Coronavirus disease (COVID-19) has been the main agenda of the whole world ever since it
came into sight. X-ray imaging is a common and easily accessible tool that has great …

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its
first known detection in December 2019. As a result, accurate and reliable advance warning …

In this work, we propose a novel approach called Operational Support Estimator Networks
(OSENs) for the support estimation task. Support Estimation (SE) is defined as finding the …

Compressive imaging aims to recover a latent image from under-sampled measurements,
suffering from a serious ill-posed inverse problem. Recently, deep neural networks have …

In this study, we propose a novel approach to predict the distances of the detected objects in
an observed scene. The proposed approach modifies the recently proposed Convolutional …

Here, a novel image‐based quantized compressive sensing (QCS) framework based on
complex‐valued Vandermonde (Vander) matrix is proposed. In the proposed QCS …

Visual object tracking, crucial in aerial applications such as surveillance, cinematography,
and chasing, faces challenges despite AI advancements. Current solutions lack full …

The efforts in compressive sensing (CS) literature can be divided into two groups: finding a
measurement matrix that preserves the compressed information at its maximum level, and …

The research outlined in this thesis was carried out at Tampere University, Finland, between
the years 2020 and 2023. I greatly acknowledge the acquired funding from the National …

This chapter presents deep learning methodologies for medical imaging tasks. The chapter
starts with echocardiography for early detection of myocardial infarction (MI) or commonly …