Two-Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection

Mashta, Faten; Altabban, Wissam; Wainakh, Mohiedin

doi:10.4018/ijitn.2020100102

Cited by 2 publications

(2 citation statements)

References 13 publications

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“…Two-stage algorithm. The two-stage algorithms are widely used to solve image processing problems such as Image Super-Resolution [24], single-image reflection removal [25], segmentation task [26,27], intrusion detection [28], eigenvalues Detection [29], image restoration [30] and image denoising [31,32]. For example, Fan et al [24] achieved the optimal balance between accuracy and speed by employing a deep residual design with constrained depth and combining the two-stage structure with the lightweight two-layer PConv residual block design.…”

Section: Related Workmentioning

confidence: 99%

TTH-Net: Two-Stage Transformer–CNN Hybrid Network for Leaf Vein Segmentation

Song,

Yu,

Zhang

2023

Applied Sciences

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Leaf vein segmentation is crucial in species classification and smart agriculture. The existing methods combine manual features and machine learning techniques to segment coarse leaf veins. However, the extraction of the intricate patterns is time consuming. To address the issues, we propose a coarse-to-fine two-stage hybrid network termed TTH-Net, which combines a transformer and CNN to accurately extract veins. Specifically, the proposed TTH-Net consists of two stages and a cross-stage semantic enhancement module. The first stage utilizes the Vision Transformer (base version) to extract globally high-level feature representations. Based on these features, the second stage identifies fine-grained vein features via CNN. To enhance the interaction between the two stages, a cross-stage semantic enhancement module is designed to integrate the strengths of the transformer and CNN, which also improves the segmentation accuracy of the decoder. Extensive experiments on the public dataset LVN are conducted, and the results prove that TTH-Net has significant advantages over other methods in leaf vein segmentation.

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Section: Related Workmentioning

confidence: 99%

TTH-Net: Two-Stage Transformer–CNN Hybrid Network for Leaf Vein Segmentation

Song,

Yu,

Zhang

2023

Applied Sciences

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“…She received the B.Sc degree in telecommunication engineering from Higher Institute for AppliedSciences and Technology (HIAST), in 2007. In 2014, 2020 received the M.Sc. and PhD degree from HIAST, respectively.…”

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confidence: 99%

An Integrated Parallel Multistage Spectrum Sensing for Cognitive Radio

Mashta

Wainakh

Altabban

2021

International Journal of Embedded and Real-Time Communication Systems

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Spectrum sensing for cognitive radio requires speed and good detection performance at very low SNR ratios. There is no single-stage spectrum sensing technique that is perfect enough to be implemented in practical cognitive radio. In this paper, the authors propose a new parallel fully blind multistage detector. They assume the appropriate stage based on the estimated SNR values that are achieved from the SNR estimator. Energy detection is used in first stage for its simplicity and sensing accuracy at high SNR. For low SNRs, they adopt the maximum eigenvalues detector with different smoothing factor in higher stages. The sensing accuracy for the maximum eigenvalue detector technique improves with higher value of the smoothing factor. However, the computational complexity will increase significantly. They analyze the performance of two cases of the proposed detector: two-stage and three-stage schemes. The simulation results show that the proposed detector improves spectrum sensing in terms of accuracy and speed.

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Two-Stage Spectrum Sensing for Cognitive Radio Using Eigenvalues Detection

Cited by 2 publications

References 13 publications

TTH-Net: Two-Stage Transformer–CNN Hybrid Network for Leaf Vein Segmentation

TTH-Net: Two-Stage Transformer–CNN Hybrid Network for Leaf Vein Segmentation

An Integrated Parallel Multistage Spectrum Sensing for Cognitive Radio

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