Iet Optoelectronics最新文献

The authors aim to build a theoretical system for calibrating the position detection of a single quadrant photodiode (QPD) attached to 2D rotators and to suppress noise using a Sugeno-type fuzzy inference system. According to the results, if the original ANFIS network (NW_o) is trained under the conditions of resolution (rs_o), traing samples (N_o), and the possibility of random noise (p_o) and presented as {original, rs_o, N_o, p_o}, with the #X testing case presented as {case #X, rs_x, N_x, p_x}, case #X is expected to be the all-pass case of the two root-mean-squared error criteria under the conditions of rs_x ≥ rs_o and p_x ≤ p_o. Thus, the measurement accuracy is improved without tracking historical data. Furthermore, the two conditions indicate the limitations of the proposed ANFIS-based method. These conditions can be employed to save time and money. If a company produces many types of QPD rotators with different values of rs, the engineers can optimise the design to train the ANFIS network with only one case with the smallest rs and a larger p for all types of QPD rotators with any value of rs and p. This demonstrates the potential industrial application of the proposed method.

The cover image is based on the Article ANFIS-based controlled spherical rotator with quadrant photodiode to improve position detection accuracy by Bing-Yuh Lu et al., https://doi.org/10.1049/ote2.12127.

A coherent optical receiver digital signal processing (DSP) scheme is proposed based on spectral clustering, which utilises spectral clustering to cluster the signals outputted by the DSP. Compared to existing blind DSP algorithms for coherent optical receivers operating at over 100Gbps with PM-mQAM, the proposed scheme effectively suppresses various physical impairments, achieving lower bit error rates (BERs) at the same transmission distance, thus enabling longer transmission distances under the same forward error correction threshold. Furthermore, simulations of the proposed scheme on a 14GBaud PM-16QAM coherent optical transmission system show that, at a BER of 1.9E-2, the maximum transmission distance increases from 1700 to 2000 km, and at a BER of 3.8E-3, it increases from 700 to 900 km.

The authors present continuous wave (CW) high-power broad area and ridge waveguide lasers with laser emission at 626 nm at room temperature. For this, the authors employ GaAs-based diode lasers in the AlGaInP system for laser emission at 626 nm at room temperature. The laser structure is grown on three-inch wafers by metal organic vapour phase epitaxy. Broad area and ridge waveguide lasers are fabricated. Pulsed broad area laser characterisation on bar level shows laser operation at 20 C heat sink temperature. The authors measured peak lasing wavelengths as short as 625 nm and total maximum output power of both facets up to 1.4 W at an injection current of 2 A. Both, broad area and ridge waveguide lasers show laser operation and CW excitation at room temperature. The ridge waveguide lasers emit output powers of over 90 mW at 626 nm at a maximum injection current of 200 mA with a nearly diffraction-limited beam profile.

Free space optical communication (FSO) has become a popular research direction due to its high bandwidth, easy deployment, and inherent security. Its channel state is unstable because of atmospheric environment, especially in long-distance ground transmission system. Interleaving combined with forward error correction coding (FEC) have been utilised to improve the stability of system. However, there is little detailed experimental results of deep interleaving combined with FEC under different atmospheric turbulence intensity over long-distance FSO system. A deep interleaving with FEC method was designed and implemented on a 7 km long online experiment. The performance of different depth interleaving is analysed under weak and strong atmospheric turbulence state. The experiment results show that deep interleaving performs better under weak turbulence for the larger correlation factor of the channel and the outage probability of the system with deep interleaving can be greatly reduced.

Coherent Rayleigh scattering-based distributed fibre optic sensing technology enables real-time acquisition of vibration and acoustic information along the optical fibres. However, the complexity of monitoring environments often leads to false alarms and missed detections during the process of information source identification with distributed acoustic sensing (DAS). Therefore, it becomes crucial to effectively extract meaningful signal features and perform accurate pattern recognition in the presence of external noise disturbance. The authors provide a comprehensive review of signal feature extraction and pattern recognition techniques applied in DAS technology. After introducing the fundamentals of DAS, specific applications are considered, and the following techniques have been analysed and compared: feature extraction algorithms based on wavelet decomposition, feature extraction schemes utilising other decomposition models, traditional recognition classifiers, and neural network-based recognition classifiers using deep learning. The advantages and limitations of each scheme are discussed, along with their potential applications in various scenarios. The aim is to provide insights into the latest technologies in signal processing and pattern recognition for DAS, fostering further advancements in this field.

