Communications Signal Processing Networking
Welcome to the CoSiNe Lab at Clarkson University. Learn more about our work and our team here.
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.
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
Our work is supported in part by funding from the National Science Foundation, Qualcomm Inc., Andro CS, and Facebook Research.