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A correlation optical time domain reflectometry (COTDR) based on broadband random optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. Different from the conventional COTDR that utilizes an external feedback semiconductor laser as the chaotic source, the proposed scheme employs a random optoelectronic oscillator as the random signal source. With the assistance of randomly distributed feedback in the OEO caused by the random fiber Bragg grating, the generated signals have random and broadband characteristics, whose frequencies are not restricted by a fixed cavity length in the OEO. Due to the avoidance of relaxation oscillations in semiconductor chaotic laser, the generated random signal has a wide and flat spectrum as well as excellent autocorrelation features. The experimental results illustrate that the proposed COTDR can achieve a range-independent spatial resolution of about 10 mm and its dynamic range is at least 78.47 km. • A correlation optical time domain reflectometry based on broadband random optoelectronic oscillator is proposed and experimentally demonstrated. • A random optoelectronic oscillator is employed as the random signal source with a wide and flat spectrum alongside excellent autocorrelation features. • The proposed COTDR can achieve a range-independent spatial resolution of about 10 mm and its dynamic range is at least 78.47 km. [ABSTRACT FROM AUTHOR]

• An actively mode-locked OEO with intensity feed-forward path is proposed to generate MFCs with high power flatness and wide spectrum range. • An intensity feed-forward path with the same delay as the main loop is introduced to realize signal power equalization. • Comb lines within ± 10 dB power variations increase from 15 to 78 and power difference decreases from 8.2 dB to 1.5 dB due to the use of intensity feed-forward technology. An actively mode-locked optoelectronic oscillator (OEO) with intensity feed-forward path is proposed and experimentally demonstrated to generate microwave frequency combs (MFCs) with high power flatness and wide spectrum range. An intensity feed-forward path with the same time delay as the delay of the main loop is inserted into an actively mode-locked OEO, which optimizes the power flatness and spectrum range of the generated MFCs signal. Experimental results show that comb lines within ± 10 dB power variations increase from 15 to 78 and power difference decreases from 8.2 dB to 1.5 dB when the intensity feed-forward path is working in the loop. Moreover, the performance of single sideband (SSB) phase noise with different lengths of SMF inserted in the proposed scheme are also investigated. [ABSTRACT FROM AUTHOR]

In this paper, a novel all-optical architecture for multi-order nonlinear distortions (NDs) suppression in the intensity-modulation direct-detection (IM-DD) analog photonic link (APL) is proposed. A systematic analysis of the relationship between the NDs and all optical sidebands (OSBs) is summarized. Numerical simulation results show that, by optimizing the amplitude and phase of OSB, the 3rd-order intermodulation distortion (IMD3) can be significantly suppressed. Consequently, the spurious-free dynamic range (SFDR) of 107.1 dB Hz 8 / 9 is obtained, which is improved by 16.17 dB compared with a single quadrature biased Mach–Zehnder modulator (MZM) used in APL. [ABSTRACT FROM AUTHOR]

Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces. [ABSTRACT FROM AUTHOR]

In recent years, significant strides have been made in the inverse design of metamaterial perfect absorbers (MPAs) using deep learning techniques. However, this progress has been hindered by the functional homogeneity arising from the structural uniformity of the inverse-designed MPAs. In this paper, we address this limitation by designing reconfigurable MPAs (RMPAs) with three distinct structures and propose a residual parallel neural network (RPNN) that incorporates the optimized residual fully connected neural network (RFC-NN) for the inverse design of multifunctional MPAs. The trained RPNN accurately predicts the structural parameters and their corresponding absorption spectra with remarkable precision, yielding R2 values of 0.9981 and 0.9928, respectively. With this model, we successfully inverted the design of MPAs with three functions: broadband absorption, dual-band absorption, and triple-band absorption properties. A particularly noteworthy achievement was the realization of absorption bandwidth shifts using liquid crystal (LC) materials. Our RPNN showcases its proficiency in designing RMPAs with multifunctionality, all within a single network model. This marks a significant advancement over previous research methodologies. The proposed methodology holds great promise in diverse applications such as solar energy harvesting, detection, and filtration. [ABSTRACT FROM AUTHOR]

A new kind of electron Pearcey‐Gaussian beam (EPGB) with auto‐focusing properties in the presence of an additional magnetic field is introduced, which is composed of the multiplication of the Pearcey function and the Gaussian function. The closed‐form expressions of the EPGBs are obtained. Based on different initial transverse velocities, different dynamics of the EPGBs are presented. The effect of the magnetic field toward the maximum focusing plane is also demonstrated. The analytical results are in excellent agreement with direct numerical simulations. [ABSTRACT FROM AUTHOR]

To compensate for the deficiencies of low spectrum efficiency (SE) and scarce spectrum resources in Radio Frequency (RF), the FSO/THz-RF dual-hop downlink based on Non-Orthogonal Multiple Access (NOMA) is proposed, which utilizes the superior SE of NOMA and the spectrum resources of Free-Space Optical (FSO) and Terahertz (THz). Closed-form expressions of outage probability (OP) and ergodic capacity (EC) are derived for single-user and NOMA system, accounting for joint effects of channel fading, pointing error, and path loss. Additionally, corresponding asymptotic OP and diversity gains provide valuable physical insights. Results show that asymptotic OP and diversity gains are equal for both single-user and NOMA system, with diversity gains dependent on the performance parameters of the first link, independent of the total number and order of users. Moreover, better system performance is achieved with a smaller power allocation coefficient (PAC) mean deviation and NOMA system integer coefficient (NSIC). Finally, numerical results illustrate the superiority of the system. [Display omitted] • NOMA FSO/THz-RF links are modeled to improve SE and to economize spectrum resources. • The OP, AOP and EC are calculated and the coding gain and diversity gain are derived. • An algorithm to determine the optimal PAC and NSIC and two lower bounds are studied. [ABSTRACT FROM AUTHOR]

Deep learning-based research on metamaterial absorbers (MAs) has received increasing attention. However, the problem of homogeneity of structure and material of MAs has constrained their further development. In this paper, we designed MA with a top metal layer consisting of eight rectangular nano-rods, and adjusting their lengths can form various structures. In addition, we formed a material database for constructing MAs with the results of random combinations of eight materials and represented them in a coded manner. Meanwhile, we design MAs with ultra-wideband and dual absorption bandwidths using a dual-channel tandem neural network (DTNN). Compared with the existing methods, our method not only simplifies the steps of selecting materials and structures but also enables the design of MAs with different absorption properties. [ABSTRACT FROM AUTHOR]

As one of the spatial dimensions, orbital angular momentum (OAM) expands the channel capacity of underwater wireless optical communication (UWOC) system. However, the performance of the OAM-UWOC system is degraded due to the distortion of the OAM by oceanic turbulence. In this paper, we present a 7-layer convolutional neural network (CNN) and validate its ability to identify OAM patterns carried by Bessel Gaussian (BG) beams in oceanic turbulence channels. The trained CNN performs the task of recognizing OAM patterns with an accuracy of over 99% under weak-to-moderate oceanic turbulence at 100 m. Furthermore, in order to demonstrate the performance and robustness of the designed CNN, the effects of training sample number, epoch, turbulence intensity, balance parameter, transmission distance, type of training dataset, and coding method on the recognition ability of CNN are fully investigated. We hope that the method devised in our work will contribute to UWOC and facilitate its further development. [ABSTRACT FROM AUTHOR]