Search Results (19)

Ultra-high-speed integrated communication and sensing (ICS) transmission techniques are highly desired for next-generation highly reliable optical transport networks (OTNs). The inherent multiple-channel advantage of uncoupled multi-core fibers (MCFs) empowers the evolution of ICS techniques. In this paper, we demonstrate an ultra-high-speed ICS OTN system utilizing 130 Gbaud probability constellation shaping 64-ary quadrature amplitude modulation (PCS-64QAM) 1.2 Tb/s OTN transponders and polarization-based sensing technique over a field-deployed seven-core MCF cable for the first time. A real-time 267.8 Tb/s 2 × 70.76 km transmission is achieved by only utilizing C-band signals thanks to the high-performance 1.2 Tb/s OTN transponders. Moreover, the ICS system can sense environmental impacts on the MCF cable such as shaking, striking, etc., in real time. The capacity of the transmission system can also be further enhanced by using signals in the L-band. Our work demonstrates the feasibility of simultaneously achieving ultra-high-speed data transmission and the real-time sensing of environmental disturbances over a field-deployed MCF cable, which we believe is a crucial milestone for next-generation ultra-high-speed highly reliable optical transmission networks. Full article

Simulating atmospheric turbulence phase screens is essential for optical system research and turbulence compensation. Traditional methods, such as multi-harmonic power spectrum inversion and Zernike polynomial fitting, often suffer from sampling errors and limited diversity. To overcome these challenges, this paper proposes an improved denoising diffusion probabilistic model (DDPM) for generating high-fidelity atmospheric turbulence phase screens. The model effectively captures the statistical distribution of turbulence phase screens using small training datasets. A refined loss function incorporating the structure function enhances accuracy. Additionally, a self-attention module strengthens the model’s ability to learn phase screen features. The experimental results demonstrate that the proposed approach significantly reduces the Fréchet Inception Distance (FID) from 154.45 to 59.80, with the mean loss stabilizing around 0.1 after 50,000 iterations. The generated phase screens exhibit high precision and diversity, providing an efficient and adaptable solution for atmospheric turbulence simulation. Full article

The rotational Doppler effect (RDE) has been playing a vital role in the detection of rotational motion in recent years. However, the results obtained from this detection method are easily influenced by the detection conditions. It means that the characteristic of the frequency signals varies greatly under different detection conditions. How to efficiently and automatically classify and recognize different signal features is of great significance for the accurate extraction of rotating speed information in the future. Based on the well-known support vector machine (SVM) model, we built an SVM learn model to automatically classify and recognize RDE signals under different detection conditions. The results show that the SVM learn model can effectively recognize the category of detection signals with high accuracy. By accurately identifying signal categories, it can provide a great foundation for extracting target speed and other information in the future, and has broad application prospects in engineering practice. Full article

The space-division multiplexed (SDM) transmission technique based on uncoupled multi-core fibers (MCF) shows great implementation potential due to its huge transmission capacity and compatibility with existing transceivers. In this paper, we demonstrate a real-time single-span 106 km field trial over deployed 4-core MCF cable using commercial 800 Gb/s optical transport network (OTN) transceivers. The transceivers achieved a modulation rate of 130 Gbaud with the optoelectronic multiple-chip module (OE-MCM) packaging technique, which enabled the adoption of a highly noise-tolerant probability constellation shaping a 16-array quadrature amplitude modulation (PCS-16QAM) modulation format for 800 Gb/s OTN transceivers, and could realize unrepeated long-span transmission. The 4-core 800 Gb/s transmission systems achieved a real-time transmission capacity of 256 Tb/s with fully loaded 80-wavelength channels over the C+L band. The performance of different kinds of 800 G OTN transceivers with different modulation formats under this long-span unrepeated optical transmission system is also estimated and discussed. This field trial demonstrates the feasibility of applying uncoupled MCF with 800 Gb/s OTN transceivers in unrepeated long-span transmission scenarios and promotes its field implementation in next-generation high-speed optical interconnection systems. Full article

