Search Results (158)

Improving city crossroads safety is a critical problem for modern smart transportation systems (STS). This article presents the results of developing, upgrading, and comprehensively experimentally testing an acoustic monitoring system prototype designed for rapid accident detection. Unlike conventional camera- or lidar-based approaches, the proposed solution uses passive sound source localization to operate effectively with no direct visibility and in adverse weather conditions, addressing a key limitation of camera- or lidar-based systems. Generalized Cross-Correlation with Phase Transform (GCC-PHAT) algorithms were used to develop a hardware–software complex featuring four microphones, a multichannel audio interface, and a computation module. This study focuses on the gradual upgrading of the algorithm to reduce the mean localization error in real-life urban conditions. Laboratory and complex field tests were conducted on an open-air testing ground of a university campus. During these tests, the system demonstrated that it can accurately determine the coordinates of a sound source imitating accidents (sirens, collisions). The analysis confirmed that the system satisfies the V2X infrastructure integration response time requirement (<200 ms). The results suggest that the system can be used as part of smart transportation systems. Full article

Electric-field-sensitive dielectrics play a crucial role in electric field induction sensing and related capacitive conversion, with interfacial polarization and charge accumulation largely determining the signal output. This paper introduces graphene/transition metal dichalcogenide (TMD) (MoSe₂, MoS₂, and WS₂) heterojunctions as functional fillers to enhance the dielectric response and electric-field-induced voltage output of flexible polydimethylsiloxane (PDMS) composites. Density functional theory (DFT) calculations were used to evaluate the stability of the heterojunctions and interfacial electronic modulation, including binding behavior, charge redistribution, and Fermi level-referenced band structure/total density of states (TDOS) characteristics. The calculations show that the graphene/TMD interface is primarily controlled by van der Waals forces, exhibiting negative binding energy and significant interfacial charge rearrangement. Based on these theoretical results, graphene/TMD heterojunction powders were synthesized and incorporated into polydimethylsiloxane (PDMS). Structural characterization confirmed the presence of face-to-face interfacial contacts and consistent elemental co-localization within the heterojunction filler. Dielectric spectroscopy analysis revealed an overall improvement in the dielectric constant of the composite materials while maintaining a stable loss trend within the studied frequency range. More importantly, calibrated electric field induction tests (based on pure PDMS) showed a significant enhancement in the voltage response of all heterojunction composite materials, with the WS₂-G/PDMS system exhibiting the best performance, exhibiting an electric-field-induced voltage amplitude 7.607% higher than that of pure PDMS. This work establishes a microscopic-to-macroscopic correlation between interfacial electronic modulation and electric-field-sensitive dielectric properties, providing a feasible interface engineering strategy for high-performance flexible dielectric sensing materials. Full article

This paper presents a novel method for the precise localization of remote radio-signal sources using a formation of unmanned aerial vehicles (UAVs). The approach is based on time-difference-of-arrival (TDoA) measurements and the geometric analysis of hyperbolas formed by pairs of UAVs. By studying the asymptotic intersections of these hyperbolas, the method ensures unique determination of the source position, even in the presence of multiple intersection points. Theoretical analysis confirms that the correct intersection point is located at a significantly larger distance from the UAV formation center compared to spurious intersections, providing a rigorous criterion for resolving localization ambiguity. The proposed framework also addresses secure inter-UAV communication via optical-fiber links and supports expansion of UAV groups with directional antennas and low-power signal relays. Additionally, the study discusses practical UAV configurations, including hybrid propulsion and jet-assisted kamikaze platforms, demonstrating the applicability of the method in contested environments. The results indicate that this approach provides a robust mathematical basis for unambiguous emitter localization and enables scalable, secure, and resilient multi-UAV systems, with potential applications in electronic-warfare scenarios, surveillance, and tactical operations. Full article

