Search Results (97)

Gas pipeline leakages in urban underground utility tunnels pose a severe threat to public safety. Leakages of varying aperture sizes trigger differentiated risks of diffusion and explosion; thus, achieving precise identification of leakage hole size has become a critical issue in safety management. To address the difficulty of traditional methods in effectively separating the acoustic features of different leakage levels within complex utility tunnel environments, this paper proposes a gas pipeline leakage risk level identification method based on a lightweight Spatial–Temporal Graph Neural Network (ST-GNN). First, relying on a real utility tunnel simulation platform, acoustic signals under different pressures and leakage hole size are collected, and time-frequency magnitude features are constructed through Short-Time Fourier Transform (STFT). Furthermore, each acoustic sample is independently converted into a graph with STFT time frames as nodes, where temporal neighborhood edges and K-nearest neighbor edges jointly encode local dynamics and non-local spectral similarities. This transforms unstructured acoustic signals into graph-structured data that embodies spatial–temporal coupling relationships. Building upon this, a lightweight Chebyshev graph convolutional network is designed to progressively extract discriminative features strongly correlated with leakage levels using multi-layer convolution. Experimental results on the actual utility tunnel simulation platform dataset demonstrate that the proposed method achieves excellent performance in a three-level leakage classification task. The t-SNE visualization reveals the effective separation of features, progressing from complete mixing in the input layer to distinct separation in the output layer. Through multiple training statistics and ablation experiments, the impact of dataset size and the number of network layers on the identification performance is analyzed, validating the robustness of the proposed model under limited samples and the effectiveness of its lightweight structure. This provides a feasible solution for the automated and refined identification of gas pipeline leakage levels in underground utility tunnels. Full article

High-precision identification of acoustic convergence zones (CZs) and acoustic shadow zones (SZs) is a core prerequisite for deep-sea sonar performance prediction and long-range underwater target detection. However, in data-scarce marine environments, traditional acoustic identification methods suffer from high environmental sensitivity and significant computational costs, while pure data-driven deep learning methods face dilemmas such as a lack of physical consistency and poor generalization on small samples. To address these issues, a three-level cascaded recognition framework based on physics-prior-augmented deep learning is proposed in this paper, enabling accurate segmentation of CZs and intelligent classification of sound field types under data-scarce scenarios. In this framework, physical acoustic principles are incorporated exclusively as priors through a training dataset generated by a Gaussian beam acoustic propagation code (Bellhop) and through hand-crafted geometric features derived post hoc from the initial segmentation outputs. Taking a typical deep-sea area in the Northwest Pacific Ocean as the research object, a hybrid dataset comprising 5000 simulated transmission loss images and 500 simulated images from a geographically distinct sea area is constructed. The sound field is categorized into four types: strong convergence, usable convergence, weak convergence, and shadow zone. In the first stage, the ResNet-34 backbone is improved by integrating deformable convolution and a global statistical feature module, which, combined with a joint loss function, achieves high-precision pixel-level segmentation of CZs and SZs, with the regional gray contrast reaching 86.9%. In the second stage, a customized dual-channel VGG16 architecture is designed to fuse the extracted geometric priors and visual features, achieving a sound field classification accuracy of 89.91%. In the third stage, a hybrid data augmentation technique combining Mixup and convolutional autoencoder is adopted alongside a transfer learning strategy to mitigate the data scarcity under cross-domain conditions, boosting the small-sample classification accuracy to 84.45%. The experimental results demonstrate that the models in each stage of the proposed framework significantly outperform traditional methods and baseline networks. This study provides a novel methodology and technical support for intelligent sound field identification in data-scarce marine environments. Finally, the core contributions and current limitations are summarized, and future research directions, such as constructing a dynamic hydrological parameter feedback mechanism and identifying three-dimensional complex sound fields, are prospected. Full article

The deconvolution approach for the mapping of acoustic sources (DAMAS) based on the microphone array is proved effective in various acoustic imaging applications. Generally, DAMAS and its variations result in heavy computation load due to the nature of large-scale linear equations and the iterative solver, which prevent the deployment of DAMAS to platforms with limited resources, such as the edge devices of the internet of things (IoT). In order to enhance the computational efficiency of DAMAS, a fast algorithm based on DAMAS with grid compression by the morphological operations on the acoustic images is proposed in this work. The proposed approach intentionally neglects the physics behind the acoustic imaging, but emphasizes the general visual features of acoustic images, as if they were natural images. A low computation load can be guaranteed regardless of the complicated acoustic environments, which alternatively ensures the robustness of proposed algorithm. Numerical simulations demonstrate that the proposed algorithm effectively accelerates the acoustic image reconstruction. In practical experiments, the proposed method reduces the algorithm time to be within 26% of DAMAS. In certain scenarios, both the algorithm time and localization accuracy of the proposed method outperform the conventional methods. Full article

