Universal BPS structure of scalar kinks in static geometries
We present a geometric extension of the Bogomolny-Prasad-Sommerfield (BPS) construction for scalar kinks in (1+1) dimensions embedded in static curved spacetimes. By introducing a nonminimal coupling between the scalar prepotential and the extrinsic curvature of the static foliation, the flat-space first-order Bogomolny equation remains exactly valid for arbitrary static backgrounds. As a consequence, the kink profile is unchanged, while the effective potential and vacuum structure acquire a controlled geometric dependence. We show that these curved-space BPS kinks are always linearly stable. However, the existence of the translational zero mode is not guaranteed: its normalizability depends on the competition between the intrinsic length scale of the kink and the asymptotic curvature scale of the geometry. When the geometric scale dominates, the zero mode is removed and the soliton becomes geometrically pinned, despite remaining an exact BPS solution. Explicit realizations in AdS2 demonstrate how different static slicings of the same spacetime lead to qualitatively distinct physical outcomes, ranging from preserved translational invariance to its complete removal by horizons. These results establish geometry as a precise mechanism for controlling solitonic moduli without compromising linear stability.
Asymmetric CNN-based equalization for clipping distortion compensation in LED-based VLC systems
Visible Light Communication (VLC) systems utilizing Direct Current-biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) are inherently limited by a high Peak-to-Average Power Ratio (PAPR), which leads to non-linear distortions due to the restricted dynamic range of LEDs. Conventional PAPR reduction techniques often impose significant computational overhead on the transmitter or severely degrade the Error Vector Magnitude (EVM). This paper proposes an asymmetric deep learning-based framework to mitigate clipping noise. We introduce a low-complexity clipping approach at the transmitter ((𝑁)) and shift the compensation burden to a Convolutional Neural Network (CNN) at the receiver. Two clipping ratios were evaluated: CR = 0.9 (moderate, 2.9 dB PAPR reduction) and CR = 0.7 (aggressive, 5.8 dB PAPR reduction). Experimental results from 20 spatial positions within a 2.0 × 1.0 m2 test area demonstrate that the proposed CNN effectively recovers the signal under both clipping conditions. For 64-QAM modulation with CR = 0.7,
the CNN achieves a mean EVM of 7.07%, below the 8% reference threshold defined by 3GPP standards, and a mean BER of 8.98 × 10−4, while the conventional MMSE equalizer reaches 15.05% EVM and 5.37 × 10−2 BER. The architecture shows consistent generalization and spatial robustness compared to standard Deep Neural Networks (DNNs), suggesting its potential for LED-based lighting applications.
RVFL-Based Nonlinearity Compensation for MISO VLC Systems: Experimental Validation and Performance Analysis
Visible Light Communication (VLC) systems employing multiple Light-Emitting Diode (LED) transmitters face significant challenges from aggregate nonlinear distortions that degrade signal quality and limit practical deployment. This work presents experimental validation of Random Vector Functional Link (RVFL) networks for post-distortion compensation in Multiple-Input Single-Output (MISO) VLC systems. RVFL’s closed-form training and memoryless architecture offer distinct computational advantages over iterative methods. Comprehensive experimental validation across variable drive amplitude (300-900 mV) and bias current (50-350 mA) in a 2×1 dual-luminaire testbed demonstrates that RVFL achieves EVM of 7.81% for 16-QAM and 3.97% for 64-QAM at 200 cm distance, enabling reliable transmission where uncompensated signals exceed thresholds. Comparative analysis reveals superior computational efficiency: RVFL inference requires 45.5 ms versus 502.5 ms for Artificial Neural Networks (ANN) and 338.5 ms for Volterra, achieving 11× faster inference than ANN. These results position RVFL as an attractive candidate for practical VLC receivers where computational efficiency is critical.
Influence of vertical X-ray beam angulation and receptor-holders on the radiographic detection of implant abutment–prosthesis misfit
Periapical radiographs have been the primary adjunctive diagnostic method of evaluating abutment-prosthesis misfit. However, whether different radiographic receptor‑holders interfere with diagnostic accuracy depending on the vertical X-ray beam angulation remains unclear.
