This paper presents a novel localization method that leverages two sets of distributed microphone arrays using the Generalized Cross-Correlation Phase Transform (GCC-PHAT) technique to improve the performance of anti-drone systems. In contrast to conventional sound source localization techniques, the proposed approach enhances localization accuracy by precisely estimating the azimuth angle while considering the unique acoustic characteristics of drones. The effectiveness of the proposed method was validated through both simulations and field tests. Simulation results revealed that, in ideal channel conditions, the proposed method significantly reduced the mean and variance of localization errors compared to existing techniques, resulting in more accurate positioning. Furthermore, in noisy environments, the proposed approach consistently outperformed the comparison method across various Signal-to-Noise Ratio (SNR) levels, achieving up to 2.13 m of improvement at SNR levels above 0 dB. While the comparison method exhibited decreased localization accuracy along the y-axis and z-axis, the proposed method maintained stable performance across all axes by effectively distinguishing between azimuth and elevation angles. Field test results closely mirrored the simulation outcomes, further confirming the robustness and reliability of the proposed localization approach.

In this study, we propose a generative data augmentation technique to overcome the challenges of severely limited data when designing a deep learning-based automated strabismus diagnosis system. We implement a generative model based on the StyleGAN2-ADA model for system design and assess strabismus classification performance using two classifiers. We evaluate the capability of our proposed method against traditional data augmentation techniques and confirm a substantial enhancement in performance. Furthermore, we conduct experiments to explore the relationship between the diagnosis agreement among ophthalmologists and the generation performance of the generative model. Beyond FID, we validate the generative samples on the classifier to establish their practicality. Through these experiments, we demonstrate that the generative model-based data augmentation improves overall quantitative performance in scenarios of extreme data scarcity and effectively mitigates overfitting issues during deep learning model training.

Background/Objectives: Atropine and orthokeratology (OK) are used for childhood myopia control, but comparisons of atropine monotherapy, OK monotherapy, and combination therapy remain limited. This study compared 12-month axial length changes and the proportions of fast, intermediate, and slow progressors among these treatments. Methods: This retrospective study included 154 children followed for at least 12 months: 70 received atropine monotherapy, 64 received OK monotherapy, and 20 received combination therapy. Axial length changes at 6 and 12 months were analyzed using a linear mixed-effects model adjusted for age at treatment initiation, sex, baseline axial length and baseline spherical equivalent refractive error. Based on 12-month axial elongation, progression was classified as fast (≥0.30 mm), intermediate (0.15 to <0.30 mm), or slow (<0.15 mm). Results: At 12 months, mean axial elongation was 0.294 ± 0.209 mm, 0.238 ± 0.228 mm, and 0.160 ± 0.109 mm in the atropine, OK, and combination groups, respectively. The group-by-time interaction was significant (p = 0.006). No significant pairwise differences were observed at 6 months. At 12 months, axial elongation was significantly smaller in the combination group than in the atropine and OK groups (adjusted mean differences, 0.161 and 0.115 mm; 95% CI, 0.078–0.245 and 0.024–0.207; Holm-adjusted p < 0.001 and 0.027, respectively). There was no significant difference between the atropine and OK groups (p = 0.155). The pattern of progression differed among groups (p = 0.017), with fast progressors least frequent in the combination group (5.0% vs. 32.9% and 32.8%). Conclusions: In this observational cohort, combination therapy was associated with less axial elongation and fewer fast progressors than either monotherapy.