The core-division-multiplexed (CDM) transmission technique utilizing uncoupled multi-core fiber (MCF) is considered a promising candidate for next-generation long-haul optical transport networks (OTNs) due to its high-capacity potential. For the field implementation of MCF, it is of great significance to explore its long-haul transmission capability using high-speed OTN transceivers over deployed MCF cable. In this paper, we investigate the real-time long-haul transmission capability of a deployed seven-core MCF cable using commercial 138-Gbaud 400 Gb/s backbone OTN transceivers with a dual-polarization quadrature phase shift keying (DP-QPSK) modulation format. Thanks to the highly noise-tolerant DP-QPSK modulation format enabled by the high baud rate, a real-time 256 Tb/s transmission over a 990.64 km (14 × 70.76 km) deployed seven-core fiber cable with more than 600 fusion splices is field demonstrated for the first time, which achieves a real-time capacity–distance product of 221.9 Pb/s∙km. Specifically, the long-haul CDM transmission is simulated by cascading the fiber cores of two segments of 70.76 km seven-core fibers. And dynamic gain equalizers (DGEs) are utilized to mitigate the impacts of stimulated Raman scattering (SRS) and the uneven gain spectra of amplifiers in broadband transmissions by equalizing the power of signals with different wavelengths. This field trial demonstrates the feasibility of applying uncoupled MCF in long-haul OTN transmission systems and will contribute to its field implementation in terrestrial fiber cable systems. Full article

Fiber-reinforced composites, such as glass fiber-reinforced polymer (GFRP), are widely used across industries but are susceptible to inclusion defects during manufacturing. Detecting and classifying these defects is crucial for ensuring material integrity. This study classifies four common inclusion defects—metal, peel ply, release paper, and PTFE film—in GFRP using terahertz technology and machine learning. Two GFRP sheets with inclusion defects at different depths were fabricated. Terahertz time-domain signals were acquired, and a cross-correlation-based deconvolution algorithm extracted impulse responses. LSTM-RNN, Bi-LSTM RNN, and 1D-CNN models were trained and tested on time-domain, frequency-domain, and impulse response signals. The defect-free region exhibited the highest classification accuracy. Bi-LSTM RNN achieved the best recall and macro F1-score, followed by 1D-CNN, while LSTM-RNN performed worse. Training with impulse response signals improved classification while maintaining accuracy. Full article

With the development of the Internet of Things, cloud networking, and 4K/8K high-definition video, global internet traffic has seen a dramatic increase. This surge in traffic has placed higher demands on the performance of optical networks, featuring higher data rates, lower latency, and lower cost. The passive optical network (PON) is a representative scenario of optical access networks. Issues such as burst-mode detection in upstream PON scenarios, flexible rate allocation in downstream scenarios, and the simplification of hardware complexity at the optical network unit (ONU) side have attracted considerable attention. Compared to intensity modulation/direct detection (IM/DD), a recently proposed coherent PON incorporates a local oscillator laser at the receiver, enabling superior receiver sensitivity, spectrally efficient modulation, linear optical field recovery, and flexible channel selection. These features significantly enhance the flexibility and data rates of PON systems. This paper provides a comprehensive review of the development of coherent PONs, particularly in aspects of preamble design for burst-mode detection in upstream scenarios, the design of flexible rate PONs in downstream scenarios, and solutions for reducing hardware complexity at the ONU side. Full article

Shack–Hartmann wavefront sensors (SHWFSs) are crucial for detecting distortions in adaptive optics systems, but the accuracy of wavefront reconstruction is often hampered by low guide star brightness or strong atmospheric turbulence. This study introduces a new method of using the Vision Transformer model to process image information from SHWFSs. Compared with previous traditional methods, this model can assign a weight value to each subaperture by considering the position and image information of each subaperture of this sensor, and it can process to obtain wavefront reconstruction results. Comparative evaluations using simulated SHWFS light intensity images and corresponding deformable mirror command vectors demonstrate the robustness and accuracy of the Vision Transformer under various guide star magnitudes and atmospheric conditions, compared to convolutional neural networks (CNNs), represented in this study by Residual Neural Network (ResNet), which are widely used by other scholars. Notably, normalization preprocessing significantly improves the CNN performance (improving Strehl ratio by up to 0.2 under low turbulence) while having a varied impact on the Vision Transformer, improving its performance under a low turbulence intensity and high brightness (Strehl ratio up to 0.8) but deteriorating under a high turbulence intensity and low brightness (Strehl ratio reduced to about 0.05). Overall, the Vision Transformer consistently outperforms CNN models across all tested conditions, enhancing the Strehl ratio by an average of 0.2 more than CNNs. Full article

