13 papers
Distributed Multi-Sensor Control for Multi-Target Tracking Using Adaptive Complementary Fusion for LMB Densities
Aidan Blair, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar +2
Tracking multiple targets in dynamic environments using distributed sensor networks is a fundamental problem in statistical signal processing. In such scenarios, the network of mob…
Automated Keypoint Estimation for Self-Piercing Rivet Joints Using micro-CT Imaging and Transfer Learning
Wei Qin Chuah, Ruwan Tennakoon, Amanda Freis +3
The structural integrity of self-piercing rivet (SPR) joints is critical in automotive industries, yet its evaluation poses challenges due to the limitations of traditional destruc…
IT-RUDA: Information Theory Assisted Robust Unsupervised Domain Adaptation
Shima Rashidi, Ruwan Tennakoon, Aref Miri Rekavandi +7
Distribution shift between train (source) and test (target) datasets is a common problem encountered in machine learning applications. One approach to resolve this issue is to use…
Distributed Complementary Fusion for Connected Vehicles
James Klupacs, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar +2
We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled m…
Interaction-Aware Labeled Multi-Bernoulli Filter
Nida Ishtiaq, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar +1
Tracking multiple objects through time is an important part of an intelligent transportation system. Random finite set (RFS)-based filters are one of the emerging techniques for tr…
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks
WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad +2
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an impo…