New Study Unveils Precise Calibration Method for Event Cameras in Autonomous Systems

Abstract The conventional checkerboard-based calibration for standard cameras faces fundamental limitations when applied to bio-inspired event cameras. Specifically, this stems from two challenges: (i) Events are triggered asynchronously at different timestamps along motion trajectories. If we accumulate them directly on the image plane, it causes temporal misalignment and produces blurred edges. (ii) Checkerboard corners on event cameras show near-zero event occurrence at the corner itself. This hinders reliable corner localization and makes calibration difficult. To address these issues, we present a novel calibration framework that directly detects checkerboard corners from event data without learning-based grayscale image reconstruction. We first mathematically analyze the absence of events at corner points. Based on this fact, we then leverage edge-driven event cues to initialize corner positions. Using the nearzero event occurrence at checkerboard corners, we gradually refine the estimated corner toward low event-density regions, achieving sub-pixel accuracy. Furthermore, we extend the corner detection to fiducial markers such as AprilTag, resulting in reliable detection even under partial visibility or occlusion. Evaluations on self-collected and public data demonstrate reliable checkerboard corner detection and stable camera calibration. A research team affiliated with UNIST has introduced a new calibration technique for event cameras—an essential sensor for high-speed robotics and autonomous vehicles. Unlike conventional methods, this approach uses standard checkerboard patterns to calibrate the sensors directly from event data, eliminating the need for image reconstruction. Event cameras detect only changes in brightness at individual pixels, enabling rapid perception in challenging conditions, such as low light or fast motion. However, calibrating these sensors—particularly with common checkerboard targets—has been problematic because the key points at the intersections of black and white squares rarely produce detectable events. Led by Professor Kyungdon Joo from UNIST's Graduate School of Artificial Intelligence, the team developed a computer vision approach that bypasses this challenge. Instead of locating checkerboard corners directly within event data, the method first detects the pattern's boundary lines. It then identifies the corners as intersections where these lines meet and where minimal activity occurs—since brightness changes cancel out at intersections. This insight, grounded in the mathematical behavior of event signals, enables precise corner detection without converting event data into traditional images. The team also improved the clarity of the detected grid. Because event signals are recorded asynchronously across pixels, slight movements can cause the pattern to blur. Their technique aligns and refines these signals, reconstructing sharp grid lines and significantly enhancing calibration accuracy. Furthermore, the method extends to AprilTags—square fiducial markers similar to QR codes used for localization. It can identify and decode these tags solely from event data, even when some are partially obscured or outside the camera's field of view. First author Taehun Ryu explains, “Previous methods had to convert event data into grayscale images to find corners, which could introduce blurring and errors. Our approach directly extracts reference points from raw signals, greatly improving calibration precision.” Professor Joo highlights the broader significance, “Accurate camera calibration is fundamental to many vision systems. Our work paves the way for deploying event cameras in real-world robots and vehicles.” The study has been selected as a Highlight at the 2026 Conference on Computer Vision and Pattern Recognition (CVPR), scheduled for June 3–7 in Denver, USA. Only about 3.5% of submissions earn this recognition, which honors outstanding contributions to the field. The research was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP), the Ministry of Science and ICT (MSIT), the National Research Foundation of Korea (NRF), and UNIST's Graduate School of Artificial Intelligence. Additional support came from projects including the Development of AI Bots Collaboration Platform and Self-organizing and the Geometric and Physical Commonsense Reasoning based Behavior Intelligence for Embodied AI . Journal Reference Taehun Ryu, Changwoo Kang, and Kyungdon Joo, "From Corners to Fiducial Tags: Revisiting Checkerboard Calibration for Event Cameras," '26 CVPR, (2026).

