Statistical properties for an optically injected chaotic semiconductor laser with a high-pass filter.

Chaotic waveforms with Gaussian distributions are significant for laser-chaos-based applications such as random number generation. By exploring the injection parameter space of the optical injection semiconductor lasers, we numerically investigate the associated probability density functions of the generated chaotic waveforms when different high-pass filters with different cutoff frequencies are used. Our results demonstrate that the chaotic waveforms with Gaussian probability density functions can be obtained once the cutoff frequency of the high-pass filter is larger than the laser relaxation resonance frequency. Especially, we find that the Gaussian probability density function can reach a superhigh coefficient of determination R2 ≥ 99.5% and an ultralow skewness |S|<0.1 in a large parameter space by jointly controlling the injection parameter and cutoff frequency.

Bias-drift insensitive full-field frequency response characterization of a thin-film lithium niobate-based intensity modulator.

We propose a rapid and precise scheme for characterizing the full-field frequency response of a thin-film lithium niobate-based intensity modulator (TFLN-IM) via a specially designed multi-tone microwave signal. Our proposed scheme remains insensitive to the bias-drift of IM. Experimental verification is implemented with a self-packaged TFLN-IM with a 3 dB bandwidth of 30 GHz. In comparison with the vector network analyzer (VNA) characterization results, the deviation values of the amplitude-frequency response (AFR) and phase-frequency response (PFR) within the 50 GHz bandwidth are below 0.3 dB and 0.15 rad, respectively. When the bias is drifted within 90% of the Vπ range, the deviation fluctuation values of AFR and PFR are less than 0.3 dB and 0.05 rad, respectively. With the help of the full-field response results, we can pre-compensate the TFLN-IM for the 64 Gbaud PAM-4 signals under the back-to-back (B2B) transmission, achieving a received optical power (ROP) gain of 2.3 dB. The versatility of our proposed full-field response characterization scheme can extend to various optical transceivers, offering the advantage of low cost, robust operation, and flexible implementation.

Low-loss and compact photonic lantern based on a step-index double cladding fiber.

The fulfilment of the adiabatic criterion is indispensable for the realization of a low-loss photonic lantern (PL), concurrently imposing a stringent restriction on the taper transition length of the PL. Here, by relaxing the adiabatic criterion, a low-loss and compact PL based on a step-index double cladding fiber (SI-DCF) is theoretically proposed and experimentally demonstrated. The use of SI-DCF can reduce the mode field diameter (MFD) expansion ratio during the tapering processing and greatly decrease the taper transition length required for adiabatic tapering. We initially evaluate the variation of both MFD and effective refractive index (RI) along the fiber tapering based on three types of fiber structures, including the modified standard single-mode fiber (SSMF), the graded-index fiber (GIF), and the proposed SI-DCF. In comparison with the commonly used fiber geometry, the SI-DCF can reduce the MFD expansion ratio from 77.73% to 38.81%, leading to more than half reduction of the tapering length for both 3-mode and 6-mode PLs. Then, two kinds of SI-DCF with different core diameters are fabricated to realize a 3-mode PL. The fabricated PL possesses a 1.5 cm tapering length and less than 0.2 dB insertion loss (IL). After splicing with the commercial few-mode fiber, the PL has an average IL of 0.6 dB and more than 13 dB LP11 mode purity over the C-band. Finally, a transfer matrix measurement indicates that the fabricated PLs have a mode coupling of less than -10 dB at 1550 nm.

Blind frequency offset estimation method based on a minimum phase correction error for a full spectrum modulated NFDM system.

