Search Results (817)

This paper proposes an underwater free-space optical code division multiple-access (FSO-CDMA) communication system that integrates differential pulse position modulation (DPPM) with a developed hybrid prime code (HPC) and Manchester encoding, alongside a multiple-access interference cancellation (MAIC) receiver. The system is designed to address the challenges posed by underwater turbulence, absorption, and scattering. A 1-watt laser source operating at a wavelength of 455 nm is utilized to mitigate these effects, thereby reducing link absorption, scattering, and attenuation. The MAIC receiver is employed to minimize noise and interference, significantly enhancing the system’s bit-error rate (BER) performance under turbulent conditions. The paper details the construction and correlation analysis of the HPC, and investigates both instantaneous and average BER performance based on the proposed modulation-coding technique and MAIC receiver. Results demonstrate that the proposed system achieves a 438 m link distance with an 8.33 Gbps data rate and 1.33 Tbps × user network throughput over a 184,900 m² coverage area, maintaining a BER of 10⁻⁹. Full article

This study theoretically investigates the coupling efficiency of multi-Gaussian Shell-mode (MGSM) beams in ocean turbulence. The expression for the fiber-coupling efficiency of the MGSM beams propagating through oceanic turbulent media is derived using the cross-spectral density function. Numerical simulations are performed to examine the relationship between fiber-coupling efficiency and the beam order, and the scintillation index of the MGSM beams in ocean turbulence is also examined. In the analysis of transmission efficiency, the effects of the receiving aperture and source coherence on transmission efficiency are investigated, taking into account ocean turbulence induced by salinity and temperature fluctuations. The analysis of the fiber-coupling efficiency for MGSM beams presented in this work provides insights for optimizing the design of free-space optical communication systems. Full article

The rapid development of continuous-variable quantum communication has driven an increasing demand for high-performance quantum signal processing modules. Among these, the balanced homodyne detector (BHD) has emerged as a leading solution for practical quantum state measurement due to its capability to provide complete quantum mechanical characterization. However, its performance is often constrained by limited bandwidth and high noise levels, primarily due to the reliance on bulk optical components and discrete receiver electronics. The dominant noise source in these systems typically stems from electronic noise, while imbalances in the optical path further degrade the signal-to-noise ratio (SNR) of the BHD. In this work, we present an adjustable integrated optical path to enhance the balance within the BHD system, along with a low-noise transimpedance amplifier (TIA) by employing optoelectronic co-design. Our design achieves a bandwidth of 2.5 GHz, an input-referred noise current of only 2 pA/√Hz in 180 nm CMOS technology, and a measured quantum shot noise clearance of 15 dB generated from a 700 μA photocurrent. This is the maximum quantum shot noise clearance at the same BHD photocurrent reported to date above the GHz bandwidth. Full article

Space gravitational wave detection, which could help humanity explore the mysteries of the universe, is a significant objective in the scientific world today, and several different countries and scientific groups have organized programs targeting its realization. The telescope for gravitational wave detection is a crucial component in the detection satellite, as it is the means of receiving and transmitting the interferometric laser beam; therefore, its structural design is very significant. This paper focuses on the telescope in the TianQin program. First, a structural design scheme is given based on a five-mirror optical system Then, some of the component’s parts are refined to improve its mechanical performance. Finally, a mechanical simulation analysis is performed to verify its reliability and feasibility during the rocket launch. The results reveal that the presented structural design scheme for the telescope is both safe and viable. Full article

We experimentally demonstrate an 80 Gbit/s Nyquist-dense wavelength division multiplexed (Nyquist-DWDM) transmission system operating in a simulated atmospheric turbulence channel. The system utilizes eight wavelength-tunable lasers with 100 GHz spacing, modulated by cascaded Mach–Zehnder modulators, to generate phase-locked Nyquist pulse sequences with a 10 GHz repetition rate and a temporal width of 66.7 ps. Each channel is synchronously modulated with a 10 Gbit/s pseudo-random bit sequence (PRBS) and transmitted through controlled weak turbulence conditions generated by a temperature-gradient convection chamber. Experimental measurements reveal that, as the turbulence intensity increases from $C_n^2=1.01\times {10}^{-16}$ to $5.71\times {10}^{-16} {\mathrm{m}}^{-2/3}$, the signal-to-noise ratio (SNR) of the edge channel (C29) and central channel (C33) decreases by approximately 6.5 dB while maintaining stable Nyquist waveform profiles and inter-channel orthogonality. At a forward-error-correction (FEC) threshold of $3.8\times {10}^{-3}$, the minimum receiver sensitivity is −17.66 dBm, corresponding to power penalties below 5 dB relative to the back-to-back condition. The consistent SNR difference (<2 dB) between adjacent channels confirms uniform power distribution and low inter-channel crosstalk under turbulence. These findings verify that Nyquist pulse shaping substantially mitigates phase distortion and scintillation effects, demonstrating the feasibility of high-capacity DWDM free-space optical (FSO) systems with enhanced spectral efficiency and turbulence resilience. The proposed configuration provides a scalable foundation for future multi-wavelength FSO links and hybrid fiber-wireless optical networks. Full article