2026.06.05

Novel Network Analysis Algorithm for Targeted Community Detection Without Full Data Access

Abstract With the proliferation of social networks, identifying meaningful community structures efficiently is essential for analyzing complex interactions. This paper introduces Local Sketch Modularity (LSM), a novel modularity that measures community quality without relying on the entire structural information of the network, enabling a more targeted and practical approach to find the query-centric community. We validate the efficacy of the proposed modularity LSM through theoretical analyses, showing robustness against the free-rider effect. We further formulate the Local Modularity Optimization for Size-Constrained Community Search (LMSC) problem, which leverages LSM to identify the query-centric community without requiring knowledge of the entire graph. We prove that LMSC is NP-hard and propose two efficient and effective algorithms. Extensive experiments on real-world networks demonstrate both the effectiveness and efficiency of the proposed method, confirming its applicability for large-scale network analysis. A research team led by Professor JungHoon Kim from the Department of Computer Science and Engineering at UNIST has introduced an innovative network analysis algorithm capable of identifying relevant communities around a target node without requiring access to the entire network. This advancement has the potential to enhance targeted marketing, improve fraud detection, and generate new insights in biological network research. Traditional community detection tools often struggle with large-scale data or are limited by privacy restrictions that prevent access to full network information. They tend to include unrelated nodes or overlook tightly connected groups around a specific target. The new method addresses these challenges by focusing solely on the neighborhood of a designated node, expanding the community incrementally while adhering to a predefined size. Starting from the target node—such as a potential customer, a suspicious account, or a biological molecule—the algorithm examines neighboring candidates and assesses whether adding each one improves the overall coherence of the group. It balances internal connectivity with external separation, preventing the community from becoming excessively large. To capture subtle relationships, it also considers small, tightly linked subgroups that might otherwise be missed. Testing on real-world networks shows this approach significantly outperforms existing methods, with F1 scores up to 1.39 times higher and ARI scores up to 5.95 times higher. These results demonstrate more accurate detection of relevant communities while reducing false positives. “In many real-world situations, obtaining full network data isn't feasible, and the target community size is often fixed,” said Professor Kim. “Our method quickly identifies meaningful groups around a specific node, making it applicable to areas like customer segmentation, fraud prevention, and biological research.” Supported by the National Research Foundation of Korea (NRF), the study was led by first author Dahee Kim. Their work has been accepted for presentation at the ACM Special Interest Group on Management of Data (SIGMOD) 2026, scheduled to take place in Bengaluru, India, from May 31 to June 5, 2026. As one of the most respected conferences in data management, SIGMOD offers a platform for pioneering research in the field. Journal Reference Dahee Kim, Taejoon Han, Kaiyu Feng, et al., "LMSC: Local Sketch Modularity Optimization for Size-Constrained Community Search in Networks,"'26 SIGMOD, (2026).

2026.06.05

New High-Efficiency Wireless Charging for Implantable Medical Devices

Abstract This article presents a single-input–dual-output (SIDO) wireless power transfer (WPT) system that maintains high link efficiency across both heavy and light load conditions. Conventional SIDO architectures employ a fixed matching network, which leads to significant efficiency degradation when the load changes. To address this limitation, the proposed system classifies the two outputs into heavy-load and light-load domains and employs dedicated matching networks optimized for each operating condition, thereby maximizing link efficiency during charging of either load. To minimize the power overhead of mode switching while enabling single-stage AC–DC regulation, a charging-phase controller, a full/half-window mode controller, and an adaptive window generator are employed. Furthermore, an active rectifier with a slowdown unit cell (SUC)-based on/off controller (SOOC) is proposed to improve power conversion efficiency (PCE). In addition, an early turn-on switching (ETS) is implemented to achieve higher PCE compared to conventional zero-voltage switching (ZVS). Fabricated in a 0.18- μ m BCD process, the system is implemented as two chips, a standalone rectifier for accurate PCE measurement and a SIDO integrating the same rectifier. The SIDO demonstrates stable full/half-window operation without multiple pulsing. Measured link efficiency reaches 94.4% at 3mA and 92.7% at 30mA at a coupling coefficient k = 0.2, confirming the effectiveness of the load-optimized matching. The rectifier achieves 94.5% peak PCE and maintains above 92.3% PCE across a 2.5–5.0V input range. A new wireless charging technology promises to enhance the safety and longevity of implantable medical devices, such as pacemakers and neural stimulators. Led by Professor Franklin Bien of the Department of Electrical Engineering, the research team developed a system that employs load-specific matching networks and adaptive control to optimize power delivery. Implantable devices often incorporate circuits with varying power demands—high-current circuits for stimulation and low-current circuits for data processing. Traditional wireless chargers rely on fixed configurations, which can lead to inefficiencies and heat buildup, potentially damaging surrounding tissue. The new system detects changes in load and switches between dedicated matching networks tailored for high and low power conditions, thereby optimizing energy transfer. Furthermore, the team improved the efficiency of the rectifier circuit that converts received alternating current (AC) into direct current (DC). By precisely controlling switching points during power conversion, they minimized energy loss and enhanced overall performance. Experimental results demonstrated a link efficiency of 94.4% at a low load of 3mA and 92.7% at a high load of 30mA. The active rectifier achieved a peak power conversion efficiency of up to 94.5%, maintaining above 92% even as input voltage varied from 2.5V to 5V. The research team anticipates that this technology will extend the operational lifespan of implantable devices, reducing the need for frequent surgical replacements and associated risks. It also holds promise for application in wearable electronics and compact Internet of Things (IoT) devices that require reliable, low-loss wireless power. The findings of this research were published online in the IEEE Transactions on Very Large Scale Integration (VLSI) Systems on April 29, 2026. The study has been s upported by the Ministry of Science and ICT (MSIT) and the Institute for Information & Communications Technology Planning & Evaluation (IITP). Journal Reference Sungmin Shin, Seongbin Kwon, Geonwoo Baek, et al ., “A Single-Input Dual-Output Wireless Power Transfer System With Load-Optimized Matching Network,” IEEE TVLSI. , (2026).

