This paper describes the application of state-estimation techniques for the real-time prediction
of the state-of-charge (SoC) and state-of-health (SoH) of lead-acid cells. Specifically, …

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment,
and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from …

One of the biggest issues facing the use of machine learning in medical imaging is the lack
of availability of large, labelled datasets. The annotation of medical images is not only …

Abstract Machine learning, particularly deep learning has boosted medical image analysis
over the past years. Training a good model based on deep learning requires large amount of …

Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities
and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable …

Wavelets and wavelet transforms are a relatively new topic in signal processing. Their
development and, in particular, their application remains an active area of research. This paper …

Convolutional neural networks (CNNs) have revolutionized medical image analysis over the
past few years. The U-Net architecture is one of the most well-known CNN architectures for …

This paper describes a novel adaptive battery model based on a remapped variant of the
well-known Randles' lead-acid model. Remapping of the model is shown to allow improved …

A critical decision-step in the emergency treatment of ischemic stroke is whether or not to
administer thrombolysis — a treatment that can result in good recovery, or deterioration due to …

Evidence from lesion and cortical-slice studies implicate the neocortical cholinergic system
in the modulation of sensory, attentional and memory processing. In this review we consider …