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High-security image encryption by multiplexing phase encoding in domains of dual optical transforms

Li Zhihui, Gao Bin, Pan Xiaoou, Li Linlin, Wang Chenxuan, Zuo Weizhuo, Ji Yu, Liu Shutian, Liu Zhengjun

Optica Applicata

2023

Vol. 53, nr 3

447--466

A novel optical image encryption is proposed based on multiplexing of the random phase encoding with shift and rotation operations in domains of two transforms, extended fractional Fourier transform (eFrFT) and Fresnel transform. The original image is subjected to eFrFT with the action of the random phase mask. The mask is shifted and rotated to enhance the security of this encryption method. The image obtained from eFrFT is entered into Fresnel diffraction by the use of the phase mask to obtain the final encrypted image. We plan for the phase keys to be multiplexed in order to decrease the amount of keys that need to be stored in an application. Here, the displacement, rotation angle, and wavelength in this system can be used as additional keys to improve the security and reliability of the encryption system. Numerical experiments are conducted to verify the effectiveness and security of the method. The findings demonstrate that the keys are sufficiently sensitive for high security.

Generative adversarial network and convolutional neural network-based EEG imbalanced classification model for seizure detection

Gao Bin, Zhou Jiazheng, Yang Yuying, Chi Jinxin, Yuan Qi

Biocybernetics and Biomedical Engineering

2022

Vol. 42, no. 1

1--15

Automatic seizure detection technology is of great significance to reduce workloads of neurologists for epilepsy diagnosis and treatments. Imbalanced classification is a challenge in seizure detection from long-term continuous EEG recordings, as the durations of the seizure events are much shorter than the non-seizure periods. An imbalanced deep learning model is proposed in this paper to improve the performance of seizure detection. To modify imbalanced EEG data distribution, a generative adversarial network (GAN) that is a strong candidate for data enhancement is built to produce the seizure-period EEG data used for forming a more balanced training set. Next, a pyramidal one-dimensional convolutional neural network (1DCNN) is designed to deal with 1D EEG signals and trained on the augmented training set that consists of both original and generated EEG data. Compared to the conventional 2DCNNs, the deep architecture of the 1DCNN reduces the training parameters so as to greatly increase the training speed. The proposed method is evaluated on three publicly available EEG databases. After data augmentation by the GAN, the designed 1DCNN shows much better classification for seizure detection, achieving competitive results over the three EEG databases, which demonstrates the generalizability of this method across different databases. Comparison with other published methods indicates its enhanced detection performance for imbalanced EEG data.

Multiphysics coupling study on the effect of blood flow pulsation in patients with pulsatile tinnitus

Mu Zhenxia, Sun Yufeng, Li Xiaoshuai, Qiu Xiaoyu, Gao Bin, Liu Youjun, Zhao Pengfei, Wang Zhenchang

Biocybernetics and Biomedical Engineering

2021

Vol. 41, no. 3

1197--1207

This work aimed to investigate the effect of blood flow pulsation in patients with pulsatile tinnitus (PT). Nine blood flow patterns with different pulsations were designed on the basis of the blood flow data of a patient with PT (control group; cases with blood flow pulsation indexes that had decreased by 20%, 40%, 60%, and 80%; and cases with blood flow pulsation indexes that had increased by 20%, 40%, 60%, and 80%). The transient-state multiphysics coupling method was used to clarify the biomechanical and acoustic changes that had occurred under different blood flow pulsations. Results showed that blood flow impacted directly the vessel wall in the sigmoid sinus wall dehiscence (SSWD) area. The impact velocity of the blood flow slowed with the decrement in blood flow pulsation index but increased with the increase in blood flow pulsation index. With the reduction in blood flow pulsation index, the average pressure of the SSWD area decreased by 1.84, 3.93, 6.23, and 8.31 Pa; the average displacement of the vessel wall in the SSWD area decreased by 0.11, 0.23, 0.36, and 0.48 mm; and the average sound pressure level at the tympanum decreased by 2.2, 4.16, 7.14, and 12.97 dB. By contrast, with the increase in blood flow pulsation index, the average pressure increased by 2.46, 4.21, 6.28, and 8.56; the average displacement increased by 0.14, 0.2, 0.36, and 0.49 mm; and the average sound pressure level increased by 1.32, 2.87, 4.15, and 5.19 dB. Statistical analysis revealed that the pathological induction of PT had a strong quadratic correlation with the pulsation of blood flow. The degree of PT could be attenuated by reducing blood flow pulsation. Such an approach would aid clinical treatment. The conclusion obtained in this work would present a theoretical basis for the treatment of patients with PT.