2026.06.04

UNIST Unveils Transfer-Free Method for 2D Semiconductor LED Production

Abstract We investigated an epitaxial strategy for fabricating MoS2 light-emitting diodes (LEDs). A full-coverage MoS2 active layer was grown on p-type GaN, and n-type ZnO nanorods were then vertically aligned on the MoS2 to form ap–n junction with negligible damage to the MoS2. All materials have nearly matched hexagonal structures, enabling single-crystal alignment. Although the continuous MoS2 film formed multiple layers (MLs), the ZnO/MoS2/GaN heterostructure yielded favorable optical characteristics of the ML-MoS2, including internal quantum efficiency comparable to that of the single-layer MoS2. The ZnO/MoS2/GaN LED exhibited stable A and B exciton emissions, which implies direct bandgap transition with spin–orbit coupling. Without mechanically exfoliated or transferred 2D films, this epitaxial approach satisfies the key requirements for fabricating 2D-based optoelectronic and quantum light sources. The strength of epitaxy, such as large-scale scalability and multiple quantum-well formation, will further advance 2D optoelectronics, making them more practical and efficient. A research team, affiliated with UNIST has demonstrated a new way to produce light-emitting diodes (LEDs) using atomically thin layers of molybdenum disulfide (MoS2). By growing the material directly on a substrate, they eliminate the need for transferring fragile 2D films—a step that has limited previous efforts to scale and uniformity. Led by Professor Kunook Chung from the Graduate School of Semiconductor Materials and Devices Engineering, the team developed a process to grow high-quality MoS₂ directly on gallium nitride (GaN), then add zinc oxide (ZnO) nanorods on top to form a complete p–n junction. This approach simplifies manufacturing and results in consistent, scalable devices. MoS2, a 2D semiconductor capable of emitting visible light at just a few atomic layers, has long promised applications in quantum light sources and integrated photonics. However, traditional fabrication methods involve synthesizing the material separately and then transferring it onto a substrate, which often introduces defects, contamination, and variability. By growing MoS2 directly on GaN, the team avoided these issues. The process begins with depositing GaN, then carefully epitaxially growing MoS2 at high temperature. The ZnO nanorods are subsequently grown vertically on the MoS₂ layer, creating a well-aligned, high-quality heterostructure. These devices emit red light at wavelengths of 630 nm and 705 nm, confirmed through optical testing. The emission features quantum effects like spin–orbit coupling, suggesting potential for quantum photonic applications. Professor Chung explained, “Transfer processes have limited the scalability of 2D LEDs. Our method shows that direct growth can produce uniform, high-quality devices—similar to traditional semiconductor fabrication. This opens the door to large-scale, practical 2D optoelectronics.” He further added, “Further improvements in efficiency could lead to applications such as micro-LED displays or quantum light sources, especially in the red spectrum.” The findings of this research have been featured as the Supplementary Cover of Nano Letters on April 21, 2026. The study has been supported by the National Research Foundation of Korea (NRF), the Ministry of Science and ICT (MSIT), the Korea Institute for Advancement of Technology (KIAT), and the Ministry of Trade, Industry, and Resources (MOTIE). Journal Reference Imasda Rahmatulloh, Daryll JC Dalayoan, Asad Ali, et al ., “Epitaxial n-ZnO/MoS2/p-GaN Heterostructure Light-Emitting Diodes,” Nano Letters, (2026).

