Category: News

Abstract: Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect objects with low metal content. One successful, previously investigated approach is the Multiple Instance Adaptive […]

Our labmates James Bocinsky, Connor McCurley and Joshua Peeples recently presented at SPIE in Baltimore, MD and they all did a great job! James Bocinsky talked about “Investigation of initialization strategies for the Multiple Instance Adaptive Cosine Estimator” and more details about it can be found here. Connor McCurley talked about”Comparison of Hand-held WEMI Target […]

Congratulations to our labmate Daniel Suen for receiving his ECE undergraduate research award! This award is given yearly to the top ECE undergraduate student who has excelled in her/his research during the 2018 calendar year.

The Machine Learning and Sensing Lab is excited to welcome our newest lab member Sofia Vega! Sofia is a third year Statistics and Mathematics at the University of Florida. Through her statistics courses, she has gained the data analysis skills that she puts into her work in the lab. With a desire to do research, […]

Abstract: Synthetic aperture sonar (SAS) imagery can generate high resolution images of the seafloor. Thus, segmentation algorithms can be used to partition the images into different seafloor environments. In this paper, we compare two possibilistic segmentation approaches. Possibilistic approaches allow for the ability to detect novel or outlier environments as well as well known classes. […]

Abstract: Wide-band Electromagnetic Induction Sensors (WEMI) have been used for a number of years in subsurface detection of explosive hazards. While WEMI sensors have proven effective at localizing objects exhibiting large magnetic responses, detecting objects lacking or containing very low amounts of conductive materials can be challenging. In this paper, we compare a number of […]

Dr. David LeBauer, the director of data science for the Arizona experiment station at University of Arizona gave a talk about how to integrates theory with observation to optimize food an fuel production. He will talk about the challenges opportunities for agriculture in the digital age and give examples about his research.

This Wednesday, Alina Zare presented a talk on Machine Learning and Applications in Remote Sensing in Global Centra Spring 2019 Webinars. Most supervised machine learning algorithms assume that each training data point is paired with an accurate training label. However, obtaining accurate training label information is often time consuming and expensive, making it infeasible for […]

Gators giving back! In February, the UF Machine Learning Lab hosted a two hour workshop for GatorTrax, a free K-12 outreach program run by the UF College of Engineering and honor society. Conner McCurley, Susan Meerdink, and Weihuang Xu helped with a youth STEM outreach program called GatorTRAX and taught 40 kids about machine learning […]

Congratulations to James Bocinsky who defended his Master’s thesis titled “Learning Multiple Target Concepts From Uncertain, Ambiguous Data Using The Adaptive Cosine Estimator and Spectral Match Filter” this week on Monday, March 11th. His research focused on developing algorithms for explosive hazard detection. His thesis included investigating multiple instance learning, clustering, and various target detection […]