Precise positioning is critical for autonomous uncrewed aerial vehicle (UAV) navigation, especially in GNSS-denied environments where radio-based signals are unreliable. This study presents an optical camera communication (OCC)-based positioning method that enables real-time 3D coordinate estimation using aviation obstruction light-emitting diodes (LEDs) as optical transmitters and a UAV-mounted camera as the receiver. In the proposed system, absolute positional identifiers are encoded into color-shift-keying-modulated optical signals emitted by fixed LEDs and captured by the UAV camera. The UAV’s 3D position is estimated by integrating the decoded LED information with geometric constraints through the Perspective-n-Point algorithm, eliminating the need for satellite or RF-based localization infrastructure. A virtual offshore wind farm, developed in Unreal Engine, was used to experimentally evaluate the feasibility and accuracy of the method. Results demonstrate submeter localization precision over a 50,000 cm flight path, confirming the system’s capability for reliable, real-time positioning. These findings indicate that OCC-based positioning provides a cost-effective and robust alternative for UAV navigation in complex or communication-restricted environments. The offshore wind farm inspection scenario further highlights the method’s potential for industrial operation and maintenance tasks and underscores the promise of integrating optical wireless communication into autonomous UAV systems. Full article

This study presents a near-real-time water stress monitoring framework for tropical heterogeneous landscapes by integrating optical and radar remote sensing data within the Google Earth Engine platform. Five complementary indices, vertical transmit/vertical receive–vertical transmit/horizontal receive (VV/VH) ratio, Dual Polarimetric Radar Vegetation Index (DpRVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Ratio Drought Index (RDI), were analyzed across three contrasting agricultural systems: paddy, sugarcane, and rubber, revealing distinct phenological and water stress dynamics. Radar-derived structural indices captured patterns of biomass accumulation and canopy development, with VV/VH values ranging from 4.2 to 12.3 in paddy and 5.4 to 6.0 in rubber. In parallel, optical moisture indices detected crop physiological stress; for instance, NDMI dropped from 0.26 to 0.06 during drought in sugarcane. Cross-index analyses demonstrated strong complementarity; synchronized VV/VH and RDI peaks characterized paddy inundation, whereas lagged NDMI–VV/VH responses captured stress-induced defoliation in rubber trees. Temporal profiling established crop-specific diagnostic signatures, with DpRVI peaking at 0.75 in paddy, gradual RDI decline in sugarcane, and NDMI values of 0.2–0.3 in rubber. The framework provides spatially explicit, temporally continuous, and cost-effective monitoring to support irrigation, drought early warning, and agricultural planning. Multi-year validation and field-based calibration are recommended for operational implementation. Full article

Diabetic retinopathy (DR) and diabetic macular edema (DME) remain major causes of vision loss among working-age adults. Artificial intelligence (AI), particularly deep learning, has gained attention in ophthalmic imaging, offering opportunities to improve both diagnostic accuracy and efficiency. This review examined applications of AI in DR and DME published between 2010 and 2025. A narrative search of PubMed and Google Scholar identified English-language, peer-reviewed studies, with additional screening of reference lists. Eligible articles evaluated AI algorithms for detection, classification, prognosis, or treatment monitoring, with study selection guided by PRISMA 2020. Of 300 records screened, 60 met the inclusion criteria. Most reported strong diagnostic performance, with sensitivities up to 96% and specificities up to 98% for detecting referable DR on fundus photographs. Algorithms trained on optical coherence tomography (OCT) data showed high accuracy for identifying DME, with area under the receiver operating characteristic curve (AUC) values frequently exceeding 0.90. Several models also predicted anti-vascular endothelial growth factor (anti-VEGF) treatment response and recurrence of fluid with encouraging results. Autonomous AI tools have gained regulatory approval and have been implemented in clinical practice, though performance can vary depending on image quality, device differences, and patient populations. Overall, AI demonstrates strong potential to improve screening, diagnostic consistency, and personalized care, but broader validation and system-level integration remain necessary. Full article

