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Currently, 2D-based industrial anomaly detection has been used and developed in various fields, but multimodal industrial anomaly detection based on 3D point clouds and RGB images is still uncharted territory. Existing multimodal industrial anomaly detection methods directly connect multimodal features, resulting in strong disturbance between features and deteriorating detection performance. Therefore, In this study, we propose Hybrid Fusion Anomaly Detection (HFAD), a new multimodal anomaly detection method using FFL and hybrid fusion method to improve multimodal industrial anomaly detection performance. HFAD is an approach that meets all the requirements and advantages of industrial anomaly detection, protects data privacy and supports efficient use of resources. Additionally, it shows improved I-AUROC, AUPRO, and P-AUROC performance in all 10 categories of the MVTec AD-3D dataset. [ABSTRACT FROM AUTHOR]

This paper proposes a compact bandpass filter using a folded ring resonator, which occupies an area, one-quarter the size of a conventional ring resonator. For the filter design, circuit modeling is used to derive the coupling coefficients between the even- and odd-mode resonances of the proposed resonator with the perturbation stub. The validity of the proposed filter is demonstrated by designing and fabricating a two-pole bandpass filter with a 100 MHz bandwidth at the central frequency of 2.45 GHz [ABSTRACT FROM AUTHOR]