15 citations · 15 across the 8 of their papers we have counts for
8 papers
Personalization on a Budget: Minimally-Labeled Continual Learning for Resource-Efficient Seizure Detection
Amirhossein Shahbazinia, Jonathan Dan, Jose A. Miranda +2
Objective: Epilepsy, a prevalent neurological disease, demands careful diagnosis and continuous care. Seizure detection remains challenging, as current clinical practice relies on…
VCO-CARE: VCO-based Calibration-free Analog Readout for Electrodermal activity sensing
Leidy Mabel Alvero-Gonzalez, Matias Miguez, Eric Gutierrez +4
Continuous monitoring of electrodermal activity (EDA) through wearable devices has attracted much attention in recent times. However, the persistent challenge demands analog front-…
MEDEA: A Design-Time Multi-Objective Manager for Energy-Efficient DNN Inference on Heterogeneous Ultra-Low Power Platforms
Hossein Taji, José Miranda, Miguel Peón-Quirós +1
The growing demand for on-device AI necessitates energy-efficient execution of DNN based applications on resource-constrained ultra-low power (ULP) platforms. Heterogeneous archite…
FADE: Forecasting for Anomaly Detection on ECG
Paula Ruiz-Barroso, Francisco M. Castro, José Miranda +3
Cardiovascular diseases, a leading cause of noncommunicable disease-related deaths, require early and accurate detection to improve patient outcomes. Taking advantage of advances i…
Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs
Christodoulos Kechris, Jonathan Dan, Jose Miranda +1
Deep learning time-series processing often relies on convolutional neural networks with overlapping windows. This overlap allows the network to produce an output faster than the wi…
DC is all you need: describing ReLU from a signal processing standpoint
Christodoulos Kechris, Jonathan Dan, Jose Miranda +1
Non-linear activation functions are crucial in Convolutional Neural Networks. However, until now they have not been well described in the frequency domain. In this work, we study t…
KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch
Christodoulos Kechris, Jonathan Dan, Jose Miranda +1
Accurate extraction of heart rate from photoplethysmography (PPG) signals remains challenging due to motion artifacts and signal degradation. Although deep learning methods trained…
STRELA: STReaming ELAstic CGRA Accelerator for Embedded Systems
Daniel Vazquez, Jose Miranda, Alfonso Rodriguez +3
Reconfigurable computing offers a good balance between flexibility and energy efficiency. When combined with software-programmable devices such as CPUs, it is possible to obtain hi…