Among the emerging photovoltaic technologies, solid-state dye-sensitised solar cells (ssDSSCs) have attracted considerable interest due to their cost-effective production, adjustable characteristics, and potential for lightweight and flexible applications. Nevertheless, achieving efficiencies comparable to established technologies, such as perovskite and silicon-based solar devices, have proven challenging. Herein, the device structure, Pt/PEDOT: PSS/N719 dye/PC₆₁BM/ITO is investigated theoretically using the solar cell capacitance simulator (SCAPS-1D). Groundbreaking advancement is introduced in ssDSSC design, achieving remarkable theoretical power conversion efficiency of 20.73%, surpassing the performance reported in traditional dye-based solar cell technologies. The model ssDSSC demonstrates an exceptional Fill factor of 86.64%, indicating efficient current collection; along with a modest short-circuit current density (J_sc) of 22.38 mA/cm² and an impressive open-circuit voltage (V_oc) of 1.0691 V, highlighting efficient light absorption and charge separation. Mott–Schottky capacitance analysis and parasitic resistances (series and shunt) have been thoroughly discussed. Despite the fact that only numerical simulation is involved, the proposed ssDSSCs structure gives insights into the fabrication of a highly efficient solar cell that can be injected into the production workflow in order to advance the photovoltaic technology of the solid-state DSSC.

Waveguide imaging is considered as one of the most important and widely used techniques in biomedical endoscopic applications. Recently, many attempts have been made to develop ever miniaturised in vivo imaging devices for minimally invasive clinical inspections. However, miniaturisation implies using a smaller optical aperture waveguide, which may introduce pixilation artefacts and pixel-to-pixel distortion to deteriorate overall imaging quality. To overcome the constraints imposed by miniaturised waveguides, the deep learning algorithms can be an effective tool to cure the imaging distortion via post-processing, which already had encouraging results in many scenes of automatic machine-learnt imaging restoration. The authors introduce the waveguide imaging transmission and the restoration algorithms, and then discuss their possible combinations. The results show that the integration of advanced waveguides and optimised algorithms can achieve unprecedented imaging restoration than before. In the future, in order to fill the need for high-quality reconstructed images, we should not only improve ability of software to optimise restoration algorithms but also correspondingly concern hardware progress in waveguides. The practical sense of it is to help researchers better master and take advantage of these combinations to make next generation high-fidelity endoscopes.

A model is presented for calculating the electric field profiles of transverse modes in fibres. Using this model, the electric field propagation of transverse modes was calculated and compared in both bent and straight Yb and Ho fibres. Our simulations revealed that the field intensities of the modes did not consistently increase or decrease by bending as expected, but rather fluctuated as the bending radii of the fibres changed from 1 to 100 cm. Additionally, it was observed that at certain bending radii, the modes of the fibres became deformed and transitioned into higher modes. Furthermore, the extent to which the modes stretched towards cladding due to bending was calculated. To provide a more comprehensive analysis of the results and investigate the effects of the physical parameters of the fibres on the bent fibre modes, simulation results were presented for two different V numbers and core radii.

A method that combines phase-sensitive optical time domain reflectometry with deep learning to construct new voting fully convolution neural networks (VoteFCNs) is proposed. Compared to the traditional convolutional network, the VoteFCN can be input with data of random size and requires less parameters so that the training speed can be improved greatly. The recognition results can be more accurate and more reliable if we use classification voting count and average recognition rate as the criteria to judge network training quality. At last, the training and identification were conducted by simulating such several disturbance events: walking, raining, climbing fence, hammering the ground optical fibre and normal outdoor environments. The results show that the average test accuracy of this method is about 93.4%.

Point-to-point (P2P) flexible transceivers are the key technical enabler to cost-effectively offer fast, dynamic, and ‘just-the-right-size’ ultra-dense P2P connectivity for various applications including remote equipment control and distributed fibre networks. However, existing flexible transceivers originally designed for hub-and-spoke traffic patterns are sub-optimal. To effectively address such technical issue, a P2P flexible transceiver incorporating a cascaded inverse fast fourier transform/fast fourier transform-based multi-channel aggregation/de-aggregation technique and analogue in-phase and quadrature (IQ) mixers is proposed and numerically evaluated in a 56Gbps@20 km intensity modulation and direct detection transmission system. The proposed P2P flexible transceivers not only support adaptive and flexible variations in both channel count and channel line rate but also offer additional physical layer network security.