Spatial Covariance Matrix Reconstruction for DOA Estimation in Hybrid Massive MIMO Systems with Multiple Radio Frequency Chains

Multiple signal classification (MUSIC) has been widely applied in multiple-input multiple-output (MIMO) receivers for direction-of-arrival (DOA) estimation. To reduce the cost of radio frequency (RF) chains operating at millimeter-wave bands, hybrid analog-digital structure has been adopted in massive MIMO transceivers. In this situation, the received signals at the antennas are unavailable to the digital receiver, and as a consequence, the spatial covariance matrix (SCM), which is essential in MUSIC algorithm, cannot be obtained using traditional sample average approach. Based on our previous work, we propose a novel algorithm for SCM reconstruction in hybrid massive MIMO systems with multiple RF chains. By switching the analog beamformers to a group of predetermined DOAs, SCM can be reconstructed through the solutions of a set of linear equations. In addition, based on insightful analysis on that linear equations, a low-complexity algorithm, as well as a careful selection of the predetermined DOAs, will be also presented in this paper. Simulation results show that the proposed algorithms can reconstruct the SCM accurately so that MUSIC algorithm can be well used for DOA estimation in hybrid massive MIMO systems with multiple RF chains.

FocusNetv2: Imbalanced Large and Small Organ Segmentation with Adversarial Shape Constraint for Head and Neck CT Images

Radiotherapy is a treatment where radiation is used to eliminate cancer cells. The delineation of organs-at-risk (OARs) is a vital step in radiotherapy treatment planning to avoid damage to healthy organs. For nasopharyngeal cancer, more than 20 OARs are needed to be precisely segmented in advance. The challenge of this task lies in complex anatomical structure, low-contrast organ contours, and the extremely imbalanced size between large and small organs. Common segmentation methods that treat them equally would generally lead to inaccurate small-organ labeling. We propose a novel two-stage deep neural network, FocusNetv2, to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation. In addition to our original FocusNet, we employ a novel adversarial shape constraint on small organs to ensure the consistency between estimated small-organ shapes and organ shape prior knowledge. Our proposed framework is extensively tested on both self-collected dataset of 1,164 CT scans and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, which shows superior performance compared with state-of-the-art head and neck OAR segmentation methods.

Phase-field modeling of non-isothermal grain coalescence in the unconventional sintering techniques

A thermodynamically consistent phase-field model is developed to study the non-isothermal grain coalescence during the sintering process, with a potential application to the simulation in unconventional sintering techniques, e.g. spark plasma sintering, field-assisted sintering, and selective laser sintering, where non-equilibrium and high temperature gradient exist. In the model, order parameters are adopted to represent the bulk and atmosphere/pore region, as well as the crystallographic orientations. Based on the entropy analysis, the temperature-dependent free energy density is developed, which includes contributions from the internal energy (induced by the change of temperature and order parameters) and the order parameter related configurational entropy. The temperature-dependent model parameters are determined by using the experimental data of surface and grain boundary energies and interface width. From laws of thermodynamics, the kinetics for the order parameters and the order-parameter-coupled heat transfer are derived. The model is numerically implemented by the finite element method. Grain coalescence from two identical particles shows that non-isothermal condition leads to the unsymmetric morphology and curved grain boundary due to the gradients of on-site surface and grain-boundary energies induced by the local temperature inhomogeneity. More simulations on the non-isothermal grain coalescence from two non-identical and multiple particles present the temporal evolution of grain shrinkage/growth, neck growth, and porosity, demonstrating the capability and versatility of the model. It is anticipated that the work could provide a contribution to the research community of unconventional sintering techniques that can be used to model the non-isothermal related microstructural features.