Investigating Optical Transport Network Performance:A Recurrence Plot Approach

In future optical transport networks,lightpath performance analysis is of great practical significance for fully automated management.In general,the quality of transmission(QoT)of lightpaths,measured by optical quality factor or optical signal-to-noise ratio,has a complex time-varying process,along with the interactions of the other lightpath state parameters(LSPs),such as transmit power,chromatic dispersion,polarization mode dispersion.Current studies are mostly focused on lightpath QoT estimation,but ignoring lightpath-level data analytics.In this case,our article proposes a novel lightpath performance analysis method considering recurrence plot(RP)and cross recurrence plot(CRP).Firstly,we give a detailed interpretation on the recurrence patterns of LSPs via a qualitative 2-D RP representation and its quantitative measure.It can potentially enable the accurate and fast lightpath failure detection with a low computational burden.On the other hand,CRP is devoted to modeling the relationships between lightpath QoT and LSPs,and the correlation degree is determined by a geometric mean of multiple indexes of cross recurrence quantification analysis.From the view of application,such CRP analysis can provide the effective knowledge sharing to guarantee more credible QoT prediction.Extensive experiments on a real-world optical network dataset have clearly demonstrated the effectiveness of our proposal.

Luminescence regulation of Sb^(3+)in 0D hybrid metal halides by hydrogen bond network for optical anti-counterfeiting

The Sb^(3+) doping strategy has been proven to be an effective way to regulate the band gap and improve the photophysical properties of organic-inorganic hybrid metal halides(OIHMHs).However,the emission of Sb^(3+) ions in OIHMHs is primarily confined to the low energy region,resulting in yellow or red emissions.To date,there are few reports about green emission of Sb^(3+)-doped OIHMHs.Here,we present a novel approach for regulating the luminescence of Sb^(3+) ions in 0D C_(10)H_(2)_(2)N_(6)InCl_(7)·H_(2)O via hydrogen bond network,in which water molecules act as agents for hydrogen bonding.Sb^(3+)-doped C_(10)H_(2)2N_(6)InCl_(7)·H_(2)O shows a broadband green emission peaking at 540 nm and a high photoluminescence quantum yield(PLQY)of 80%.It is found that the intense green emission stems from the radiative recombination of the self-trapped excitons(STEs).Upon removal of water molecules with heat,C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7) generates yellow emis-sion,attributed to the breaking of the hydrogen bond network and large structural distortions of excited state.Once water molecules are adsorbed by C_(10)H_(2)_(2)N_(6)In_(1-x)Sb_(x)Cl_(7),it can subsequently emit green light.This water-induced reversible emission switching is successfully used for optical security and information encryption.Our findings expand the under-standing of how the local coordination structure influences the photophysical mechanism in Sb^(3+)-doped metal halides and provide a novel method to control the STEs emission.

Optical Neural Networks:Analysis and Prospects for 5G Applications

With the capacities of self-learning,acquainted capacities,high-speed looking for ideal arrangements,solid nonlin-ear fitting,and mapping self-assertively complex nonlinear relations,neural systems have made incredible advances and accomplished broad application over the final half-century.As one of the foremost conspicuous methods for fake insights,neural systems are growing toward high computational speed and moo control utilization.Due to the inborn impediments of electronic gadgets,it may be troublesome for electronic-implemented neural systems to make the strides these two exhibitions encourage.Optical neural systems can combine optoelectronic procedures and neural organization models to provide ways to break the bottleneck.This paper outlines optical neural networks of feedforward repetitive and spiking models to give a clearer picture of history,wildernesses,and future optical neural systems.The framework demonstrates neural systems in optic communication with the serial and parallel setup.The graphene-based laser structure for fiber optic communication is discussed.The comparison of different balance plans for photonic neural systems is made within the setting of hereditary calculation and molecule swarm optimization.In expansion,the execution comparison of routine photonic neural,time-domain with and without extending commotion is additionally expounded.The challenges and future patterns of optical neural systems on the growing scale and applications of in situ preparing nonlinear computing will hence be uncovered.

