Search Results for author: Marco Levorato

Found 10 papers, 5 papers with code

Spatio-Temporal Split Learning for Autonomous Aerial Surveillance using Urban Air Mobility (UAM) Networks

no code implementations15 Nov 2021 Yoo Jeong Ha, Soyi Jung, Jae-Hyun Kim, Marco Levorato, Joongheon Kim

This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets.

Supervised Compression for Resource-Constrained Edge Computing Systems

1 code implementation21 Aug 2021 Yoshitomo Matsubara, Ruihan Yang, Marco Levorato, Stephan Mandt

There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.

Data Compression Edge-computing +2

Personalized Stress Monitoring using Wearable Sensors in Everyday Settings

no code implementations31 Jul 2021 Ali Tazarv, Sina Labbaf, Stephanie M. Reich, Nikil Dutt, Amir M. Rahmani, Marco Levorato

Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies.

Heart Rate Variability Photoplethysmography (PPG)

A Deep Learning Approach to Predict Blood Pressure from PPG Signals

no code implementations30 Jul 2021 Ali Tazarv, Marco Levorato

Based on these similarities, in recent years several methods were proposed to predict BP from the PPG signal.

Photoplethysmography (PPG)

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

no code implementations8 Mar 2021 Yoshitomo Matsubara, Marco Levorato, Francesco Restuccia

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others.

Autonomous Vehicles Image Classification +1

Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems

2 code implementations20 Nov 2020 Yoshitomo Matsubara, Davide Callegaro, Sabur Baidya, Marco Levorato, Sameer Singh

In this paper, we propose to modify the structure and training process of DNN models for complex image classification tasks to achieve in-network compression in the early network layers.

Edge-computing Knowledge Distillation +1

Frequency-based Multi Task learning With Attention Mechanism for Fault Detection In Power Systems

no code implementations15 Sep 2020 Peyman Tehrani, Marco Levorato

The prompt and accurate detection of faults and abnormalities in electric transmission lines is a critical challenge in smart grid systems.

Fault Detection Multi-Task Learning +1

Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks

2 code implementations31 Jul 2020 Yoshitomo Matsubara, Marco Levorato

However, poor conditions of the wireless channel connecting the mobile devices to the edge servers may degrade the overall capture-to-output delay achieved by edge offloading.

Edge-computing Real-Time Object Detection

Split Computing for Complex Object Detectors: Challenges and Preliminary Results

2 code implementations27 Jul 2020 Yoshitomo Matsubara, Marco Levorato

Following the trends of mobile and edge computing for DNN models, an intermediate option, split computing, has been attracting attentions from the research community.

Edge-computing Image Classification

Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems

2 code implementations1 Oct 2019 Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, Sameer Singh

Offloading the execution of complex Deep Neural Networks (DNNs) models to compute-capable devices at the network edge, that is, edge servers, can significantly reduce capture-to-output delay.

Edge-computing Knowledge Distillation +1

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