Communications Signal Processing Networking


Welcome to the CoSiNe Lab at Clarkson University. Learn more about our work and our team here.

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

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:

Apps, Demos, and Workshops

Demo on Password Hardening

Impostor Attack

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


The Team

PI: Mahesh K. Banavar, Assistant Professor, Department of Electrical and Computer Engineering, Clarkson University.

PhD Students:

MS Student:

  • Monalisa Achalla


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