Search Results (196)

Reconfigurable intelligent surfaces (RISs) are regarded as a transformative technique for future wireless networks. Currently, the majority of research efforts have focused on channel estimation scenarios in communication systems assisted by a single passive RIS. However, single-RIS-assisted systems suffer from limited coverage performance, with significant performance degradation observed in dense obstacle environments. To mitigate the adverse impacts imposed by environmental factors, a dual-RIS-assisted communication system exhibits superior adaptability to practical scenarios. This work focuses on investigating such a system. It is worth noting that fully passive RISs lack the capability to process signals independently. Furthermore, when employing pilot-aided algorithms to acquire channel state information (CSI), wireless systems often encounter challenges arising from large channel matrix dimensions, thereby leading to substantial pilot overhead. To address the aforementioned issues, this paper proposes a novel semi-blind channel estimation method for multiple-input multiple-output (MIMO) systems aided by double reconfigurable intelligent surfaces (D-RISs). Specifically, we construct two tensor models, namely the Parallel Factor (PARAFAC) model and the Parallel Tucker2 model, for the received signal in two separate stages. By means of tensor decomposition, the joint channel estimation and symbol detection problem is reformulated as a least squares problem and solved using a two-stage algorithm. In the first stage, the ALS algorithm is adopted to estimate the transmitted symbols and provide initialization for the second stage. Then, in the second stage, the TALS algorithm is employed to obtain the final estimation results of the three sub-channels. Simulation results verify the effectiveness of the proposed receiver. Full article

At racing speeds above 300 km/h (≈83 m/s), hazard awareness becomes a vehicular-communications problem: 100 ms already correspond to about 8.3 m of blind travel before an alert can influence braking, line choice, or torque delivery. Cloud-only telemetry is therefore insufficient under intermittent coverage and variable round-trip delay, while conventional trackside and pit-wall links do not provide direct inter-bike hazard dissemination. We propose Hybrid Epistemic Offloading (HEO), an edge–mesh–cloud architecture for high-mobility V2V/V2X hazard dissemination that explicitly separates an ephemeral safety plane from a durable cloud-analytics plane. On-bike edge nodes ingest high-rate ECU/IMU signals over CAN and persist full-fidelity traces into standardized ASAM MDF containers, enabling loss-tolerant buffering, deterministic replay, and post hoc auditability across coverage gaps. For real-time safety, motorcycles form a local V2V mesh that disseminates compact hazard digests using latency-bounded gossip with adaptive fanout, TTL-based suppression, and redundancy-aware forwarding over sidelink-capable V2X links. The hazard channel is formulated as uncertainty-aware to account for localization error and propagation delay at race pace. We evaluate the system in two stages: (i) a reproducible mobility-coupled simulation/emulation campaign for mesh dissemination and durable edge → gateway → cloud delivery; and (ii) an MDF4 replay-based Jerez pilot for stability-oriented co-design analysis. Under the tested conditions, the durable MQTT path achieved an 83.4 ms median, 175.9 ms p95, and 303.74 ms maximum end-to-end latency with no observed event loss. In the Jerez pilot, the co-design workflow reduced mean wheel slip from 6.26% to 3.75% (−40.10%) and a control-volatility proxy from 0.1290 to 0.0212 (−83.58%). Full article

Moderate-to-severe atopic dermatitis (AD) requires safe, long-term management strategies to complement conventional pharmacotherapy. This study evaluated the efficacy and safety of VGH4, a standardized multi-herb traditional Chinese medicine (TCM) formula, as an adjunct to standard care. In a randomized, double-blind, placebo-controlled crossover pilot trial, 19 patients with moderate-to-severe AD (SCOring Atopic Dermatitis Index (SCORAD) ≥ 25) received VGH4 or placebo for 6 weeks, separated by a 2-week washout. Primary outcomes assessed disease severity (SCORAD), while secondary outcomes included quality of life (DLQI/CDLQI) and safety. Eighteen patients completed the study. VGH4 yielded a median within-patient SCORAD reduction 10.2 points greater than placebo (p = 0.054). The primary endpoint did not reach statistical significance at the α = 0.05 level (p = 0.054); nevertheless, the observed magnitude of improvement exceeded the established minimal clinically important differences (MCIDs). The subjective SCORAD component showed a significant between-treatment difference favoring VGH4 (p = 0.015), and a statistically significant improvement in quality of life was also observed in adult patients (p = 0.023). In conclusion, VGH4 was generally well tolerated in this short-term pilot trial, with no serious adverse events, and showed preliminary signals of possible benefits in patient-reported outcomes as an adjunct therapy. These exploratory findings warrant confirmation in larger, adequately powered trials. Full article

