• 제목/요약/키워드: OSCR

검색결과 2건 처리시간 0.016초

Coexistence of OSCR-Based IR-UWB System with IEEE 802.11a WLAN

  • Wu, Weiwei;Huang, Han;Yin, Huarin;Wang, Weidong;Wang, Dong-Jin
    • ETRI Journal
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    • 제28권1호
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    • pp.91-94
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    • 2006
  • Impulse radio (IR) is a competitive candidate for ultra-wideband (UWB) systems. In this letter, we evaluated the coexistence of an IR-UWB system based on an orthogonal sinusoidal correlation receiver (OSCR) with an IEEE 802.11a WLAN through a detailed simulation. The coexistence performance of the two systems is characterized in terms of the receiver's bit-error rates. Then, some approaches to interference mitigation are discussed.

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Improving the Recognition of Known and Unknown Plant Disease Classes Using Deep Learning

  • Yao Meng;Jaehwan Lee;Alvaro Fuentes;Mun Haeng Lee;Taehyun Kim;Sook Yoon;Dong Sun Park
    • 스마트미디어저널
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    • 제13권8호
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    • pp.16-25
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    • 2024
  • Recently, there has been a growing emphasis on identifying both known and unknown diseases in plant disease recognition. In this task, a model trained only on images of known classes is required to classify an input image into either one of the known classes or into an unknown class. Consequently, the capability to recognize unknown diseases is critical for model deployment. To enhance this capability, we are considering three factors. Firstly, we propose a new logits-based scoring function for unknown scores. Secondly, initial experiments indicate that a compact feature space is crucial for the effectiveness of logits-based methods, leading us to employ the AM-Softmax loss instead of Cross-entropy loss during training. Thirdly, drawing inspiration from the efficacy of transfer learning, we utilize a large plant-relevant dataset, PlantCLEF2022, for pre-training a model. The experimental results suggest that our method outperforms current algorithms. Specifically, our method achieved a performance of 97.90 CSA, 91.77 AUROC, and 90.63 OSCR with the ResNet50 model and a performance of 98.28 CSA, 92.05 AUROC, and 91.12 OSCR with the ConvNext base model. We believe that our study will contribute to the community.