Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications | Paper Citation Record

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This paper cites Artificial intelligence in aviation safety: Systematic review and biometric analysis,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Artificial intelligence in aviation safety: Systematic review and biometric analysis,

Reference 1

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correction dated 2024-11-26. Source: crossref record 10.1007/s44196-024-00707-1->10.1007/s44196-024-00671-w:correction, observed 2026-07-11T02:57:41.122644+00:00. This notice travels one citation hop only.

Observation c6bb8580-b409-4b6f-8e3c-8fdf76a1db7f · outbound

This paper cites A comprehensive review and a taxonomy of edge machine learning: Requirements, paradigms, and techniques,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A comprehensive review and a taxonomy of edge machine learning: Requirements, paradigms, and techniques,

Reference 2

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Observation 08cb4e45-1f74-480c-94a2-257aa5248b56 · outbound

This paper cites Edge computing for aviation autonomy,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Edge computing for aviation autonomy,

Reference 3

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Observation 75aa629e-3691-463c-9ccc-8b2e1b1fd24a · outbound

This paper cites A survey on collaborative DNN inference for edge intelligence,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A survey on collaborative DNN inference for edge intelligence,

Reference 4

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Observation b8c797ee-c941-4b1e-83a3-b580dec76c51 · outbound

This paper cites Edge intelligence: The confluence of edge computing and artificial intelligence,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Edge intelligence: The confluence of edge computing and artificial intelligence,

Reference 5

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This paper cites Mobile edge intelligence for large language models: A contemporary survey,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Mobile edge intelligence for large language models: A contemporary survey,

Reference 6

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Observation 62d66a1e-837e-4c4a-afcb-1e0f674d160e · outbound

This paper cites Edge intelligence for smart airport runway: Architectures and enabling technologies,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Edge intelligence for smart airport runway: Architectures and enabling technologies,

Reference 7

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Observation 7ade1dec-f377-48cb-b3bc-b776ac6a5e33 · outbound

This paper cites How edge computing can bolster aviation sector innovation,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications How edge computing can bolster aviation sector innovation,

Reference 8

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Observation 73690dc3-96e5-432f-98cf-2fea82d9bdd1 · outbound

This paper cites Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

Reference 9

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Observation 04cfd91d-9de4-4bdd-b649-74c9b8f621dc · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T12:04:13.787799Z

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Observation 359fd83c-3061-4c0e-941f-6f2118c6b145 · outbound

This paper cites QLoRA: Efficient Finetuning of Quantized LLMs.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications QLoRA: Efficient Finetuning of Quantized LLMs

Reference 11

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Observation b0f3f082-03a9-4ca9-9626-3535c7ba7734 · outbound

This paper cites AWQ: Activation -aware weight quantization for on -device LLM compression and acceleration,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications AWQ: Activation -aware weight quantization for on -device LLM compression and acceleration,

Reference 12

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Observation 58addd3d-31a5-432d-b841-db8c9f9d2675 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 13

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Observation a32228b5-c235-4152-960b-a3ebecdb9bf7 · outbound

This paper cites SqueezeLLM: Dense-and-Sparse Quantization.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications SqueezeLLM: Dense-and-Sparse Quantization

Reference 14

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Observation 532b285f-6a2e-4a0c-9bfe-268b32c6a838 · outbound

This paper cites SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot

Reference 15

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Observation 183701db-2b89-4318-bf70-1c3b62182471 · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A Simple and Effective Pruning Approach for Large Language Models

Reference 16

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Observation ccba9851-a3d8-418a-b54c-dcd2ce4a348d · outbound

This paper cites Fluctuation-based Adaptive Structured Pruning for Large Language Models.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Fluctuation-based Adaptive Structured Pruning for Large Language Models

Reference 17

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Observation b02e7974-bf27-49d4-bd95-1031627f7eac · outbound