Interest in classical psychedelics as potential treatments for ADHD has grown alongside broader psychiatric psychedelic research, but ADHD-specific evidence remains limited. This systematic review examined prospective and experimental studies on whether classical psychedelics, including microdosing-like use and retreat-based exposure, are associated with changes in adult ADHD symptoms and related functioning. A PRISMA-guided systematic review was conducted using a PECO/PICO framework focused on adults (≥18 years) with diagnosed ADHD and/or elevated ADHD symptomatology who were exposed to a classical psychedelic and assessed prospectively with quantitative ADHD outcomes. Major databases were searched, with reference screening and targeted checks for recent or registered trials. Risk of bias was assessed using RoB 2 for the RCT and ROBINS-I for non-randomized studies. Because of heterogeneity and the small number of studies, findings were synthesized narratively. Five studies met the inclusion criteria. Five prospective/experimental studies were included: three naturalistic online microdosing cohorts, one randomized double-blind placebo-controlled phase 2A trial of low-dose LSD, and one pre-post ayahuasca retreat pilot. In uncontrolled naturalistic microdosing studies, participants reported short-term reductions in ADHD symptom ratings together with improvements in well-being and affect-related functioning; however, these studies were highly vulnerable to self-selection, expectancy, attrition, and non-standardized exposure. In contrast, the only randomized placebo-controlled ADHD trial found improvement in both LSD and placebo groups, with no statistically significant advantage for LSD on clinician-rated or self-reported ADHD outcomes. Objective cognitive findings were limited and inconsistent, and safety data outside the supervised trial context were sparse. Naturalistic studies provide, at most, low-certainty signals of perceived short-term improvement, but the strongest controlled evidence does not demonstrate drug-specific efficacy of repeated low-dose LSD for core ADHD symptoms. Current evidence therefore does not allow separation of pharmacological effects from expectancy, setting, self-monitoring, and broader experiential/contextual influences, and is insufficient to support psychedelics as an evidence-based treatment for ADHD. Full article

Cross-interface architectures are increasingly central to large-scale ocean observation systems, where underwater sensor nodes transmit data to spatially distributed buoys that relay information to terrestrial networks. In these deployments, the inherent broadcast nature of underwater acoustic (UWA) propagation enables a single node’s signals to be captured by multiple buoys. However, substantial and dynamic propagation delays lead to inherent reception asynchrony and severe multi-user interference. Conventional detection relies on large hydrophone arrays on single platforms and assumes strict synchronization, hindering scalability and elevating costs. This study proposes a distributed asynchronous reception framework for buoy-assisted UWA networks. Under a cloud software-defined acoustic (C-SDA) architecture, spatially separated buoys are treated as a virtual distributed multiple-input multiple-output (MIMO) receiver. We introduce a minimum-delay-based equivalent reconstruction to regularize the asynchronous structure, followed by blind channel identification and pilot-assisted synchronization for robust multi-user detection. By leveraging long-delay broadcast propagation as a source of spatial diversity, the framework facilitates scalable and cost-effective multi-user access. The results demonstrate that the architecture provides a practical paradigm for the underwater Internet of Things and long-term ocean observation. Full article

High-power jamming may drive the radio-frequency (RF) front end of a satellite receiver into a nonlinear regime, thereby invalidating the linear superposition assumption underlying conventional excision and blanking methods. We formulate dual-receiver direct-sequence spread-spectrum (DSSS) anti-jamming as a nonlinear source-separation problem in complex baseband using stacked I/Q observations. We then propose a time-domain separator that jointly estimates the desired DSSS signal and the jammer on a designated reference receiver. The separator combines a multi-scale convolutional front end with a Transformer encoder and is pretrained on synthetic nonlinear mixtures that include multi-tone or burst jamming as well as typical satellite impairments, including Doppler/carrier-frequency offset (CFO), phase noise, multipath, and additive white Gaussian noise (AWGN). Robustness under high-jammer-to-signal-ratio (JSR) conditions is improved through high-JSR oversampling and JSR-aware loss reweighting. After Stage I supervised pretraining on labeled synthetic mixtures, an optional Stage II mixture-only adaptation step further refines the separator using nonlinear reconstruction consistency and lightweight communication-motivated priors. Across 1000 test mixtures with JSRs from −5 to 15 dB, SNRs from 15 to 25 dB, and cubic coefficients $a\in \left[\mathrm{0,0.5}\right]$, the proposed method improves the desired-signal scale-invariant signal-to-noise ratio (SI-SNR) from −4.79 dB for the mixture baseline to 13.32 dB after supervised pretraining and to 17.73 dB after mixture-only blind fine-tuning. Over the same test set, the failure rate (SI-SNR < 0 dB) decreases from 60.7% to 2.3%. Full article

