On the public citation graph
Experimental Demonstration of STT-MRAM-based Nonvolatile Instantly On/Off System for IoT Applications: Case Studies
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labelled with the one it came from. Neither is a complete citation index — absence here is not
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1 of the 64 references listed on this page is in the Retraction Watch database as retracted (export 2026-11-11): 10.1016/j.jnca.2019.01.012 (notice 2019-05-22).
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Retraction Watch data via Crossref Labs, used with credit; OpenCitations Index (CC0) and Meta (CC BY 4.0);
dataset doi:10.7910/DVN/WT8VOA (CC BY 4.0).
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What this work says
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inferredcites →https://doi.org/10.1016/j.renene.2021.03.022openalex-snapshotExperimental evaluation of a hybrid photovoltaic and thermal solar energy collector with integrated phase change material (PVT-PCM) in comparison with a traditional photovoltaic (PV) module
inferredcites →https://doi.org/10.1109/tec.2020.3004191openalex-snapshotOptimal Control of Lossy Energy Storage Systems With Nonlinear Efficiency Based on Dynamic Programming and Pontryagin's Minimum Principle
inferredcites →https://doi.org/10.1109/tii.2019.2933867openalex-snapshotLearning-Based Energy-Efficient Resource Management by Heterogeneous RF/VLC for Ultra-Reliable Low-Latency Industrial IoT Networks
inferredcites →https://doi.org/10.1145/3317679openalex-snapshotOptimal Power Management with Guaranteed Minimum Energy Utilization for Solar Energy Harvesting Systems
inferredcites →https://doi.org/10.1145/3505283openalex-snapshotIntroduction to the Special Issue on Memory and Storage Systems for Embedded and IoT Applications
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inferred← citeshttps://doi.org/10.2139/ssrn.4782177openalex-extractedInteraxnn: A Reconfigurable and Approximate In-Memory Processing Accelerator for Ultra-Low-Power Binary Neural Network Inference in Intermittently Powered Systems
inferred← citeshttps://doi.org/10.1145/3771845openalex-snapshotInterAxNN: Reconfigurable and Approximate in-Memory Processing Accelerator for Ultra-Low-Power Binary Neural Network Inference in Intermittently Powered Systems
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