.com/clarkson.edu/cu-cosine-lab/home/rapid-project-on-covid-19-hotspot-detection

Apps, Demos, and Workshops

Demo on Password Hardening

Impostor Attack

Ultrasound AI

Fluid detection in ultrasound images 

Reflections App

Available on Google Play

Reflections Lite App

Available on Google Play

A Tool for Simulation and Visualization of Distributed Estimation in Wireless Sensor Networks

Workshop

Role of Generative AI in Classrooms

Workshop

The Team

Mahesh K. Banavar, 

Professor, Department of Electrical and Computer Engineering

Associate Director of Faculty Support, Institute for STEM Education

Clarkson University. 

PhD Students: 

• Arfina Rahman

• Derek Pelkey

MS Students:

• Fatemeh Ghassemi

• Olaolu Olugbenle

• Shaira Torsa

Acknowledgements

Our work is supported in part by funding from the National Science Foundation, NIST, Qualcomm Inc., Andro CS, and Facebook Research. 

A full list of publications (updated semi-frequently) is available here. 

MB's Google Scholar Page.