Space-based automatic dependent surveillance–broadcast (ADS-B) systems offer the potential for comprehensive global aircraft surveillance. However, they face substantial challenges due to severe signal collisions resulting from the simultaneous reception of asynchronous ADS-B transmissions from multiple aircraft within a satellite’s expansive coverage area. Traditional collision mitigation approaches, such as serial interference cancellation and multichannel blind source separation, often have high computational costs, impose strict signal structure constraints, or rely on multiple-antenna configurations, all of which limit their practicality in satellite scenarios. To address these limitations, this paper proposes two novel deep learning–based models, designated SplitNet-2 and SplitNet-3. SplitNet-2 leverages a Transformer-inspired self-attention architecture specifically designed to separate two overlapping ADS-B signals, while SplitNet-3 employs a convolutional residual U-shaped network optimized for disentangling three simultaneous, colliding signals. Extensive simulations under realistic satellite reception conditions demonstrate that the proposed models significantly outperform conventional methods, achieving lower bit error rates (BERs) and improved demodulation accuracy. These advancements offer a promising solution to the critical problem of underdetermined signal separation in space-based ADS-B reception and significantly enhance the reliability and coverage of satellite-based ADS-B surveillance systems. Full article

On-load tap changers (OLTCs) are critical components of power transformers. In recent years, condition monitoring technologies for OLTCs based on vibration signals have attracted increasing research interest. However, practical applications still face several challenges, including background noise interference, insufficient characterization of transient signals, signal complexity, difficulty in detecting subtle anomalies, and ambiguous associations between fault modes and signal features. To address these issues, this paper proposes an OLTC acoustic fingerprint feature recognition method based on multidimensional phase-space trajectory analysis. First, an OLTC fault simulation platform was established, in which typical mechanical faults—such as fastener loosening, contact wear, and insufficient spring energy storage—were physically simulated. Corresponding vibration signals were then acquired under different operating conditions. Considering the independence of vibration characteristics at different locations of the distribution transformer, a blind source separation method based on endpoint detection was employed to separate OLTC vibration signals from the operational noise of the transformer body. Given the nonlinear and chaotic characteristics of OLTC vibration signals, phase-space reconstruction was introduced for signal analysis. Based on the reconstructed phase space, characteristic patterns and geometric feature parameters corresponding to different mechanical states of the OLTC were extracted. Furthermore, a two-dimensional membership function was constructed using the phase-space trajectories, and fuzzy inference based on predefined fuzzy rules was applied to compute representative feature parameters. A feature parameter database was subsequently established to enable OLTC condition identification. Experimental results demonstrate that the proposed diagnostic model can effectively classify and identify OLTC fault conditions using vibration signals, achieving an average classification accuracy exceeding 91.25%. The proposed method provides an effective non-intrusive approach for online monitoring and mechanical fault diagnosis of OLTCs without interrupting normal transformer operation. Full article

At present, most of the switchgear partial discharge detection means are offline detection and cannot monitor multiple partial discharge sources online at the same time. Based on this, this paper investigates the application of ultrasonic technology in localized discharge fault localization in high-voltage switchgear, removes the background noise of localized discharge in switchgear by using soft and hard filtering; proposes a generalized cubic correlation algorithm on the basis of TODA, improves the accuracy of the time difference acquisition in the case of low signal-to-noise ratio; determines the number of multiple localized discharging power sources by using the single-channel signal blind source separation technique and singularity spectral analysis; and determines the number of multiple localized discharging power sources by using independent component analysis to separate them. As well as for the problem that TDOA cannot be directly applied to the localization of multiple partial discharge sources, independent component analysis is used to separate the mixed signals, and the disordered coordinate selection method is proposed to determine the coordinates of multiple partial discharge sources. The experimental results show that (1) the noise reduction method is able to remove the excess interference while preserving the localized discharge signals; (2) the improved generalized cubic inter-correlation algorithm is more resistant to interference and has less error than other time delay estimation algorithms. The localization error is reduced by 60 mm~68 mm compared to the basic correlation algorithm, 41 mm~47 mm compared to the twice correlation algorithm, and 17 mm~20 mm compared to the three times correlation algorithm, which is a big improvement compared to the pre-improved algorithm. (3) It is able to locate the multiple localized power sources, and the accuracy of the number of localized power sources reaches 88%. Full article

