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A more accurate and efficient millimeter-wave imaging algorithm for a monostatic system is presented in this paper. Firstly, a more precise physical model is implemented, which considers the propagation loss of both the incident wave and scattered wave. However, this physical model cannot be processed using classical mathematical methods such as spherical wave decomposition and Weyl identity. Instead, the method of stationary phase (MSP) is applied to derive the imaging formula. As a result, the proposed algorithm achieves superior imaging results for multiple targets at different ranges. Additionally, the range stacking technique is introduced to avoid time-consuming interpolation, which also contributes to improving computational efficiency comparing to the algorithms employing interpolation. The simulation results show that the proposed algorithm reduces the computation time by half. The proposed algorithm has been further verified through real data collected with our in-house developed prototype. [ABSTRACT FROM AUTHOR]

Non-invasive inspection and imaging techniques are used to acquire non-visible information embedded in samples. Typical applications include medical imaging, defect evaluation, and electronics testing. However, existing methods have specific limitations, including safety risks (e.g., X-ray), equipment costs (e.g., optical tomography), personnel training (e.g., ultrasonography), and material constraints (e.g., terahertz spectroscopy). Such constraints make these approaches impractical for everyday scenarios. In this article, we present a method that is low-cost and practical for non-invasive inspection in everyday settings. Our prototype incorporates a miniaturized near-infrared spectroscopy scanner driven by a computer-controlled 2D-plotter. Our work presents a method to optimize content embedding, as well as a wavelength selection algorithm to extract content without human supervision. We show that our method can successfully extract occluded text through a paper stack of up to 16 pages. In addition, we present a deep-learning-based image enhancement model that can further improve the image quality and simultaneously decompose overlapping content. Finally, we demonstrate how our method can be generalized to different inks and other layered materials beyond paper. Our approach enables a wide range of content embedding applications, including chipless information embedding, physical secret sharing, 3D print evaluations, and steganography. [ABSTRACT FROM AUTHOR]