Search Results (171)

Navigating through doorways remains a daily challenge for wheelchair users, often leading to frustration, collisions, or dependence on assistance. These challenges highlight a pressing need for intelligent doorway detection algorithm for assistive wheelchairs that go beyond traditional object detection. This study presents the algorithmic development of a lightweight, vision-based doorway detection and alignment module with contextual awareness. It integrates channel and spatial attention, semantic feature fusion, unsupervised depth estimation, and doorway alignment that offers real-time navigational guidance to the wheelchairs control system. The model achieved a mean average precision of 95.8% and a F1 score of 93%, while maintaining low computational demands suitable for future deployment on embedded systems. By eliminating the need for depth sensors and enabling contextual awareness, this study offers a robust solution to improve indoor mobility and deliver actionable feedback to support safe and independent doorway traversal for wheelchair users. Full article

Robust indoor positioning, crucial to modern location-based services, increasingly leverages Channel State Information (CSI) for its superior multipath resolution over the traditional RSSI. However, current CSI-based methods are hampered by three key limitations: susceptibility to skewed, non-Gaussian noise; informational redundancy from multi-AP configurations; and spatial discontinuities arising from Euclidean-based modeling. To address these challenges, we propose a unified framework that synergistically combines three innovations: (1) an adaptive filtering pipeline that uses wavelet decomposition and dynamic Kalman updates to suppress skewed noise; (2) a graph attention network that optimizes AP selection by modeling spatiotemporal correlations; and (3) a hyperbolic covariance model that captures the intrinsic non-Euclidean geometry of signal propagation. Evaluations on experimental data demonstrate that our framework achieves superior positioning accuracy and environmental robustness over state-of-the-art methods. Crucially, the hyperbolic representation enhances resilience to obstructions by preserving the signal manifold’s true structure, thereby advancing the practical deployment of fingerprinting systems. Full article

In contemporary wireless communication systems, achieving precise localization of communicating devices and accurate channel estimation is crucial for enhancing operational efficiency and reliability. This study introduces a novel approach that integrates the localization task and channel estimation into a single framework. We present a multi-task neural network architecture capable of simultaneously estimating channels from multiple base stations in a blind manner while estimating user terminal coordinates in given indoor environments. This approach exploits the relationship between channel characteristics and spatial information, using the same channel state information (CSI) data to perform both tasks with a single model. We evaluate the proposed solution, assessing its effectiveness across differing antenna spacing configurations and indoor test environments using both WiFi and 5G orthogonal frequency-division multiplexing (OFDM) systems. The results show performance benefits, achieving comparable channel estimation results to other studies while simultaneously providing a localization estimate, resulting in reduced model overhead while leveraging spatial context. The presented system demonstrates potential to improve the efficiency of communication systems in real-world applications, aligning with the goals of emerging integrated sensing and communication (ISAC) systems. Results based on experimental data using the proposed solution show a 50th percentile localization error of 1.62 m for 3-tap channels and 0.89 m for 10-tap channels. Full article

Single image dehazing is a fundamental task in computer vision, aiming to recover a clear scene from a hazy input image. To address the limitations of traditional dehazing algorithms—particularly in global feature association and local detail preservation—this study proposes a novel Transformer-based dehazing model enhanced by an interactive channel attention mechanism. The proposed architecture adopts a U-shaped encoder–decoder framework, incorporating key components such as a feature extraction module and a feature fusion module based on interactive attention. Specifically, the interactive channel attention mechanism facilitates cross-layer feature interaction, enabling the dynamic fusion of global contextual information and local texture details. The network architecture leverages a multi-scale feature pyramid to extract image information across different dimensions, while an improved cross-channel attention weighting mechanism enhances feature representation in regions with varying haze densities. Extensive experiments conducted on both synthetic and real-world datasets—including the RESIDE benchmark—demonstrate the superior performance of the proposed method. Quantitatively, it achieves PSNR gains of 0.53 dB for indoor scenes and 1.64 dB for outdoor scenes, alongside SSIM improvements of 1.4% and 1.7%, respectively, compared with the second-best performing method. Qualitative assessments further confirm that the proposed model excels in restoring fine structural details in dense haze regions while maintaining high color fidelity. These results validate the effectiveness of the proposed approach in enhancing both perceptual quality and quantitative accuracy in image dehazing tasks. Full article