Photonic Spatial Switching Techniques Using Semiconductor Optical Amplifiers
This work presents a detailed analysis of the key switching parameters of semiconductor optical amplifiers (SOAs). Based on a numerical model of an InPhenix SOA, a dataset was generated, enabling a rigorous examination of the principal parameters of the PISIC and MISIC techniques. Furthermore, a Multi-Layer Perceptron (MLP) was employed to solve the inverse SOA problem, achieving a low mean squared error.
Optimizing L-Band EDFA Pumping Schemes via Recurrent Neural Networks and Water Cycle Algorithm
This work presents an analysis of L-band Erbium-Doped Fiber Amplifier (EDFA) performance under various pumping configurations. We employed an artificial neural network and a multi-objective optimization algorithm to find the amplifier Pareto front for gain and noise figure. To achieve this, we utilized a recurrent neural network (RNN), trained with experimentally acquired data, jointly with the Multi-Objective Water Cycle Algorithm (MOWCA). Results demonstrate the feasibility of achieving reasonable gains (up to 22 dB) with low noise figures (around 4 dB) across various pumping configurations. Furthermore, we observed a potential gain increment of up to 3.5 dB without significant noise figure degradation when employing bi-directional pumping compared with forward pumping. These insights may contribute to the design of more efficient L-band EDFAs.
Versatile neuromorphic photonic processor for next-generation optical communication systems
Building upon an 8-tap delayed silicon-on-insulator (SOI) photonic neural network (PNN) pre-equalizer validated at 10-GHz bandwidth orthogonal frequency-division multiplexing (OFDM) intensity-modulation direct detection (IMDD), we experimentally probe the operational boundaries of this architecture by scaling to 20-GHz bandwidth operation and 16-QAM modulation. We quantify performance across 5/10/20-GHz OFDM bandwidths and QAM4/QAM16, approaching the sampling limit imposed by the fixed 25-ps tap spacing. In this way, we experimentally demonstrate its applicability to preserve the signal integrity near the Nyquist limit with improvements in all tested configurations.
Analytical FIR Optical Filter Based on Time-Delayed Photonic Neural Networks for 40 Gbps NRZ Dispersion Compensation
This paper presents an analytical method for designing FIR filters based on the hardware of time-delayed photonic neural networks for chromatic dispersion compensation in long-reach passive optical networks. The model employs a Fourier-based synthesis of the compensation function, allowing for the closed-form determination of phase and amplitude weights for each optical path. Different implementation strategies are considered, including solutions with phase-only control and others with additional amplitude control using tunable attenuators. Simulations over a 100 km standard single-mode fiber link demonstrate effective dispersion compensation for NRZ-OOK signals. In particular, the symmetric FIR-based design with 23 optical paths and 25 ps delay spacing achieved BER < 10-3 at data rates up to 40 Gbps under amplified spontaneous emission noise. These results confirm the analytical approach's potential to provide scalable, power-efficient, and high-speed solutions for future long-reach optical access networks.
Integrated Photonic Enabling Massive Data Transfer: A Short Review
We have been living in a digital revolution, with the creation of new online services, the popularization of electronics, the advent of generative AI, and a growing trend of virtualization of things, allowing the creation of a digital twin version of a real object. As this revolution pushes society forward, there is a high performance demand for its infrastructure, such as telecommunication networks. There is a constant need to transmit a massive amount of data, which is increasing with the market demand. The scientific community has been working to bring new solutions to this demand. One possibility is simplifying the edge devices, adding open issues, and reducing system latency. In this context, photonic integrated circuits can play a significant role. This work concisely reviews the current development of photonics integrated circuits (PIC) for optical systems with strict requirements and the demand for massive data transfer. As the current telecommunication scenario rapidly evolves due to market demands and the creation of new requirement-sensitive services, it is crucial to understand the role of integrated photonics in telecommunication networks as discussed in this work. Also, it is presented how open issues, such as chromatic dispersion, can be handled by PIC, leveraging the overall performance of optical telecommunication systems.
Experimental Crosstalk Analysis in SDM Systems with Multimode Fiber Geometry Mismatch
The crosstalk performance of different 50μ m GIMMFs with 45 spatial modes is studied using a packaged multi-plane light converter and a 96×96 optical switch for full cardinality characterization. The impact of combining different refractive index geometries and of environment-induced perturbations are addressed.