To improve the spectral efficiency of a full spectrum modulated nonlinear frequency division multiplexing (FS-NFDM) system, a blind frequency offset estimation (FOE) method has been proposed. The approach based on the minimum phase correction error can achieve high estimation accuracy of sub-MHz without need of any training symbols. Furthermore, in order to reduce the computational complexity, an eigenvalue-shift method is used to get a coarse search interval of FO, and then the one-dimensional optimization algorithm based on golden section search and parabolic interpolation is used to get the optimal FOE for the coarse search interval. The feasibility and reliability of the proposed blind FOE approach have been demonstrated in both BTB and fiber transmission scenarios. Compared with the grid search method, the proposed solving scheme can save hundreds of times of the searches. The experimental results reveal that the proposed method is robust to the amplified spontaneous emission noise and phase noise and has the capabilities of a wide FOE range and a high FOE accuracy.

Experimental demonstration of optical waveform transmission with multiple-eigenvalue 16-APSK modulation.

The multi-eigenvalue multiplexing-based discrete spectrum-modulated nonlinear frequency-division multiplexing (DS-NFDM) system with higher-order modulation format has been demonstrated experimentally. After designing the coefficients of the eigenvalue set and the constellation point distribution of 16-amplitude phase shift keying (16-APSK), the realizations of 14-, 30-, and 46-eigenvalue multiplexed DS-NFDM signals have been implemented. The results show that 46-eigenvalue and 30-eigenvalue multiplexed DS-NFDM signals can transmit 50 km and 400 km over a nonzero dispersion-shifted fiber (NZDSF) under soft-decision forward error correction (SD-FEC) threshold of 2.4E-2, respectively. This demonstration shows for the first time, to the best of our knowledge, the record for multiplexed eigenvalue number and data rate of the multiple-eigenvalue-based DS-NFDM system.

Vectorial holography over a multimode fiber.

Vectorial holography through a strongly scattering medium can facilitate various applications in optics and photonics. However, the realization of vectorial holography with arbitrary distribution of optical intensity is still limited because of experimental noise during the calibration of vectorial transmission matrix (TM) and reconstruction noise during the retrieval of input wavefront for a given holographic target. Herein, we propose and experimentally demonstrate the vectorial holography with arbitrary distribution of optical intensity over a multimode fiber (MMF) using the Tikhonov regularization. By optimizing the noise factor, the performance of vectorial holography over an MMF is improved compared with the conjugate transpose and inverse TM methods. Our results might shed new light on the optical communication and detection mediated by MMFs.

Pairwise Tomlinson-Harashima precoding for multiple-lane IM-DD transmissions.

As for the photonic interconnection based on the multiple-lane intensity modulation direct detection (IM-DD) transmission, both intra-channel inter-symbol-interference (ISI) originating from bandwidth constraint, and inter-channel performance discrepancy emerging from inter-channel component differences are the major bottleneck for the throughput enhancement. Here, we propose a pairwise Tomlinson-Harshima precoding (P-THP) scheme, in order to simultaneously deal with both intra-channel ISI and inter-channel performance discrepancy. The effective function of the proposed P-THP scheme is experimentally evaluated by transmitting 4-channel 81-GBaud PAM4 signals over 2 km standard single-mode fiber (SSMF). Compared with the conventional scheme with only applying THP on individual wavelength channel, the required optical received power (ROP) under the back-to-back (B2B) transmission can be reduced by 0.75∼1 dB with the help of proposed P-THP in different experimental component configurations, at the 7% hard decision forward error correction (HD-FEC) threshold of BER = 3.8 × 10-3. After the 2 km SSMF transmission, only the use of proposed P-THP can guarantee to reach the designated HD-FEC threshold, leading to a net rate of >600 Gbit/s.

Simultaneous realization of high gain and low DMG of four-mode EDFA under bidirectional hybrid-mode pump.

