121 documents found, page 1 of 13

TREE: bridging the gap between reconfigurable computing and secure execution

Trusted Execution Environments (TEEs) have become a pivotal technology for securing a wide spectrum of security-sensitive applications. With modern com puting systems shifting to heterogeneous architectures, integrating TEE support into these systems is paramount. One promising line of research has proposed leveraging FPGA technology to provide promising TEE solutions. Despite their potential, current implement...

FOG-Zip: Bitstream Compression for Octree-Encoded LiDAR Data

Light detection and ranging (LiDAR) sensors play a critical role in enabling precise and reliable environmental perception for autonomous vehicles. However, handling the large amounts of data they generate presents a significant challenge. With the emergence of standards, such as the geometry based point cloud compression (G-PCC) standard, octrees have been used as a key data structure for compressing 3-D light...

Comparison of feature selection methods: modelling COPD outcomes

Selecting features associated with patient-centered outcomes is of major relevance yet the importance given depends on the method. We aimed to compare stepwise selection, least absolute shrinkage and selection operator, random forest, Boruta, extreme gradient boosting and generalized maximum entropy estimation and suggest an aggregated evaluation. We also aimed to describe outcomes in people with chronic obstru...

Persistence Landscapes—Implementing a Dataset Verification Method in Resource-S...

As more and more devices are being deployed across networks to gather data and use them to perform intelligent tasks, it is vital to have a tool to perform real-time data analysis. Data are the backbone of Machine Learning models, the core of intelligent systems. Therefore, verifying whether the data being gathered are similar to those used for model building is essential. One fantastic tool for the performance...

SecureQNN: introducing a privacy-preserving framework for QNNs at the deep edge

Recent concerns about real-time inference and data privacy are making Machine Learning (ML) shift to the edge. However, training efficient ML models require large-scale datasets not available for typical ML clients. Consequently, the training is usually delegated to specific Service Providers (SP), which are now worried to deploy proprietary ML models on untrusted edge devices. A natural solution to increase th...

Persistence landscapes - implementing a dataset verification method in resource...

As more and more devices are being deployed across networks to gather data and use them to perform intelligent tasks, it is vital to have a tool to perform real-time data analysis. Data are the backbone of Machine Learning models, the core of intelligent systems. Therefore, verifying whether the data being gathered are similar to those used for model building is essential. One fantastic tool for the performance...

A multiobjective optimization approach to pulmonary rehabilitation effectivenes...

Chronic obstructive pulmonary disease (COPD) is a common disease that accounts for a significant individualand societal burden. Pulmonary rehabilitation (PR) is a key management strategy but it is highly inaccessible, makingprioritisation highly needed. This study aimed to determine and optimize predictive models of PR outcomes and builda tool to help healthcare professionals in their clinical decision-making a...

Assessment of the stability of a procedure for variables selection in high dime...

0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Em...

Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH in...

Automotive LiDAR technology: a survey

Nowadays, and more than a decade after the first steps towards autonomous driving, we keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being a key instrument for the success of this technology. Such advances trigger the emergence of new players in the automotive industry, and along with car manufacturers, this sector represents a multibillion-dollar market where everyone wants ...