Search Results (532)

The Internet of Things (IoT) provides technical solutions for monitoring assets in facility layouts, and this is further strengthened by the development of sophisticated software tools for intralogistics operations. The present research provides a taxonomy for the existing tracking technologies and a comparison matrix for supporting decision making when selecting the most suitable technology for real-time tracking in indoor areas. Although numerous tracking technologies exist, ultra-wideband (UWB) technology has gained significant attention in recent years due to its exceptional positioning accuracy and its ability to operate effectively in challenging environments with numerous obstacles, even under non-line-of-sight (NLOS) conditions. Specifically, this research focuses on a real-time location system (RTLS) that is designed and implemented to monitor assets based on UWB technology. Additionally, a new algorithm is introduced to reduce localization errors by attempting to exclude NLOS measurements from the tag’s position calculations. The experiments showcased that the proposed algorithm improves the overall positioning error by 24%, reporting an RMSE of 0.124 m in comparison to the 0.163 m of the normal trilateration method. The experimental results highlight the efficiency of the proposed solution for fast and accurate localization and tracking in real-world environments. Full article

Pseudolite positioning systems offer precise localization when GPS signals are unavailable, advancing the development of intelligent transportation systems. However, in confined indoor environments such as kilometer-long tunnels, where vehicles move at high speeds, traditional pseudolite algorithms struggle to establish accurate physical models linking signals to spatial domains. This study introduces a deep learning-based pseudolite positioning algorithm leveraging a spatio-temporal fusion framework to address challenges such as signal attenuation, multipath effects, and non-line-of-sight (NLOS) effects. The Enconv1d model we developed is based on the spatio-temporal characteristics of the pseudolite observation signals. The model employs the encoder module from the Transformer to capture multi-step time constraints while introducing a multi-scale one-dimensional convolutional neural network module (1D CNN) to assist the encoder module in learning spatial features and finally outputs the localization results of the Enconv1d model after the dense layer integration. Four experimental tests in a 4.6 km long real-world tunnel demonstrate that the proposed framework delivers continuous decimeter-level positioning accuracy. Full article

This study investigates the impact of wall roughness on the performance of the Non-Line-of-Sight (NLOS) component in Visible Light Communication (VLC) systems designed for underground mining environments, adhering to safety and communication standards such as IEC 60079-28(intrinsic safety in explosive atmospheres) and IEEE 802.15.7 (VLC parameters). Using probabilistic models aligned with the ITU-R P.1238 propagation guidelines, the research evaluates how wall materials (e.g., coal, shale, limestone) and their irregular geometries, characterized by surface roughness profiles compliant with ISO 8503-2,influence reflection coefficients (0.05–0.85 range), incidence angles (0°–90°), and irradiance angles (5°–180°), which are critical for signal propagation. Simulation scenarios, parameterized with material reflectivity data from ASTM E423, explore the effects of statistical distributions (uniform, normal with $\mu$ = 0.3, $\sigma$ = 0.2; exponential $\lambda$ = 2; gamma $\alpha$ = 0.5, $\beta$ = 0.2) on power distribution, channel impulse response, and reflection coefficients. The results indicate variations in maximum received power: a decrease of 80% for uniform distribution, an increase of 150% for exponential distribution, and a 100% increase for gamma distribution in reflection conditions. Under incidence and irradiance conditions, uniform distribution exhibited a 158.62% increase, whereas exponential distribution and gamma distribution experienced reductions of 72.22% and 7.04%, respectively. These variations align with IEC 62973-1 EMI limits and emphasize the role of roughness (Ra = 0.8–12.5 μm per ASME B46.1). Full article

The second-order nonlinear optical (NLO) properties of the known heteroleptic complex [Cu(1,10-phenanthroline)xantphos][PF₆] (complex 1) and the related new complexes [Cu(5-NO₂-1,10-phenanthroline)xantphos][PF₆] and [Cu(5-NO₂-1,10-phenanthroline)(dppe)][PF₆] (dppe = 1,2-bis(diphenylphosphino)ethane) (complexes 2 and 3) were investigated in solution by the EFISH (Electric Field-Induced Second Harmonic generation) technique, working at a non-resonant wavelength of 1907 nm. It turned out that they are characterized by large μβ values (957–1100 × 10⁻⁴⁸ esu), much higher than that of the Disperse Red One benchmark. Unexpectedly, the homoleptic complex [Cu(2-mesityl-1,10-phenanthroline)₂][PF₆] (complex 4) shows a similar high second-order NLO response. Quantum chemical calculations based on Density Functional Theory (DFT) methods have been carried out to give insight into the electronic structure of the investigated complexes in relation to NLO properties. This investigation, which represents the first EFISH study on copper(I) complexes, opens a convenient route for the development of low-cost dipolar NLO-active heteroleptic [Cu(P^P)(N^N)][PF₆] and homoleptic [Cu(N^N)₂][PF₆] complexes. Full article

