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1. Broadband angular spectrum differentiation using dielectric metasurfaces

   https://doi.org/10.1038/s41467-024-46537-9  

   Deng, Ming; Cotrufo, Michele; Wang, Jian; Dong, Jianji; Ruan, Zhichao; Alù, Andrea; Chen, Lin (December 2024, Nature Communications)

   Abstract Signal processing is of critical importance for various science and technology fields. Analog optical processing can provide an effective solution to perform large-scale and real-time data processing, superior to its digital counterparts, which have the disadvantages of low operation speed and large energy consumption. As an important branch of modern optics, Fourier optics exhibits great potential for analog optical image processing, for instance for edge detection. While these operations have been commonly explored to manipulate the spatial content of an image, mathematical operations that act directly over the angular spectrum of an image have not been pursued. Here, we demonstrate manipulation of the angular spectrum of an image, and in particular its differentiation, using dielectric metasurfaces operating across the whole visible spectrum. We experimentally show that this technique can be used to enhance desired portions of the angular spectrum of an image. Our approach can be extended to develop more general angular spectrum analog meta-processors, and may open opportunities for optical analog data processing and biological imaging. 

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2. Highly Sensitive Readout Interface for Real-Time Differential Precision Measurements with Impedance Biosensors

   https://doi.org/10.3390/bios13010077  

   Neshani, Sara; Momeni, Kasra; Chen, Degang J.; Neihart, Nathan M. (January 2023, Biosensors)

   Field deployment is critical to developing numerous sensitive impedance transducers. Precise, cost-effective, and real-time readout units are being sought to interface these sensitive impedance transducers for various clinical or environmental applications. This paper presents a general readout method with a detailed design procedure for interfacing impedance transducers that generate small fractional changes in the impedance characteristics after detection. The emphasis of the design is obtaining a target response resolution considering the accuracy in real-time. An entire readout unit with amplification/filtering and real-time data acquisition and processing using a single microcontroller is proposed. Most important design parameters, such as the signal-to-noise ratio (SNR), common-mode-to-differential conversion, digitization configuration/speed, and the data processing method are discussed here. The studied process can be used as a general guideline to design custom readout units to interface with various developed transducers in the laboratory and verify the performance for field deployment and commercialization. A single frequency readout unit with a target 8-bit resolution to interface differentially placed transducers (e.g., bridge configuration) is designed and implemented. A single MCU is programmed for real-time data acquisition and sine fitting. The 8-bit resolution is achieved even at low SNR levels of roughly 7 dB by setting the component values and fitting algorithm parameters with the given methods. 

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3. Enhanced Knowledge Graph Attention Networks for Efficient Graph Learning

   https://doi.org/10.1109/HPEC62836.2024.10938526  

   Buschmann, Fernando Vera; Du, Zhihui; Bader, David A (September 2024, 28th Annual IEEE High Performance Extreme Computing Conference (HPEC))

   This paper introduces an innovative design for Enhanced Knowledge Graph Attention Networks (EKGAT), focusing on improving representation learning for graph-structured data. By integrating TransformerConv layers, the proposed EKGAT model excels in capturing complex node relationships compared to traditional KGAT models. Additionally, our EKGAT model integrates disentanglement learning techniques to segment entity representations into independent components, thereby capturing various semantic aspects more effectively. Comprehensive experiments on the Cora, PubMed, and Amazon datasets reveal substantial improvements in node classification accuracy and convergence speed. The incorporation of TransformerConv layers significantly accelerates the convergence of the training loss function while either maintaining or enhancing accuracy, which is particularly advantageous for large-scale, real-time applications. Results from t-SNE and PCA analyses vividly illustrate the superior embedding separability achieved by our model, underscoring its enhanced representation capabilities. These findings highlight the potential of EKGAT to advance graph analytics and network science, providing robust, scalable solutions for a wide range of applications, from recommendation systems and social network analysis to biomedical data interpretation and real-time big data processing. 

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4. Exploring Surface Texture Quantification in Piezo Vibration Striking Treatment (PVST) Using Topological Measures

   https://doi.org/10.1115/MSEC2022-86659  

   Yesilli, Melih C.; Chumley, Max M.; Chen, Jisheng; Khasawneh, Firas A.; Guo, Yang (January 2022, International Manufacturing Science and Engineering Conference)

   Abstract Surface texture influences wear and tribological properties of manufactured parts, and it plays a critical role in end-user products. Therefore, quantifying the order or structure of a manufactured surface provides important information on the quality and life expectancy of the product. Although texture can be intentionally introduced to enhance aesthetics or to satisfy a design function, sometimes it is an inevitable byproduct of surface treatment processes such as Piezo Vibration Striking Treatment (PVST). Measures of order for surfaces have been characterized using statistical, spectral, and geometric approaches. For nearly hexagonal lattices, topological tools have also been used to measure the surface order. This paper explores utilizing tools from Topological Data Analysis for measuring surface texture. We compute measures of order based on optical digital microscope images of surfaces treated using PVST. These measures are applied to the grid obtained from estimating the centers of tool impacts, and they quantify the grid’s deviations from the nominal one. Our results show that TDA provides a convenient framework for characterization of pattern type that bypasses some limitations of existing tools such as difficult manual processing of the data and the need for an expert user to analyze and interpret the surface images. 

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5. Physics-informed machine learning for inverse design of optical metamaterials

   Sulagna Sarkar, Anqi Ji (October 2023, Advanced Photonics Research)

   Optical metasurfaces consist of densely arranged unit cells that manipulate light through various light confinement and scattering processes. Due to its unique advantages, such as high performance, small form factor and easy integration with semiconductor devices, metasurfaces have been gathering increasing attention in fields such as displays, imaging, sensing and optical computation. Despite advances in fabrication and characterization, a viable design prediction for suitable optical response remains challenging for complex optical metamaterial systems. The computation cost required to obtain the optimal design exponentially grows as the design complexity increases. Furthermore, the design prediction is challenging since the inverse problem is often ill-posed. In recent years, deep learning (DL) methods have shown great promise in the area of inverse design. Inspired by this and the capability of DL to produce fast inference, we introduce a physics-informed DL framework to expedite the computation for the inverse design of metasurfaces. Addition of the physics-based constraints improve generalizability of the DL model while reducing data burden. Our approach introduces a tandem DL architecture with physics-based learning to alleviate the nonuniqueness issue by selecting designs that are scientifically consistent, with low error in design prediction and accurate reconstruction of optical responses. To prove the concept, we focus on the inverse design of a representative plasmonic device that consists of metal gratings deposited on a dielectric film on top of a metal substrate. The optical response of the device is determined by the geometrical dimensions as well as the material properties. The training and testing data are obtained through Rigorous Coupled-Wave Analysis (RCWA), while the physics-based constraint is derived from solving the electromagnetic (EM) wave equations for a simplified homogenized model. We consider the prediction of design for the optical response of a single wavelength incident or a spectrum of wavelength in the visible light range. Our model converges with an accuracy up to 97% for inverse design prediction with the optical response for the visible light spectrum as input. The model is also able to predict design with accuracy up to 96% and optical response reconstruction accuracy of 99% for optical response of a single wavelength of light as input. 

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