This paper cites LLM Pruning and Distillation in Practice: The Minitron Approach.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications LLM Pruning and Distillation in Practice: The Minitron Approach

Reference 18

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Observation 36e83df9-f848-4c7b-b641-436b52f6530b · outbound

This paper cites Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling

Reference 19

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Observation e16e89e7-7b48-45aa-836e-d9cda3131804 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A Survey on Knowledge Distillation of Large Language Models

Reference 20

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Observation 893aae88-7363-4169-af9d-253b19fe9df0 · outbound

This paper cites Survey on knowledge distillation for large language models: Methods, evaluation, and application,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Survey on knowledge distillation for large language models: Methods, evaluation, and application,

Reference 21

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Observation 3520ed03-adb8-451d-b1fb-e46acd8bf037 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 22

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Observation ea244504-dd06-49b4-a347-f98dfc7434ef · outbound

This paper cites LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

Reference 23

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Observation 36ae74b6-8fe7-4e67-8ba3-4d50d656e4e1 · outbound

This paper cites Split computing and early exiting for deep learning applications: Survey and research challenges,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Split computing and early exiting for deep learning applications: Survey and research challenges,

Reference 24

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Observation 3d7109e7-6b75-4c60-95a1-4d7302782538 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Communication-efficient learning of deep networks from decentralized data,

Reference 25

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Observation 13df6120-7f36-473d-8bfb-e32090339c28 · outbound

This paper cites A survey on efficient federated learning methods for foundation model training,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A survey on efficient federated learning methods for foundation model training,

Reference 26

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This paper cites Federated learning survey: A multi -level taxonomy of aggregation techniques, experimental insights, and future frontiers,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Federated learning survey: A multi -level taxonomy of aggregation techniques, experimental insights, and future frontiers,

Reference 27

Resolution: verified exact; doi, observed 2026-08-01T12:09:10.700743Z

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Observation c71e99e7-7ce5-4887-b4f8-2232bc307f7e · outbound

This paper cites Recent Advances on Federated Learning: A Systematic Survey.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Recent Advances on Federated Learning: A Systematic Survey

Reference 28

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Observation a2a5c678-686f-40be-ac9a-1a813c06bcc7 · outbound

This paper cites Federated learning based on Stackelberg game in unmanned -aerial-vehicle-enabled mobile edge computing,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Federated learning based on Stackelberg game in unmanned -aerial-vehicle-enabled mobile edge computing,

Reference 29

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Observation 01633517-64f6-4d1a-bf4f-6d6eb967d36c · outbound

This paper cites Split learning for health: Distributed deep learning without sharing raw patient data.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Split learning for health: Distributed deep learning without sharing raw patient data

Reference 30

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This paper cites Edge-MSL: Split learning on the mobile edge via multi -armed bandits,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Edge-MSL: Split learning on the mobile edge via multi -armed bandits,

Reference 31

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This paper cites Security Analysis of SplitFed Learning.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Security Analysis of SplitFed Learning

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-01T12:09:10.306229Z

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Observation ef2a78b7-7ccb-4e0d-8ab3-f31ff3cebd4d · outbound

This paper cites ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications

Reference 33

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This paper cites Lessons learned in transcribing 5000 h of air traffic control communications for robust automatic speech understanding,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Lessons learned in transcribing 5000 h of air traffic control communications for robust automatic speech understanding,

Reference 34

Resolution: verified exact; doi, observed 2026-08-01T12:09:10.043110Z

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This paper cites A surveillance video real-time object detection system based on edge- cloud cooperation in airport apron,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A surveillance video real-time object detection system based on edge- cloud cooperation in airport apron,

Reference 35

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This paper cites Intelligent surveillance of airport apron: Detection and location of abnormal behavior in typical non -cooperative human objects,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Intelligent surveillance of airport apron: Detection and location of abnormal behavior in typical non -cooperative human objects,

