https://scholar.dgist.ac.kr/handle/20.500.11750/155 Department of Robotics and Mechatronics Engineering, DGIST 2026-11-11T10:58:43Z https://scholar.dgist.ac.kr/handle/20.500.11750/61155 Title: SCORE beamforming: Spectral COherence REfinement for high-contrast ultrafast Doppler imaging Author(s): Guezzi, Nizar; Lee, Sangheon; Nam, Sangwoo; Seo, Youngho; Kim, Myeongchan; Eom, Kisang; Hyun, Jung Ho; Yu, Jaesok Abstract: Delay-and-sum (DAS) beamforming is widely used in ultrasound flow imaging due to its computational simplicity; however, its high sidelobe levels significantly degrade image contrast. Coherence factor (CF) beamforming partially alleviates this limitation by emphasizing spatial coherence, yet residual incoherent energy remains and continues to impair image quality. In this study, we propose a dual-stage beamforming approach that integrates CF weighting with a frequency-domain filtering stage designed to discriminate and suppress incoherent energy while preserving coherent signal components. The proposed method, termed SCORE (Spectral COherence REfinement), introduces an additional coherence-based discrimination mechanism beyond conventional CF beamforming. Simulation experiments involving single-target, multi-target, and vascular scenarios demonstrate that SCORE achieves substantial sidelobe suppression while maintaining signal integrity. In vivo validation using an open-source contrast-enhanced rat brain and kidney dataset, as well as contrast-free human spleen further confirms improved flow detectability, reduced background noise, and enhanced power Doppler imaging performance, as evidenced by increases in SNR and CNR approaching 10 dB compared to CF and GCF beamforming. Overall, the SCORE beamformer provides a robust framework for high-contrast vascular ultrasound imaging, offering superior sidelobe and noise suppression relative to DAS, CF and GCF. To enhance computational efficiency, the beamforming pipeline was implemented on a graphics processing unit (GPU) using an optimized CUDA framework, achieving real-time beamforming performance with processing times below 30 ms per frame. 2026-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61147 Title: Immobilized microalgae-driven triboelectric nanogenerators for sustainable bioelectricity production Author(s): Kaja, Kushal Ruthvik; Janpum, Chalampol; Komkhum, Tanakit; Hajra, Sugato; Vivekananthan, Venkateswaran; Vittayakorn, Naratip; Thinsurat, Kamon; Ngamcharussrivichai, Chawalit; Kim, Hoe Joon; In-na, Pichaya Abstract: Triboelectric nanogenerators (TENG) are emerging as promising solutions for decentralised energy generation due to the growing need for sustainable power sources. These devices convert wasted mechanical energy into electricity under ambient conditions, offering advantages such as eco-friendly operation, material versatility, and effective energy scavenging. Despite these benefits, their relatively low electrical output compared to conventional sources like batteries and fuel cells remains a limitation. Microalgae have attracted attention for their ability to produce bioelectricity through photosynthesis and respiration while simultaneously capturing carbon dioxide. Immobilising microalgal cells on conductive substrates improves electron transfer and metabolic activity. In this context, living Chlorella vulgaris TISTR 8580 with varied cell densities was immobilised on aluminium electrodes and incorporated into a TENG platform to explore energy harvesting from solid-solid and solid-liquid interactions. The highest output of 110V and 330 nA was generated, confirming the microalgae as a promising tribolayer and extending the conventional triboelectric series. However, sustaining cell viability over extended periods remains a challenge, highlighting the need for optimised light and nutrient conditions in future developments. 2026-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61142 Title: Dynamic-junction triboelectric nanogenerators: Underlying physics, interface charge-generation mechanisms, and design frameworks for enhanced harvesting and sensing performance Author(s): Sriphan, Saichon; Vittayakorn, Wanwilai; Maluangnont, Tosapol; Hajra, Sugato; Kim, Hoe Joon; Pakawanit, Phakkhananan; Charoonsuk, Thitirat; Vittayakorn, Naratip Abstract: The increasing demand for sustainable energy solutions has driven the advancement of technologies capable of converting ambient mechanical energy into electrical power. Among these, triboelectric nanogenerators (TENGs) have emerged as a prominent platform for broad-based energy harvesting. This review provides an overview of recent developments in dynamic junction-based TENGs, highlighting approaches that extend beyond conventional solid-solid (S-S) contact-separation mechanisms to encompass charge generation at liquid-solid (L-S), liquid-liquid (L-L), and liquid-gas (L-G) interfaces. Dynamic-junction TENGs are defined as triboelectric systems in which mechanically evolving interfaces regulate charge generation, separation, or collection. Under this framework, dynamic junctions encompass both material interfaces—such as S–S, L–S, L–L, L–G, and G–S—and electronically active semiconductor junctions, including Schottky, p–n, and heterojunction-based tribovoltaic nanogenerators. We provide a comparative analysis of the fundamental working principles of TENGs and the distinct charge generation mechanisms intrinsic to each interface type. The review discusses the theoretical frameworks relevant to dynamic-junction TENGs according to their domains of applicability, including the capacitive model and Maxwell’s displacement current for electrostatic-induction-based devices, Wang’s electron-cloud model for microscopic solid–solid contact electrification, and electric-double-layer-based models for liquid-containing interfaces. Furthermore, we categorize and scrutinize advanced design strategies, from interface-specific innovations and materials engineering to system-level integration and hybridization, all aimed at significantly enhancing output performance. The review highlights the representative applications of dynamic junction-based TENGs in diverse fields such as wearable electronics, environmental energy harvesting, intelligent robotics, and smart infrastructure. Finally, we discuss critical challenges, including stability, durability, and scalability, and outline future research directions, aiming to provide insights for the ongoing development and practical implementation of highly efficient, self-powered systems. 2026-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61134 Title: Steering feedback torque prediction using a driving-condition clustering-enhanced sequence-to-sequence network Author(s): Wang, Jinjie; Xu, Jianjun; Wu, Xiaodong; Yao, Wenguang; Nam, Kanghyun; Yao, Pengju Abstract: Accurate prediction of Steering Feedback Torque (SFT) is a key challenge in steer-by-wire (SbW) development, as it directly determines the realism and controllability of the steering experience. Traditional physics-based models rely on complex calibration and struggle to capture the nonlinear, time-varying dynamics inherent in real-world vehicle signals. To address this, a clustering-enhanced SFT prediction framework that integrates driving state clustering with a sequence-to-sequence (S2S) network is proposed. A multi-channel signal dataset, comprising steering, inertial, and wheel-speed measurements, is acquired from on-road experiments on an instrumented test vehicle across diverse driving scenarios. Driving features extracted from these signals are first clustered via unsupervised learning, and the resulting cluster membership probabilities are embedded as supplementary features into the multivariate time series input. This enhances the model's generalization across driving conditions and improves interpretability by linking model behavior to identifiable driving patterns. An S2S network is then trained to capture the temporal dependencies specific to the steering system. Hardware-in-the-loop experiments with driver-in-the-loop operation confirm that the proposed method outperforms conventional static regression models and non-clustered baselines in both common and extreme driving scenarios. 2026-09-30T15:00:00Z