https://scholar.dgist.ac.kr/handle/20.500.11750/15726 Wed, 07 Oct 2026 21:10:27 GMT 2026-10-07T21:10:27Z 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/20.500.11750/60621 Title: Low-Complexity Single-Chain ISAC Receiver via Beat-Signal Domain FRFT Author(s): Kim, Bong-Seok; Lee, Jonghun; Kim, Sangdong Abstract: This letter introduces a single-chain Fractional Fourier Transform (FRFT)-based receiver for integrated sensing and communication (ISAC) that eliminates the need for matched-filter banks. While Slope-Shift Keying (SSK) is a promising modulation technique for automotive FMCW radar, conventional coherent receivers require a bank of matched filters, making the receiver processing complexity scale linearly with the modulation order. To overcome this limitation, we apply the FRFT in the beat-signal domain. By exploiting the chirp-rate-dependent energy focusing property, this approach converts slope discrimination into a fractional-domain peak detection problem. Consequently, it enables efficient high-order symbol detection using only a single standard receive processing chain. Simulation results demonstrate that the proposed scheme reduces the modulation-order-dependent digital processing burden while maintaining stable bit error rate (BER) performance and radar sensing performance under high-mobility Rician multipath V2V conditions. Wed, 31 Dec 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/60621 2025-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59897 Title: Enhancing photoelectrochemical CO2 reduction with CuBi2O4-cellulose nanofiber hybrid photocathodes Author(s): Cho, A. Young; Yoon, Ji Hyun; Lee, Sangwoo; Yun, Heeseo; Ma, Joonhee; Park, Jun-Young; Kim, Soo Young; Lee, Jonghun; Choi, Taekjib Abstract: The photoelectrochemical (PEC) conversion of carbon dioxide (CO2) into valuable chemicals and fuels offers a promising strategy to address global challenges such as climate change and glacier retreat. However, developing high-performance photocathodes for the CO2 reduction reaction (CO2RR) is challenging, particularly in optimizing the surface morphology and active site distribution of the electrodes. In this study, we propose a CuBi2O4 (CBO)-based photocathode capable of gas-phase CO2RR through hybridization with cellulose nanofiber (CNF). Our results reveal that the CBO-CNF membrane exhibits inherent hydrophilicity and significantly larger active sites compared to a CBO film prepared with a Nafion binder, leading to reduced charge transfer resistance on the photocathode surface. Moreover, the simultaneous hydrothermal synthesis of the CBO-CNF composite precursor solution effectively inhibits the formation of undesirable CuO nanoparticles on the surface, which would otherwise increase charge transport resistance within the photocathode bulk. Consequently, the CBO-CNF membrane demonstrates superior PEC activities for CO2RR, achieving a photocurrent density of - 5.69 mA/cm2 at - 0.4 VRHE and an onset potential of 0.015 VRHE. Furthermore, the incorporation of CNF improves the long-term PEC stability of the photocathode by promoting charge carrier participation in CO2RR rather than undesired self-reduction reaction. This enhanced stability, coupled with the improved PEC performance, highlights the potential of CNF to replace existing polymer binder materials. These results suggest the feasibility of developing a new type of CBO photocathode with a porous membrane structure suitable for gas-phase PEC cells, marking a significant step forward in PEC technology for CO2 conversion. Sat, 31 Jan 2026 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59897 2026-01-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59387 Title: Radar Foot Gesture Recognition with Hybrid Pruned Lightweight Deep Models Author(s): Son, Eungang; Song, Seungeon; Kim, Bong-Seok; Kim, Sangdong; Lee, Jonghun Abstract: Foot gesture recognition using a continuous-wave (CW) radar requires implementation on edge hardware with strict latency and memory budgets. Existing structured and unstructured pruning pipelines rely on iterative training–pruning–retraining cycles, increasing search costs and making them significantly time-consuming. We propose a NAS-guided bisection hybrid pruning framework on foot gesture recognition from a continuous-wave (CW) radar, which employs a weighted shared supernet encompassing both block and channel options. The method consists of three major steps. In the bisection-guided NAS structured pruning stage, the algorithm identifies the minimum number of retained blocks—or equivalently, the maximum achievable sparsity—that satisfies the target accuracy under specified FLOPs and latency constraints. Next, during the hybrid compression phase, a global L1 percentile-based unstructured pruning and channel repacking are applied to further reduce memory usage. Finally, in the low-cost decision protocol stage, each pruning decision is evaluated using short fine-tuning (1–3 epochs) and partial validation (10–30% of dataset) to avoid repeated full retraining. We further provide a unified theory for hybrid pruning—formulating a resource-aware objective, a logit-perturbation invariance bound for unstructured pruning/INT8/repacking, a Hoeffding-based bisection decision margin, and a compression (code-length) generalization bound—explaining when the compressed models match baseline accuracy while meeting edge budgets. Radar return signals are processed with a short-time Fourier transform (STFT) to generate unique time–frequency spectrograms for each gesture (kick, swing, slide, tap). The proposed pruning method achieves 20–57% reductions in floating-point operations (FLOPs) and approximately 86% reductions in parameters, while preserving equivalent recognition accuracy. Experimental results demonstrate that the pruned model maintains high gesture recognition performance with substantially lower computational cost, making it suitable for real-time deployment on edge devices. Fri, 31 Oct 2025 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/59387 2025-10-31T15:00:00Z