Phosphogypsum leachate significantly accelerates carbonate rock dissolution in karst regions. The dissolution mechanism of phosphogypsum leachate associated with carbonate rock interaction and the corresponding numerical simulation need further study. In this study, 3D digital core imaging was used to scan undisturbed carbonate rock specimens from phosphogypsum landfill sites, and corresponding 3D structural models were constructed. We carried out indoor dissolution experiments in which we used Scanning Electron Microscopy as well as Energy Dispersive Spectrometer to observe changes in the surface micromorphology and elemental content of the rock specimens under different dissolution conditions. A reactive numerical model was developed based on the 3D structural model obtained from 3D digital core imaging, and numerical simulation studies were conducted. The dissolution reaction between phosphogypsum leachate and carbonate rocks exhibited an initial rapid phase followed by gradual stabilization. The pH of the leachate showed an exponential negative correlation with the dissolution amount per unit area of the rock specimens, while a power-law negative correlation was observed between pH and chemical dissolution rates. The numerical model effectively reproduced the reactant concentration states observed in experiments, confirming its capability to simulate reaction processes within rock specimens. Simulation results demonstrated that preferential flow through fracture channels led to higher reactant concentrations near fractures due to incomplete reactions, whereas lower concentrations occurred in sub-fracture regions. As the fracture aperture increased, the concentration disparity between these regions became more pronounced, with higher concentration of reactants at the outlet. Full article

Reconfigurable intelligent surfaces (RIS) and massive multiple input and multiple output (M-MIMO) are the two major enabling technologies for next-generation networks, capable of providing spectral efficiency (SE), energy efficiency (EE), array gain, spatial multiplexing, and reliability. This work introduces an RIS-assisted millimeter wave (mmWave) M-MIMO system to harvest the advantages of RIS and mmWave M-MIMO systems that are required for beyond fifth-generation (B5G) systems. The performance of the proposed system is evaluated under 3GPP TR 38.901 V16.1.0 5G channel models. Specifically, we considered indoor hotspot (InH)—indoor office and urban microcellular (UMi)—street canyon channel environments for 28 GHz and 73 GHz mmWave frequencies. Using the SimRIS channel simulator, the channel matrices were generated for the required number of realizations. Monte Carlo simulations were executed extensively to evaluate the proposed system’s average bit error rate (ABER) and sum rate performances, and it was observed that increasing the number of transmit antennas from 4 to 64 resulted in a better performance gain of ∼10 dB for both InH—indoor office and UMi—street canyon channel environments. The improvement of the number of RIS elements from 64 to 1024 resulted in ∼7 dB performance gain. It was also observed that ABER performance at 28 GHz was better compared to 73 GHz by at least ∼5 dB for the considered channels. The impact of finite resolution RIS on the considered 5G channel models was also evaluated. ABER performance degraded for 2-bit finite resolution RIS compared to ideal infinite resolution RIS by ∼6 dB. Full article

Accurate indoor localization is essential for Internet of Things (IoT) systems and autonomous navigation in the 6G communication system. However, achieving precision in environments affected by signal multipath effects and interference remains a challenge for 6G communication systems. We employ a Residual Neural Network (ResNet) augmented with channel and spatial attention mechanisms to enhance indoor localization performance using time-domain data. Through extensive experimentation, our models, when equipped with an attention mechanism, can achieve accurate location under 20% interference. Numerical results show that the ResNet with a Channel Local Attention Block (CLAB) can reduce the localization error by about 12% even when the interference is high. Similarly, the ResNet with a Spatial Local Attention Block (SLAB) can also improve the localization accuracy. While a ResNet combining both CLAB and SLAB can reduce the position error to about 7 cm. Full article

Despite the widespread adoption of indoor positioning technology, the existing solutions still face significant challenges. On one hand, Wi-Fi-based positioning struggles to balance accuracy and efficiency in complex indoor environments and architectural layouts formed by pre-existing access points (APs). On the other hand, vision-based methods, while offering high-precision potential, are hindered by prohibitive costs associated with binocular camera systems required for depth image acquisition, limiting their large-scale deployment. Additionally, channel state information (CSI), containing multi-subcarrier data, maintains amplitude symmetry in ideal free-space conditions but becomes susceptible to periodic positioning errors in real environments due to multipath interference. Meanwhile, image-based positioning often suffers from spatial ambiguity in texture-repeated areas. To address these challenges, we propose a novel hybrid indoor positioning method that integrates multi-granularity and multi-modal features. By fusing CSI data with visual information, the system leverages spatial consistency constraints from images to mitigate CSI error fluctuations while utilizing CSI’s global stability to correct local ambiguities in image-based positioning. In the initial coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, CSI features from Wi-Fi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. The experimental results show that our proposed method synergistically combines the strengths of Wi-Fi fingerprints and visual positioning, resulting in a substantial enhancement in positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 m for 45% of positioning points and 0.8 m for 67% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology. Full article

