Passive multifrequency microwave sensors frequently struggle with difficulties of nonuniform spatial resolution among multiple channels. The raw measurements in the land-sea transition zone are seriously contaminated. Conventional analytical deconvolution techniques suffer from the tradeoff between spatial resolution enhancement and noise amplification, leading to low data integrity in the practical spatial resolution matching application. To provide multichannel microwave radiometer (MWR) data with matching levels of spatial resolution, a method based on iterative deconvolution with close loop priors (ICLP) is proposed. Specifically, a destriping module is first utilized as a preprocessing step to maintain high data integrity. Then, the close loop mechanism using sparse adaptive priors is proposed to balance the spatial resolution and data integrity enhancement. Also, progressively iterative deconvolution is introduced to realize controllable levels of spatial resolution enhancement (spatial resolution matching) for multichannel data to reach a consistent level. Experiments performed using both simulated and actual microwave radiation imager (MWRI) data demonstrate the validity and effectiveness of the method.

Federated learning (FL) enables collaborative learning between parties, called clients, without sharing the original and potentially sensitive data. To ensure fast convergence in the presence of such heterogeneous clients, it is imperative to timely select clients who can effectively contribute to learning. A realistic but overlooked case of heterogeneous clients are Mavericks, who monopolize the possession of certain data types, e.g., children hospitals possess most of the data on pediatric cardiology. In this paper, we address the importance and tackle the challenges of Mavericks by exploring two types of client selection strategies. First, we show theoretically and through simulations that the common contribution-based approach, Shapley Value, underestimates the contribution of Mavericks and is hence not effective as a measure to select clients. Then, we propose FedEMD, an adaptive strategy with competitive overhead based on the Wasserstein distance, supported by a proven convergence bound. As FedEMD adapts the selection probability such that Mavericks are preferably selected when the model benefits from improvement on rare classes, it consistently ensures the fast convergence in the presence of different types of Mavericks. Compared to existing strategies, including Shapley Value-based ones, FedEMD improves the convergence speed of neural network classifiers with FedAvg aggregation by 26.9% and its performance is consistent across various levels of heterogeneity.

Robust bonding of Cu quasi-nanoparticles sintering for Ag coated chip and bare copper substrate was achieved. The effect of temperature, pressure and time on the sintering bonding strength and microstructural evolution was deeply studied. 36.5 MPa shear strength was achieved when applied 5 MPa pressure. By increasing to 30 MPa, it shows the best die shear strength of 116 MPa, accomplished with a sintering temperature of 250 °C for 3 min. Temperature also influenced the shear strength a lot. Between 210 °C and 230 °C can already provide strength over 30 MPa. When increased to 270 °C, the strength was extremely enhanced to over 120 MPa. Inspection on the fracture behaviors and cross-section of sheared off samples was conducted by SEM, EDS, and XRD. It is found that low bonding performance is due to both of the incomplete burnt out of organics and incomplete Cu QNPs sintering. In addition, high bonding is account for the positive effect of pressure and temperature on promoting the necking growth, sintering networking formation, pores isolation and brittle-ductile fracture transformation. The recommended sintering profile is 250 °C, 3 min, 20 MPa. Finally, the feasibility for SiC MOSFET power electronics DA was verified by testing its static characteristics at both room temperature and 150 °C.