2026.06.04

Breakthrough Chameleon MXene Promises Advanced 6G Communication and Next-Gen Batteries

Abstract Device-level performance in MXenes is dictated by architecture—planar nanosheets are optimal for electromagnetic interference (EMI) shielding, while scrolled structures enhance ion transport for energy storage—particularly when morphology is programmed at synthesis. Whether such architectures can be deterministically encoded through precursor stoichiometry remains unresolved. Here, we demonstrate that precise carbon stoichiometry control in Ti3AlCxO2-x MAX phases tunes internal lattice strain and thereby directs the emergent MXene architecture. Carbon-rich precursors (x = 1.94) yield strain-relieved, high-crystalline nanosheets with metallic conductivity (∼23 300 S cm−1), enabling ultrathin films with record-high EMI shielding performances across 8 µm) and robust W-band retention after 5,000 bending cycles (r = 2.5 mm). In contrast, carbon-deficient precursors (x = 1.71) introduce lattice compression and oxygen substitution, triggering spontaneous scrolling upon delamination. The resulting nanoscrolls offer exceptional ion accessibility, achieving 657 F g−1 at 2 mV s−1 with 99.4% retention over 12 000 cycles. This stoichiometry-programmed approach establishes a synthesis-stage lever linking MAX chemistry to MXene architecture and function, enabling application-specific architecture design within established MAX/MXene synthesis and solution-processing workflows for next-generation electronics and energy storage. Researchers at UNIST have discovered a simple way to control MXene’s structure by adjusting the carbon content in its precursor. This breakthrough enables the creation of tailored MXene materials optimized for high-frequency electromagnetic interference (EMI) shielding and rapid energy storage. MXene, a two-dimensional material composed of metal and carbon layers, is celebrated for its excellent electrical conductivity and adaptability. Its potential spans batteries, sensors, and flexible electronics. With the rise of 6G technology and advanced radar systems, shielding devices against high-frequency interference has become critical. At the same time, the demand for quick, reliable energy storage continues to grow. Led by Professor Soon-Yong Kwon from the Graduate School of Semiconductor Materials and Devices Engineering and Professor EunMi Choi from the Department of Electrical Engineering, the team showed that changing the carbon content in MAX precursors influences how MXene forms during synthesis. This control method results in two distinctly different structures. When using carbon-rich precursors, the process yields flat, highly crystalline nanosheets that exhibit excellent electrical conductivity, effective electromagnetic shielding at 100 GHz, and maintain flexibility and durability under repeated bending. Conversely, employing carbon-deficient precursors induces the spontaneous formation of nanoscrolls, which significantly enhance ion transport. These nanoscroll structures enable high-capacity energy storage, achieving a capacitance of 657 F/g and a cycle life exceeding 12,000 cycles. This work confirms that simple adjustments to precursor composition can produce MXene structures tailored for specific applications—flat sheets for shielding and scrolls for energy storage—within a streamlined process. First author Jaeeun Park explains, “We showed that flat MXene sheets are ideal for electromagnetic shielding, while scroll structures are better suited for energy storage. By controlling the precursor, we can design the material for different functions without changing the synthesis method.” Professor Kwon adds, “Last year, we enhanced MXene’s conductivity and broadband shielding through nitrogen doping. Now, we’ve demonstrated how to design the material’s shape from the start. The ultra-thin, flexible MXene performs well at 100 GHz and withstands bending, making it a promising lightweight, flexible shield for future 6G and radar systems.” Supported by the Ministry of Science and ICT (MSIT) and the National Research Foundation of Korea (NRF), the study was published in Advanced Materials on May 18, 2026. Journal Reference Jaeeun Park, Ju-Hyoung Han, Yujin Chae, et al ., “Stoichiometry-Programmed MXenes via Precursor Engineering for High-Performance EMI Shielding and Energy Storage,” Adv. Mater ., (2026).

