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Sensor-fusion for Smartphone Location Tracking using Hybrid Multimodal Deep Neural Networks
Published in Sensors Special Issue "Multisensors Indoor Localization", 2021
Recommended citation: Xijia Wei, Zhiqiang Wei and Valentin Radu. Sensor-fusion for Smartphone Location Tracking using Hybrid Multimodal Deep Neural Networks. Sensors https://www.mdpi.com/1424-8220/21/22/7488/htm
MM-Loc: Cross-sensor Indoor Smartphone Location Tracking using Multimodal Deep Neural Networks
Published in IEEE International Conference on Indoor Positioning and Indoor Navigation 2021 (IPIN), 2021
Recommended citation: Xijia Wei, Zhiqiang Wei and Valentin Radu, MM-Loc: Cross-sensor Indoor Smartphone Location Tracking using Multimodal Deep Neural Networks in Proc. Int. Conf. Indoor Positioning Indoor Navigat. (IPIN), Nov. 2021, pp. 1–8. https://ieeexplore.ieee.org/document/9662519
An LSTM-based method for Message Queue Throughput Prediction
Published in ACAI 2022: International Conference on Algorithms, Computing and Artificial Intelligence, 2022
Recommended citation: Wei Z, Zhao X, et al. An LSTM-based method for Message Queue Throughput Prediction[C]//Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence. 2022: 1-6. https://dl.acm.org/doi/abs/10.1145/3579654.3579724
SGANFuzz: A Deep Learning-based MQTT Fuzzing Method using Generative Adversarial Networks
Published in IEEE Access, 2024: International Conference on Algorithms, Computing and Artificial Intelligence, 2024
Recommended citation: Wei Z, Wei X, Zhao X, et al. SGANFuzz: A Deep Learning-based MQTT Fuzzing Method using Generative Adversarial Networks[J]. IEEE Access, 2024. https://ieeexplore.ieee.org/document/10433531