In the context of mobile phones, the local optimal global navigation satellite systems (GNSS) shadow matching algorithm, which is based on the urban three-dimensional model, can effectively reduce the error of GNSS pseudo-range single-point positioning. However, the positioning accuracy of this algorithm is susceptible to noise, and its continuous signal-to-noise ratio (SNR) scoring method does not fully exploit the probability density and probability distribution information contained in the SNR. Therefore, this paper proposes two improvements for the local optimal shadow matching algorithm: (1) utilizing low-pass filtering to filter SNR, thereby reducing the influence of noise on the algorithm and (2) introducing a probability-based SNR scoring method to fully leverage the probability density and probability distribution information of SNR. In dynamic single-point positioning, the improved algorithm attains an absolute positioning accuracy of up to 3 m, representing a decimeter-level enhancement over the original algorithm. Experiments confirm that using the SNR statistical information of non-line of sight (NLOS) and line-of-sight (LOS) as prior information results in better positioning accuracy compared to when this information is distorted by multipath effects. Additionally, to address the issue of high time complexity in the shadow matching algorithm, especially when considering local optima, this paper presents a scheme to simplify the algorithm’s flow, reducing its time complexity by approximately 75%. Full article

This paper presents an innovative approach to improving visible light communication (VLC) systems in total shadowing conditions by integrating intelligent reflecting surfaces (IRSs), angle diversity receivers (ADRs), and recurrent neural networks (RNNs). Two ADR configurations (pyramidal and hemispherical) are evaluated, along with signal combination mechanisms: maximum ratio combining (MRC) and select best combining (SBC). The RNN is employed to dynamically optimize the IRS placement, maximizing the signal-to-noise ratio (SNR) at the ADRs and enhancing overall system performance in non-line-of-sight (NLoS) scenarios. This study investigates the spatial distribution of SNRs in VLC systems using RNN-optimized IRSs, comparing the performance of different ADR configurations and signal combination methods. The results demonstrate significant improvements in received power and the SNR compared to non-optimized setups, showcasing the effectiveness of RNN-based optimization for robust signal reception. This article highlights the potential of machine learning in enhancing VLC technology, offering a practical solution for real-world indoor applications. The findings emphasize the importance of adaptive IRS placement and spur further exploration of advanced algorithms and ADR designs to address challenges in complex indoor environments. Full article

Unmanned aerial vehicles (UAVs) significantly shape the evolution of 5G and 6G technologies in India, particularly in reconfiguring communication networks. Through their deployment as base stations or relays, these aerial vehicles substantially enhance communication performance and extend network coverage in areas characterized by high demand and challenging topographies. Accurate modelling of the UAV-to-ground channel is imperative for gaining valuable insights into UAV-assisted communication systems, particularly within India’s rapidly expanding metropolitan cities and their diverse topographical complexities. This study proposes an approach to model low-altitude channels in urban areas, offering specific scenarios and tailored solutions to facilitate radio frequency (RF) planning for Indian metropolitan cities. The proposed model leverages the International Telecommunication Union recommendation (ITU-R) for city mapping and utilizes frequency ranges from 1.8 to 6 GHz and altitudes up to 500 m to comprehensively model both line-of-sight (LoS) and non-line-of-sight (NLoS) communications. It employs the uniform theory of diffraction to calculate the additional path loss for non-line-of-sight (NLoS) communication for both vertical and horizontal polarizations. The normal distribution for additional shadowing loss is discerned from simulation results. This study outlined the approach to derive a comprehensive statistical channel model based on the elevation angle and evaluate model parameters at various frequencies and altitudes for both vertical and horizontal polarization. The model was subsequently compared with existing models for validation, showing close alignment. The ease of implementation and practical application of this proposed model render it an invaluable tool for planning and simulating mobile networks in urban areas, thus facilitating the seamless integration of advanced communication technologies in India. Full article

The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machine learning (ML) to LOS/NLOS classification and this paper is part of this trend, but not all the different aspects of ML have been analyzed. In the general ML domain, poisoning and adversarial attacks and the related mitigation techniques are an active area of research. Such attacks aim to hamper the ML classification process by poisoning the data set. Mitigation techniques are designed to counter this threat using different approaches. Poisoning attacks in LOS/NLOS classification have not received significant attention by the wireless communication community and this paper aims to address this gap by proposing the application of a specific mitigation technique based on outlier detection algorithms. The rationale is that poisoned samples can be identified as outliers from legitimate samples. In particular, the study described in this paper proposes a recent outlier detection algorithm, which has low computing complexity: the sparse data observers (SDOs) algorithm. The study proposes a comprehensive analysis of both conventional and novel types of attacks and related mitigation techniques based on outlier detection algorithms for UltraWideBand (UWB) channel classification. The proposed techniques are applied to two data sets: the public eWINE data set with seven different UWB LOS/NLOS different environments and a radar data set with the LOS/NLOS condition. The results show that the SDO algorithm outperforms other outlier detection algorithms for attack detection like the isolation forest (iForest) algorithm and the one-class support vector machine (OCSVM) in most of the scenarios and attacks, and it is quite competitive in the task of increasing the UWB LOS/NLOS classification accuracy through sanitation in comparison to the poisoned model. Full article