2026.06.02

AI-Driven Satellite Detects Methane—84 Times More Potent Than CO2—Faster and More Precise

Abstract Methane (CH4) is a dominant driver of near-term warming, yet global emission monitoring remains constrained by slow processing and large uncertainties. Hyperspectral spectrometers enable sensitive detection of CH4 plumes, but the relative advantages of enhancement-based (ENH) and radiance-based (RAD) approaches have not been systematically evaluated. Here we introduce a dual-path deep-learning framework that systematically compares both approaches using globally distributed, expert-validated CH4 plume datasets from EMIT and Tanager-1. The ENH models exhibit higher segmentation accuracy across plume scales, whereas the RAD models, operating directly on 49 shortwave-infrared channels, avoid computationally expensive preprocessing (eg, matched filtering) and enable rapid screening. Both pathways markedly reduce labor-intensive workflows and latency relative to traditional processing while maintaining competitive performance by utilizing deep learning. Explainable AI analyzes demonstrate that the models learn spatial-spectral features consistent with CH4 absorption structure and plume morphology, providing evidence of scientific validity. Cross-sensor evaluation demonstrates architectural robustness across EMIT and Tanager-1, establishing a physics-grounded framework adaptable across hyperspectral sensors. Methane (CH4) is a powerful greenhouse gas—84 times more potent than carbon dioxide over 20 years. Quickly identifying leaks is crucial, but current methods rely on slow, manual analysis of satellite images. Researchers at UNIST have created an AI-based system that automates methane plume detection, making monitoring faster and more precise. Led by Professor Jungho Im from the Department of Civil, Urban, Earth, and Environmental Engineering, the team built a deep learning model to identify methane leaks in satellite imagery. They tested different data types and modeling techniques to determine the most effective approach for global deployment. Hyperspectral satellites capture reflected sunlight across hundreds of narrow wavelengths, revealing detailed surface information. The team trained their models on data from NASA's EMIT satellite, which orbits the International Space Station. The models detect methane by recognizing specific infrared wavelengths absorbed by the gas—clear indicators of leaks. The system successfully identified methane emissions from diverse sources, including oil and gas facilities, waste sites, and coal mines across regions such as Turkmenistan, Algeria, and the US The analysis revealed that the AI system does not merely recognize visual patterns; It interprets physical signals such as absorption features and plume shapes consistent with real-world physics. They tested three deep learning architectures—CNN-ASPP, Inception U-Net, and SegFormer—using two data types: raw radiance and processed data that highlights methane concentration. Models trained on the enhanced data performed better overall. But models trained on raw data responded faster, making them useful for quick scans. Applying these models to Tanager-1 satellite data—commercial hyperspectral imagery—yield similar results, demonstrating the approach's versatility across different sensors. First authors Seyoung Yang and Yejin Kim played key roles in this work. The models work across various resolutions and conditions, relying on physical principles to quickly flag large leaks for further investigation. Professor Im emphasized the importance, “Fast leak detection is essential for reducing methane emissions. Traditional methods are slow and require experts. By combining hyperspectral data with AI, we can identify potential trouble spots in real time and strengthen global monitoring efforts.” Supported by the Ministry of Environment and the Ministry of Education, this research was published in npj Climate and Atmospheric Science on March 25, 2026. Journal Reference Seyoung Yang, Yejin Kim, Minki Choo, et al ., “Beyond localized methane plume detection: a dual-path deep learning framework for sensor-agnostic global hyperspectral methane plume monitoring,” npj Clim. Atmos. Sci., (2026).

2026.06.01