Multivariate time series with missing values are common in a wide range of applications,
including energy data. Existing imputation methods often fail to focus on the temporal …

This paper presents a novel place recognition approach to autonomous vehicles by using
low-cost, single-chip automotive radar. Aimed at improving recognition robustness and fully …

Recent research on spatial and spatio-temporal model checking provides novel image
analysis methodologies, rooted in logical methods for topological spaces. Medical imaging …

Several application domains, including healthcare, smart building, and traffic monitoring,
require the continuous publishing of data, also known as time series. In many cases, time …

In this paper, multiple-object vehicle tracking system by affinity matching using min-cost
linear cost assignment is proposed. This tracking system is targeted to scene recordings …

Global traveltime modeling is an essential component of modern seismological studies with
a whole gamut of applications ranging from earthquake source localization to seismic …

AI models of code have made significant progress over the past few years. However, many
models are actually not learning task-relevant source code features. Instead, they often fit …

Unmanned Aerial Vehicles (UAVs) provide a flexible method for acquiring high-resolution
imagery with relative simple operation and cost-effectiveness. This technology emerged 30 …

In the previous studies, machine learning models generally used the majority vote or
average value to predict agricultural product freshness. However, freshness evaluation is …

So far, detection and quantification of bio-aerosols requires genotypic or phenotypic
identification of every single particle. Successful use of optical particle counters, as a time …