Search Results - "Luo, Aiwen"

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  1. 1

    LMFFNet: A Well-Balanced Lightweight Network for Fast and Accurate Semantic Segmentation by Shi, Min, Shen, Jialin, Yi, Qingming, Weng, Jian, Huang, Zunkai, Luo, Aiwen, Zhou, Yicong

    “…Real-time semantic segmentation is widely used in autonomous driving and robotics. Most previous networks achieved great accuracy based on a complicated model…”
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    Journal Article
  2. 2

    ELANet: Effective Lightweight Attention-Guided Network for Real-Time Semantic Segmentation by Yi, Qingming, Dai, Guoshuai, Shi, Min, Huang, Zunkai, Luo, Aiwen

    Published in Neural processing letters (01-10-2023)
    “…Deep neural networks have greatly facilitated the applications of semantic segmentation. However, most of the existing neural networks bring massive…”
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    Journal Article
  3. 3

    An efficient adaptive h‐refinement for the RWG‐based electric field integral equation applied to antennas with waveport by Ding, Ning, Yin, Lei, Zhao, Xunwang, Lin, ZhongChao, Zhang, Yu, Luo, Aiwen, Dang, Xiaojie

    Published in IET microwaves, antennas & propagation (01-03-2024)
    “…An approximation scheme that combines approximate solutions with error estimators to accelerate the adaptive h‐refinement of the RWG‐based method of moments…”
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    Journal Article
  4. 4

    A Hardware Architecture for Cell-Based Feature-Extraction and Classification Using Dual-Feature Space by An, Fengwei, Zhang, Xiangyu, Luo, Aiwen, Chen, Lei, Mattausch, Hans Jurgen

    “…Many computer-vision and machine-learning applications in robotics, mobile, wearable devices, and automotive domains are constrained by their real-time…”
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    Journal Article
  5. 5

    A Hardware-Efficient Recognition Accelerator Using Haar-Like Feature and SVM Classifier by Luo, Aiwen, An, Fengwei, Zhang, Xiangyu, Mattausch, Hans Jurgen

    Published in IEEE access (2019)
    “…Significantly improved performance of the various learning algorithms has revived the interest in computer vision for recognition applications during the…”
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    Journal Article
  6. 6

    Energy Efficiency of Force-Sensor-Controlled Humanoid-Robot Walking on Indoor Surfaces by Bhattacharya, Sandip, Dutta, Sunandan, Luo, Aiwen, Miura-Mattausch, Mitiko, Ochi, Yoshihiro, Mattausch, Hans Jurgen

    Published in IEEE access (2020)
    “…We report an energy-efficiency analysis for the walking pattern of a humanoid robot on different indoor surfaces with different walking speeds. The walking…”
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    Journal Article
  7. 7

    Force-Sensor-Based Surface Recognition With Surface-Property-Dependent Walking-Speed Adjustment of Humanoid Robot by Bhattacharya, Sandip, Luo, Aiwen, Dutta, Sunandan, Miura-Mattausch, Mitiko, Mattausch, Hans Jurgen

    Published in IEEE access (2020)
    “…We report the development of a biped-robot system with real-time surface recognition and walking-speed adjustment to control the robot motion during walking on…”
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    Journal Article
  8. 8

    Markov Chains Based Dynamic Bandwidth Allocation in DiffServ Network by Geng, Xianmin, Luo, Aiwen, Sun, Zhijun, Cheng, Yu

    Published in IEEE communications letters (01-10-2012)
    “…This letter proposes a Markov chain based model for dynamic bandwidth allocation in DiffServ networks. At a time-slot, the proposed Markov chain is used to…”
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    Journal Article
  9. 9

    Surface-Property Recognition With Force Sensors for Stable Walking of Humanoid Robot by Bhattacharya, Sandip, Luo, Aiwen, Maiti, Tapas Kumar, Dutta, Sunandan, Ochi, Yoshihiro, Miura-Mattausch, Mitiko, Jurgen Mattausch, Hans

    Published in IEEE access (2019)
    “…In this paper, we propose a surface-identification system for stable humanoid-robot walking on various types of surfaces using force sensors mounted under the…”
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    Journal Article
  10. 10

    A hardware-oriented histogram of oriented gradients algorithm and its VLSI implementation by Zhang, Xiangyu, An, Fengwei, Nakashima, Ikki, Luo, Aiwen, Chen, Lei, Ishii, Idaku, Mattausch, Hans Jürgen