The multidimensional parameter measurement of microwave signals, including temporal, spatial, and frequency, is essential for electronic warfare and radar systems. In this article, we present a photonic scheme for real-time microwave frequency and angle-of-arrival (AOA) measurement based on stimulated Brillouin scattering (SBS). In the proposed system, the unknown signal under test (SUT) received by adjacent antennas is injected into a dual-drive Mach–Zehnder modulator (DDMZM). Two branches of the SUT with phase difference interfere in the optical domain, converting phase difference into the power of optical sidebands. These optical sidebands are scanned by combining SBS with frequency-to-time mapping (FTTM) to achieve simultaneous measurement of the AOA and frequency. Consequently, the frequency and AOA of the SUT are mapped to the time interval and normalized amplitude of the output electrical pulses, respectively. Results show that the system can achieve the frequency measurement of multiple RF signals in the range of 5–15 GHz and AOA measurement in the range of −70° to 70°, with measurement errors of ±5 MHz and ±2°, respectively. Furthermore, the frequency measurement range can be flexibly adjusted by tuning the pump optical driving signals. Full article

This study explores the application of JAYA optimization algorithms to significantly enhance the performance of indoor optical wireless communication (OWC) systems. By strategically optimizing photo-signal parameters, the system was able to improve signal distribution and reception within a confined space using circular and randomly positioned diffuse spots. The primary objective was to maximize signal-to-noise ratio (SNR) and minimize delay spread (DS), two critical factors that affect transmission quality in OWC systems. Given the challenges posed by background noise and multipath dispersion, an effective optimization strategy was essential to ensure robust signal integrity at the receiver end. Key achievements of JAYA optimization include significant performance gains, such as a 29% improvement in SNR, enhancing signal clarity and reception, and a 23.3% reduction in delay spread, ensuring stable and efficient transmission. System stability also improved, with the standard deviation of SNR improving by up to 5%, leading to a more consistent performance, while the standard deviation of delay spread improved by up to 9.9%, minimizing variations across receivers. Resilience against environmental challenges: Optimization proved effective even in the presence of ambient light noise and complex multipath dispersion effects, reinforcing its adaptability in real-world applications. The findings of this study confirm that JAYA optimization algorithms offer a powerful solution for overcoming noise and dispersion issues in indoor OWC systems, leading to more reliable and high-quality optical wireless communications. These results underscore the importance of algorithmic precision in enhancing system performance, paving the way for further advancements in indoor optical networking technologies. Full article

Optical Wireless Communication (OWC) technologies are emerging as promising complements to radio-frequency systems, offering high bandwidth, spatial confinement, and license-free operation. Within this domain, Visible Light Communication (VLC) and Optical Camera Communication (OCC) represent two distinct paradigms with divergent performance and security profiles. While VLC leverages LED-photodiode links for high-speed data transfer, OCC exploits ubiquitous image sensors to decode modulated light patterns, enabling flexible but lower-rate communication. Despite their potential, both remain vulnerable to various attacks, including eavesdropping, jamming, spoofing, and privacy breaches. This work applies—and extends—the Risk Matrix (RM) methodology to systematically evaluate the security of VLC and OCC across reconnaissance, denial, and exploitation phases. Unlike prior literature, which treats VLC and OCC separately and under incompatible threat definitions, we introduce a unified, domain-specific risk framework that maps empirical channel behavior and attack feasibility into a common set of impact and likelihood indices. A normalized risk rank (NRR) is proposed to enable a direct, quantitative comparison of heterogeneous attacks and technologies under a shared reference scale. By quantifying risks for representative threats—including war driving, Denial of Service (DoS) attacks, preshared key cracking, and Evil Twin attacks—our analysis shows that neither VLC nor OCC is intrinsically more secure; rather, their vulnerabilities are context-dependent, shaped by physical constraints, receiver architectures, and deployment environments. VLC tends to concentrate confidentiality-driven exposure due to optical leakage paths, whereas OCC is more sensitive to availability-related degradation under adversarial load. Overall, the main contribution of this work is the first unified, standards-aligned, and empirically grounded risk-assessment framework capable of comparing VLC and OCC on a common security scale. The findings highlight the need for technology-aware security strategies in future OWC deployments and demonstrate how an adapted RM methodology can identify priority areas for mitigation, design, and resource allocation. Full article

