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Mozhgan Navardi
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2020 – today
- 2023
- [j3]Mozhgan Navardi
, Edward Humes
MetaE2RL: Toward Meta-Reasoning for Energy-Efficient Multigoal Reinforcement Learning With Squeezed-Edge You Only Look Once. IEEE Micro 43(6): 29-39 (2023) - [c3]Mozhgan Navardi, Tinoosh Mohsenin:
MLAE2: Metareasoning for Latency-Aware Energy-Efficient Autonomous Nano-Drones. ISCAS 2023: 1-5 - [i2]Tejaswini Manjunath, Mozhgan Navardi, Prakhar Dixit, Bharat Prakash, Tinoosh Mohsenin:
ReProHRL: Towards Multi-Goal Navigation in the Real World using Hierarchical Agents. CoRR abs/2308.08737 (2023) - [i1]Edward Humes, Mozhgan Navardi, Tinoosh Mohsenin:
Squeezed Edge YOLO: Onboard Object Detection on Edge Devices. CoRR abs/2312.11716 (2023) - 2022
- [j2]Aidin Shiri
Efficient Language-Guided Reinforcement Learning for Resource-Constrained Autonomous Systems. IEEE Micro 42(6): 107-114 (2022) - [j1]Mozhgan Navardi, Behnaz Ranjbar
Peak-Power Aware Life-Time Reliability Improvement in Fault-Tolerant Mixed-Criticality Systems. IEEE Open J. Circuits Syst. 3: 199-215 (2022) - [c2]Mozhgan Navardi, Aidin Shiri, Edward Humes, Nicholas R. Waytowich, Tinoosh Mohsenin:
An Optimization Framework for Efficient Vision-Based Autonomous Drone Navigation. AICAS 2022: 304-307 - [c1]Mozhgan Navardi, Edward Humes, Tinoosh Mohsenin:
E2EdgeAI: Energy-Efficient Edge Computing for Deployment of Vision-Based DNNs on Autonomous Tiny Drones. SEC 2022: 504-509
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