Conventional adaptive active noise control (ANC) techniques, such as filtered-x normalized least mean square (FxNLMS), frequently run into issues when the noise environment changes, leading to longer reaction times. Moreover, fixed-filter approaches may lose the essential phase information necessary for efficient noise cancellation. This paper introduces 2D-BiSpecNet, a novel, effectively delayless feedforward active noise control system that uses a deep learning co-processor to address these difficulties. The technique converts one-dimensional audio signals into 64 × 64 bispectrum matrices, which transform sounds into visual representations. Therefore, it focuses on nonlinear quadratic phase couplings (QPCs), which provide robust and amplitude-independent views of the noise structure. The system then applies a quick multilabel classifier to examine these representations and immediately generates a control filter via 15 parallel subcontrol filters. The paper specifies a 5 × 5 convolutional receptive field that had the maximum efficacy. Simulations with real acoustic data indicate that this configuration yields an average noise reduction of −14.48 dB for aircraft noise, outperforming the usual FxNLMS technique by nearly 6 dB. The technology conducts classification and filtering nearly seven times faster than adaptive approaches, thus reducing convergence delays and delivering a more reliable and low-latency solution for noise-canceling environments. Full article

Deep-sea mining operations demand continuous, drift-free positioning over multi-day missions—a requirement that traditional acoustic dead-reckoning systems struggle to meet due to cumulative error accumulation and frequent DVL bottom-lock loss in sediment plume environments. Inspired by Google Cartographer’s 2D grid mapping paradigm, we present a prior map-based visual localization framework that decouples offline mapping from real-time localization, fundamentally eliminating drift through absolute image registration against pre-built seabed mosaics. By integrating adaptive keyframe selection, Multi-Scale Retinex (MSR) enhancement, and the AD-LG deep feature matching architecture, our system constructs globally consistent seabed maps for absolute positioning. The framework leverages deformable convolutions and LightGlue to effectively mitigate challenges such as low texture and non-rigid distortion. Quantitative validation on tank simulation datasets demonstrates significant superiority over IMU-only and standard fusion schemes; qualitative deployment on real Pacific CCZ imagery confirms near-real-time operational feasibility on an embedded Jetson Orin NX platform. This system establishes visual navigation as a viable backup to acoustic systems, addressing a critical gap in deep-sea mining vehicle autonomy. Full article

Search and rescue (SAR) missions in collapsed or underground environments remain challenging due to GPS unavailability, which hinders localization and autonomous navigation. Systems that rely on single-sensor inputs or structured settings often degrade under smoke, dust, or dynamic clutter. This paper presents an autonomous ground robot for GPS-denied SAR that integrates low-cost thermal, visual, inertial, and acoustic cues within a unified, computation-efficient architecture. The stack combines Thermal–Visual Odometry (TV–VO) with Zero-Velocity Updates (ZUPT) for drift-resistant localization, RescueGraph for multimodal survivor detection, and a Proximal Policy Optimization (PPO) planner for adaptive navigation under uncertainty. Across simulated disaster scenarios and benchmark corridor runs, the system shows embedded-feasible runtime behavior and supports return to base without external beacons under the evaluated conditions. Quantitatively, TV–VO+ZUPT reduces drift in short internal evaluations, while RescueGraph attains an F1-score of 0.6923 and an area under the ROC curve (AUC) of 0.976 for survivor detection. At the system level, the integrated navigation stack achieves full mission completion in the reported SAR-style trials, while the separate A*/PPO comparison highlights a trade-off between completion rate, traversal time, and collisions. Overall, the results support the practical promise of a low-cost sensor-fusion and learning-assisted navigation framework for GPS-denied SAR robotics. Full article