This paper proposes a free-space optical (FSO) system for early warning and detection of natural disasters. The system consists of multiple sensor nodes equipped with mirrors, motors, and controllers. Under normal weather conditions, signals are transmitted between nodes. However, in the presence of frost, signal reflection is directed to a base station (BS) by adjusting the mirrors’ orientation based on the reflection angle determined using Snell’s law. By monitoring the round-trip time of the signal to the BS, the frost-affected node can be identified. The power received at various nodes is analyzed, considering reflections from mirrors and path attenuation. The results indicate that sufficient power levels can be achieved for six nodes, covering an area of approximately 314.16 km². The total time required to send an alarm signal to the BS is calculated and compared with the systems proposed in the literature. The proposed system demonstrates a time reduction of up to 69.7% compared to systems where the signal traverses all nodes before reaching the BS and a 7% reduction compared to systems employing dedicated transmitters and receivers. The proposed system is easy to deploy in a crop area with a fast response and relatively low power consumption, making it an efficient solution for early warning of frost. To the author’s best knowledge, the proposed system is the first one to exploit mirrors in free-space optics for early warning of frost. The analysis presented in this paper is very helpful for further investigations into using mirrors in FSO systems for early warning and notification of natural disasters such as frost. Full article

The development of minimally invasive diagnostic devices in the biomedical field has grown significantly, especially those that take advantage of photoacoustic phenomena. Photoacoustic imaging is an imaging technique that exploits the photoacoustic effect, relying on the conversion of absorbed light into ultrasound waves. Thanks to lab-on-fiber technology, optical fiber can be functionalized to generate and receive a photoacoustic signal. Weak acoustic signals often limit this process, as conversion efficiency can be influenced by factors such as tissue heterogeneity, light scattering, and the attenuation of the acoustic waves within tissues. Consequently, there is significant interest in the development of highly sensitive systems with broad bandwidths. While the literature has largely focused on standard devices utilizing the interferometric effect in homogeneous slabs, this study explores the potential of multilayer structures that leverage Bragg reflection to be realized on the fiber tip. We numerically investigated both periodic and aperiodic designs of polymeric multilayer structures to further enhance the optical performance of opto-acoustic sensors. We demonstrate an enhancement in sensitivity of up to about three orders of magnitude without compromising bandwidth. This work highlights the advantages of multilayer sensor designs in improving sensitivity and performance for high-frequency opto-acoustic sensing. Full article

For the first time, a highly energy-efficient 32-parallel 64-point FFT scheme for IMDD OFDM-PON is proposed and implemented on a Xilinx ML605 platform. By experimentally verifying the power consumption model for the FPGA logic resources utilized in the FFT, the relationship between FFT calculating consumption and FPGA logic resource usage is established. Based on this relationship, we derive a resource selection principle for the FFT bit resolution optimization to minimize power consumption under different levels of received optical power. Consequently, the proposed FFT achieves a 76.1% reduction in power consumption compared to the traditional Spiral FFT at a received optical power of −21 dBm. Based on the proposed FFT, the real-time OFDM-PON receiver power consumption can save up to 43% compared with traditional OFDM-PON system. Full article

Visible light positioning (VLP) has emerged as a promising indoor positioning technology, owing to its high accuracy and cost-effectiveness. In practical scenarios, signal attenuation, multiple light reflections, or light-deficient regions, particularly near room corners or furniture, can significantly degrade the light quality. In addition, the non-uniform light distribution by light-emitting diode (LED) luminaires can also introduce errors in VLP estimation. To mitigate these challenges, recent studies have increasingly explored the use of machine learning (ML) techniques to model the complex nonlinear characteristics of indoor optical channels and improve VLP performance. Convolutional neural networks (CNNs) have demonstrated strong potential in reducing positioning errors and improving system robustness under non-ideal lighting conditions. However, the performance of CNN-based systems is highly sensitive to their hyperparameters, including learning rate, dropout rate, batch size, and optimizer selection. Manual tuning of these parameters is not only time-consuming but also often suboptimal, particularly when models are applied to new or dynamic environments. Therefore, there is a growing need for automated optimization techniques that can adaptively determine optimal model configurations for VLP tasks. In this work, we propose and demonstrate a VLP system that integrates received signal strength (RSS) signal pre-processing, a CNN, and particle swarm optimization (PSO) for automated hyperparameter tuning. In the proof-of-concept VLP experiment, three different height layer planes (i.e., 200, 225, and 250 cm) are employed for the comparison of three different ML models, including linear regression (LR), an artificial neural network (ANN), and a CNN. For instance, the mean positioning error of a CNN + pre-processing model at the 200 cm receiver (Rx)-plane reduces from 9.83 cm to 5.72 cm. This represents an improvement of 41.81%. By employing a CNN + pre-processing + PSO, the mean error can be further reduced to 4.93 cm. These findings demonstrate that integrating PSO-based hyperparameter tuning with a CNN and RSS pre-processing significantly enhances positioning accuracy, reliability, and model robustness. This approach offers a scalable and effective solution for real-world indoor positioning applications in smart buildings and Internet of Things (IoT) environments. Full article