Magnetic localization technology plays a significant role in medical device navigation and human–computer interaction. However, existing localization methods based on local optimization suffer from poor initial solutions and slow convergence. To address the aforementioned challenges, this paper presents a hybrid localization approach, referred to as the Improved Artificial Lemming Algorithm (IALA) Integrated with Levenberg–Marquardt (LM) Optimization. Building upon the Artificial Lemming Algorithm (ALA), the proposed method incorporates an adaptive Gaussian–Lévy hybrid mutation strategy designed to enhance search performance through improved exploration–exploitation dynamics, as quantitatively demonstrated by the diversity-based analysis where IALA maintains higher exploration percentages on multimodal functions while achieving superior optimization results on high-dimensional problems. By introducing a competitive foraging mechanism inspired by the aggressive behavior of the Tasmanian Devil Optimization (TDO) algorithm, it enhances population diversity and search initiative. Furthermore, a time-varying tracking and escape strategy is adopted to improve dynamic optimization performance in complex solution spaces. The proposed method leverages IALA to generate high-quality initial solutions, significantly accelerating the convergence speed and stability of the LM algorithm, thereby improving the overall performance of the permanent magnet localization system. The experimental results show that, using a horizontal test platform of 60 mm × 60 mm with 41 uniformly distributed test points, and acquiring data at vertical heights ranging from 15 mm to 65 mm in 5 mm increments for two distinct orientations of the permanent magnet, the IALA-LM algorithm achieves an average localization success rate of 96.9% over 902 trials, with a mean position error of 1.1 mm and a mean orientation error of 0.17°. Compared with the standard LM algorithm, the proposed IALA-LM algorithm reduces the position error by approximately 66.7% (from 3.3 mm to 1.1 mm) and the orientation error by approximately 94.3% (from 3.0° to 0.17°). Consequently, the proposed method enables high-precision, high-stability, and high-efficiency localization of permanent magnets. It can provide reliable spatial pose estimation support for demanding applications such as miniature implantable or ingestible medical devices (e.g., capsule endoscopy, intramedullary nail fixation, and tumor localization), human–computer interaction, and industrial inspection. Full article

Sound source localization (SSL) has evolved from traditional signal-processing methods to sophisticated deep-learning architectures. However, applying these to distributed microphone arrays in adverse environments is complicated by high reverberation and potential sensor asynchrony, which can corrupt crucial Time-Difference-of-Arrival (TDoA) information. We introduce SpaceNet, a multimodal deep-learning architecture designed to address such issues by explicitly fusing audio features with sensor geometry. SpaceNet features: (1) a dual-branch architecture with specialized spatial processing that decomposes microphone geometry into distances, azimuths, and elevations; and (2) a feature-normalization technique to ensure stable multimodal training. Evaluation on real-world datasets from disaster sites demonstrates that SpaceNet, when trained on ILD-only mel-spectra, achieves better accuracy compared to our baseline model (CHAWA) and identical models trained on full mel-spectrograms. This approach also reduces computational overhead by a factor of 24. Our findings suggest that for distributed arrays in adverse environments, time-invariant ILD cues are a more effective and efficient feature for localization than complex temporal features corrupted by reverberation and synchronization errors. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

Accurate user positioning in 6G networks is essential for next-generation mobile services. However, classical approaches such as time-difference-of-arrival (TDoA) remain vulnerable to dense multipath and NLoS conditions commonly found in indoor and industrial environments. This paper proposes an AI-driven RF fingerprinting framework that leverages uplink channel state information (CSI) to achieve robust and privacy-preserving 2D localization. A lightweight convolutional neural network (CNN) extracts location-specific spectral–spatial fingerprints from CSI tensors, while a federated learning (FL) scheme enables distributed training across multiple gNBs without sharing raw channel data. The proposed integration of CSI tensor processing with FL and structured pruning is introduced as a novel solution for practical 6G edge positioning. To further reduce latency and communication costs, a structured pruning mechanism compresses the model by 40–60%, lowering the memory footprint with negligible accuracy loss. A performance evaluation in 3GPP-compliant indoor factory scenarios indicates a median positioning error below 1 m for over 90% of cases, significantly outperforming TDoA. Moreover, the compressed FL model reduces the FL communication load by ~38% and accelerates local training, establishing an efficient, secure, and deployment-ready positioning solution for 6G networks. Full article

In this study, first-principles calculations were employed to analyze the effect of Si and O doping on the electronic structure of the Fe/Zn interface, aiming to reveal the mechanism underlying the degradation of its interfacial stability. Through detailed analysis of bond population, charge density, differential charge density, as well as total density of states (TDOS) and partial density of states (PDOS), the following findings were obtained: After Si and O doping, the charge distribution at the Fe/Zn interface exhibits local aggregation or sparsity. The differential charge density shows a redistribution of charges, and the charge density in the Fe-Zn bonding region changes. In terms of density of states, the contribution of orbitals related to Fe and Zn atoms to the density of states near the Fermi level is altered. The hybridization between the orbitals of Si/O atoms and those of Fe/Zn atoms affects the electronic interaction. Comprehensive analysis indicates that the degradation of Fe/Zn interfacial stability caused by Si and O doping is mainly attributed to the following factors: it modifies the chemical bonding, induces lattice distortion which generates internal stress, enhances the inhomogeneity of charge distribution, and weakens the bonding force between Fe and Zn atoms. This research provides a theoretical basis for the performance regulation of related material systems. Full article