We theoretically and experimentally verify that, the bidirectional hybrid-mode pumping scheme can address the optimization problem of trade-off between high gain and low differential modal gain (DMG) of four-mode erbium-doped fiber amplifier (4M-EDFA), in comparison with traditional both forward and backward hybrid-mode pumping scheme. It is noticed that, when the total pump power is fixed, the bidirectional hybrid-mode pumping scheme can not only achieve higher gain, but also suppress DMG due to different overlap integrals for the forward and backward pumping schemes. The bidirectional hybrid-mode pumped 4M-EDFA is developed with the forward pumping at LP02 mode and the backward pumping at LP21 mode, under a pump power ratio of 30%:70%. Thus, we can achieve an average gain of up to 21.16 dB and a low DMG of 0.43 dB at 1550 nm, and an average gain of up to 20.64 dB with a DMG of less than 1.6 dB over the C-band. In particular, the bidirectional hybrid-mode pumping scheme allows us to tailor the gain characteristics of the few-mode erbium-doped fiber amplifiers (FM-EDFAs), by adjusting the power ratio between forward and backward pumps.

Wireless-Channel Key Distribution Based on Laser Synchronization.

We propose and experimentally demonstrate a wireless-channel key distribution scheme based on laser synchronization induced by a common wireless random signal. Two semiconductor lasers are synchronized under injection of the drive signal after electrical-optical conversion and emit irregular outputs that are used to generate shared keys. Our proof-of-concept experiment using a complex drive signal achieved a secure key generation rate of up to 150 Mbit/s with a bit error rate below 3.8 × 10-3. Numerical simulation results show that the proposed scheme has the potential to achieve a distribution distance of several hundred meters. It is believed that common-signal-induced laser synchronization paves the way for high-speed wireless physical-layer key distribution.

Scalable parallel ultrafast optical random bit generation based on a single chaotic microcomb.

Random bit generators are critical for information security, cryptography, stochastic modeling, and simulations. Speed and scalability are key challenges faced by current physical random bit generation. Herein, we propose a massively parallel scheme for ultrafast random bit generation towards rates of order 100 terabit per second based on a single micro-ring resonator. A modulation-instability-driven chaotic comb in a micro-ring resonator enables the simultaneous generation of hundreds of independent and unbiased random bit streams. A proof-of-concept experiment demonstrates that using our method, random bit streams beyond 2 terabit per second can be successfully generated with only 7 comb lines. This bit rate can be easily enhanced by further increasing the number of comb lines used. Our approach provides a chip-scale solution to random bit generation for secure communication and high-performance computation, and offers superhigh speed and large scalability.

Non-sweep DC component estimation method for a virtual-carrier assisted Kramers-Kronig receiver.

The Kramers-Kronig (KK) receiver has attracted much attention in short-range optical interconnection because of its ability to recover the phase of the signal from the intensity information through KK algorithm. In high-speed KK systems, such as virtual-carrier (VC) assisted ones, an alternating current (AC) coupled photo-detector (PD) is preferred due to relaxing the requirements of analog-to-digital converter (ADC) and electronic amplifier by filtering direct current (DC) component. However, the loss of the DC component will cause the KK algorithm to break down, so it is necessary to accurately recover DC value in the digital domain with multiple-sweep. In this paper, we propose what we believe is a novel non-sweep DC component estimation scheme based on optimized digital carrier-to-signal power ratio (OD-CSPR) method, which can accurately estimate the DC component with only 3-4 iterations in the scenario of VC-assisted KK receiver optical transmission. The scheme utilizes the one-dimensional search optimization algorithm based on golden section search and parabolic interpolation without sweeping. The simulation and experimental results of the proposed non-sweep OD-CSPR method show that the DC component can be estimated accurately in a large CSPR range, and the system performance is close to that of the conventional DC-sweep method. Compared with the typical defined digital CSPR (DD-CSPR) based optimization method, the proposed one can realize optical signal-to-noise ratio (OSNR) gains of 0.9 dB in the back-to-back (B2B) and 0.7 dB under 80 km fiber transmission scenarios respectively with a total bit rate of 160Gb/s.

Non-orthogonal optical multiplexing empowered by deep learning.

