Deep Learning for Optical Sensor Applications: An Insight Review

Al‐Ashwal, Nagi H.; Soufy, Khaled A. M. Al; Hamza, Mohga E.; Swillam, Mohamed A.

doi:10.20944/preprints202306.0860.v1

Cited by 5 publications

(1 citation statement)

References 122 publications

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“…Application field: Optics 334 Comin and Hartschuh [29] 2018 GA and ANN optimization framework 335 Backer [98] 2019 ANN and Steepest-descent Inverse design 336 Liu, Zhang, Dan, et al [124] 2019 ANN, DecisionTree, ExtraTree, Inverse design KNN and Random forest 337 Mukherjee, Bhattacharjee, Halder, et al [126] [120] autoencoder and PCA Liu, Zhu, Lee, et al [3] 2020 ANN and Cooperative Inverse design convolution algorithm Moon, Choi, Lee, et al [121] 2020 ANN, t-Stochastic Neighbor Classification and clustering Embedding AND k-means Tao, Zhang, You, et al [176] 2020 GA and ANN Surrogate model Liu, Zhang, Dai, et al [509] 2020 GA, PSO, multi-traversal optimization framework direct-binary search and simulated annealing Du, You, Zhang, et al [125] 2021 ANN and Forest regression Surrogate model and Support vector regression Li, Chen, Xiong, et al [28] 2022 MLP,CNN,RNN Optimization Framework Li, Deng, Liu, et al [510] 2022 Autoencoder, GAN Optimization Framework Al-Ashwal, Al Soufy, Hamza, et al [511] 2023 DNN,CNN,GAN Optimization Framework End of Table 4…”

Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Machine intelligence continues to rise in popularity as an aid to the design and discovery of novel metamaterials. The properties of metamaterials are essentially controllable via their architectures and until recently, the design process has relied on a combination of trial-and-error and physics-based methods for optimization. These processes can be time-consuming and challenging, especially if the design space for metamaterial optimization is explored thoroughly. Artificial intelligence (AI) and machine learning (ML) can be used to overcome challenges like these as pre-processed massive metamaterial datasets can be used to very accurately train appropriate models. The models can be broad, describing properties, structure, and function at numerous levels of hierarchy, using relevant inputted knowledge. Here, we present a comprehensive review of the literature where state-of-the-art machine intelligence is used for the design, discovery and development of metamaterials. In this review, individual approaches are categorized based on methodology and application. We further present machine intelligence trends over a wide range of metamaterial design problems including: acoustics, photonics, plasmonics, mechanics, and more. Finally, we identify and discuss recent research directions and highlight current gaps in knowledge. KeywordsMetamaterials • Artificial intelligence • Machine learning • Neural Networks • Evolutionary algorithms • Surrogate modelling • Optimization 42 solely related to the properties of material constituents. 43 Properties can therefore be controlled and manipulated by 44 changing metamaterial 'unit cell' topology and while the 45 bulk properties of the constituent materials have influence, 46 these properties are often not the sole dominating variable 47 used in designing metamaterials. When designing with 48 respect to topology, the properties of metamaterials can 49 be customized within a very large design space, and this 50 is one of the reasons for the exponential growth in meta-51 materials R&D across the breadth of modern engineering.

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Section: Continuation Of Tablementioning

confidence: 99%

Machine Intelligence in Metamaterials Design

Cerniauskas,

Sadia,

Alam

2023

Preprint

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Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

Esmaeili,

Cassie,

Nguyen

et al. 2023

Bioengineering

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Nanomaterial-based aptasensors serve as useful instruments for detecting small biological entities. This work utilizes data gathered from three electrochemical aptamer-based sensors varying in receptors, analytes of interest, and lengths of signals. Our ultimate objective was the automatic detection and quantification of target analytes from a segment of the signal recorded by these sensors. Initially, we proposed a data augmentation method using conditional variational autoencoders to address data scarcity. Secondly, we employed recurrent-based networks for signal extrapolation, ensuring uniform signal lengths. In the third step, we developed seven deep learning classification models (GRU, unidirectional LSTM (ULSTM), bidirectional LSTM (BLSTM), ConvGRU, ConvULSTM, ConvBLSTM, and CNN) to identify and quantify specific analyte concentrations for six distinct classes, ranging from the absence of analyte to 10 μM. Finally, the second classification model was created to distinguish between abnormal and normal data segments, detect the presence or absence of analytes in the sample, and, if detected, identify the specific analyte and quantify its concentration. Evaluating the time series forecasting showed that the GRU-based network outperformed two other ULSTM and BLSTM networks. Regarding classification models, it turned out signal extrapolation was not effective in improving the classification performance. Comparing the role of the network architectures in classification performance, the result showed that hybrid networks, including both convolutional and recurrent layers and CNN networks, achieved 82% to 99% accuracy across all three datasets. Utilizing short-term Fourier transform (STFT) as the preprocessing technique improved the performance of all datasets with accuracies from 84% to 99%. These findings underscore the effectiveness of suitable data preprocessing methods in enhancing neural network performance, enabling automatic analyte identification and quantification from electrochemical aptasensor signals.

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Image-Range Stitching and Semantic-Based Crack Detection Methods for Tunnel Inspection Vehicles

Tian,

Li,

et al. 2023

Remote Sensing

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This study introduces two innovative methods in the research for use in vision−based tunnel inspection vehicles. First, the image−range stitching method is used to map the sequence images acquired by a camera onto a tunnel layout map. This method reduces the tunnel image−stitching problem to the appropriate parameters, thus solving the problem of mapping equations, ranging from camera pixels to the tunnel layout map. The parameters are obtained using a laser scanner. Secondly, traditional label−based deep learning solely perceives the consistency between pixels and semantically labeled samples, making it challenging to effectively address issues with uncertainty and multiplicity. Consequently, we introduce a method that employs a bidirectional heuristic search approach, utilizing randomly generated seed pixels as hints to locate targets that concurrently appear in both the image and the image semantic generation model. The results reveal the potential for cooperation between laser−scanning and camera−imaging technologies and point out a novel approach of crack detection that appears to be more focused on semantic understanding.

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Deep Learning for Optical Sensor Applications: An Insight Review

Cited by 5 publications

References 122 publications

Machine Intelligence in Metamaterials Design

Machine Intelligence in Metamaterials Design

Utilizing Deep Learning Algorithms for Signal Processing in Electrochemical Biosensors: From Data Augmentation to Detection and Quantification of Chemicals of Interest

Image-Range Stitching and Semantic-Based Crack Detection Methods for Tunnel Inspection Vehicles

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