Plasmonic sensor for rapid detection of water adulteration in honey and quantitative measurement of lactose concentration in solution

Haque, Moez; Rahad, Rummanur; Rakib, A.K.M.; Sharar, Shadman Shahriar; Sagor, Rakibul Hasan

doi:10.1016/j.rinp.2023.106733

Cited by 25 publications

(1 citation statement)

References 81 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DL learns rules for inputs and outputs from large amounts of data, enabling the construction of non-linear models for various applications. Deep learning optimization methods can be applied in the fields of metasurface device design, including sensor [22][23][24][25], demultiplexer [26,27], coupler [28,29], inferometer [30], etc, to improve their design efficiency.The use of neural networks to implement data-driven models provides a new approach for the design of electromagnetic structures [31][32][33][34][35][36], such as EIT [37], broadband absorption [38] and perfect absorption [39]. Deep learning uses neural networks to learn patterns in data, and after training and optimizing on a dataset of metasurfaces, neural networks can effectively predict the best metasurface design.…”

Section: Introductionmentioning

confidence: 99%

Optimized design for absorption metasurface based on autoencoder (AE) and BiLSTM-Attention-FCN-Net

Zhu,

Du,

Dong

et al. 2024

Phys. Scr.

View full text Add to dashboard Cite

In order to speed up the process of optimizing design of metasurface absorbers, an improved design model for metasurface absorbers based on autoencoder (AE) and BiLSTM-Attention-FCN-Net (including bidirectional long-short-term memory network, attention mechanism, and fully-connection layer network) is proposed. The metasurface structural parameters can be input into the forward prediction network to predict the corresponding absorption spectra. Meantime, the metasurface structural parameters can be obtained by inputting the absorption spectra into the inverse prediction network. Specially, in the inverse prediction network, the bidirectional long-short-term memory (BiLSTM) network can effectively capture the context relationship between absorption spectral sequence data, and the attention mechanism can enhance the BiLSTM output sequence features, which highlight the critical feature information. After the training, the mean square error (MSE) value on the validation set of the reverse prediction network converges to 0.0046, R2 reaches 0.975, and our network can accurately predict the metasurface structure parameters within 1.5s with a maximum error of 0.03mm. Moreover, this model can achieve the optimal design of multi-band metasurface absorbers, including the single-band, dual-band, and three-band absorptions. The proposed method can also be extended to other types of metasurface optimization design.

show abstract