Orthogonality among channels is a canonical basis for optical multiplexing featured with division multiplexing, which substantially reduce the complexity of signal post-processing in demultiplexing. However, it inevitably imposes an upper limit of capacity for multiplexing. Herein, we report on non-orthogonal optical multiplexing over a multimode fiber (MMF) leveraged by a deep neural network, termed speckle light field retrieval network (SLRnet), where it can learn the complicated mapping relation between multiple non-orthogonal input light field encoded with information and their corresponding single intensity output. As a proof-of-principle experimental demonstration, it is shown that the SLRnet can effectively solve the ill-posed problem of non-orthogonal optical multiplexing over an MMF, where multiple non-orthogonal input signals mediated by the same polarization, wavelength and spatial position can be explicitly retrieved utilizing a single-shot speckle output with fidelity as high as ~ 98%. Our results resemble an important step for harnessing non-orthogonal channels for high capacity optical multiplexing.

Application of flash GC e-nose and FT-NIR combined with deep learning algorithm in preventing age fraud and quality evaluation of pericarpium citri reticulatae.

Pericarpium citri reticulatae (PCR) is the dried mature fruit peel of Citrus reticulata Blanco and its cultivated varieties in the Brassicaceae family. It can be used as both food and medicine, and has the effect of relieving cough and phlegm, and promoting digestion. The smell and medicinal properties of PCR are aged over the years; only varieties with aging value can be called "Chenpi". That is to say, the storage year of PCR has a great influence on its quality. As the color and smell of PCR of different storage years are similar, some unscrupulous merchants often use PCRs of low years to pretend to be PCRs of high years, and make huge profits. Therefore, we did this study with the aim of establishing a rapid and nondestructive method to identify the counterfeiting of PCR storage year, so as to protect the legitimate rights and interests of consumers. In this study, a classification model of PCR was established by e-eye, flash GC e-nose, and Fourier transform near-infrared (FT-NIR) combined with machine learning algorithms, which can quickly and accurately distinguish PCRs of different storage years. DFA and PLS-DA models were established by flash GC e-nose to distinguish PCRs of different ages, and 8 odor components were identified, among which (+)-limonene and γ-terpinene were the key components to distinguish PCRs of different ages. In addition, the classification and calibration model of PCRs were established by the combination of FT-NIR and machine learning algorithms. The classification models included SVM, KNN, LSTM, and CNN-LSTM, while the calibration models included PLSR, LSTM, and CNN-LSTM. Among them, the CNN-LSTM model built by internal capsule had significantly better classification and calibration performance than the other models. The accuracy of the classification model was 98.21 %. The R2P of age, (+)-limonene and γ-terpinene was 0.9912, 0.9875 and 0.9891, respectively. These results showed that the combination of flash GC e-nose and FT-NIR combined with deep learning algorithm could quickly and accurately distinguish PCRs of different ages. It also provided an effective and reliable method to monitor the quality of PCR in the market.

Dual-Domain Reconstruction Network Incorporating Multi-Level Wavelet Transform and Recurrent Convolution for Sparse View Computed Tomography Imaging.

Sparse view computed tomography (SVCT) aims to reduce the number of X-ray projection views required for reconstructing the cross-sectional image of an object. While SVCT significantly reduces X-ray radiation dose and speeds up scanning, insufficient projection data give rise to issues such as severe streak artifacts and blurring in reconstructed images, thereby impacting the diagnostic accuracy of CT detection. To address this challenge, a dual-domain reconstruction network incorporating multi-level wavelet transform and recurrent convolution is proposed in this paper. The dual-domain network is composed of a sinogram domain network (SDN) and an image domain network (IDN). Multi-level wavelet transform is employed in both IDN and SDN to decompose sinograms and CT images into distinct frequency components, which are then processed through separate network branches to recover detailed information within their respective frequency bands. To capture global textures, artifacts, and shallow features in sinograms and CT images, a recurrent convolution unit (RCU) based on convolutional long and short-term memory (Conv-LSTM) is designed, which can model their long-range dependencies through recurrent calculation. Additionally, a self-attention-based multi-level frequency feature normalization fusion (MFNF) block is proposed to assist in recovering high-frequency components by aggregating low-frequency components. Finally, an edge loss function based on the Laplacian of Gaussian (LoG) is designed as the regularization term for enhancing the recovery of high-frequency edge structures. The experimental results demonstrate the effectiveness of our approach in reducing artifacts and enhancing the reconstruction of intricate structural details across various sparse views and noise levels. Our method excels in both performance and robustness, as evidenced by its superior outcomes in numerous qualitative and quantitative assessments, surpassing contemporary state-of-the-art CNNs or Transformer-based reconstruction methods.

