Search Results (457)

Ray-based blind deconvolution (RBD) is a technique for estimating the source-to-receiver array channel impulse response (CIR) without prior knowledge of the source waveform. Given its diverse applications, including source–receiver range estimation and the inversion of ocean waveguide parameters, RBD has been actively studied in underwater acoustics. However, the accuracy of CIR estimation in RBD may be compromised by phase uncertainty in the source waveform, necessitating enhancements in its performance. This paper proposes a method to improve RBD performance by estimating the phase of the source waveform using an optimization algorithm. Specifically, the particle swarm optimization (PSO) algorithm is employed to minimize phase estimation errors by optimizing the time delay for each receiver to maximize the beamformer output. The effectiveness of the proposed method was evaluated using two types of source signals: ship noise and linear frequency modulation (LFM), which corresponded to relatively low- and high-frequency sources, respectively. Performance comparisons with conventional RBD across various source-to-vertical line array distances revealed that the proposed method yielded more compact arrival paths with reduced time spread and a higher signal-to-noise ratio at short distances in the low-frequency band, and it consistently outperformed conventional RBD at all distances in the high-frequency band. Full article

This paper describes a simulation study on the hard turning of AISI 52100 alloy steel with coated carbide tools under minimal cutting fluid conditions using the commercial software AdvantEdge. A finite element analysis coupled with adaptive meshing was carried out to accurately capture temperature gradients. To minimise the number of experiments while optimising the cutting parameters along with fluid application parameters, a cutting speed (v) of 80 m/min, feed rate (f) of 0.05 mm/rev, depth of cut (d) of 0.15 mm, nozzle stand-off distance (NSD) of 20 mm, jet angle (JA) of 30°, and jet velocity (JV) of 50 m/s were observed to be the optimal process parameters based on the combined response’s signal-to-noise ratios. The effects of each parameter on machined surface temperature, cutting force, cutting temperature, and tool–chip contact length were determined using ANOVA. The depth of cut affected cutting force, while cutting speed and jet velocity affected cutting temperature and tool–chip contact length. Cutting speed influenced machined surface temperature significantly, whereas other parameters showed minimal effect. Nozzle stand-off distance exhibited less significant effect. Taguchi optimisation determined the optimal combination of process parameters for minimising thermal effects during hard turning. Cutting temperature and cutting force simulation results were found to be highly consistent with experimental results. On the other hand, the simulated results for the tool–chip contact length and machined surface temperature were very close to the values found in the literature. The result validated the finite element model’s ability to accurately simulate thermal behaviour during hard-turning operations. Full article

Brain–computer interfaces (BCIs) provide a direct communication pathway between the central nervous system and external environments, enabling human–machine interaction control. Among them, event-related potential (ERP)-based BCIs are among the most accurate and reliable BCI systems. However, current mainstream classification algorithms struggle to eliminate calibration requirements and rely heavily on costly labeled data, limiting the practical usability of ERP-based BCIs. To address this, the development of unsupervised algorithms is critical for advancing real-world BCI applications. In this study, we propose the spatio-temporal equalization sliding-window distribution distance maximization (STE-sDDM) algorithm, which introduces spatio-temporal equalization (STE) to unsupervised ERP classification for the first time and integrates it with a novel unsupervised classification method, sliding-window distribution distance maximization (sDDM). STE estimates and removes colored noise interference in background noise to enhance the signal-to-noise ratio of inputs for sDDM. Meanwhile, sDDM leverages an enhanced inter-class divergence metric based on the ergodic hypothesis theory, utilizing sliding windows to emphasize temporally discriminative features, thereby improving unsupervised classification accuracy. The experimental results demonstrate that the integration of STE and sDDM significantly enhances ERP feature separability, outperforming state-of-the-art unsupervised online classification algorithms in spelling accuracy and the information transfer rate (ITR), facilitating more accurate and faster plug-and-play real-time control for BCI systems. Additionally, static spatio-temporal equalizer architectures were found to outperform dynamic architectures when combined with this framework. Full article

Underwater acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication. Full article

