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91 to 100 of 298 Results
Related Data for: Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis
Aug 30, 2024 - Narendra VISHWAKARMA
Vishwakarma, Narendra, 2024, "Related Data for: Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis", https://doi.org/10.21979/N9/XRDOTT, DR-NTU (Data), V1
MATLAB source code the publication title: "Intelligent-reflecting-surfaces-assisted hybrid FSO/RF communication with diversity combining: a performance analysis" These code will produce the outage probability and Bit error rate plots for the above paper
Narendra VISHWAKARMA(Nanyang Technological University)
Aug 30, 2024
Appointment: Research Fellow (Former)
Replication Data for: Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing
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; Kumar, Niraj; 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
Coronavirus Neutralizing Antibodies(Nanyang Technological University)
Jun 20, 2024KWOH Chee Keong
Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
Jun 20, 2024 - S-Lab for Advanced Intelligence
Wu, Haoning; Zhang, Erli; Liao, Liang; Chen, Chaofeng; Hou, Jingwen; Wang, Annan; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach", https://doi.org/10.21979/N9/ELWDPE, DR-NTU (Data), V1
A large-scale in-the-wild VQA database, named Maxwell, created to gather more than two million human opinions across 13 specific quality-related factors, including technical distortions e.g. noise, flicker and aesthetic factors e.g. contents.
Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
Jun 20, 2024 - S-Lab for Advanced Intelligence
Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Li, Chunyi; Sun, Wenxiu; Yan, Qiong; Zhai, Guangtao; Lin, Weisi, 2024, "Replication Data for: Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision", https://doi.org/10.21979/N9/M41ERD, DR-NTU (Data), V1
We present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.
Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Jun 20, 2024 - S-Lab for Advanced Intelligence
Wu, Haoning; Zhang, Zicheng; Zhang, Erli; Chen, Chaofeng; Liao, Liang; Wang, Annan; Xu, Kaixin; Li, Chunyi; Hou, Jingwen; Zhai, Guangtao; Xue, Geng; Sun, Wenxiu; Yan, Qiong; Lin, Weisi, 2024, "Replication Data for: Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models", https://doi.org/10.21979/N9/GPLPNI, DR-NTU (Data), V1
The dataset consisting of human natural language feedback on low-level vision.
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