Research on the effects of processing Heishunpian from Aconiti lateralis radix praeparata on components and efficacy using the "step knockout" strategy.

Heishunpian is obtained through complex processing of Aconiti lateralis radix praeparata. However, the impact of each processing step on chemical compositions and pharmacological activities is still unclear. The mechanism of the processing needs to be further studied. The samples were all prepared using the "step knockout" strategy for UPLC-QTOF-MS analysis, and analgesic and anti-inflammatory efficacy evaluation. Each sample was analyzed by UPLC-QTOF-MS to determine the component differences. The hot plate test and acetic acid writhing test were used to evaluate the analgesic effect. Anti-inflammatory efficacy was evaluated by xylene-induced ear edema test. The correlation between components and efficacies was studied to screen the effective components for further investigating the processing of Heishunpian. Mass spectrum analysis results showed that 49 components were identified, and it appeared that brine immersion and rinsing had a great influence on the components. In the hot plate test, ibuprofen and Heishunpian had the most significant effect, while ibuprofen and the sample without rinsing showed the best efficacy for the acetic acid writhing test. The sample without dyeing had the best effect on ear edema. The correlation analysis indicated that mesaconine, aconine, 3-deoxyaconine, delbruine, and asperglaucide were potentially considered effective analgesic components. It is not recommended to remove brine immersion and rinsing. Boiling and steaming are necessary processes that improve efficacy. Dyeing, which does not have a significant impact on components and efficacy, may be an unnecessary process. This research has been of great significance in identifying anti-inflammatory and analgesic components and optimizing processing for Heishunpian.

Ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry combined with network pharmacology for analysis of potential quality markers of three processed products of Qingpi.

Qingpi, a well-known traditional Chinese medicine for qi-regulating and commonly processed into three types of pieces, has been widely used in the clinical application of liver disease for thousands of years. In this study, an ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry approach along with multivariate statistical analysis was developed to assess and characterize the differentiations of three processed products and confirm the potential quality markers of Qingpi. In addition, a systematic analysis combined with network pharmacology and molecular docking was performed to clarify the potential mechanism of Qingpi for the treatment of liver disease. As a result, 18 components were identified and an integrated network of Qingpi-Components-Target-Pathway-Liver Disease was constructed. Eight compounds were finally screened out as the potential quality markers acting on ten main targets and pathways of liver disease. Molecular docking analysis results indicated that the quality markers had a good binding activity with the targets. Overall, this work preliminarily identified the potential quality markers of three processed products of Qingpi, and predicted its targets in the prevention and treatment of liver disease, which can provide supporting information for further study of the pharmacodynamic substances and mechanisms of Qingpi.

Security optimization of synchronization in DFB lasers under constant-amplitude random-phase drive light by reducing drive-response correlation.

Common-signal-induced laser synchronization promoted a promising paradigm of high-speed physical key distribution. Constant-amplitude and random-phase (CARP) light was proposed as the common drive signal to enhance security by reducing the correlation between the drive and the laser response in intensity. However, the correlation in light phase is not examined. Here, we numerically reveal that the correlation coefficient of the CARP light phase and the response laser intensity (denoted as CCR-φD) can reach a value close to 0.6. Effects of parameters including optical frequency detuning, and modulation depth and noise bandwidth and transparency carrier density for CARP light generation are investigated in detail. By optimizing the optical frequency, modulation depth, and noise bandwidth, respectively, CCR-φD can be reduced to 0.32, 0.18, and 0.10. In the meantime, CCR-φD can be further reduced through secondary optimizing of parameters. CCR-φD can be further reduced by increasing transparent carrier density provided response laser synchronization is achieved. This work gives a new insight about the laser synchronization induced by common CARP light, and also contributes a suggestion of security improvement for physical key distribution based on laser synchronization.