Reference 36

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This paper cites Edge-cloud collaborative streaming video analytics with multi-agent deep reinforcement learning,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Edge-cloud collaborative streaming video analytics with multi-agent deep reinforcement learning,

Reference 37

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This paper cites Large-scale video analytics with cloud – edge collaborative continuous learning,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Large-scale video analytics with cloud – edge collaborative continuous learning,

Reference 38

Resolution: verified exact; doi, observed 2026-08-01T12:09:09.895015Z

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This paper cites Application of physical - structure-driven deep learning and compensation methods in aircraft engine health management,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Application of physical - structure-driven deep learning and compensation methods in aircraft engine health management,

Reference 39

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Observation 55d7c2c9-d801-4781-9c4c-4c3b5b3310b2 · outbound

This paper cites A health state -related ensemble deep learning method for aircraft engine remaining useful life prediction,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A health state -related ensemble deep learning method for aircraft engine remaining useful life prediction,

Reference 40

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This paper cites Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Predictive maintenance analytics and implementation for aircraft: Challenges and opportunities,

Reference 41

Resolution: verified exact; doi, observed 2026-08-01T12:09:09.783499Z

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Observation f2d052be-aeca-4a78-a4d8-2d445a4f9083 · outbound

This paper cites A computer vision-based standalone system for automated operational data collection at non-towered airports,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A computer vision-based standalone system for automated operational data collection at non-towered airports,

Reference 42

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This paper cites A survey on UAV-enabled edge computing: Resource management perspective,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A survey on UAV-enabled edge computing: Resource management perspective,

Reference 43

Resolution: verified exact; doi, observed 2026-08-01T12:09:09.657964Z

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This paper cites A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A Comprehensive Survey on Aerial Mobile Edge Computing: Challenges, State-of-the-Art, and Future Directions

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-01T12:09:09.577599Z

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Observation e1198068-453a-49a7-9d41-16bf623aae0e · outbound

This paper cites Conformal alignment: Knowing when to trust foundation models with guarantees,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Conformal alignment: Knowing when to trust foundation models with guarantees,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-01T12:04:17.746843Z

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Observation 64360e57-9069-4888-9eb5-e41ecf4444fb · outbound

This paper cites Deep Correlated Prompting for Visual Recognition with Missing Modalities.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Deep Correlated Prompting for Visual Recognition with Missing Modalities

Reference 46

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This paper cites A survey on efficient federated learning methods for foundation model training,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications A survey on efficient federated learning methods for foundation model training,

Reference 47

Resolution: verified exact; doi, observed 2026-08-01T12:09:09.408936Z

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This paper cites Task-oriented energy scheduling in wireless rechargeable sensor networks,.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Task-oriented energy scheduling in wireless rechargeable sensor networks,

Reference 48

Resolution: verified exact; doi, observed 2026-08-01T12:09:09.342026Z

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Observation 8c0c943e-db64-4055-983b-2d64753ff33b · outbound

This paper cites HEMM: Holistic Evaluation of Multimodal Foundation Models.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications HEMM: Holistic Evaluation of Multimodal Foundation Models

Reference 49

Resolution: verified exact; local_arxiv, observed 2026-08-01T12:09:09.189658Z

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Observation a6963e0a-d27a-4f4a-9dd4-d0cb9fe8e9b8 · outbound

This paper cites Deep Multimodal Learning with Missing Modality: A Survey.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Deep Multimodal Learning with Missing Modality: A Survey

Reference 50

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This paper cites TerraMind: Large-Scale Generative Multimodality for Earth Observation.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications TerraMind: Large-Scale Generative Multimodality for Earth Observation

Reference 51

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This paper cites AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models

Reference 52

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This paper cites Available: https://www.kbr.com/sites/default/files/documents/2023-09/aaiml- 7507.pdf.

Edge Intelligence in Civil Aviation: Paradigms, Techniques, and Applications Available: https://www.kbr.com/sites/default/files/documents/2023-09/aaiml- 7507.pdf

Reference 2023

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