Hardware Acceleration of SVM Training for Real-Time Embedded Systems: Overview

Amezzane, Ilham; Fakhri, Yadolah; Aroussi, Mohamed El; Bakhouya, Mohamed

doi:10.1007/978-3-030-35202-8_7

Cited by 5 publications

(1 citation statement)

References 15 publications

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“…Generally, machine learning techniques are considered computationally expensive and challenging to implement on embedded hardware . However, several recent studies have shown the potential for the SVM algorithm to be implemented on a variety of embedded devices including VLSI integrated circuits and FPGAs (Amezzane et al, 2020;.…”

Section: Potential For Implementation On Low-cost Hardwarementioning

confidence: 99%

Generalized Time-Series Analysis for In-Situ Spacecraft Observations: Anomaly Detection and Data Prioritization using Principal Components Analysis and Unsupervised Clustering

Finley,

Ledesma,

Paterson

et al. 2024

Preprint

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In-situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near-Earth space and beyond. However, on-orbit telemetry constraints can severely limit the capability of spacecraft to transmit high-cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission’s science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist-In-The-Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high-cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low-cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One-Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data’s potential for scientific utility. The technique’s performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e-POP/Swarm-Echo and MMS missions.

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Section: Potential For Implementation On Low-cost Hardwarementioning

confidence: 99%

Generalized Time-Series Analysis for In-Situ Spacecraft Observations: Anomaly Detection and Data Prioritization using Principal Components Analysis and Unsupervised Clustering

Finley,

Ledesma,

Paterson

et al. 2024

Preprint

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FPGA implementation of breast cancer detection using SVM linear classifier

Laxmisagar

Hanumantharaju²

2023

Multimed Tools Appl

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Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering

Finley,

Martinez‐Ledesma,

Paterson

et al. 2024

Earth and Space Science

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In situ spacecraft observations are critical to our study and understanding of the various phenomena that couple mass, momentum, and energy throughout near‐Earth space and beyond. However, on‐orbit telemetry constraints can severely limit the capability of spacecraft to transmit high‐cadence data, and missions are often only able to telemeter a small percentage of their captured data at full rate. This presents a programmatic need to prioritize intervals with the highest probability of enabling the mission's science goals. Larger missions such as the Magnetospheric Multiscale mission (MMS) aim to solve this problem with a Scientist‐In‐The‐Loop (SITL), where a domain expert flags intervals of time with potentially interesting data for high‐cadence data downlink and subsequent study. Although suitable for some missions, the SITL solution is not always feasible, especially for low‐cost missions such as CubeSats and NanoSats. This manuscript presents a generalizable method for the detection of anomalous data points in spacecraft observations, enabling rapid data prioritization without substantial computational overhead or the need for additional infrastructure on the ground. Specifically, Principal Components Analysis and One‐Class Support Vector Machines are used to generate an alternative representation of the data and provide an indication, for each point, of the data's potential for scientific utility. The technique's performance and generalizability is demonstrated through application to intervals of observations, including magnetic field data and plasma moments, from the CASSIOPE e‐POP/Swarm‐Echo and MMS missions.

show abstract

Hardware Acceleration of SVM Training for Real-Time Embedded Systems: Overview

Cited by 5 publications

References 15 publications

Generalized Time-Series Analysis for In-Situ Spacecraft Observations: Anomaly Detection and Data Prioritization using Principal Components Analysis and Unsupervised Clustering

Generalized Time-Series Analysis for In-Situ Spacecraft Observations: Anomaly Detection and Data Prioritization using Principal Components Analysis and Unsupervised Clustering

FPGA implementation of breast cancer detection using SVM linear classifier

Generalized Time‐Series Analysis for In Situ Spacecraft Observations: Anomaly Detection and Data Prioritization Using Principal Components Analysis and Unsupervised Clustering

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