In complex indoor environments, target tracking performance is impacted by non-line-of sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a double extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an extended Kalman filter (EKF). Time of arrival (TOA) measurements collected by multiple stationary ultra-wideband (UWB) sensors are used. The residual errors between the measured TOA and that of the first KF are predicted, and the covariance of the first KF is adjusted correspondingly. Then, we use the estimated distance state of the first KF as a measurement vector for the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with good accuracy even without or with only one LOS TOA measurement for a period of time without prior information about the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not greatly decrease when NLOS noises exist for a long period of time. Finally, the proposed DEKF can maintain the high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model in the LOS/NLOS environment. Our simulation and experimental results show that the proposed algorithm performs much better than other algorithms in SOTA, particularly in severe mixed LOS/NLOS environments. Full article

In complex indoor environments, target tracking performance is impacted by non-line-of-sight (NLOS) noises and other measurement errors. In order to fix NLOS errors, a Double Extended Kalman filter (DEKF) algorithm is proposed, which refers to a kind of cascaded structure composed of two Kalman filters. In the proposed algorithm, the first filter is a classic Kalman filter (KF) and the second is an Extended Kalman filter (EKF). The time of arrival (TOA) measurements collected by multiple stationary ultra-wide band (UWB) sensors are used. Residual errors between the measured TOA and the prediction from the first KF are used to adjust the covariance of the first KF accordingly. Then, we use the estimated distance state of the first KF as a measurement vector of the second EKF in order to obtain a smoother observation. One of the advantages of the proposed algorithm is that it is able to perform target tracking with a good accuracy even without or with only one line-of-sight(LOS) TOA measurement for a period of time without prior information of the NLOS noise, which may be difficult to obtain in practical applications. Another advantage is that the accuracy does not significantly decrease when NLOS noises persist for a long period of time. Finally, the proposed DEKF can maintain high-precision positioning characteristics in both the constant velocity (CV) model and the constant acceleration (CA) model for LOS/NLOS environments. In the case of mixed LOS/NLOS environments, the RMSE of the proposed algorithm can be kept within 5 cm, while the RMSEs of other compared algorithms are easily beyond tens of centimeters. At the same time, when the confidence of RMSE is set to 95% for 1000 MC simulations, the confidence interval of the proposed algorithm is the smallest, and the mean value is 3–5 times closer to the true value compared to other algorithms. Simulation and experimental results show that the proposed algorithm performs much better than other state-of-the-art algorithms, particularly in severe mixed LOS/NLOS environments. Full article

This work presents a novel approach to modeling an underwater optical wireless communications (UOWC) channel based on a deterministic analysis specifically for non-line-of-sight (NLOS) configurations. The model considers the presence of a wavy ocean surface, providing a more accurate representation of realistic conditions. By expanding the possibilities for communication in complex underwater environments, our model offers a comprehensive analysis of the ocean waves’ impact. A significant achievement of this study is the capacity of the model to accurately compute the variable size of the width of the beam (footprint) on the receiver plane reflected by the sea surface and the time intervals during which the receiver remains illuminated. Additionally, the model determines the precise position of the reflected beam on the receiver plane and accurately identifies the time intervals during which communication is feasible, offering invaluable insight into the system performance under oceanic wave variability. The results confirmed that oceanic wave variability induces severe misalignment in optical links, creating intermittent opportunities for effective communication. The optical–geometric analysis contributed significantly to understanding the novel impact of ocean waves on NLOS-UOWC systems. These findings enhance the preliminary considerations in NLOS link design, particularly in scenarios with autonomous underwater vehicles in constant motion, aiding in the reduction of pointing errors. Full article

As large-scale low Earth orbit (LEO) constellations continue to expand, the potential of their signal strength for positioning applications should be fully leveraged. For high-precision angle of arrival (AOA) estimation, current spectrum search algorithms are computationally expensive. To address this, we propose a method that downscales the 2D joint spectrum search algorithm by incorporating satellite ephemeris a priori information. The proposed algorithm efficiently and accurately determines the azimuth and elevation angles of NLOS (non-line-of-sight) signals. Furthermore, an NLOS virtual satellite construction method is introduced for integrating NLOS satellite data into the positioning system using previously estimated azimuth and elevation angles. Simulation experiments, conducted with a uniform planar array antenna in environments containing both LOS (line-of-sight) and NLOS signals, demonstrate the effectiveness of the proposed solution. The results show that the azimuth determination algorithm reduces computational complexity without sacrificing accuracy, while the NLOS virtual satellite construction method significantly enhances positioning accuracy in NLOS environments. The geometric dilution of precision (GDOP) improved significantly, decreasing from values exceeding 10 to an average of less than 1.42. Full article

