A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression by Xufeng Li, Li Zhou, Yan Zhu

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From Light to Logic: Recent Advances in Optoelectronic Logic Gate by Woochul Kim, Dante Ahn, Minz Lee, Namsoo Lim, Hyeonghun Kim, Yusin Pak

“…OELGs present significant advantages over traditional electronic logic gates, including enhanced processing speed, bandwidth, and energy efficiency. …”
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Neuromorphic Floating-Gate Memory Based on 2D Materials by Chao Hu, Lijuan Liang, Jinran Yu, Liuqi Cheng, Nianjie Zhang, Yifei Wang, Yichen Wei, Yixuan Fu, Zhong Lin Wang, Qijun Sun

“…This novel methodology emulates the biological synaptic mechanisms for information processing, enabling efficient data transmission and computation at the identical position. …”
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Vertical Electrolyte‐Gated Transistors: Structures, Materials, Integrations, and Applications by Bin Bao, Ting Zhang, Junlei Xie, Jin Wu, Gang He, Lang Jiang, Yanlin Song, Shouguo Wang

“…Abstract Biological synapse‐inspired electrolyte‐gated transistors have received broad attention recently as a competitive candidate for constructing artificial intelligence (AI) systems to meet the data memorizing and processing challenges brought by the big data era. …”
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Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction by Deyong Guan, Na Ren, Ke Wang, Qi Wang, Hualong Zhang

“…The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. …”
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Utilizing active learning and attention-CNN to classify vegetation based on UAV multispectral data by Sheng Miao, Chuanlong Wang, Guangze Kong, Xiuhe Yuan, Xiang Shen, Chao Liu

“…The model achieves accurate identification of vegetation types in the study area by utilizing multispectral data obtained from preprocessing of unmanned aerial vehicle (UAV) remote sensing equipment. …”
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100 Gbps Low-Latency Protocol Processing and FPGA Implementation by Liangwei Lei, Funan Zhu, Shaowen Lu, Yaohui Du, Jiawei Li, Xia Hou

“…With the ongoing evolution of integrated space-air-ground networks, space laser communications face increasingly stringent demands for low latency and high throughput in high-speed data transmission. To meet these requirements, this paper presents a low-latency protocol stack processing architecture based on Field Programmable Gate Array (FPGA) and provides an in-depth investigation into its implementation at the hardware level. …”
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Deep Learning-Based Sentiment Analysis Using Gated Recurrent Unit by Najeem Olawale Adelakun, Mariam Adenike Lasisi

“…The research employs a systematic methodology that begins with data collection from various financial sources. This is followed by rigorous preprocessing, including data cleaning, tokenization, and downsampling to balance sentiment classes. …”
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Multivariate bidirectional gate recurrent unit for improving accuracy of energy prediction by Quota Alief Sias, Rahma Gantassi, Yonghoon Choi

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Deep reinforcement learning approach for real-time airport gate assignment by Haonan Li, Xu Wu, Marta Ribeiro, Bruno Santos, Pan Zheng

“…We bridge this gap by looking at gate assignments as a dynamic decision-making process. …”
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BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention by Watshara Shoombuatong, Pakpoom Mookdarsanit, Nalini Schaduangrat, Lawankorn Mookdarsanit

“…Therefore, this study proposes an innovative deep learning-based hybrid framework (termed BGATT-GR) that leverages a data augmentation method, a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (ATT) to attain more accurate identification of GR antagonists. …”
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Bidirectional f-Divergence-Based Deep Generative Method for Imputing Missing Values in Time-Series Data by Wen-Shan Liu, Tong Si, Aldas Kriauciunas, Marcus Snell, Haijun Gong

“…The imputation process is achieved by training two neural networks, implemented using bidirectional modified gated recurrent units, with f-divergence serving as the objective function to guide optimization. …”
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Stroke risk prediction: a deep learning approach for identifying high-risk patients by Afeez A. Soladoye, Kazeem M. Olagunju, Sunday A. Ajagbe, Ibrahim A. Adeyanju, Precious I. Ogie, Pragasen Mudali

“…This study developed a stroke prediction system with a modified Gated Recurrent Unit (GRU), a structured stroke dataset was gotten from Kaggle, which went through different preprocessing techniques like label Encoder, Normalization with MinMax, dropping of irrelevant values. …”
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Soft Electronic Switches and Adaptive Logic Gates Based on Nanostructured Gold Networks by Giacomo Nadalini, Alexander Dallinger, Davide Sottocorno, Francesco Greco, Francesca Borghi, Paolo Milani

“…Abstract The advent of neuromorphic substrates is promoting the development of in materia autonomous and adaptive devices, employed as hardware solutions to reduce the current inefficiencies of traditional data processing techniques, in terms of energy requirements. …”
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Alternative Approach of Developing Optical Binary Adder Using Reversible Peres Gates by Dhoumendra Mandal, Sumana Mandal, Mrinal Kanti Mandal, Sisir Kumar Garai

“…The authors have also proposed a method of designing an optical reversible full adder, using two such Peres gates and subsequently a data recovery circuit which can recover the input data of the adder. …”
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Construction of a Web Game for the Teaching-Learning Process of Electronics during the COVID-19 Pandemic by Ricardo-Adán Salas-Rueda, Clara Alvarado-Zamorano, Jesús Ramírez-Ortega

“…The aim of this mixed research was the construction and usage analysis of the Digital Game for the teaching-learning process on Electronics (DGE) version 3.0 in the Combinational Circuits unit through data science. …”
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A Recommendation Algorithm Incorporating Adaptive Gating Mechanisms and Knowledge Graph Enhancements by Yi Yu, Teng Lv

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Recognition of Ground Clutter in Single-Polarization Radar Based on Gated Recurrent Unit by Jiaxin Wang, Haibo Zou, Landi Zhong, Zhiqun Hu

“…A new method is proposed for identifying ground clutter in single-polarization radar data based on the gated recurrent unit (GRU) neural network. …”
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3D segmentation of uterine fibroids based on deep supervision and an attention gate by ZhiWei Liu, ChengNv Sun, ChengWei Li, FaJin Lv, FaJin Lv

“…We introduce attention gates during the upsampling process to enhance focus on areas of interest. …”
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Downscaling and Gap-Filling GRACE-Based Terrestrial Water Storage Anomalies in the Qinghai–Tibet Plateau Using Deep Learning and Multi-Source Data by Jun Chen, Linsong Wang, Chao Chen, Zhenran Peng

“…While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). …”
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