To address the escalating security challenges posed by unauthorized Unmanned Aerial Vehicles, this paper presents a Sim2real physics-informed audio–visual fusion simulation platform designed to enhance Counter-Unmanned Aerial Vehicle detection and tracking performance. The proposed method integrates two complementary sensing pipelines: a physics-based acoustic localization system utilizing Time Difference of Arrival principles and a deep learning-driven visual detection framework. To ensure robust surveillance against non-cooperative targets, these pipelines are not only fused through strict spatiotemporal synchronization but also mutually reinforce each other—acoustic data guides visual attention in low-visibility scenarios typical of adversarial intrusions, while visual detections refine acoustic parameter estimation. Building upon prior work in multi-modal perception, we extend the framework to dynamic environments characterized by configurable visual obstructions, including smoke and fog, which frequently compromise conventional optical anti-drone systems. Experiments demonstrate that the fusion system progressively adapts to degraded visual conditions, extending tracking continuity from approximately 50% coverage under vision-only operation to near-continuous target awareness, with a moderate trade-off in average angular precision when acoustic-only segments are included. Physical validation with quadrotor Unmanned Aerial Vehicles confirms the platform’s capability to bridge simulation-to-reality gaps. Our results highlight the system’s robustness against sensor degradation and its potential to accelerate the development of resilient multisensor Counter-Unmanned Aerial Vehicle systems while reducing dependency on costly field testing. Full article

As a critical barrier for power network safety, insulating materials are susceptible to internal microcracks, delamination, and other hidden defects that can trigger dielectric strength degradation and space charge accumulation, ultimately leading to insulation breakdown. Ultrasonic shear wave non-destructive testing enables defect identification without damaging the material. Therefore, this paper focuses on the identification and imaging of internal defects in insulating components using ultrasonic shear waves. First, a physical model for ultrasonic shear wave NDT is established. Based on the refraction and reflection characteristics of ultrasonic waves in materials with different acoustic impedances, a defect localization formula is derived. Through simulation verification, for the three defects set at different positions in the defect model, the positioning error is less than 0.5 mm. Subsequently, defects such as circular holes, triangular shapes, cracks, and bottom grooves were simulated. Analysis of the echo data revealed a correlation between the distance from the sensor to the defect and the echo amplitude. For groove defect imaging, the differential SAFT algorithm was employed, achieving a width error of 1 mm for imaging a 2 mm wide by 5 mm high groove, clearly presenting the defect morphology. Finally, an imaging software program for defect structure reconstruction was developed based on the simulation model presented in this article. We collected side and back view data through the constructed ultrasonic transverse wave non-destructive testing experimental platform, and visualized defects in insulation materials with grooves using this ultrasonic imaging program. This study achieved defect localization and imaging through simulation of various defect types combined with synthetic aperture focused imaging algorithms, providing a reference for visualization and industrial application of ultrasonic shear wave non-destructive testing technology. Full article

Cooperative task allocation is one of the critical enablers for multi-Autonomous Underwater Vehicle (AUV) missions, but existing approaches often assume reliable communication that rarely holds in real underwater acoustic environments. We study here the performance and robustness of the Consensus-Based Bundle Algorithm (CBBA) for multi-AUV task allocation under realistically degraded underwater communication conditions with dynamically appearing tasks. An integrated simulation framework that incorporates a Dubins-based kinematic model with minimum turning radius constraints, a configurable underwater acoustic communication model (range, delay, packet loss, and bandwidth), and a full implementation of improved CBBA with new features, complemented by 3D trajectory and network-topology visualization. We define five communication regimes, from ideal fully connected networks to severe conditions with short range and high packet loss. Within these regimes, we assess CBBA based on task allocation quality (total bundle value and task completion rate), convergence behavior (iterations and convergence rate), and communication efficiency (message delivery rate, average delay, and network connectivity), with additional metrics on the number of conflicts during dynamic task reallocation. Our simulation results indicate that CBBA maintains performance close to the optimum when the conditions are good and moderate but degrades significantly when connectivity becomes intermittent. We then introduce a local-communication-based conflict resolution strategy in the face of frequent task conflicts under very poor conditions: neighborhood-limited information exchange, negotiation within task areas, and decentralized local decisions. The proposed conflict resolution strategy significantly reduces the occurrence of conflicts and improves task completion under stringent communication constraints. This provides practical design insights for deploying multi-AUV systems under weak underwater acoustic networks. Full article

