Appointment: Associate Professor

Research topics:
• Real-Time Systems
• Model-based Design Methodologies
• Energy Management

Datasets from the CPS@NTU research group in the School of Computer Science and Engineering

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1 to 9 of 9 Results
Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
Aug 21, 2024 - Resource Allocation for Edge-Cloud System
Gao, Chuanchao; Niraj Kumar; Easwaran, Arvind, 2024, "Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing", https://doi.org/10.21979/N9/VJTMBM, DR-NTU (Data), V2, UNF:6:ujvYZ07RwxVvj5sepdEDNw== [fileUNF]
Experiment data for paper "Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing".
Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems
Jul 3, 2024 - Safe ML
Yuhas, Michael John; Easwaran, Arvind, 2024, "Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems", https://doi.org/10.21979/N9/YIOFK8, DR-NTU (Data), V2
Replication Data for: Co-Design of Out-of-Distribution Detectors for Autonomous Emergency Braking Systems
Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
Jul 2, 2024 - Safe ML
Yuhas, Michael; Ng, Daniel Jun Xian; Easwaran, Arvind, 2024, "Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems", https://doi.org/10.21979/N9/UZY54Q, DR-NTU (Data), V1
Replication Data for: Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems
Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Jul 1, 2024 - Safe ML
Yuhas, Michael; Rahiminasab, Zahra; Easwaran, Arvind, 2024, "Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder", https://doi.org/10.21979/N9/0YI4HT, DR-NTU (Data), V1
Replication Data for: Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder
Replication Data for: Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot
Oct 25, 2022 - Safe ML
Yuhas, Michael John, 2022, "Replication Data for: Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot", https://doi.org/10.21979/N9/INOCLV, DR-NTU (Data), V2
Replication Data for "Demo Abstract: Real-Time Out-of-Distribution Detection on a Mobile Robot" This data set contains a series of scenes (sequential images) gathered by a DB21M Duckiebot navigating a straight track inside the HESL (Hardware and Embedded Systems Laboratory). The...
Replication Data for: Deadline-constrained Multi-resource Task Mapping and Allocation for Edge-Cloud Systems
Aug 9, 2022 - Resource Allocation for Edge-Cloud System
Gao, Chuanchao; Shaan, Aryaman; Easwaran, Arvind, 2022, "Replication Data for: Deadline-constrained Multi-resource Task Mapping and Allocation for Edge-Cloud Systems", https://doi.org/10.21979/N9/5D1FBL, DR-NTU (Data), V1
experiment data for paper "Deadline-constrained Multi-resource Task Mapping and Allocation for Edge-Cloud Systems"
Resource Allocation for Edge-Cloud System(Nanyang Technological University)
Aug 9, 2022
Experiment data for research papers
Replication Data for: Embedded out-of-distribution detection on an autonomous robot platform
Jun 20, 2021 - Safe ML
Yuhas, Michael John, 2021, "Replication Data for: Embedded out-of-distribution detection on an autonomous robot platform", https://doi.org/10.21979/N9/FVVHNK, DR-NTU (Data), V1
This data set contains the raw images and timing data gathered during the experiments listed in the paper "Embedded out-of-distribution detection on an autonomous robot platform," which appeared in Proceedings of the Workshop on Design Automation for CPS and IoT, 2021.
Safe ML(Nanyang Technological University)
Jun 20, 2021
This Dataverse contains datasets generated and used during the course of the Safe_ML project in the CPS research group.
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