Training-based symbol detection with temporal convolutional neural network in single-polarized optical communication system

In order to reduce the physical impairment caused by signal distortion,in this paper,we investigate symbol detection with Deep Learning(DL)methods to improve bit-error performance in the optical communication system.Many DL-based methods have been applied to such systems to improve bit-error performance.Referring to the speech-to-text method of automatic speech recognition,this paper proposes a signal-to-symbol method based on DL and designs a receiver for symbol detection on single-polarized optical communications modes.To realize this detection method,we propose a non-causal temporal convolutional network-assisted receiver to detect symbols directly from the baseband signal,which specifically integrates most modules of the receiver.Meanwhile,we adopt three training approaches for different signal-to-noise ratios.We also apply a parametric rectified linear unit to enhance the noise robustness of the proposed network.According to the simulation experiments,the biterror-rate performance of the proposed method is close to or even superior to that of the conventional receiver and better than the recurrent neural network-based receiver.

Bee Colony Optimization Algorithm for Routing and Wavelength Assignment Based on Directional Guidance in Satellite Optical Networks

With the development of satellite communication,in order to solve the problems of shortage of on-board resources and refinement of delay requirements to improve the communication performance of satellite optical networks,this paper proposes a bee colony optimization algorithm for routing and wavelength assignment based on directional guidance(DBCO-RWA)in satellite optical networks.In D-BCORWA,directional guidance based on relative position and link load is defined,and then the link cost function in the path search stage is established based on the directional guidance factor.Finally,feasible solutions are expanded in the global optimization stage.The wavelength utilization,communication success probability,blocking rate,communication hops and convergence characteristic are simulated.The results show that the performance of the proposed algorithm is improved compared with existing algorithms.

Machine Learning-Based Alarms Classification and Correlation in an SDH/WDM Optical Network to Improve Network Maintenance

The evolution of telecommunications has allowed the development of broadband services based mainly on fiber optic backbone networks. The operation and maintenance of these optical networks is made possible by using supervision platforms that generate alarms that can be archived in the form of log files. But analyzing the alarms in the log files is a laborious and difficult task for the engineers who need a degree of expertise. Identifying failures and their root cause can be time consuming and impact the quality of service, network availability and service level agreements signed between the operator and its customers. Therefore, it is more than important to study the different possibilities of alarms classification and to use machine learning algorithms for alarms correlation in order to quickly determine the root causes of problems faster. We conducted a research case study on one of the operators in Cameroon who held an optical backbone based on SDH and WDM technologies with data collected from 2016-03-28 to “2022-09-01” with 7201 rows and 18. In this paper, we will classify alarms according to different criteria and use 02 unsupervised learning algorithms namely the K-Means algorithm and the DBSCAN to establish correlations between alarms in order to identify root causes of problems and reduce the time to troubleshoot. To achieve this objective, log files were exploited in order to obtain the root causes of the alarms, and then K-Means algorithm and the DBSCAN were used firstly to evaluate their performance and their capability to identify the root cause of alarms in optical network.

Hybrid Policy of Routing and Spectrum Assignment in Elastic Optical Networks

The major challenge in elastic optical networks is to determine the path of a connection and to allocate spectral resources on the links of this path. This problem consists of two sub-problems, routing and spectrum allocation. In the literature, these sub-problems are solved with a predefined order for all topology node pairs. Recent work proposes hybrid resolution algorithms based on connection demand and network state to provide a solution to these problems. However, the blocking rate of new connection requests has become problematic. In this work, we propose a hybrid routing and spectrum assignment policy to improve blocking rate of new connection requests. The proposed solution consists to change the routing policy of a pair node if the connection request is blocked. This algorithm improves the blocking rate of new connection requests.

