Suchergebnisse - "parallel encoder"

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  1. 1

    Simultaneous multiclass retinal lesion segmentation using fully automated RILBP-YNet in diabetic retinopathy von Geetha Pavani, P., Biswal, B., Gandhi, Tapan Kumar

    ISSN: 1746-8094, 1746-8108
    Veröffentlicht: Elsevier Ltd 01.09.2023
    Veröffentlicht in Biomedical signal processing and control (01.09.2023)
    “… •The proposed model, RILBP-YNet (Residual Inception Local Binary Pattern - YNetwork) is implemented for segmenting multiple lesions like Microaneurysms, …”
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  2. 2

    Passive Non-Line-of-Sight Imaging with Parallel Encoder von Du, Xiaolong, Geng, Ruixu, Zhang, Jiarui, Chen, Yan, Hu, Yang

    ISSN: 2379-190X
    Veröffentlicht: IEEE 06.04.2025
    “… Passive non-line-of-sight (NLOS) imaging has developed rapidly in recent years. However, existing models generally suffer from low-quality reconstruction due …”
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  3. 3

    MCPT: Mixed Convolutional Parallel Transformer for Polarimetric SAR Image Classification von Wang, Wenke, Wang, Jianlong, Lu, Bibo, Liu, Boyuan, Zhang, Yake, Wang, Chunyang

    ISSN: 2072-4292, 2072-4292
    Veröffentlicht: Basel MDPI AG 05.06.2023
    Veröffentlicht in Remote sensing (Basel, Switzerland) (05.06.2023)
    “… Vision transformers (ViT) have the characteristics of massive training data and complex model, which cannot be directly applied to polarimetric synthetic …”
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    Journal Article
  4. 4

    DB-DCAFN: dual-branch deformable cross-attention fusion network for bacterial segmentation von Wang, Jingkun, Ma, Xinyu, Cao, Long, Leng, Yilin, Li, Zeyi, Cheng, Zihan, Cao, Yuzhu, Huang, Xiaoping, Zheng, Jian

    ISSN: 2524-4442, 2096-496X, 2524-4442
    Veröffentlicht: Singapore Springer Nature Singapore 04.07.2023
    Veröffentlicht in Visual computing for industry, biomedicine and art (04.07.2023)
    “… Sputum smear tests are critical for the diagnosis of respiratory diseases. Automatic segmentation of bacteria from sputum smear images is important for …”
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  5. 5

    Parallel Enhanced DETR: Improving Feature Utilization in Object Detection von Han, Ping, Shi, Jiangyu, Li, Longshuang, Zuo, Huahong

    ISSN: 2642-6471
    Veröffentlicht: IEEE 12.07.2025
    “… Deformable DETR(DEtection TRansformer) has improved object detection performance. However, it still suffers from insufficient utilization of features extracted …”
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  6. 6

    A Image Segmentation Network Based on Dual Parallel Encoders von Guo, Qianchen

    Veröffentlicht: IEEE 28.06.2025
    “… With the advancements in deep learning over recent years, deep learning-driven image processing has achieved remarkable breakthroughs in tasks such as semantic …”
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  7. 7

    A Lightweight CNN-Transformer Parallel Fusion Architecture for Accurate Remote Sensing Image Classification von Peng, Jianzhong, Lu, Huimin, Guo, Jianwei, Zhu, Bingxue

    Veröffentlicht: IEEE 25.04.2025
    “… Remote sensing image classification plays a significant role in urban planning, agricultural monitoring and other areas. However, achieving a lightweight and …”
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  8. 8

    DPE-Net: Dual-Parallel Encoder Based Network for Semantic Segmentation of Polyps von Manan, Malik Abdul, Jinchao, Feng, Ahmed, Shahzad, Raheem, Abdul

    ISSN: 2642-6471
    Veröffentlicht: IEEE 12.07.2024
    “… In medical imaging, efficient segmentation of colon polyps plays a pivotal role in minimally invasive solutions for colorectal cancer. This study introduces a …”
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  9. 9

    Parallel Transformer-CNN Model for Medical Image Segmentation von Zhou, Mingkun, Nie, Xueyun, Liu, Yuhang, Li, Doudou

    ISSN: 2159-1288
    Veröffentlicht: IEEE 12.04.2024
    “… In recent years, methods based on deep learning have made significant progress in the field of medical image segmentation, especially the method combining …”
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  10. 10

    A Low Complexity Parallel QC-LDPC Encoder von Yao, Xiaoxia, Li, Lintao, Liu, Jihong, Li, Qian

    Veröffentlicht: IEEE 23.05.2021
    “… In this paper, a low complexity parallel QC (quasi-cyclic)-LDPC (low-density parity-check) Codes encoding algorithm and encoder architecture is introduced. In …”
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  11. 11

    Designing fault-secure parallel encoders for systematic linear error correcting codes von Piestrak, S.J., Dandache, A., Monteiro, F.

    ISSN: 0018-9529, 1558-1721
    Veröffentlicht: New York IEEE 01.12.2003
    Veröffentlicht in IEEE transactions on reliability (01.12.2003)
    “… We consider the open problem of designing fault-secure parallel encoders for various systematic linear ECC. The main idea relies on generating not only the …”
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  12. 12

    10Gb/s orthogonally concatenated BCH encoder for fiber communications von Xinxin Liu, Qingsheng Hu

    Veröffentlicht: IEEE 01.12.2013
    “… This paper presents a 10Gb/s concatenated BCH encoder of super FEC. The concatenated BCH encoder module contains 32 BCH column encoder groups and 2 BCH row …”
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  13. 13

    A Quasi-Parallel Encoder of Quasi-Cyclic LDPC Codes in IEEE 802.16e von Zhuo Ma, Ying Li, Xinmei Wang

    ISBN: 142444909X, 9781424449095
    ISSN: 2160-1283
    Veröffentlicht: IEEE 01.12.2009
    “… We present a quasi-parallel LDPC encoder based on the quasi-cyclic characteristic of the parity check matrix of the LDPC code in IEEE 802.16e. A bidirectional …”
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  14. 14

    PESO: A Seq2Seq-Based Vessel Trajectory Prediction Method with Parallel Encoders and Ship-Oriented Decoder von Zhang, Yuanben, Han, Zhonghe, Zhou, Xue, Zhang, Lili, Wang, Lei, Zhen, Enqiang, Wang, Sijun, Zhao, Zhihao, Guo, Zhi

    ISSN: 2076-3417, 2076-3417
    Veröffentlicht: Basel MDPI AG 01.03.2023
    Veröffentlicht in Applied sciences (01.03.2023)
    “… Vessel trajectory prediction supports navigation services and collision detection. To maintain safety and efficiency in maritime transportation, vessel …”
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    Journal Article
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    Latent Feature Separation and Extraction with Multiple Parallel Encoders for Convolutional Autoencoder von Kim, Jaehyun, Kim, Myungjun, Shin, Hyunjung

    ISSN: 2375-9356
    Veröffentlicht: IEEE 01.01.2022
    “… Much of the real-world image data is unlabeled or mislabeled. Therefore, even if there is no label, if similar images can be grouped together with image data …”
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