This paper addresses the consensus control challenge for multiple autonomous underwater vehicles’ (AUVs) formation operating under external disturbances and communication delays. A fixed-time disturbance observer (FxTDO) is developed to precisely estimate external disturbances within a fixed time. A fixed-time state observer (FxTSO) is designed to reconstruct the leader’s position and velocity states, effectively compensating for communication delays. Building upon these observer estimates, an event-triggered sliding mode controller is proposed to achieve formation consensus with guaranteed convergence time while significantly reducing communication frequency through its triggering mechanism. The entire approach ensures fixed-time convergence of the closed-loop system, and rigorous theoretical proof of this stability is provided. Simulation results confirm the effectiveness of the proposed scheme in handling external disturbances and delays, achieving accurate formation tracking with improved communication efficiency. This work provides a robust solution for multi-AUV coordination in challenging environments. Full article

In this research article, a silicon carbide (SiC) nanocluster has been designed and characterized as an anode electrode for lithium (Li), sodium (Na), potassium (K), beryllium (Be), magnesium (Mg), boron (B), aluminum (Al) and gallium (Ga)-ion batteries through the formation of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. A vast study on energy-saving by SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC complexes was probed using computational approaches accompanying density state analysis of charge density differences (CDDs), total density of states (TDOS) and molecular electrostatic potential (ESP) for hybrid clusters of SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC. The functionalization of Li, Na, K, Be, Mg, B, Al and Ga metal/metalloid elements can raise the negative charge distribution of carbon elements as electron acceptors in SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters. Higher Si/C content can increase battery capacity through SiLiC, SiNaC, SiKC, SiBeC, SiMgC, SiBC, SiAlC and SiGaC nanoclusters for energy storage processes and to improve the rate performance by enhancing electrical conductivity. Full article

Clownfish (Amphiprion ocellaris) are a staple commodity in the marine aquarium trade and an emerging model organism for research. Bottlenecks during larviculture affect the survival of juvenile fish and continued reliance on live feeds, such as rotifers (Brachionus spp.) and Artemia spp. nauplii, increasing the complexity and cost of raising this species. This study utilized known digestive physiology of clownfish larvae to experimentally reduce the use of live feeds. First, larvae were weaned from rotifers to Artemia at three time points (3, 5, and 7 days post-hatch [DPH]), demonstrating that larvae can be transitioned to Artemia as early as 5 DPH without negative impacts on survival, total length (TL), or whole-body cortisol. A second weaning trial tested the introduction of a commercial microdiet (MD) at 5, 8, and 11 DPH. Survival was greatest when the MD was introduced at 5 DPH (mean ± SD; 64.47 ± 0.10%), and no differences in TL nor whole-body cortisol were detected, suggesting that Artemia may not be required prior to MD weaning. Next, three commercially available MDs were tested for their effects on survival, growth, and coloration of clownfish larvae. Survival and growth did not differ among diets, but fish fed TDO Chroma Boost™ exhibited significantly red-shifted hues, higher saturation, and greater brightness scores in some body regions compared to fish fed Golden Pearl or GEMMA Micro 300. A partial budget analysis indicated a net profit increase of ~$1.60 per fish, highlighting the potential for cost savings and streamlined clownfish production. Full article

Background/Objectives: Idiopathic pulmonary fibrosis (IPF) is a progressive disease characterized by lung scarring, impaired function, and high mortality. Effective therapies to reverse fibrosis are lacking. This study aims to uncover the molecular mechanisms of IPF, explore diagnostic biomarkers, and identify therapeutic targets. Methods: Multi-omics data were integrated to identify biomarkers with causal associations to IPF using Mendelian randomization and transcriptomic analysis. Machine learning was employed to construct a diagnostic model, and single-cell transcriptomic analysis determined gene expression patterns in fibrotic lung tissue. Results: Seven core genes (GREM1, UGT1A6, CDH2, TDO2, HS3ST1, ADGRF5, and MPO) were identified, showing strong diagnostic potential (AUC = 0.987, 95% CI: 0.972–0.987). These genes exhibited distinct distribution patterns in fibroblasts, endothelial cells, epithelial cells, macrophages, and dendritic cells. Conclusions: This study highlights key genes driving IPF, involved in pathways related to metabolism, immunity, and inflammation. However, their utility as fluid-based biomarkers remains unproven and requires protein-level validation in prospective cohorts. By integrating genomic, immunological, and cellular insights, it provides a framework for targeted therapies and advances mechanism-based precision medicine for IPF. Full article

