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Found 7 papers, 1 papers with code
Model-Driven Dataset Generation for Data-Driven Battery SOH Models
Estimating the State of Health (SOH) of batteries is crucial for ensuring the reliable operation of battery systems.
Energy-efficient Wearable-to-Mobile Offload of ML Inference for PPG-based Heart-Rate Estimation
In this work, we propose a collaborative inference approach that uses both a smartwatch and a connected smartphone to maximize the performance of heart rate (HR) tracking while also maximizing the smartwatch's battery life.
Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays
Ultra-low-resolution Infrared (IR) array sensors offer a low-cost, energy-efficient, and privacy-preserving solution for people counting, with applications such as occupancy monitoring.
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge
Neural Architecture Search (NAS) is quickly becoming the go-to approach to optimize the structure of Deep Learning (DL) models for complex tasks such as Image Classification or Object Detection.
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time.
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs
In this work, we demonstrate that an accurate detection of social distance violations can be achieved processing the raw output of a 8x8 IR array sensor with a small-sized Convolutional Neural Network (CNN).
C-NMT: A Collaborative Inference Framework for Neural Machine Translation
Collaborative Inference (CI) optimizes the latency and energy consumption of deep learning inference through the inter-operation of edge and cloud devices.