In recent years, the Swept-Source Phase Microscope (SS-PM) has gained more attention due to its greater robustness to sample motion and lower signal decay with depth. However, the mechanical wavelength tuning of the swept source creates small variations in the wavenumber sampling of spectra that introduce serious phase noise. We present a software post-processing method to eliminate phase noise in SS-PM. This method does not require high-quality swept light sources or high-precision synchronization devices and achieves ~72 pm displacement sensitivity using a conventional SS-PM system. We compare the performance of this method with traditional software-based methods by measuring phase fluctuations. The phase fluctuations in the traditional software-based method are five times those of the proposed method, which means the proposed method has better sensitivity. Using this method, we reconstructed phase images of air wedges and resolution plates to demonstrate the SS-PM’s potential for high-sensitivity surface profiling measurement. Finally, we discuss the advantages of SS-PM over traditional Spectral-Domain PM techniques. Full article

Alignment precision is a crucial factor that directly impacts overlay accuracy, which is one of three fundamental indicators of lithography. The alignment method based on the Moiré fringe has the advantages of a simple measurement optical path and high measurement accuracy. However, it requires strict control of the distance between the mask and wafer to ensure imaging quality. This limitation restricts its application scenarios. A depth–DOF (depth of focus) Moiré fringe alignment by broad–spectrum modulation is presented to enhance the range of the alignment signals. This method establishes a broad–spectrum Moiré fringe model based on the Talbot effect principle, and it effectively covers the width of dark field (WDF) between different wavelength imaging ranges, thereby extending the DOF range of the alignment process, and employs a hybrid of genetic algorithms and the particle-swarm optimization (GA–PSO) algorithm to combine various spectral components in a white spectrum. By calculating the optimal ratio of each wavelength and using white light incoherent illumination in combination with this ratio, it achieves the optimal DOF range of a broad–spectrum Moiré fringe imaging model. The simulation results demonstrate that the available DOF range of the alignment system has been expanded from 400 μm to 800 μm. Additionally, the alignment precision of the system was analyzed, under the same conditions, and the accuracy analysis of the noise resistance, translation amount, and tilt amount was conducted for the Moiré fringe and broad–spectrum Moiré fringe. Compared to a single wavelength, the alignment precision of the broad–spectrum Moiré fringe decreased by an average of 0.0495 nm, equivalent to a 1.5% reduction in the original alignment precision, when using a 4 μm mask and a 4.4 μm wafer. However, the alignment precision can still reach 3.795 nm, effectively enhancing the available depth of focus range and reducing the loss of alignment precision. Full article

Source mask optimization (SMO), a primary resolution enhancement technology, is one of the most pivotal technologies for enhancing lithography imaging quality. Due to the high computation complexity of SMO, patterns should be selected by a selection algorithm before optimization. However, the limitations of existing selection methods are twofold: they are computationally intensive and they produce biased selection results. The representative method having the former limitation is the diffraction signature method. And IBM’s method utilizing the rigid transfer function tends to cause biased selection results. To address this problem, this study proposes a novel pattern cluster and selection algorithm architecture based on a convolutional neural network (CNN). The proposed method provides a paradigm for solving the critical pattern selection problem by CNN to transfer patterns from the source image domain to unified embeddings in a K-dimensional feature space, exhibiting higher efficiency and maintaining high accuracy. Full article