Ensuring food security requires innovative, sustainable pest management solutions. The Solar Insecticidal Lamp Internet of Things (SIL-IoT) represents such an advancement, yet its reliability in harsh, variable outdoor environments is compromised by frequent component and sensor faults, threatening effective pest control and data integrity. This paper presents a comprehensive survey on fault detection (FD) for SIL-IoT systems, systematically analyzing their unique challenges, including electromagnetic interference, resource constraints, data scarcity, and network instability. To address these challenges, we investigate countermeasures, including blind source separation for signal decomposition under interference, lightweight model techniques for edge deployment, and transfer/self-supervised learning for low-cost fault modeling across diverse agricultural scenarios. A dedicated case study, utilizing sensor fault data of SIL-IoT, demonstrates the efficacy of these approaches: an empirical mode decomposition-enhanced model achieved 97.89% accuracy, while a depthwise separable-based convolutional neural network variant reduced computational cost by 88.7% with comparable performance. This survey not only synthesizes the state of the art but also provides a structured framework and actionable insights for developing robust, efficient, and scalable FD solutions, thereby enhancing the operational reliability and sustainability of SIL-IoT systems. Full article

Focusing on the West African Craton (WAC) as a test bed, this protocol-oriented scoping review synthesizes indicators for orogenic gold and translates them into an auditable, map-first checklist that separates Fertility and Preservation, while deliberately deferring any performance estimation to a blinded, out-of-sample evaluation. There is a need for a transparent, auditable, and field-ready framework that integrates geological, structural, geophysical, and geochemical evidence. We (i) synthesize the state of knowledge into a map-first, reproducible targeting checklist, (ii) formalize an indicator decision matrix that separates Fertility from Preservation factors, and (iii) specify a deferred, out-of-sample evaluation protocol to quantify performance. We conduct a Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR)-style scoping review (2010–2025) and codify commonly used indicators (e.g., transpressional jogs, lineament density, proximity to tonalite-trondhjemite-granodiorite (TTG)/tonalite contacts, Sr/Y proxies). Indicators are operationalized as auditable pass/fail rules and assembled into a decision matrix with explicit uncertainty handling and risk logging. We further define a deferred evaluation protocol using classification and ranking metrics (receiver operating characteristic (ROC) and precision–recall (PR) curves, odds ratios), ablation/sensitivity tests, and district-level threshold calibration. We deliver (1) a unified, auditable checklist with default (tunable) thresholds; (2) an indicator decision matrix that disentangles Fertility vs. Preservation signals; and (3) a deferred evaluation protocol enabling a reproducible, out-of-sample assessment without inflating apparent performance. All numerical thresholds reported here are explicit placeholders that facilitate transparency and auditability; they are not optimized. A properly blocked train/validation/test scheme, operating-point selection criteria, null models, and uncertainty procedures are prespecified for future evaluation. By publishing the checklist, data lineage, and audit-log schema now—without performance claims—we enable reproducible adoption and stress-test the framework ahead of calibration. Full article

This manuscript presents a study on the application of blind source separation (BSS) techniques, specifically the Independent Component Analysis (ICA) method, for the detection and identification of localized faults in rolling element bearings. Bearing defects typically manifest as distinct harmonics of characteristic fault frequencies, accompanied by modulation sidebands in the vibration signal spectrum. The accurate extraction and isolation of these components are crucial for reliable fault diagnosis, particularly in systems where multiple vibration sources overlap. In this work, a linear ICA algorithm was applied to vibration signals acquired from a simplified rotating machinery setup designed to emulate common bearing fault conditions. The study investigates the effect of ICA-based signal decomposition on the statistical distribution of selected diagnostic indicators and evaluates its ability to enhance the detectability of fault-related components. The experimental results demonstrate that the application of ICA significantly improves the separation of vibration sources, leading to a more distinct representation of fault signatures. The findings confirm the effectiveness of blind source separation methods in vibration-based diagnostics and highlight the potential of ICA as a complementary tool for improving the accuracy and robustness of bearing fault detection systems in rotating machinery. Full article

