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Optical phased arrays (OPAs) are renowned for their exceptional ability to manipulate and direct light beams, positioning them as an ideal choice for solid-state light detection and ranging (LiDAR) technologies. This review provides a comprehensive examination of the current research landscape for photonic integrated circuit (PIC)-based OPAs. It begins by addressing the critical design elements at the component level necessary for optimal functionality. This review then delves into phase calibration techniques and the overarching architecture of OPAs. It concludes by emphasizing the innovative 3-D OPA design, which stands out for its enhanced optical efficiency. [ABSTRACT FROM AUTHOR]

There has been considerable interest in the search and design of two-dimensional (2D) van der Waals (vdW) compounds with exotic magnetic behavior. Here we establish the growth of phase pure crystals of Mn2In2Se5 and Mn2Ga2S5 and evaluate their structural, physical, and magnetic properties. These 2D vdW phases consist of double-octahedral thick Mn(S/Se)6 layers capped by InSe4 or GaS4 tetrahedra. Transmission electron microscopy confirms phase purity with the absence of impurity intergrowths of other closely related 2D vdW phases including Mn(In/Ga)2(Se/S)4 or In4Se3. Optical absorption measurements indicate these compounds have indirect band gaps of 1.33 and 1.58 eV, respectively. We also establish the Raman signatures for both compounds. Mn2In2Se5 and Mn2Ga2S5 are significantly frustrated magnetic materials due to the competing magnetic interactions in this double-thick triangular arrangement of metal atoms. They have magnetic transition temperatures of 7 and 13 K, respectively, compared to Weiss constants of −198 and −340 K, respectively. AC susceptibility experiments indicate that both Mn2In2Se5 and Mn2Ga2S5 exhibit significant spin glass character. The significant magnetic frustration makes these materials unique 2D magnetic vdW building blocks. [ABSTRACT FROM AUTHOR]

The problem of industrial bearing health monitoring and fault diagnosis has recently been a popular research topic. Extracting sufficient features from the input raw vibration signals and mapping them to the most likely fault labels is the essence of bearing fault diagnosis. This study proposes a novel framework for bearing defect diagnostics by merging dilated residual convolutional neural networks and attention mechanisms. In this framework, multiple parallel dilated convolutional networks can automatically learn rich fault features at each scale from vibration signals. Simultaneously, the attention approach boosts fault-related features and suppresses irrelevant ones, improving fault detection performance and generalization. According to the experimental results of two different bearing datasets, the framework achieves a higher accuracy and can accurately identify various types of faults. [ABSTRACT FROM AUTHOR]