Deep learning channel estimation algorithm for ultra-massive terahertz systems by YU Shujuan, ZHAO Yang, WEI Yuyao, ZHANG Yun, GAO Gui, ZHAO Shengmei

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Improving the performance of the MIMO-OFDM-NOMA System using a V-BLAST ZF approach based on deep CNN in IoT by Mehdi Izadi, Gholam-Reza Mohammad-Khani, Gholamreza Farahani

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Parameter-efficient adaptation with multi-channel adversarial training for far-field speech recognition by Tong Niu, Yaqi Chen, Dan Qu, Hengbo Hu, ChengRan Liu

“…To overcome the channel interference problem of FSR, we propose a multi-channel adversarial training (MCAT) approach, which incorporates a channel recognizer in an adversarial manner to guide the model in learning channel-invariant speech representations. Experimental results on multiple datasets demonstrate speech prefix tuning surpasses LoRA by a degradation relative WER of 5.76% with fewer parameters. …”
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Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication by Xin Liu, Shanghong Zhao, Yanxia Liang, Shahid Karim

“…Initially, we develop a U-Net-based signal processing algorithm that effectively reduces acoustic noise in UAV communication channels and enables real-time, accurate perception of channel states by automatically learning channel features. Subsequently, we enhance fuzzy reinforcement Q-learning by incorporating a fuzzy neural network to approximate the Q-values and integrating this approach with the allocation strategy of wireless sensing nodes. …”
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Classification of microscopic images of rock thin sections based on TLCA-ResNet34 by Zhenyu Zhao, Shucheng Tan, Hui Chen, Pengwei Wang, Qinghua Zhang, Haoyu Wei, Zhenlin Zhang

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Patch-Wise-Based Self-Supervised Learning for Anomaly Detection on Multivariate Time Series Data by Seungmin Oh, Le Hoang Anh, Dang Thanh Vu, Gwang Hyun Yu, Minsoo Hahn, Jinsul Kim

“…The proposed approach comprises four key components: (i) maintaining continuous features through patching, (ii) incorporating various temporal information by learning channel dependencies and adding relative positional bias, (iii) achieving feature representation learning through self-supervised learning, and (iv) supervised learning based on anomaly augmentation for downstream tasks. …”
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Classification from Sky: A Robust Remote Sensing Time Series Image Classification Using Spatial Encoder and Multi-Fast Channel Attention by Kwabena Sarpong, Jehoiada Kofi Jackson, Derrick Effah, Daniel Addo, Sophyani Banaamwini Yussif, Mohammad Awrangjeb, Rutherford Agbeshi Patamia, Juliana Mantebea Danso, Zhiguang Qin

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An exploration study to detect important factors influencing on learning oriented customers by Naser Azad, Sima Zomorrodian

“…The recent advances in technology have increased learning channels in industry helping organizations remove the middle level management. …”
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A fiber channel modeling method based on complex neural networks by Haifeng Yang, Yongjun Wang, Chao Li, Lu Han, Qi Zhang, Xiangjun Xin

“…To address this limitation, we propose a complex-valued conditional generative adversarial network (C-CGAN) in this paper to comprehensively learn channel features. We describe the architecture and parameters of the C-CGAN and employ complex-valued windowed construction for input data. …”
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A hybrid local-global neural network for visual classification using raw EEG signals by Shuning Xue, Bu Jin, Jie Jiang, Longteng Guo, Jing Liu

“…Specifically, we first propose a reweight module to learn channel weights adaptively. Then, a local feature extraction module is designed to capture basic EEG features. …”
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Can Digital Human Capital Promote Farmers’ Willingness to Engage in Green Production? Exploring the Role of Online Learning and Social Networks by Siyu Gong, Ludi Jiang, Zhigang Yu

“…Therefore, continuously enhancing digital human capital, emphasizing diverse learning channels, and leveraging ’acquaintance networks’ to encourage farmers to improve their awareness of green production through digital platforms are critical for promoting sustainable green agriculture in developing countries.…”
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SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification by Shandong Yuan, Zili Zou, Han Zhou, Yun Ren, Jianping Wu, Kai Yan

“…Specifically, SECNN aggregates multi-scale convolutional features as distinct semantic channels and employs Squeeze-and-Excitation (SE) blocks to learn channel-wise attention weights, thereby enabling dynamic feature recalibration based on inter-channel dependencies. …”
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Secret Key Generation Driven by Attention-Based Convolutional Autoencoder and Quantile Quantization for IoT Security in 5G and Beyond by Anas Alashqar, Ehsan Olyaei Torshizi, Raed Mesleh, Werner Henkel

“…Specifically, a two-dimensional convolutional neural network–based autoencoder (2D CNN–AE) with a spatial self-attention (SSA) mechanism is developed to efficiently extract and learn channel reciprocity features in time-division duplex (TDD)-based fifth-generation (5G) networks. …”
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Researching The Relation Between The Communicative Approach And Types Of Syllabi To Language Teaching by Fatima Bendoukha

“…The majority of the scientific tools and methods focus on the significance of the delivered learning channels and the ways of ensuring language acquisition. …”
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Digital Literacy and the Livelihood Resilience of Livestock Farmers: Empirical Evidence from the Old Revolutionary Base Areas in Northwest China by Xuefeng Ma, Liang Cheng, Yahui Li, Minjuan Zhao

“…In areas inhabited by ethnic minorities and among moderate-income groups, the role of digital literacy on the livelihood resilience of livestock farmers is more significant. (2) The improvement of digital literacy has a significant positive impact on livelihood resilience through three different pathways: the “differential mode of association”, learning channels, and types of income. (3) Digital literacy has led to the psychological aspects of rural hollowing-out problems among livestock farmers, which is particularly evident in families with only one type of caregiving burden (either only left-behind elderly people or only left-behind children). …”
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SCCA-YOLO: Spatial Channel Fusion and Context-Aware YOLO for Lunar Crater Detection by Jiahao Tang, Boyuan Gu, Tianyou Li, Ying-Bo Lu

“…In addition, the improved Channel Attention Concatenation (CAC) strategy adaptively learns channel-wise importance weights during feature concatenation, further optimizing multi-scale semantic feature fusion and enhancing the model’s sensitivity to critical crater features. …”
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