Korea Electronics Technology Institute

This article presents a hybrid voltage-time domain pipelined analog-to-digital converter (ADC). The proposed ADC employs a voltage-domain SAR quantizer for the coarse stage, while a time-domain quantizer is used for the fine stage. Ahybrid-domain dual-residue method is proposed to generate reference-embedded time-domain residues. It ensures full-scale matching between the converted time-domain residue and the time-domain reference of the fine stage. For the fine stage sub-quantizer, an interpolating time-to-digital converter (TDC) suited for dual-residue quantization is employed without using an external time-domain reference. A prototype ADC, implemented in a 40-nm CMOS process, occupies 0.034 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>. Operating at a sampling rate of 150 MS/s, the ADC achieves an SNDR of 61.1 dB and an SFDR of 75.5 dB at a Nyquist input, while consuming 2.54 mW, resulting in a Walden figure-of-merit (FoM<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</sub>) of 18.3 fJ/conv.-step.

This study aimed to develop and evaluate a deep learning algorithm for detecting and classifying intrasinus calcifications on paranasal sinus (PNS) computed tomography (CT) for the diagnosis of fungal sinusitis and differentiation of fungal and non-fungal sinusitis. A dataset of 277 PNS CT cases from Korea University Guro Hospital, supplemented by temporal and geographic external test sets, was utilized. A 3D U-Net model was employed to segment maxillary sinus regions. YOLO v5 identified calcifications, followed by classification into three patterns: normal sinus or chronic sinusitis without calcifications, dense peripheral dystrophic calcification, and central punctate fungal calcification. A separate convolutional neural network (CNN) refined the classification to ensure accurate categorization of calcification patterns. The 3D U-Net model achieved a Dice Similarity Coefficient of 0.9674, indicating accurate segmentation. YOLO v5 demonstrated precision of 79.50% and recall of 92.14% in detecting calcifications. The CNN classification model attained F1 scores of 94.73%, 90.60%, and 94.01%, and overall accuracies of 97.48%, 86.87%, and 94.01% for internal, temporal, and geographic test sets, respectively. This study demonstrated the capability of deep learning algorithms to accurately detect and classify fungal sinusitis-related calcifications on PNS CT scans. The developed framework achieved high accuracy in segmentation of sinus area and detection/classification of intrasinus calcifications. The framework also demonstrated its potential for broader application to radiographic imaging.

ABSTRACT All‐solid‐state batteries (ASSBs) are among the most promising candidates for next‐generation energy storage due to their high energy density and intrinsic safety. However, the practical use of lithium metal anodes in ASSBs remains limited by interfacial instability and dendritic Li growth, particularly under moderate stack pressures. Here, we report a nano‐sized tin (nSn) interlayer that forms a highly reversible Li–Sn alloy interface via spontaneous in situ lithiation, offering a robust, lithiophilic, and electronically conductive interface between the Li metal and sulfide‐based solid electrolyte. The resulting Li x Sn layer promotes uniform Li‐ion transport, suppresses dendrite formation, and enhances interfacial charge transfer. Symmetric cell measurements demonstrate markedly improved critical current densities and stable cycling, while full‐cell tests reveal high Coulombic efficiency, improved rate performance, and excellent capacity retention. The nSn interlayer was further scaled into a sheet‐type architecture using a warm isostatic press (WIP) process for integration into pouch‐type cells. The pouch cell exhibited high areal capacity, retaining 81% after 500 cycles, and delivered energy densities of 351 Wh kg −1 and 918 Wh L −1 under a low external pressure of 2 MPa. This work introduces a simple and scalable alloying‐based interfacial engineering strategy that enables high‐performance, fast‐charging ASSBs with practical viability.

This paper proposes a novel domain adaptation network to improve model accuracy in plant disease detection by leveraging structured image and text graphs. The proposed methodology constructs image graphs from segmented regions, including background, healthy, and disease regions. Class-specific text graphs are generated from prompts containing distinct color and texture attributes for each class. By aligning the structural information between image and text graphs, the network facilitates the effective transfer of class distinction capabilities from a source domain rich in data to a target domain with limited data, thereby addressing a key challenge of domain shift and data scarcity in agriculture. This approach incorporates text-image graph loss and image graph loss to enable comprehensive cross-domain class alignment, enhancing class distinction ability and ensuring consistent performance across diverse environmental conditions. Experimental evaluations across multiple source–target dataset pairs demonstrate improved class separation and stable mean IoU performance compared to existing approaches, validating the effectiveness of the proposed model in real-world agricultural scenarios. This method offers a promising pathway to enhance model adaptability and accuracy in disease detection, especially in complex and various agricultural environments.