In this paper, we propose a lightweight neural network for real-time electrocardiogram (ECG)
anomaly detection and system level power reduction of wearable Internet of Things (IoT) …

This paper presents a neuromorphic processing system with a spike-driven spiking neural
network (SNN) processor design for always-on wearable electrocardiogram (ECG) …

This brief presents an overview of current trends in the level-crossing based even-driven
systems for wireless sensors in AIoT applications. We show that orders of magnitude …

Electrocardiogram (ECG) analysis is widely used in the diagnosis of cardiovascular
diseases. This article proposes an explainable rule-mining strategy for prioritizing abnormal …

Mobile health devices with automatic electrocardiogram diagnosis models facilitate long-
term cardiac monitoring and enhance the sensitivity of detecting paroxysmal cardiovascular …

This work presents a power-efficient level crossing (LC) ADC designed to digitize sparse
signals. It uses dynamically self-biased comparators, which require minimal current when …

Efficient and effective radar emitter recognition is critical for electronic support measurement
(ESM) systems. However, in complex electromagnetic environments, intercepted pulse …

Wearable Artificial Intelligence-of-Things (AIoT) devices exhibit the need to be resource and
energy-efficient. In this paper, we introduced a quantized multilayer perceptron (qMLP) for …

This paper presents an explainable, low-complexity binary electrocardiogram (ECG)
classifier to be deployed in a resource-limited wearable edge device. The presented …

In this article, we propose a multistage electrocardiogram (ECG) classifier for distributed
machine learning (ML) inferencing across the edge-cloud continuum for wearable systems …