Ju Liu

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: Compared with the existing spectrum detection methods, such as the energy detector and the eigenvalue-based detector, the proposed K-S detectors offer superior detection performance and faster detection, and is more robust to channel uncertainty and non-Gaussian noise.

TL;DR: A novel resource optimization framework for maximizing the spectral efficiency of the Internet-of-things (IoT) networks using power domain NOMA, and demonstrates that the proposed optimal resource management scheme significantly improves the system performance compared to other schemes.

TL;DR: An algorithm is proposed to optimally allocate the bandwidth and time resources to facilitate both the EH and data transmission to maximize the area throughput of the secondary system under the performance constraint of the primary system.

TL;DR: Experimental results demonstrate that the coding efficiency of TMC2 is the best on average (especially for lossy geometry and lossy color compression) for dense point clouds while TMC13 achieves the optimal coding performance for sparse and noisy point clouds with lower time complexity.