To evaluate the potential of phase-change materials (PCMs) in improving the indoor thermal and airflow environment of Trombe walls under solar energy limitations, a computational fluid dynamics (CFDs) model was employed in this study to perform comparative simulations. Taking traditional Trombe walls (TWs) as the control group and PCM-Trombe walls (PCM-TWs) as the experimental group, the simulation analysis was carried out based on meteorological data from a typical spring day in Hangzhou in 2024. The results indicate that the application of PCM significantly reduced temperature fluctuations in the air channel, lowering the peak temperature by 8.3 °C. Meanwhile, it delayed the decline in ventilation rate, extending the effective ventilation time by approximately one hour. Moreover, by calculating the Grashof number and ventilation rate, it was observed that the buoyancy effect of PCM-TWs is weaker than that of TWs at the peak wind speed, resulting in a lower natural convection intensity. The ventilation rate variation trend of PCM-TWs was smoother, with its peak ventilation rate slightly lower than that of TWs by 0.008 kg/s. Full article

Annular flow channels, which are distinct from circular pipes, represent a complex flow structure widely applied in fields such as food engineering and petroleum engineering. Discovering the internal flow patterns is conducive to the study of heat and mass transfer laws, thereby playing a crucial role in optimizing flow processes and selecting equipment. However, the mechanism underlying the influence of annular turbulent flow on macro-pressure drop remains to be further investigated. This paper focuses on the roughness of both inner and outer pipes, as well as positive and negative eccentricities. Numerical simulation is employed to study the microscopic characteristics of the flow field, and the numerical model is validated through indoor experimental measurements of pressure drop laws. Further numerical simulations are conducted to explore the microscopic variations in the flow field, analyzed from the perspectives of wall shear force and turbulence characteristics. The results indicate that an increase in inner pipe roughness significantly enhances the wall shear force on both the inner and outer pipes, and vice versa. In the concentric case, wall shear force and turbulence characteristics exhibit central symmetry. Eccentricity leads to uneven distributions of velocity, turbulence intensity, and shear force, with such unevenness presenting axial symmetry under both positive and negative eccentricities. Additionally, eccentricity demonstrates turbulence drag reduction characteristics. This study enhances our understanding of the mechanism by which annular turbulent flow influences pressure drop. Furthermore, it offers theoretical backing for the design and optimization of annular space piping, thereby aiding in the enhancement of the performance and stability of associated industrial systems. Full article

Fingerprinting-based positioning exploiting in two dimensions the spatial side information on fingerprints from adjacent positions relative to a target position is studied. The positioning is performed at the positioning device, utilizing as fingerprints the received signal strengths of downlink radio signals, collected using a two-dimensional sensor array. The motivation is to minimize the positioning error by transferring the complexity and cost from the infrastructure to the positioning device. The goal is to learn whether spatial side information on the fingerprints can minimize the positioning error. We provide a differentiation between fingerprinting in uplink and downlink, a classification of the positioning data aggregation domains, concepts, and a related literature review. We present three pattern-matching methods for estimating the position using spatial side information, two based on regression, implemented using feedforward neural networks, and one based on classification of the fractions of the positioning area, implemented using a convolutional neural network. Fingerprinting with and without spatial side information is benchmarked using the proposed pattern-matching methods in a system simulator based on Monte Carlo methods, generating synthetic fingerprints with an indoor radio channel model and calculating the positioning error. It is observed that for the given assumptions and the system considered, fingerprinting-based positioning with spatial side information substantially reduces the positioning error. Full article