5G Flexible Optical Transport Networks with Large-Capacity, Low-Latency and High-Efficiency

The fifth generation(5G) of mobile communications are facing big challenges, due to the proliferation of diversified terminals and unprecedented services such as internet of things(IoT), high-definition videos, virtual/augmented reality(VR/AR). To accommodate massive connections and astonish mobile traffic, an efficient 5G transport network is required. Optical transport network has been demonstrated to play an important role for carrying 5G radio signals. This paper focuses on the future challenges, recent studies and potential solutions for the 5G flexible optical transport networks with the performances on large-capacity, low-latency and high-efficiency. In addition, we discuss the technology development trends of the 5G transport networks in terms of the optical device, optical transport system, optical switching, and optical networking. Finally, we conclude the paper with the improvement of network intelligence enabled by these technologies to deterministic content delivery over 5G optical transport networks.

Application of Neural Network in Fault Location of Optical Transport Network

Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixedsize vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short term memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network, and the F1-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.

Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images

AIM: To explore a segmentation algorithm based on deep learning to achieve accurate diagnosis and treatment of patients with retinal fluid.METHODS: A two-dimensional(2D) fully convolutional network for retinal segmentation was employed. In order to solve the category imbalance in retinal optical coherence tomography(OCT) images, the network parameters and loss function based on the 2D fully convolutional network were modified. For this network, the correlations of corresponding positions among adjacent images in space are ignored. Thus, we proposed a three-dimensional(3D) fully convolutional network for segmentation in the retinal OCT images.RESULTS: The algorithm was evaluated according to segmentation accuracy, Kappa coefficient, and F1 score. For the 3D fully convolutional network proposed in this paper, the overall segmentation accuracy rate is 99.56%, Kappa coefficient is 98.47%, and F1 score of retinal fluid is 95.50%. CONCLUSION: The OCT image segmentation algorithm based on deep learning is primarily founded on the 2D convolutional network. The 3D network architecture proposed in this paper reduces the influence of category imbalance, realizes end-to-end segmentation of volume images, and achieves optimal segmentation results. The segmentation maps are practically the same as the manual annotations of doctors, and can provide doctors with more accurate diagnostic data.

Differential Game Based Link Resource Management for Next Generation Optical Network

In this paper, we investigate the link resource management problem for optical networks, to achieve the resource cost during the information transmission. We use the differential game to formulate the cost control problem for the link resource management, to minimize the resource allocation cost functions, which dynamic behaviours are described by differential equations. Each link controls its transmission bandwidth based on the Nash equilibriums of the differential game. The effectiveness of the proposed model is given through numerical simulations.

Research and Progress of Service Driven Optical Switching Network in China

National R&D activities on optical switching networkare introduced. Optical switching network testbedswere established in China including 3T-net andOBS ring and mesh network test-bed with the supportof national '863' program. As an importantmodule in OPS network, a novel all-optical serialmulticast mode is discussed.

Extraordinary transmission and reflection in PT-symmetric two-segment-connected triangular optical waveguide networks with perfect and broken integer waveguide length ratios

By adjusting the waveguide length ratio, we study the extraordinary characteristics of electromagnetic waves propagating in one-dimensional(1D) parity-time-symmetric(PT-symmetric) two-segment-connected triangular optical waveguide networks with perfect and broken integer waveguide length ratios respectively. It is found that the number and the corresponding frequencies of the extremum spontaneous PT-symmetric breaking points are dependent on the waveguide length ratio. Near the extremum breaking points, ultrastrong extraordinary transmissions are created and the maximal can arrive at, respectively, 2.4079 × 10^14 and 4.3555 × 10^13 in both kinds of networks. However, bidirectional invisibility can only be produced by the networks with broken integer waveguide length ratio, whose mechanism is explained in detail from the perspective of photonic band structure. The findings of this work can be useful optical characteristic control in the fabrication of PT-symmetric optical waveguide networks, which possesses great potential in designing optical amplifiers,optical energy saver devices, and special optical filters.