The kynurenine (KYN) metabolic pathway sits at the crossroads of immunity, metabolism, and neurobiology, yet its clinical translation remains fragmented. Emerging spatial omics, wearable chronobiology, and synthetic microbiota studies reveal that tryptophan (Trp) metabolism is regulated by distinct cellular “checkpoints” along the gut–brain axis, finely modulated by sex differences, circadian rhythms, and microbiome composition. However, current interventions tackle single levers in isolation, leaving a key gap in the precision control of Trp’s fate. To address this, we drew upon an extensive body of the primary literature and databases, mapping enzyme expression across tissues at single-cell resolution and linking these profiles to clinical trials investigating dual indoleamine 2,3-dioxygenase 1 (IDO1)/tryptophan 2,3-dioxygenase (TDO) inhibitors, engineered probiotics, and chrono-modulated dosing strategies. We then developed decision-tree algorithms that rank therapeutic combinations against biomarker feedback loops derived from real-time saliva, plasma, and stool metabolomics. This synthesis pinpoints microglial and endothelial KYN hotspots, quantifies sex-specific chronotherapeutic windows, and identifies engineered Bifidobacterium consortia and dual inhibitors as synergistic nodes capable of reducing immunosuppressive KYN while preserving neuroprotective kynurenic acid. Here, we highlight a framework that couples lifestyle levers, bio-engineered microbes, and adaptive pharmaco-regimens into closed-loop “smart protocols.” By charting these intersections, this study offers a roadmap for biomarker-guided, multidisciplinary interventions that could recalibrate KYN metabolic activity across cancer, mood, neurodegeneration, and metabolic disorders, appealing to clinicians, bioengineers, and systems biologists alike. Full article

This paper presents a hybrid indoor localization framework combining time difference of arrival (TDoA) measurements with a swarm intelligence optimization technique. To address the nonlinear optimization challenges in three-dimensional (3D) indoor localization via TDoA measurements, we systematically evaluate the artificial bee colony (ABC) algorithm and chimpanzee optimization algorithm (ChOA). Through comprehensive Monte Carlo simulations in a cubic 3D environment with eight beacons, our comparative analysis reveals that the ChOA achieves superior localization accuracy while maintaining computational efficiency. Building upon the ChOA framework, we introduce a multi-beacon fusion strategy incorporating a local outlier factor-based linear weighting mechanism to enhance robustness against measurement noise and improve localization accuracy. This approach integrates spatial density estimation with geometrically consistent weighting of distributed beacons, effectively filtering measurement outliers through adaptive sensor fusion. The experimental results show that the proposed algorithm exhibits excellent convergence performance under the condition of a low population size. Its anti-interference capability against Gaussian white noise is significantly improved compared with the baseline algorithms, and its anti-interference performance against multipath noise is consistent with that of the baseline algorithms. However, in terms of dealing with UWB device failures, the performance of the algorithm is slightly inferior. Meanwhile, the algorithm has relatively good time-lag performance and target-tracking performance. The study provides theoretical insights and practical guidelines for deploying reliable localization systems in complex indoor environments. Full article

Real-time positioning is a technological field with a multitude of applications, which expand across many scopes: from positioning within a large area to localization within smaller spaces; from locating people to locating equipment; from large-scale industrial or military applications to commercially available solutions. There are at least as many implementations of real-time positioning as there are applications and challenges. Within the domain of Radio Frequency (RF) systems, positioning has been approached from multiple angles. Some of the more common solutions involve using Time of Flight (ToF) and time difference of arrival (TDoA) technologies. Within TDoA-based systems, one common limitation stems from the computational power necessary to run the multi-lateration algorithms at a high enough speed to provide high-frequency refresh rates on the tag positions. The system presented in this study implements a complete hardware and software TDoA-based real-time positioning system, using wireless Ultra-Wideband (UWB) technology. This system demonstrates improvements in the state of the art by addressing the above limitations through the use of a hybrid Machine Learning solution combined with algorithmic fine tuning in order to reduce computational power while achieving the desired positioning accuracy. This study presents the design, implementation, verification and validation of the aforementioned system, as well as an overview of similar solutions. Full article