The exploitation of high-performance third-order nonlinear optical (NLO) materials that have a favorable optical limit (OL) threshold is essential due to a rise in the application of ultra-intense lasers. In this study, a Cu-based MOF (denoted as Cu-bpy) was synthesized, and its third-order NLO and OL properties were investigated using the Z-scan technique with the nanosecond laser pulse excitation set at 532 nm. The Cu-bpy exhibits a typical rate of reverse saturable absorption (RSA) with a third-order nonlinear absorption coefficient of 100 cm GW⁻¹ and a favorable OL threshold of 0.75 J cm⁻² (at a concentration of 1.6 mg mL⁻¹), which is lower than that of most NLO materials that have been reported on so far. In addition, a DFT calculation was performed and was in agreement with our experimental results. Furthermore, the mechanism of the third-order NLO properties was illustrated as one-photon absorption (1PA). These results investigate the relationship between the structure and the nonlinear optical properties of Cu-bpy, and provide an experimental and theoretical basis for its use in optical limiting applications. Full article

In disaster-stricken areas, rapid restoration of communication infrastructure is critical to ensuring effective emergency response and recovery. Swarm UAVs, operating as mobile aerial base stations (MABS), offer a transformative solution for bridging connectivity gaps in environments where the traditional infrastructure has been compromised. This paper presents a novel hybrid path planning approach combining affinity propagation clustering (APC) with genetic algorithms (GA), aimed at maximizing coverage, and ensuring quality of service (QoS) compliance across diverse environmental conditions. Comprehensive simulations conducted in suburban, urban, dense urban, and high-rise urban environments demonstrated the efficacy of the APC-GA approach. The proposed method achieved up to 100% coverage in suburban settings with only eight unmanned aerial vehicle (UAV) swarms, and maintained superior performance in dense and high-rise urban environments, achieving 97% and 93% coverage, respectively, with 10 UAV swarms. The QoS compliance reached 98%, outperforming benchmarks such as GA (94%), PSO (90%), and ACO (88%). The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios. Mobility model analysis further underscores the adaptability of the proposed approach. The reference point group mobility (RPGM) model consistently achieved higher coverage rates (95%) than the random waypoint model (RWPM) (90%), thereby demonstrating the importance of group-based mobility patterns in enhancing UAV deployment efficiency. The findings reveal that the APC-GA adaptive clustering and path planning mechanisms effectively navigate propagation challenges, interference, and non-line-of-sight (NLOS) conditions, ensuring reliable connectivity in the most demanding environments. This research establishes the APC-GA hybrid as a scalable and QoS-compliant solution for UAV deployment in disaster response scenarios. By dynamically adapting to environmental complexities and user mobility patterns, it advances state-of-the-art emergency communication systems, offering a robust framework for real-world applications in disaster resilience and recovery. Full article

With the advent of the 5G era, high-precision localization based on mobile communication networks has become a research hotspot, playing an important role in indoor emergency rescue in shopping malls, smart factory management and tracking, as well as precision marketing. However, in complex environments, non-line-of-sight (NLOS) propagation reduces the measurement accuracy of 5G signals, causing large deviations in position solving. In order to obtain high-precision position information, it is necessary to recognize the propagation state of the signal before distance measurement or angle measurement. In this paper, we propose a dynamic multi-level optimization of comprehensive features (DMOCF) network model for line-of-sight (LOS)/NLOS identification. The DMOCF model improves the expression ability of the deep model by adding a res2 module to the time delay neural network (TDNN), so that fine-grained feature information such as weak reflections or noise in the signal can be deeply understood by the model, enabling the network to realize layer-level feature processing by adding Squeeze and Excitation (SE) blocks with adaptive weight adjustment for each layer. A mamba module with position coding is added to each layer to capture the local patterns of wireless signals under complex propagation phenomena by extracting local features, enabling the model to understand the evolution of signals over time in a deeper way. In addition, this paper proposes an improved sand cat search algorithm for network parameter search, which improves search efficiency and search accuracy. Overall, this new network architecture combines the capabilities of local feature extraction, global feature preservation, and time series modeling, resulting in superior performance in the 5G channel impulse response (CIR) signal classification task, improving the accuracy of the model and accurately identifying the key characteristics of multipath signal propagation. Experimental results show that the NLOS/LOS recognition method proposed in this paper has higher accuracy than other deep learning methods. Full article