    Published in Japanese Journal of Applied Physics (01-04-2017)
    “…A challenging and important issue for object recognition is feature extraction on embedded systems. We report a hardware implementation of the histogram of…”
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    Journal Article
  11. 11

    Rapid and Ultra-lightweight Semantic Segmentation in Urban Traffic Scene by SHI Min, SHEN Jialin, YI Qingming, LUO Aiwen

    Published in Jisuanji kexue yu tansuo (01-10-2022)
    “…Recently, with the rapid development of automatic driving, more and more researchers begin to explore the lightweight of image semantic segmentation network…”
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    Journal Article
  12. 12

    Lightweight Context-Aware Network Using Partial-Channel Transformation for Real-Time Semantic Segmentation by Shi, Min, Lin, Shaowen, Yi, Qingming, Weng, Jian, Luo, Aiwen, Zhou, Yicong

    “…Optimizing the computational efficiency of the artificial neural networks is crucial for resource-constrained platforms like autonomous driving systems. To…”
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    Journal Article
  13. 13

    Salient-Boundary-Guided Pseudo-Pixel Supervision for Weakly-Supervised Semantic Segmentation by Shi, Min, Deng, Weizhao, Yi, Qingming, Liu, Weiping, Luo, Aiwen

    Published in IEEE signal processing letters (2024)
    “…This letter presents an innovative approach for generating pixel-wise pseudo masks as supervision for image-level Weakly Supervised Semantic Segmentation…”
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    Journal Article
  14. 14

    AFANet: A Multibackbone Compatible Feature Fusion Framework for Effective Remote Sensing Object Detection by Yi, Qingming, Zheng, Mingfeng, Shi, Min, Weng, Jian, Luo, Aiwen

    “…Remote sensing object detection (RSOD) using convolutional neural networks (CNNs) continues to pose challenges in achieving high detection accuracy due to the…”
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    Journal Article
  15. 15

    A joint matrix minimization approach for multi-image face recognition by Wang, Liping, Luo, Aiwen

    Published in Science China. Mathematics (01-07-2018)
    “…The Schatten p -quasi-norm regularized minimization problem has attracted extensive attention in machine learning, image recognition, signal reconstruction,…”
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    Journal Article
  16. 16

    Real-Time Surface Identification System for Variable Walking Speeds of Biped Robots by Luo, Aiwen, Bhattacharya, Sandip, Miura-Mattausch, Mitiko, Zhou, Yicong, Mattausch, Hans J.

    Published in IEEE embedded systems letters (01-06-2024)
    “…Identifying the underlying surface at varying speeds during robotic locomotion is important for safe and efficient robot navigation. This work aims to enhance…”
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    Journal Article
  17. 17

    Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot by Luo, Aiwen, Bhattacharya, Sandip, Dutta, Sunandan, Ochi, Yoshihiro, Miura-Mattausch, Mitiko, Weng, Jian, Zhou, Yicong, Mattausch, Hans J.

    Published in IEEE sensors journal (15-04-2021)
    “…Real-time surface recognition has become a critical factor for ensuring safe walking of intelligent biped robots in a complex human living environment. This…”
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    Journal Article
  18. 18

    Resource-Efficient Object-Recognition Coprocessor With Parallel Processing of Multiple Scan Windows in 65-nm CMOS by Luo, Aiwen, An, Fengwei, Zhang, Xiangyu, Chen, Lei, Mattausch, Hans Jurgen

    “…Object recognition offers a more general implementation for vision-based applications. This paper reports a resource-efficient recognition coprocessor with…”
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    Journal Article
  19. 19

    Pixel-based pipeline hardware architecture for high-performance Haar-like feature extraction by Fujita, Yuki, Fengwei An, Aiwen Luo, Xiangyu Zhang, Lei Chen, Mattausch, Hans Jurgen

    “…Feature extraction, which is one of the basic tasks for pattern recognition, has often high computational cost and large memory usage. In this work, we propose…”
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    Conference Proceeding
  20. 20

    Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection by Luo, Aiwen, An, Fengwei, Zhang, Xiangyu, Chen, Lei, Huang, Zunkai, Mattausch, Hans Jürgen

    Published in Japanese Journal of Applied Physics (01-04-2018)
    “…Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection…”
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    Journal Article