Adaptive optimization methods for deep learning adjust the inference task to the current
circumstances at runtime to improve the resource footprint while maintaining the model's …

Wearable medical technology has become increasingly popular in recent years. One
function of wearable health devices is stress detection, which relies on sensor inputs to …

With the rapid evolution of the Internet of Things, many real-world applications utilize
heterogeneously connected sensors to capture time-series information. Edge-based …

Radar sensors offer power-efficient solutions for always-on smart devices, but processing
the data streams on resource-constrained embedded platforms remains challenging. This …

Non-intrusive load monitoring (NILM) has recently become a promising and trending topic in
energy management. Although several NILM-based solutions were proposed, a commercial …

Myocardial Infarction (MI), commonly known as heart attack, is a cardiac condition
characterized by damage to a portion of the heart, specifically the myocardium, due to the …

Deep Learning is becoming increasingly relevant in Embedded and Internet-of-things
applications. However, deploying models on embedded devices poses a challenge due to …

In this research paper we propose a novel technique is to predict the Confidence Score of
Myocardial Infarction from multilead electrocardiogram (ECG) signals. Classifying the …

Non-intrusive load monitoring (NILM) has developed as a prominent and intriguing topic in
energy management recently. Even though a variety of NILM-based solutions have been …

With ever-growing interests in personalized physical and mental healthcare, especially with
the recent COVID-19 pandemic, along with the proliferation of applied machine learning …