The jamming of Global Navigation Satellite Systems (GNSSs) is now a major threat for GNSS-based Position, Navigation, and Timing (PNT) users. A jammed receiver will lose its fix at a certain distance and will not be able to provide PNT information. At greater distances, there will be a fix, so this PNT information can be obtained; however, the information will be less accurate, as the carrier-to-noise ($C/N_0$) ratios of the received signals will be suppressed by the jammer. In this paper, the pseudo-range accuracy of a GNSS receiver under jamming conditions is investigated in order to provide more insight into the effects of a jammer on the accuracy of a GNSS receiver. The theory available in the literature will be reviewed, after which this theory will be evaluated by comparing the theoretical results with actual measurements using a high-end GNSS signal simulator and a software-defined GNSS receiver. Full article

Traditionally, microphone array networks for 3D sound source localization rely on centralized data processing, which can limit scalability and robustness. In this article, we recast the task of sound source localization (SSL) with networks of acoustic arrays as a distributed optimization problem. We then present two resolution approaches of such a problem; one is computationally centralized, while the other is computationally distributed and based on an Adapt-Then-Combine (ATC) diffusion strategy. In particular, we address 3D SSL with a network of linear microphone arrays, each of which estimates a stream of 2D directions of arrival (DoAs) and they cooperate with each other to localize a single sound source. We develop adaptive cooperation strategies to penalize the arrays with the most detrimental effects on localization accuracy and improve performance through error-based and distance-based penalties. The performance of the method is evaluated using increasingly complex DoA stream models and simulated acoustic environments characterized by various levels of reverberation and signal-to-noise ratio (SNR). Furthermore, we investigate how the performance is related to the connectivity of the network and show that the proposed approach maintains high localization accuracy and stability even in sparsely connected networks. Full article

Magnetoencephalography (MEG) is a vital tool for understanding neural dynamics, offering a noninvasive technique for measuring subtle magnetic field variations around the scalp generated by synchronized neuronal activity. Two prominent sensor technologies exist: the well-established superconducting quantum interference device (SQUID) and the more recent optically pumped magnetometer (OPM). Although many studies have compared these technologies using human-subject data in neuroscience and clinical studies, a direct hardware-level comparison using dry phantoms remains unexplored. This study presents a framework for comparing SQUID- with OPM-MEG systems in a controlled environment using a dry phantom that emulates neuronal activity, allowing strict control over physiological artifacts. Data were obtained from SQUID and OPM systems within the same shielded room, ensuring consistent environmental noise control and shielding conditions. Positioning the OPM sensors closer to the signal source resulted in a signal amplitude approximately 3–4 times larger than that detected by the SQUID-MEG system. However, the source localization error of the OPM-MEG system was approximately three times larger than that obtained by the SQUID-MEG system. The cause of the large source localization error was discussed in terms of sensor-to-source distance, sensor count, signal–noise ratio, and the spatial coverage provided by the sensor array of the source signal. Full article

Reconfigurable Intelligent Surfaces (RISs) and Non-Orthogonal Multiple Access (NOMA) have emerged as key technologies for next-generation (6G) wireless networks, attracting significant attention from researchers. As an advanced extension of RISs, Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) offer superior geometric and functional symmetry due to their capability to simultaneously reflect and transmit signals, thereby achieving full 360° spatial coverage. This symmetry not only ensures balanced energy distribution between the Transmission (T) and Reflection (R) regions but also facilitates interference cancellation through phase alignment. Furthermore, in NOMA networks, the symmetric allocation of power coefficients and the tunable transmission and reflection coefficients of STAR-RIS elements aligns with the principle of resource fairness in multi-user systems, which is crucial for maintaining fairness under asymmetric channel conditions. In this study, key factors, such as interference sources and distance effects, are considered in order to conduct a detailed analysis of the performance of STAR-RIS-assisted NOMA wireless education networks under multiple interference devices. Specifically, a comprehensive analysis of the Signal-to-Interference-plus-Noise Ratio (SINR) for both near-end and far-end devices is conducted, considering various scenarios, such as whether or not the direct communication link exists between the base station and the near-end device, and whether or not the near-end device is affected by interference. Based on these analyses, closed-form approximate expressions for the outage probabilities of the near-end and far-end devices, as well as the closed-form approximation for the system’s Spectral Efficiency (SE), are derived. Notably, the Gamma distribution is used to approximate the square of the composite channel amplitude between the base station and the near-end device, effectively reducing computational complexity. Finally, simulation results validate the accuracy of our analytical results. Both numerical and simulation results show that adjusting the base station’s power allocation, and the transmission and reflection coefficients of the STAR-RIS, can effectively mitigate the impact of interference devices on the near-end device and enhance the communication performance of receiving devices. Additionally, increasing the number of STAR-RIS elements can effectively improve the overall performance of the near-end device, far-end device, and the entire system. Full article