The Evolution Trend of Optical Core Network

Optical network is the infrastructure of telecommunicationnetwork. More than 95% of information istransported over optical network in China, wherecore network is the main trunk. How to increase thebit rate of the single wavelength channel, raise thetotal capacity of the DWDM system, extend theoptical transportation distance of electrical repeaterfree of the DWDM system and optical network intelligenceare key problems demanding high attention.The evolution trend of the optical core network isdescribed with some examples in this paper.

Software Defined Optical Networks and Its Innovation Environment

Software defined optical networks(SDONs) integrate software defined technology with optical communication networks and represent the promising development trend of future optical networks. The key technologies for SDONs include software-defined optical transmission, switching, and networking. The main features include control and transport separation, hardware universalization, protocol standardization, controllable optical network, and flexible optical network applications.This paper introduces software defined optical networks and its innovation environment, in terms of network architecture,protocol extension solution, experiment platform and typical applications. Batch testing has been conducted to evaluate the performance of this SDON testbed. The results show that the SDON testbed has good scalability in different sizes.Meanwhile, we notice that controller output bandwidth has great influence on lightpath setup delay.

PREPARATION AND SECOND-ORDER OPTICAL NONLINEARITY OF NOVEL PHENOXYSILICON NETWORKS BY SOL-GEL PROCESS

Four phenoxysilicon networks for nonlinear optical (NLO) applications were designed and prepared by an extended sol-gel process without additional H20 and catalyst. All poled polymer network films possess high second-order nonlinear optical coefficients (d33) of 10-?～10-8 esu. The investigation of NLO temporal stability at room temperature and elevated temperature (120°C) indicated that these films exhibit high d33 stability because the orientation of the chromophores are locked in the phenoxysilicon organic/inorganic networks.

Chip-Based High-Dimensional Optical Neural Network

Parallel multi-thread processing in advanced intelligent processors is the core to realize high-speed and high-capacity signal processing systems.Optical neural network(ONN)has the native advantages of high parallelization,large bandwidth,and low power consumption to meet the demand of big data.Here,we demonstrate the dual-layer ONN with Mach-Zehnder interferometer(MZI)network and nonlinear layer,while the nonlinear activation function is achieved by optical-electronic signal conversion.Two frequency components from the microcomb source carrying digit datasets are simultaneously imposed and intelligently recognized through the ONN.We successfully achieve the digit classification of different frequency components by demultiplexing the output signal and testing power distribution.Efficient parallelization feasibility with wavelength division multiplexing is demonstrated in our high-dimensional ONN.This work provides a high-performance architecture for future parallel high-capacity optical analog computing.

Review of Optical Character Recognition for Power System Image Based on Artificial Intelligence Algorithm

Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.

NEAR-INFRARED OPTICAL TOMOGRAPHY IMAGE RECONSTRUCTION APPROACH BASED ON TWO-LAYERED BP NEURAL NETWORK

An image-reconstruction approach for optical tomography is presented,in which a two-layered BP neural network is used to distinguish the tumor location.The inverse problem is solved as optimization problem by Femlab software and Levenberg–Marquardt algorithm.The concept of the average optical coefficient is proposed in this paper,which is helpful to understand the distribution of the scattering photon from tumor.The reconstructive¯µ
s by the trained network is reasonable for showing the changes of photon number transporting inside tumor tissue.It realized the fast reconstruction of tissue optical properties and provided optical OT with a new method.

Naive Echo-State-Network Based Services Awareness Algorithm of Software Defined Optical Networks

As internet services newly emerge with diversity and complexity, great challenges and demands are presented to the Open Flow controlled software defined optical networks(SDON) to achieve better match between services and SDON. With this aim, this paper proposes a naive Echo-State-Network(Naive-ESN) based services awareness algorithm of the software defined optical network, where the naive ESN model adopts the ring topology structure and generates the probability output result to determine the Qo S policy of SDON. Moreover, the Naive-ESN engine is also designed in controller node of SDON to perform services awareness by obtaining service traffic features from data plan, together with some necessary extension of the Open Flow protocol. Test results show that the proposed approach is able to improved services-oriented supporting ability of SDON.