Internet of Things (IoT) technology has continuously advanced over the past decade. As a result, device-free indoor localization functions have become a crucial part of application areas such as healthcare, safety, and energy management. Passive infrared (PIR) sensors detecting changes in temperature in an environment are one of the suitable options for human localization due to their lower cost, low energy consumption, electromagnetic tolerance, and enhanced private awareness. Although existing localization methods, including machine/deep learning, have been proposed to detect multiple persons based on signal phase and amplitude, they still face challenges regarding signal quality, ambiguity, and interference caused by the complex, interleaving movements of multiple persons. This paper proposes a novel deep learning method for multi-person localization using channel separation and template-matching techniques. The approach is based on a deep CNN-LSTM architecture with ensemble models using a mean bagging technique for achieving higher localization accuracy. Our results show that the proposed method can estimate the locations of two participants simultaneously with a mean distance error of 0.55 m, and 80% of the distance errors are within 0.8 m. Full article

To address the challenges of low accuracy in coal gangue image recognition and poor segmentation performance under the influence of dust in underground coal mines, a scaled simulation platform was constructed to replicate the longwall top coal caving face. This platform utilized real coal gangue particles as the raw material and employed dust simulation to mimic the dust conditions typically found in coal mines. Images of coal gangue without dust and under varying dust concentrations were then collected for analysis. In parallel, an improved DeeplabV3+ coal gangue image segmentation model is proposed, where ResNeSt is employed as the backbone network of DeeplabV3+, thereby enhancing the model’s capability to extract features of both coal and gangue. Furthermore, two channel attention modules (ECAs) are incorporated to augment the model’s ability to recognize edge features in coal gangue images. A class-label smoothing training strategy was adopted for model training. The experimental results indicate that, compared to the original DeepLabV3+ model, the optimized model achieves improvements of 3.14%, 4.70%, and 3.83% in average accuracy, mean intersection over union (mIoU), and mean pixel accuracy, respectively. Furthermore, the number of parameters was reduced from 44.18 M to 43.86 M, the floating-point operations decreased by 8.33%, and the frames per second (FPS) increased by 45.03%. When compared to other models such as UNet, PSANet, and SegFormer, the proposed model demonstrates superior performance in coal gangue segmentation, accuracy, and parameter efficiency. A method combining dark channel prior and Gaussian weighting was employed for defogging coal gangue images under varying dust concentration conditions. The recognition performance of the coal gangue images before and after defogging was assessed across different dust concentrations. The model’s segmentation accuracy and practical applicability were validated through defogging and segmentation of both indoor and underground dust images. The recognition accuracy of coal and gangue, before and after defogging, improved by 6.8–71.8% and 5.8–45.8%, respectively, as the dust concentration increased, thereby demonstrating the model’s effectiveness in coal gangue image defogging segmentation in underground dust environments. 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

As irrigation districts rapidly advance in terms of informatization, research on intelligent water quantity control technologies for open channels has gained increasing importance. This study aims to investigate the flow capacity and hydraulic characteristics of gates in open channels, focusing on the flow measurement and the hydraulic behavior around water-measuring structures. Although automated control in irrigation systems has achieved significant development, research on the flow characteristics near gates remains limited. To address this gap, an integrated approach combining indoor physical model experiments with theoretical analysis was used. This study explored the water surface profile, cross-sectional flow velocity distribution, vertical velocity distribution, and turbulent kinetic energy under various gate opening conditions and flow rates. The findings reveal that the water surface exhibits a sharp rise upstream of the gate, followed by a steep decline and stabilization downstream, influenced by the gate’s water-blocking effect. The flow velocities near the gate opening differ significantly in direction and magnitude from those in other cross-sections, affecting both longitudinal and vertical velocities. The turbulent kinetic energy is concentrated near the gate opening, and the turbulent kinetic energy is primarily concentrated near the sidewalls and the channel bottom; the gate’s opening size plays a crucial role in its diffusion and distribution. Linear regression analysis was utilized to fit the gate flow coefficient formula, and a comparative analysis of the measurement accuracy was conducted. The relative error between the calculated flow values and the actual measured values is within ±5%, which meets the precision requirements specified in the water measurement standards for irrigation canal systems in the irrigation district. This study pioneers an integrated approach for investigating the hydraulic characteristics of gates in open channels, merging physical model experiments with theoretical analysis. It provides novel insights into how gate openings affect water surface profiles, flow velocity distributions, and turbulent kinetic energy. This research also underscores the role of gate discharge in turbulent kinetic energy distribution, offering technical insights to enhance flow measurement accuracy and prevent sediment deposition, thereby optimizing gate applications for efficient water management. Overall, this study significantly advances the understanding of open channel flow dynamics and holds substantial significance for the refinement of water quantity control techniques in irrigation districts. Full article