The development of a device for long-term and continuous monitoring of epilepsy is a very
challenging objective, due to the high accuracy standards and nearly zero false alarms …

The long-term, continuous analysis of electroencephalography (EEG) signals on wearable
devices to automatically detect seizures in epileptic patients is a high-potential application …

With the rise of embodied foundation models (EFMs), most notably small language models
(SLMs), adapting Transformers for the edge applications has become a very active field of …

We present the WaveFormer, a neural network architecture based on a linear attention
transformer to enable long sequence inference for TinyML devices. Waveformer achieves a …

Wearable systems for the long-term monitoring of cardiovascular diseases are becoming
widespread and valuable assets in diagnosis and therapy. A promising approach for real …

This paper deploys and explores variants of TinyissimoYOLO, a highly flexible and fully
quantized ultra-lightweight object detection network designed for edge systems with a power …

Few-Shot Class-Incremental Learning (FSCIL) enables machine learning systems to expand
their inference capabilities to new classes using only a few labeled examples, without …

Wearable systems for the continuous and real-time monitoring of cardiovascular diseases
are becoming widespread and valuable assets in diagnosis and therapy. A promising …

Epilepsy is a prevalent neurological disorder that affects millions of individuals globally, and
continuous monitoring coupled with automated seizure detection appears as a necessity for …

Zeitgleich mit der rasanten Ausbreitung des Internet of Things (IoT) hat die Entwicklung von
Deep-Learning-Algorithm eine Revolution im Feld des maschinellen Lernens ausgelöst. Die …