To address the limitations in predictive capabilities of thermal error models built from single-
source, single-structure data, this paper proposes a thermal error prediction model based on …

In recent years, multi-modal fusion has attracted a lot of research interest, both in academia,
and in industry. Multimodal fusion entails the combination of information from a set of …

The importance of inference in Machine Learning (ML) has led to an explosive number of
different proposals in ML, and particularly in Deep Learning. In an attempt to reduce the …

Piezoresistive sensors are widely used in the design of underwater pressure transmitters,
whose nonlinear output is easily affected by the temperature of oil and gas medium. Most of …

In this article, a target tracking issue is investigated for a TarkBot intelligent robot with a lidar
and a camera to identify the state of the target moving around the robot. As the measurement …

This paper presents a data driven approach to multi-modal fusion where a hidden latent sub-
space between the different modalities is learned. The hidden space is estimated via a bank …

Human history is filled with inventions and other innovations that resulted in a significant and
lasting change in our civilization's course of development. From gasoline-powered vehicles …

The importance of inference in Machine Learning (ML) has led to an explosive number of
different proposals, particularly in Deep Learning. In an attempt to reduce the complexity of …

In this work, we seek to exploit the deep structure of multi-modal data to robustly exploit the
group subspace distribution of the information using the Convolutional Neural Networks …

Abstract Union of Subspaces (UoS) is a new paradigm for signal modeling and processing,
which can identify more complex trends in data sets than simple linear models. Relying on a …