Industrial workplaces, especially in vulnerable, hot, and arid developing countries, face major challenges in maintaining indoor comfort conditions due to the escalating problem of global temperature rise. This study investigates passive scenarios of adaptive retrofitting for a case study carpet and rug industrial plant in Cairo, Egypt to achieve indoor comfort conditions and energy efficiency. The research method included a Post Occupancy Evaluation (POE) for the operational phase of individual work units through measurements and simulations to investigate indoor thermal, visual, and acoustic comfort conditions as well as air quality concerns. Thus, the study presents a set of recommendations for building unit(s) and collectively for the entire facility by applying integrated application of building envelope enhancements; optimized opening design, thermal wall insulation and high-albedo (reflective) exterior coatings for wall and roof surfaces. Comparing the modified case to the base case scenario shows significant improvements. Thermal comfort achieved a 16% to 33% reduction in discomfort hours during peak summer, primarily through a 33% increase in air flow velocity and better humidity control. Visual comfort indicated improvements in daylight harvesting, with Daylighting Autonomy increasing by 47% to 64% in core areas, improving light uniformity and reducing glare potential by decreasing peak illuminance by approximately 25%. Thus, the combined envelope and system modifications resulted in a 60 to 80% reduction in monthly Energy Use Intensity (EUI). The effectiveness of the mitigation measures using acoustic insulation was demonstrated in reducing sound pollution transferring outdoors, but the high indoor sound levels require further near-source mitigation or specialized acoustic treatment for complete success. Eventually, the research method helps create a mechanism for measuring and controlling indoor comfort conditions, provide an internal baseline or benchmark to which future development can be compared against, and pinpoint areas of improvement. This can act as a pilot project for green solutions to mitigate the problem of climate change in industrial workplaces and pave the way for further collaboration with the industrial sector. Full article

Shipping expansion, offshore energy generation, fish farming, and construction work radiate high levels of underwater noise, which may critically stress marine ecosystems. Tools for simulating, analyzing, and forecasting underwater noise can be of great help in understanding the impact of underwater radiated noise both on the environment and on man-made equipment, such as underwater communication and telemetry systems. To address this challenge, we developed a web-based Marine Noise Analysis Tool (MNAT) that models, simulates, and predicts underwater radiated noise levels. To reproduce realistic shipping conditions, MNAT combines real-time Automatic Identification System data with environmental data using broadly accepted underwater acoustic propagation models, including Bellhop and RAM. Moreover, MNAT can simulate other kinds of noise sources, such as seismic airguns. It features an intuitive interface enabling real-time tracking, noise impact assessment, and interactive visualizations. MNAT’s noise modeling capabilities allow the user to design resilient communication systems in different noise conditions, analyze maritime noise data, and forecast future noise levels, with potential contributions to the design of noise-resilient systems, to the optimization of environmental monitoring device deployments, and to noise mitigation policymaking. MNAT has been made available for the community at a public GIT repository. Full article

The rapid spread of unmanned aerial vehicles (UAVs) has created new challenges for airspace security, as drones are increasingly used for surveillance, smuggling, and potentially for attacks near critical infrastructure. A key difficulty lies in reliably distinguishing UAVs from visually similar birds in electro-optical surveillance channels, where complex backgrounds and visual noise often increase false alarms. To address this, we investigated recent YOLO architectures and developed an enhanced model named YOLOv12-ADBC, incorporating an adaptive hierarchical feature integration mechanism to strengthen multi-scale spatial fusion. This architectural refinement improves sensitivity to subtle inter-class differences between drones and birds. A dedicated dataset of 7291 images was used to train and evaluate five YOLO versions (v8–v12), together with the proposed YOLOv12-ADBC. Comparative experiments demonstrated that YOLOv12-ADBC achieved the best overall performance, with precision = 0.892, recall = 0.864, mAP50 = 0.881, mAP50–95 = 0.633, and per-class accuracy reaching 96.4% for drones and 80% for birds. In inference tests on three video sequences simulating realistic monitoring conditions, YOLOv12-ADBC consistently outperformed baselines, achieving a detection accuracy of 92.1–95.5% and confidence levels up to 88.6%, while maintaining real-time processing at 118–135 frames per second (FPS). These results demonstrate that YOLOv12-ADBC not only surpasses previous YOLO models but also offers strong potential as the optical module in multi-sensor fusion frameworks. Its integration with radar, RF, and acoustic channels is expected to further enhance system-level robustness, providing a practical pathway toward reliable UAV detection in modern airspace protection systems. Full article

Urban soundscapes are complex due to the interaction of different sound sources and the influence of structures on sound propagation. Moreover, the dynamic nature of sounds over time and space adds to this complexity. Virtual reality (VR) has emerged as a powerful tool to simulate acoustic and visual environments, offering users an immersive sense of presence in controlled settings. This technology facilitates more accurate and predictive assessment of urban environments. It serves as a flexible tool for exploring, analyzing, and interpreting them under repeatable conditions. This study presents a systematic literature review focusing on research that integrates VR technology for the audiovisual reconstruction of urban environments. This topic remains relatively underrepresented in the existing literature. A total of 69 peer-reviewed studies were analyzed in this systematic review. The studies were classified according to research goals, selected urban environments, VR technologies used, technical equipment, and experimental setups. In this study, the relationship between the tools used in urban VR representations is examined, and experimental setups are discussed from both technical and perceptual perspectives. This paper highlights existing challenges and opportunities in using VR to assess soundscapes and offers practical insights for future applications of VR in urban environments. Full article