The precise ranging of ultra-wideband (UWB) fuzes relies on extracting time delay information from echo signals. However, ground multipath propagation effects induce a significant time-delay spread in the echo signals. This manifests as a channel impulse response (CIR) composed of numerous, closely spaced components, creating a challenging super-resolution problem that severely constrains the ranging accuracy and reliability of the fuze. Therefore, accurately estimating the CIR that characterizes these multipath structures from a single echo observation is crucial for the UWB fuze to perceive terrain structures and enhance ranging capabilities. This study proposes the following methods: (1) establishing an equivalent discrete multipath model(EDMM) of the ground to characterize the CIR; (2) proposing a sparse blind deconvolution(SBD) method via the ADMM-based framework under an asymmetric structured prior (ASP), which employs parametric projections to constrain the physical morphology of the unknown source signal, and designing a periodic sparse cluster projection operator to achieve super-resolution recovery of the discrete multipath structure of the channel h by enforcing the EDMM prior. Through three-variable robust decomposition, it actively separates dispersed clutter and enhances performance under low signal-to-noise ratio (SNR) conditions. Experimental results from both simulations and measured data demonstrate that the proposed algorithm exhibits excellent robustness and recovery accuracy in complex low-SNR scenarios, providing a foundational offline analysis method for understanding complex channel characteristics and guiding the development of improved real-time ranging algorithms. Full article

Background/Objectives: Binaural beat audio has gained popularity as a non-invasive tool to promote relaxation and enhance cognitive performance, though empirical support has been inconsistent. We developed a novel algorithm integrating real-time electroencephalography (EEG) feedback to dynamically tailor binaural beats to induce relaxed brain states. This study aimed to examine the efficacy and feasibility of this algorithm in a clinical trial. Methods: In a randomized, double-blinded, sham-controlled crossover trial, 25 healthy adults completed two 30 min sessions (EEG-guided intervention versus sham). EEG (Fp1) was recorded using a consumer-grade single-electrode headset, with auditory stimulation adjusted in real time based on EEG data. Outcomes included EEG frequency profiles, stop signal reaction time (SSRT), and novelty encoding task performance. Results: The intervention rapidly reduced dominant EEG frequency in all participants, with 100% achieving <8 Hz and 96% achieving <4 Hz within median 7.4 and 9.0 min, respectively. Compared to the sham, the intervention was associated with an faster novelty encoding reaction time (p = 0.039, dz = −0.225) and trends towards improved SSRT (p = 0.098, dz = −0.209), increased boundary separation in stop trials (p = 0.065, dz = 0.350), and improved inhibitory drift rate (p = 0.067, dz = 0.452) within the limits of the exploratory nature of these findings. Twenty-four (96%) participants reached a target level of <4 Hz with the intervention, while none reached this level with the sham. Conclusions: Real-time EEG-guided binaural beats may rapidly induce low-frequency brain states while potentially preserving or enhancing aspects of executive function. These findings support the feasibility of personalized, closed-loop auditory entrainment for promoting “relaxed alertness.” The results are preliminary and hypothesis-generating, warranting larger, multi-channel EEG studies in ecologically valid contexts. Full article

The difficulty in precisely extracting single-fault signatures from hydraulic pump composite faults, which stems from structural complexity and coupled multi-source vibrations, is tackled herein via a new diagnostic technique based on underdetermined blind source separation (UBSS). Utilizing sparse component analysis (SCA), the proposed method achieves blind source separation without relying on prior knowledge or multiple sensors. However, conventional SCA-based approaches are limited by their reliance on a predefined number of sources and their high sensitivity to noise. To overcome these limitations, an adaptive source number estimation strategy is proposed by integrating information–theoretic criteria into density peak clustering (DPC), enabling automatic source number determination with negligible additional computation. To facilitate this process, the short-time Fourier transform (STFT) is first employed to convert the vibration signals into the frequency domain. The resulting time–frequency points are then clustered using the integrated DPC–Bayesian Information Criterion (BIC) scheme, which jointly estimates both the number of sources and the mixing matrix. Finally, the original source signals are reconstructed through the minimum L1-norm optimization method. Simulation and experimental studies, including hydraulic pump composite fault experiments, verify that the proposed method can accurately separate mixed vibration signals and identify distinct fault components even under low signal-to-noise ratio (SNR) conditions. The results demonstrate the method’s superior separation accuracy, noise robustness, and adaptability compared with existing algorithms. Full article