Progress in the field of machine learning has enhanced the development of self-adjusting optical systems capable of autonomously adapting to changing environmental conditions. This study demonstrates the concept of self-adjusting optical systems and presents a new approach based on reinforcement learning methods. We integrated reinforcement learning algorithms into the setup for tuning the laser radiation into the fiber, as well as into the complex for controlling the laser-plasma source. That reduced the dispersion of the generated X-ray signal by 2–3 times through automatic adjustment of the position of the rotating copper target and completely eliminated the linear trend arising from the ablation of the target surface. The adjustment of the system was performed based on feedback signals obtained from the spectrometer, and the movement of the target was achieved using a neural network-controlled stepper motor. As feedback, the second harmonic of femtosecond laser radiation was used, the intensity of which has a square root dependence on the X-ray yield. The developed machine learning methodology allows the considered systems to optimize their performance and adapt in real time, leading to increased efficiency, accuracy, and reliability. Full article

Functional laser surface texturing (LST) arose in recent years as a very powerful tool for tailoring the surface properties of parts and components to their later application. As a result, self-cleaning surfaces with an improved wettability, efficient engine components with optimized tribological properties, and functional implants with increased biocompatibility can be achieved today. However, with increasing capabilities in functional LST, the prediction of resulting surface properties becomes more and more important in order to reduce the development time of those functionalities. Consequently, advanced approaches for the prediction of the properties of laser-processed surfaces—the so-called predictive modelling—are required. This work introduces the concept of predictive modelling with respect to LST by means of direct laser writing (DLW). Fundamental concepts for the prediction of surface properties are presented employing machine learning approaches, theoretical concepts, and statistical methods. The modelling takes into consideration the used laser parameters, the analysis of topographical, and other process-relevant information in order to predict the resulting surface roughness. For this purpose, two different algorithms, namely artificial neural network and random forest, were trained with experimental data for stainless steel and Stavax surfaces. Statistical results indicate that both models can predict the desired surface topography with high accuracy, despite the use of a small dataset for the training process. The approaches can be used to further optimize the laser process regarding the process efficiency, overall throughput, and other process outcomes. Full article

In inter-datacenter elastic optical networks, multi-controller deployment is adopted to improve the stability and scalability of the control plane. As the network scale increases, the traditional multi-controller deployment scheme ignores the dynamic characteristics of traffic, resulting in unbalanced load among multiple controllers. In response to this problem, the existing switch migration mechanism is proposed to achieve balanced distribution of control loads. However, most of the existing research work does not consider the additional cost of switch migration, and the load balancing performance of the controller is not significantly improved after switch migration. In this paper, we propose a cost-aware switch migration (CASM) strategy for controller load balancing. The proposed CASM strategy first measures the controller load through multiple performance indicators that affect the controller load, and then judges whether the controller is overloaded or underloaded based on the controller’s response time to the request message, thereby improving the load balancing performance of the controller. Additionally, when selecting the switch to be migrated, the CASM selects the optimal switch for migration based on minimizing the migration cost, thereby reducing the cost of switch migration. The performance evaluation shows that CASM significantly improves load balancing performance of controllers and reduces the migration cost compared to existing solutions. Full article

In order to meet the massively increasing requirements of big-data applications, data centers (DCs) are key infrastructures to cope with the associated demands, such as high performance, easy scalability, low cabling complexity and low power consumption. Many research efforts have been dedicated to traditional wired data center networks (DCNs). However, DCNs’ static and rigid topology based on optical cables significantly limits their flexibility, scalability, and even reconfigurability. The limitations of this wired connection can be addressed with optical wireless technology, which avoids cable complexity problems while allowing dynamic adaption and fast reconfiguration. Here, we propose and investigate a novel optical wireless data-center network (OW-DCN) architecture based on nanoseconds semiconductor optical amplifier (SOA)-based wavelength selectors and arrayed waveguide grating router (AWGR) controlled by fast field-programmable gate array (FPGA)-based switch schedulers. The full architecture, including the design, packet-switching strategy, contention solving methodology, and reconfiguration capability, is presented and demonstrated. Dynamic switch scheduling with a FPGA-based switch scheduler processing optical label and software-defined network (SDN)-based reconfiguration were experimentally confirmed. The proposed OW-DCN was also achieved with a power penalty of less than 2 dB power penalty at BER < 1 × 10⁻⁹ for a 50 Gb/s OOK transmission and packet-switching transmission. Full article