Hollow heart, and other crop defects, can be devastating to farmers. Hollow heart is not a disease but a physiological disorder affected by temperature, soil moisture, plant density, and other factors. These defects can cause substantial annual losses for farmers. Currently, potatoes are shipped and inspected from producers to shipping points and markets. At these facilities, samples are inspected for defects. Detection of hollow heart consists of halving potatoes and visually inspecting for defects. The defect size is compared to USDA hollow heart classification charts for acceptance or rejection. An automatic, non-destructive system to identify hollow heart has the potential to improve quality. Two methods have been developed to collect data for such a system: acoustic signal capture and visual/vibration signal capture. Data is collected and stored for one potato at a time. The procedure includes the collection of weight, proportional size, and volume, as well as the generation of an acoustic sound signal through a drop test and a motion signal captured through a vision system. To simulate hollow heart, potatoes are cored and retested by producing a new set of data. Each potato is manually cut and inspected for true hollow heart. The generated data includes over 1000 samples, each comprising proportional volume, weight, proportional size, motion, and acoustic data. Such a dataset does not exist in the current literature and can serve for the development of machine learning algorithms to detect hollow heart nondestructively. In this paper, the data is also analyzed in terms of its statistical properties, as applied for possible feature engineering in machine learning. Full article

ADHD and misophonia are developmental neurological disorders that are currently increasing in prevalence due to excessive acoustic and visual pollution. ADHD, which is characterized by a lack of attention and excessive impulsive hyperactivity, and misophonia, which is hypersensitivity to sounds accompanied by a severe emotional and psychological reaction, are both affected by the user’s spatial environment to a great extent. Spatial design can contribute to increasing or decreasing these unfavorable sensory triggers that affect individuals with ADHD and/or Misophonia. However, the role of architectural spatial design as a therapeutic approach to alleviate the symptoms of Misophonia and ADHD has never been proposed before in the literature, despite its accumulative and chronic effects on the user’s experience in everyday life in terms of well-being and productivity. Therefore, the current work discusses this problem of neglecting the potential effect of architectural spatial design on alleviating Misophonia and ADHD. Thus, the objective of the current work is to propose customized architectural spatial design as a therapeutic approach to alleviate Misophonia and ADHD through adopting the compatible architectural trends of minimal and metaphysical architecture. The methodology of the current work includes a theoretical proposal of this customized architectural spatial design for alleviating these two special neurological conditions. This includes introducing and analyzing these two neurological conditions and their relation to and interaction with architectural spatial design, analyzing minimal and metaphysical architectural trends employed in the proposed therapeutic architectural design, and then proposing augmented and virtual reality as auxiliary add-ons to the architectural spatial design to boost its therapeutic effect. Minimal architecture achieves the “no emotion” criteria through reduced forms, patterns, and colors and adopts simple geometry and natural materials to reduce sensory stressors or stimuli, in order to alleviate the loss of attention and distraction prevalent in those with ADHD, as well as allowing the employment of acoustic materials to achieve acoustic comfort and noise blockage for Misophonia relief. Metaphysical architecture leads the hierarchy of sensory experience through the symbolistic, dynamic, and enigmatic composition of forms and colors, which enhance the spatial analysis and cognitive capacities of the inhabitants. Meanwhile, the use of customized virtual and augmented reality environments is an effective add-on to minimal and metaphysical architectural spaces thanks to its proven therapeutic effect in alleviating various neurological disorders and injuries. At this level of intervention, VR/AR can be used as an add-on to minimal-architecture design, to simulate varied scenarios, as minimal design offers a clean canvas for simulating these varied virtual environments. The other option is to build these customized VR/AR scenarios around a specific architectural element as an add-on metaphysical architecture design to lead the sensory experience and enable the user to detach from the physical constraints of the space. AI-generated designs were used as a proof of concept for the proposed customized architectural spatial design following minimal and metaphysical architecture, as well as to provide AR and VR scenarios as add-on architecture to enhance the therapeutic effect of these architectural spaces for Misophonia and ADHD patients. Furthermore, the validity of VR/AR as a therapeutic approach, alongside the customized architectural design, was discussed, and it was concluded that this study proves the need for extended clinical studies on its efficiency in the long run, which will be conducted in the future. Full article