https://scholar.dgist.ac.kr/handle/20.500.11750/127 Sat, 03 Oct 2026 23:46:54 GMT 2026-10-03T23:46:54Z https://scholar.dgist.ac.kr/handle/20.500.11750/60890 Title: High-fidelity transcranial ultrasound multi-focal stimulation via physics-aware hologram technique Author(s): Lee, Moon Hwan; Khan, Mohd Afzal; Ashiquzzaman, Akm; Lee, Eunbin; Lee, Jonghun; Chung, Euiheon; Kwon, Hyuk-Sang; Hwang, Jae Youn Abstract: Introduction: Transcranial ultrasound stimulation (TUS) is an emerging non-invasive neuromodulation modality that offers deep brain access with high spatial precision. However, its broader application is limited by the difficulty of reliably generating complex transcranial acoustic fields, particularly for multi-target stimulation through the skull. These limitations can lead to focal distortion, off-target exposure, and reduced reliability of neuromodulation outcomes. Materials and methods: Here, we introduce a physics-aware thickness-only acoustic hologram (TOAH) technique for precise transcranial ultrasound neuromodulation. Unlike conventional approaches that rely on simplified phase-based approximations, TOAH directly generates fabrication-ready holographic implementations while preserving consistency between numerical field synthesis and physical acoustic realization. This enables accurate formation of single-, dual-, and tri-focal stimulation patterns under transcranial conditions. We validated TOAH through in silico simulations, ex vivo acoustic measurements through skulls, and in vivo experiments. Results: Compared with state-of-the-art methods, TOAH improved focal reconstruction, energy confinement, and multi-focal balance while reducing off-target acoustic leakage. Human-skull simulations further supported robust multi-focal reconstruction under clinically relevant transcranial conditions. In a neuropathic pain mouse model, bilateral thalamic stimulation induced measurable changes in neuronal activity, reflected by reduced c-Fos expression, together with preliminary improvements in pain-related behavioral responses. These findings support the capability of the proposed technique to enable spatially localized and reproducible neuromodulation in vivo. Conclusion: Collectively, this work provides a practical proof-of-concept strategy for achieving high-precision, multi-target transcranial neuromodulation and supports further investigation for neuroscience research and future therapeutic applications. Tue, 30 Jun 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60890 2026-06-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60456 Title: Fractionation of multiscale particle mixtures using acoustic field-flow fractionation with steric and normal mode combination Author(s): Jeong, Soowoong; Hwang, Jae Youn; Yang, In-Hwan Abstract: An acoustic field-flow fractionation (FFF) system was developed to fractionate particle mixtures with a wide size distribution by combining steric and normal mode mechanisms. The system utilized piezoelectric transducers on the top surface of the channel to generate a stable quarter-wavelength standing wave in the carrier liquid flow, controlled by the sinusoidal voltage amplitude. The acoustic radiation force from the ultrasonic standing wave suppressed Brownian diffusion, allowing for the vertical equilibrium distribution of Brownian-diffusive particles to be confined. This enabled their transport and elution by the carrier liquid velocity at the centroid of their distribution without impacting non-diffusive particles. Experimental results showed that the acoustic radiation force generated by the established standing acoustic wave field, which increases with applied voltage, effectively inhibited Brownian diffusion of 1.0 & micro;m particles at an applied voltage of 20 Vpp, thereby directing their transport according to the steric mode. Additionally, successful fractionation of a particle mixture comprising particles with radii of 350 nm, 550 nm, 1.0 & micro;m, 2.5 & micro;m, and 5.0 & micro;m demonstrated that the acoustic FFF system could separate particles across a wide size range using a hybrid separation mode that combines steric and normal modes. Theoretical predictions suggested that at an applied voltage of 80 Vpp, the acoustic FFF channel could extend the normal mode fractionation to particles below 45 nm, facilitating the separation of multiscale particle mixtures without the need for preprocessing. Thu, 30 Apr 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60456 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58607 Title: Expert-level differentiation of incomplete Kawasaki disease and pneumonia from echocardiography via multiple large receptive attention mechanisms Author(s): Lee, Haeyun; Lee, Kyungsu; Lee, Moon Hwan; Kim, Sewoong; Eun, Yongsoon; Eun, Lucy Youngmin; Hwang, Jae Youn Abstract: Background: Incomplete Kawasaki disease (KD) is challenging to diagnose due to its lack of classic clinical features, yet it has a higher incidence of coronary artery lesions, making early detection crucial. Echocardiography plays a vital role in identifying these lesions, but differentiating incomplete KD from other febrile illnesses, such as COVID-19, is difficult. Algorithms capable of achieving expert-level performance are needed to aid diagnosis, particularly in the absence of pediatric cardiologists. Methods: To address this need, we developed two novel deep learning models: the Multiple Receptive Attention Network (MRANet) and the Multiple Large Receptive Attention Network (MLRANet). These models incorporate multiple receptive attention layers and multiple large receptive attention layers to enhance their ability to identify KD-related coronary artery abnormalities on echocardiography. The models were trained and tested on 203 echocardiographic datasets and compared with advanced deep learning models to assess diagnostic performance. Results: Both MRANet and MLRANet outperformed existing deep learning models, achieving diagnostic accuracy comparable to experienced pediatric cardiologists. Notably, MLRANet demonstrated the highest sensitivity (93.48%) and specificity (66.15%), exceeding expert-level performance in detecting coronary artery abnormalities. Furthermore, MLRANet was able to distinguish incomplete KD from pneumonia effectively, showing diagnostic results aligned with the KD specialists. Conclusions: MLRANet has proven to be a valuable tool for computer-aided diagnosis of incomplete KD, offering accurate and reliable detection of coronary artery abnormalities without requiring specialist input. These findings suggest that MLRANet can facilitate timely and precise incomplete KD diagnosis, improving patient outcomes and addressing the shortage of pediatric cardiologists worldwide. © 2025 Elsevier Ltd Sun, 31 Aug 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/58607 2025-08-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/58156 Title: SoN: Selective Optimal Network for smartphone-based indoor localization in real-time Author(s): Lee, Kyungsu; Lee, Haeyun; Hwang, Jae Youn Abstract: Deep learning-based scene recognition algorithms have been developed for real-time application in indoor localization systems. However, owing to the slow calculation time resulting from the deep structure of convolutional neural networks, deep learning-based algorithms have limitations in the usage of real-time applications, despite their high accuracy in classification tasks. To significantly reduce the computation time of these algorithms and slightly improve their accuracy, we thus propose a path-selective deep learning network, denoted as Selective Optimal Network (SoN). The SoN selectively uses the depth-variable networks depending on a new indicator, denoted as the classification-complexity of a source image. The SoN reduces the prediction time by selecting optimal depth for the baseline networks corresponding to the input samples. The network was evaluated using two public datasets and two custom datasets for indoor localization and scene classification, respectively. The experimental results indicated that, compared to other deep learning models, the SoN exhibited improved accuracy and enhanced the processing speed by up to 78.59%. Additionally, the SoN was applied to a smartphone-based indoor positioning system in real-time. The results indicated that the SoN shows excellent performance for rapid and accurate classification in real-time applications of indoor localization systems. © 2025 Wed, 30 Apr 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/58156 2025-04-30T15:00:00Z