In order to meet the reliability requirements of communication for underwater resource exploration, this study develops an underwater wireless optical communication (UWOC) system utilizing a blue semiconductor laser as the light source. At the receiver, a fully digital automatic gain control (AGC) module, implemented on a field-programmable gate array (FPGA), is designed to mitigate signal fluctuations induced by underwater turbulence. Digital filtering techniques, including median filtering (MF) and bilateral edge detection filtering (BEDF), are also employed to improve signal demodulation reliability. An improved Reed–Solomon (RS) coding scheme is further adopted to significantly reduce the bit error rate (BER). The design of a highly reliable UWOC system was realized based on the above techniques. The system’s performance was evaluated across a range of signal-to-noise ratios (SNRs) and bubble intensities. The results show that the digital AGC module can provide a gain range from −3.2 dB to 16 dB, adapting to varying signal strengths, which greatly bolsters the system’s resilience against underwater turbulence. Filtering techniques and RS coding further enhance the system’s immunity to interference and reduce the system BER. Communication experiments were conducted over various distances under three distinct water quality conditions. The results demonstrate that, within the detection range of the avalanche photodiode (APD), the system consistently maintained a BER below 3.8 × 10⁻³ across all water types, thereby confirming its high reliability. In clear seawater, the system demonstrated reliable information transmission over a 10 m distance at a data rate of 10 Mbps, achieving a BER of 2 × 10⁻⁸. Theoretical calculations indicate that the maximum transmission distance in clear seawater can reach 111.35 m. Full article

Fault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems. To address the challenge that traditional single-end positioning methods experience when identifying the reflected wave head and that the adaptability of wave head calibration methods is typically limited, a single-end location method of modulus wave velocity differences based on marine predator algorithm optimized multivariate variational mode decomposition (MVMD) and morphological gradient is proposed. Firstly, the minimum comprehensive entropy kurtosis ratio is used as the fitness function, and the marine predator algorithm is used to realize the automatic optimization of the mode number and penalty factor of the multivariate variational mode decomposition. Therefore, with the goal of decomposing the traveling wave characteristic signals with the most significant traveling wave characteristic information and the lowest noise component, the line-mode traveling wave and the zero-mode traveling wave are accurately decomposed. Secondly, the intrinsic mode function component with the smallest entropy kurtosis ratio is selected as the line-mode traveling wave characteristic signal and the zero-mode traveling wave characteristic signal, respectively, and the arrival time of the wave head is accurately calibrated by combining the morphological gradient value. Finally, the fault distance is calculated by the modulus wave velocity difference location formula and compared with the variational mode decomposition-Teager energy operator (VMD-TEO) method and the empirical mode decomposition _first-order difference method. The results show that the proposed method has the highest accuracy of positioning results, and the algorithm time is significantly reduced compared with the VMD-TEO method, and it has strong adaptability to different line types of faults, different fault initial conditions, and noise interference. Full article

Solar irradiance is a critical factor influencing the reliability of optical inter-satellite links (O-ISLs). Despite its significance, limited research has focused on addressing this challenge. This work investigates the impact of solar irradiation on the optimal path configuration. A multi-directional field-of-view (FoV) model is used to practically accommodate the solar irradiance imposed on each optical transceiver module in a single satellite. The effectiveness of the optimal path configurations is evaluated through detour mitigation strategies, comparing inter-plane and inter-shell link alternatives in intercontinental scenarios within the northern hemisphere. In the scenarios, it is found that there is a tradeoff between the FoV and the level of the signal-to-noise ratio (SNR) required to overcome the effects of solar irradiance. Also, seasonal alterations in the sun’s incident direction significantly influence the link availability, with unusable link rates nearly doubling in summer compared to spring because of orbital inclinations tending to be aligned more closely with the solar direction toward Earth. The proposed inter-shell-based path optimization reduces the total link distance by up to 2500 km compared to those of the inter-plane configurations, demonstrating superior performance in mitigating impairment due to solar irradiance. Full article

Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms^–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms^–1. Increased sensing speeds (0.4–0.5 ms^–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms^–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring. Full article

Raman spectroscopy is capable of precisely identifying and analyzing the composition and properties of samples collected from the lunar surface, providing crucial data support for lunar scientific research. However, in situ Raman spectroscopy on the lunar surface faces challenges such as weak Raman scattering from targets, alongside requirements for lightweight and long-distance detection. To address these challenges, time-gated Raman spectroscopy (TG-LRS) based on a passively Q-switched pulsed laser and a linear intensified charge-coupled device (ICCD), which enable simultaneous signal amplification and background suppression, has been developed to evaluate the impact of key operational parameters on Raman signal detection and to explore miniaturization optimization. The TG-LRS system includes a 40 mm zoom telescope, a passively Q-switched 532 nm pulsed laser, a fiber optic delay line, a miniature spectrometer, and a linear ICCD detector. It achieves an electronic gating width under 20 ns. Within a detection range of 1.1–3.0 m, the optimal delay time varies linearly from 20 to 33 ns. Raman signal intensity increases with image intensifier gain, while the signal-to-noise ratio peaks at a gain range of 800–900 V before declining. Furthermore, the effects of focal depth, telescope aperture, laser energy, and integration time were studied. The Raman spectra of lunar minerals were successfully obtained in the lab, confirming the system’s ability to suppress solar background light. This demonstrates the feasibility of in situ Raman spectroscopy on the lunar surface and offers strong technical support for future missions. Full article

In recent years, although a variety of deep learning models have been developed for magnitude estimation, the complex and variable nature of earthquakes limits the generalizability and accuracy of these models. In this study, we selected the waveform data of the Japan earthquake. We applied four deep learning techniques (MagNet combined with bidirectional long- and short-term memory network Bi-LSTM, DCRNN with deepened CNN layers, DCRNNAmp with the introduction of a global scale factor, and Exams with a multilayered CNN architecture) for real-time magnitude estimation. By comparing the estimation errors of each model in the first 3 s after the earthquake, it is found that the DCRNNAmp performs the best, with an MAE of 0.287, an RMSE of 0.397, and an R² of 0.737 in the first 3 s after the arrival of the P-wave, and the inclusion of S-wave seismic-phase information is found to significantly improve the accuracy of the magnitude estimation, which suggests that S-wave seismic-phase waveform features can enrich the model’s understanding of the relationship between the seismic phases. It shows that S-wave phase waveform features can enrich the model’s knowledge of the relationship between seismic fluctuations and magnitude. The epicentral distance positively correlates with the magnitude estimation, and the model can converge faster with the improved signal-to-noise ratio. Despite the shortcomings of model design and opaque internal mechanisms, this study provides important evidence for deep learning in earthquake estimation, demonstrating its potential to improve the accuracy of on-site earthquake early warning (EEW) systems. The estimation capability can be further improved by optimizing the model and exploring new features. Full article

Semantic communication is an effective technological approach for the integration of intelligence and communication, enabling more efficient and context-aware data transmission. In this paper, we propose a bit-conversion-based semantic communication transmission framework to ensure compatibility with existing wireless systems. Specifically, a series of physical layer processing modules in end-to-end transmission are designed. Additionally, we develop a semantic communication simulator to implement and evaluate this framework. To optimize the performance of this framework, we introduce a novel physical layer metric, termed Integer Error Rate (IER), which provides a more suitable evaluation criterion for semantic communication compared to the conventional bit error rate (BER). On the basis of the IER, a minimum Manhattan distance constellation mapping scheme is proposed, which can improve the transmission quality of semantic communication under the same BER condition. Furthermore, we propose a hybrid joint source–channel coding (JSCC) and separate source–channel coding (SSCC) transmission scheme. This scheme decouples the semantic quantization output from the modulation order by segmenting the bits to be transmitted. Simulation results demonstrate that the hybrid JSCC/SSCC transmission scheme can improve the semantic performance, such as the Peak Signal-to-Noise Ratio (PSNR), in low Signal-to-Noise Ratio (SNR) environments while reducing bandwidth usage by up to 50% compared to the benchmark scheme. Full article