[RP13] Electrical and Mechanical Drone Beamforming, IITP, 2018~2023

[RP13] Electrical and Mechanical Drone Beamforming, IITP, 2018~2023 In this project, we develop electrical and mechanical drone beamforming algorithms to improve the performance of target detection and wireless communications. Actual drone systems are implemented to evaluate the proposed algorithms. We plan to employ deep learning algorithms to further improve the performance of drone control optimization and… Continue reading

[RP09]: Development of Active Wearable Systems for Crime Detection, UNIST, May 2016 ~ Dec. 2016 (collaborating with M. Ryoo at Indiana University)

[RP8] Development of Active Wearable Systems for Crime Detection, UNIST, May 2016 ~ Dec. 2017 (collaborating with M. Ryoo at Indiana University)   In this project, we develop core technologies and proof-of-concept implementation of active wearable systems for crime detection. The key technology is a learning-based action recognition based on first-vision videos.

{C31} J. Jang and H. J. Yang, “Learning-Based Distributed Resource Allocation in Asynchronous Multicell Networks,” in Proc. International Conference on ICT Convergence (ICTC), 2018.

{C31} J. Jang and H. J. Yang, “Learning-Based Distributed Resource Allocation in Asynchronous Multicell Networks,” in Proc. International Conference on ICT Convergence (ICTC), 2018. A resource allocation problem is tackled in asynchronous multicell downlink LTE-LAA networks to improve the proportional fairness by assuming limited channel state information (CSI). Previous studies solve the resource allocation problem… Continue reading

{C30} M. U. Kim and H. J. Yang, “RNN-Based Node Selection for Sensor Networks with Energy Harvesting,” in Proc. International Conference on ICT Convergence (ICTC), Jeju, Korea, Oct. 2018, pp.1316-1318.

{C30} M. U. Kim and H. J. Yang, “RNN-Based Node Selection for Sensor Networks with Energy Harvesting,” in Proc. International Conference on ICT Convergence (ICTC), Jeju, Korea, Oct.2018, pp.1316-1318. A novel recurrent neural network (RNN) based node selection is proposed for sensor networks with energy harvesting, where the downlink (DL) simultaneous wireless information and power… Continue reading