Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images ...Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.展开更多
In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing...In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.展开更多
Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement ...Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.展开更多
文摘Early diagnosis and detection are important tasks in controlling the spread of COVID-19.A number of Deep Learning techniques has been established by researchers to detect the presence of COVID-19 using CT scan images and X-rays.However,these methods suffer from biased results and inaccurate detection of the disease.So,the current research article developed Oppositional-based Chimp Optimization Algorithm and Deep Dense Convolutional Neural Network(OCOA-DDCNN)for COVID-19 prediction using CT images in IoT environment.The proposed methodology works on the basis of two stages such as pre-processing and prediction.Initially,CT scan images generated from prospective COVID-19 are collected from open-source system using IoT devices.The collected images are then preprocessed using Gaussian filter.Gaussian filter can be utilized in the removal of unwanted noise from the collected CT scan images.Afterwards,the preprocessed images are sent to prediction phase.In this phase,Deep Dense Convolutional Neural Network(DDCNN)is applied upon the pre-processed images.The proposed classifier is optimally designed with the consideration of Oppositional-basedChimp Optimization Algorithm(OCOA).This algorithm is utilized in the selection of optimal parameters for the proposed classifier.Finally,the proposed technique is used in the prediction of COVID-19 and classify the results as either COVID-19 or non-COVID-19.The projected method was implemented in MATLAB and the performances were evaluated through statistical measurements.The proposed method was contrasted with conventional techniques such as Convolutional Neural Network-Firefly Algorithm(CNN-FA),Emperor Penguin Optimization(CNN-EPO)respectively.The results established the supremacy of the proposed model.
基金National Natural Science Foundation of China(62275277,U2001601)Guangdong Project(2021QN02X055)+3 种基金Guangdong ST Programme(2024B0101030001)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(23lgbj007)Science and Technology Planning Project of Guangzhou(2024A04J9891)Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(SML2023SP231)。
文摘In this work,we demonstrate aπ-phase-shifted tilted fiber Bragg grating(π-PSTFBG)-based sensor for measuring the refractive index(RI)of NaCl solutions,achieving a real-time and online measurement system by employing a densely connected convolutional neural network(D-CNN)model to demodulate the full spectrum.The proposedπ-PSTFBG sensor is prepared by using the advanced fiber grating inscription system based on a two-beam interferometry method,which could introduce deeper features of dip-splitting for all the lossy dips in the spectrum,giving the possibility of fully measuring the change of RI.This enhanced feature gives relatively higher prediction accuracy(R^(2) of 99.67%)using the well-trained D-CNN model compared with the results achieved by pure TFBG or that with a gold coating.As a further demonstration from a practical view,a prototype integrated with the proposed D-CNN algorithm is developed to conduct RI measurement of NaCl solutions in real time using aπ-PSTFBG-based RI sensor.The results show that the proposed real-time demodulation system is capable of measuring RI with an average error of 1.6×10^(-4)RIU in a short response time of<1 s.The demonstrated spectral demodulation approach powered by deep learning shows great potential in real-time analysis for chemical solutions and point-of-care medical testing based on RI changes,especially for the portable requirements.
文摘Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images.This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks(CNN)for compression and reconstruction of the image.The proposed method does color and contrast correction for image enhancement.The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform(DWT).The decompression is done by using Inverse DWT.Further,the sub-pixel up-sampled image is de-blurred using a three-layer CNN.Residual Dense CNN(RD-CNN)is used to improve the quality of the reconstructed image after deblurring.The quality of the reconstructed images is measured using Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index Metric(SSIM).The proposed model provides better image enhancement,compression,and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN(SRCNN)respectively.
