International Journal of Optics

A new asymmetric optical double image encryption algorithm is proposed, which combines phase truncation and singular value decomposition. The plain text is encrypted with two-stage phase keys to obtain a uniformly distributed cipher text and two new decryption keys. These keys are generated during the encryption process and are different from encryption keys. It realizes asymmetric encryption and improves the security of the system. The unscrambling keys in the encryption operation are mainly related to plain text. At the same time, the system is more resistant to selective plain text attacks; it also improves the sensitivity of decryption keys. With the application of phase truncation, the key space expanded and the security of the cryptographic system is enhanced. The efficacy of the system is calculated by evaluating the estimated error between the input and retrieved images. The proposed technique provides innumerable security keys and is robust against various potential attacks. Numerical simulations verify the effectiveness and security of the proposed technique.

Frequency-modulated continuous-wave (FMCW) interference, as a new technology of laser interferometry, has the advantages of length traceability, large range, high accuracy, simple structure, and optical fiber transmission. Based on the formula of FMCW laser interference displacement, a zero-crossing phase detection algorithm is proposed, which can accurately calculate the initial phase of a cosine signal in a modulation period, and it is successfully applied to the contact laser interference displacement sensor. The experimental results show that the FMCW technology based on the zero-crossing phase detection algorithm can achieve the technical specifications of the contact displacement sensor with a measurement range greater than 15 mm and the standard deviation is less than 0.01 μm. The conversion of noncontact measurement to contact measurement can realize the direct measurement of workpieces with complex surface conditions on the production line, breaking through the limitation of optical measurement and expanding the application of optical fiber interferometry.

We demonstrate an efficient scheme to accelerate the self-similar pulse evolution and reduce the intensity noise of a free-running femtosecond fiber amplifier based on the pump wavelength optimization. Experiments and simulations indicate the enhanced tolerances of the pulse self-similar amplification to the seed signal power and pump wavelength fluctuations, with the optimum 915 nm pump wavelength. ∼20% increase in the compressed pulse quality and ∼31% reduction in the amplifier root-mean-square (RMS) relative intensity noise (RIN) (1.5 kHz to 5 MHz) have been observed, even with a more than 4 times higher pump laser diode (LD) RIN than the case of 976 nm. ∼50 fs transform-limited pulses are generated with the ∼0.03% amplifier RMS RIN. The proposed scheme can lower the requirements of low-noise self-similar femtosecond fiber amplifiers on the power stability of the seed oscillator and the thermal control of the pump LD, thus denoting potentials for the various satellite-based high-precision applications of femtosecond laser in space.

A photonic artificial intelligence chip is based on an optical neural network (ONN), low power consumption, low delay, and strong antiinterference ability. The all-optical diffractive deep neural network has recently demonstrated its inference capabilities on the image classification task. However, the size of the physical model does not have miniaturization and integration, and the optical nonlinearity is not incorporated into the diffraction neural network. By introducing the nonlinear characteristics of the network, complex tasks can be completed with high accuracy. In this study, a nonlinear all-optical diffraction deep neural network (N-D²NN) model based on 10.6 μm wavelength is constructed by combining the ONN and complex-valued neural networks with the nonlinear activation function introduced into the structure. To be specific, the improved activation function of the rectified linear unit (ReLU), i.e., Leaky-ReLU, parametric ReLU (PReLU), and randomized ReLU (RReLU), is selected as the activation function of the N-D²NN model. Through numerical simulation, it is proved that the N-D²NN model based on 10.6 μm wavelength has excellent representation ability, which enables them to perform classification learning tasks of the MNIST handwritten digital dataset and Fashion-MNIST dataset well, respectively. The results show that the N-D²NN model with the RReLU activation function has the highest classification accuracy of 97.86% and 89.28%, respectively. These results provide a theoretical basis for the preparation of miniaturized and integrated N-D²NN model photonic artificial intelligence chips.

To solve the coherent noise problem of an interference image, the method of a rotating diffuser was adopted to change the coherence of a beam to reduce the noise of the interference system. The relationship between the speed of the diffuser and the signal-to-noise ratio (SNR) of the fringe contrast system was simulated to obtain the diffuser control parameters needed for the best interference fringe state. The fringe contrast of each image and the SNR of the system were analyzed. The results showed that the increased speed of the diffuser reduced the contrast of the interference image to a certain extent, but the increased speed also effectively improved the SNR and facilitated the subsequent interference image processing. Due to the coherent noise in the interferometric system, the method of the rotated diffuser reduced the coherence of the light beam to suppress the noise of the interference image. By analyzing the coherent noise reduction characteristics of the rotated diffuser with different surface roughnesses, the relationship between the surface roughness and the noise contrast for different rotation speeds was simulated, and the effective roughness range with the noise reduction effect was selected. A noise reduction system was built based on Fizeau interference, and the noise contrast of the interference image was collected and calculated. The effective range of σ_h/λ was 0.2–0.5 when the rotation speed was 10 r/s, while the effective range of σ_h/λ was 0.4–0.6 when the rotation speed was 100 r/s. The experimental results showed that the surface roughness and wavelength ratio σ_h/λ of the rotated diffuser increased when the noise contrast tended toward 1, but the effective range of the surface roughness decreased with the increase of the rotational speed of the diffuser.

A novel PCF-based sensor has been presented in this paper to sense different chemicals and biocomponents. The proposed biochemical sensor (BCS) comprises a simple arrangement of rectangular holes. The competence of this BCS in detecting analytes is evaluated employing the full-vector finite element method (FEM). Performance metrics prove the competence of this BCS in sensing various analytes that have a refractive index in the range of 1.33–1.48. The proposed BCS shows ultralower values for both the bulk absorption and confinement loss. This BCS attains a maximum of about 95.82% relative sensitivity at 2.5 THz. Also, the dispersion for this sensor is only 0.12 ± 0.011 ps/THz/cm at the same point of interest. These results prove that the proposed BCS will play a key role in sensing toxic chemicals, illegal drugs, biocomponents, etc. Besides, the simple rectangle-based PCF structure ensures the feasibility of fabrication by practicing the existing fabrication strategies.