Transmitter dispersion eye closure quaternary assessment based on linear CNN with 1 × 1 convolutional kernel.

Transmitter dispersion eye closure quaternary (TDECQ) is a vital metric to characterize the quality of four-level pulse amplitude modulation (PAM-4) optical signals. However, the traditional TDECQ assessment scheme is complex and time consuming, with heavy iterative operations. Therefore, accelerating the TDECQ assessment has great significance for photonic data-center interconnection (DCI) applications. Here, we propose and experimentally demonstrate a TDECQ assessment based on linear-convolutional neural network (L-CNN) with the 1 × 1 convolutional kernel to reduce the implementation complexity. Our experimental results verify that the lightweight L-CNN can realize the accurate TDECQ assessment, without the involvement of nonlinear activation functions (NAFs). The mean absolute error (MAE) of 26.5625 and 53.125 GBaud PAM-4 signals are 0.16 dB and 0.18 dB, respectively, over a TDECQ range from 1.5 to 4.0 dB. Meanwhile, in comparison with existing CNN-based schemes, the L-CNN based TDECQ assessment scheme only needs 2048 multiplications, which have been reduced by five orders of magnitude.

Chaos synchronization of VCSELs with common injection of polarization-random light.

We propose and numerically demonstrate chaos synchronization of two vertical-cavity surface-emitting lasers (VCSELs) induced by common injection of constant-amplitude random-polarization light for physical key distribution. Results show that synchronization is sensitive to polarization rotation of injection light, and synchronization coefficients larger than 0.9 can be achieved as the rotation-degree mismatch is smaller than ±10°. Therefore, polarization rotation degree can serve as a hardware key parameter. Furthermore, each laser's output has no correlation to the constant amplitude of the injected light. Their components with identical polarization state, e.g. x or y polarization of VCSEL, also have low correlation coefficient smaller than 0.2. It is therefore believed that this synchronization scheme can provide a security-enhanced method of physical key distribution.

Frequency offset estimation for nonlinear frequency division multiplexing with continuous spectrum modulation.

Carrier frequency offset (CFO) estimation is very important for the optical fiber communications and has been studied widely in linear coherent systems, while only a few works have been reported for nonlinear Fourier transform (NFT) based systems. In continuous spectrum (CS) modulation nonlinear frequency division multiplexing (CS-NFDM) systems, frequency offset (FO) has a great influence on its performance, requiring an improved frequency offset estimation (FOE) method. We found that the oversampling rate R0 adopted in NFDM to ensure the accuracy of the NFT and inverse NFT (INFT) calculations, would cause the estimation accuracy of the traditional FFT-FOE method to decrease by R0 times. Moreover, CS-NFDM signals with higher baud rate require more subcarriers and then result in an oversampling factor greater than 16. This makes the traditional FFT-FOE method be ineffective to use the common training sequence (TS) overhead to meet the FOE error requirement of CS-NFDM system. Therefore, a modified FOE method based on FFT assisted by TS and autocorrelation has been proposed. The theoretical analysis and simulation results show that the proposed method is applicable to CS-NFDM system, no matter what modulation format is used. For 512 subcarriers, with a high rate of 70GBaud and the TS length of 8192, the proposed method can obtain a minimum FO estimation error about 0.1 MHz, which is better than the other two typical FFT-FOE and Schmidl & Cox methods. In addition, the proposed method can save at least 87.5% and 50% overhead. Thus, the proposed method has obvious improvement for CS-NFDM system with requiring high oversampling rate.