Search Results (72)

This study aims to investigate the effects of phase synchronisation on tonal noise reduction in a multi-rotor UAV using an electronic phase-locking system. Experiments at the University of Bristol explored the impact of relative phase angle, propeller spacing, and blade geometry on acoustic performance, including psychoacoustic annoyance. Results show that increasing the phase angle consistently reduces the sound pressure level (SPL) due to destructive interference. For the two-bladed configuration, the highest noise reduction occurred at relative phase angle $\Delta \psi ={90}^{\circ }$, with a 19 dB decrease at the first blade-passing frequency (BPF). Propeller spacing had minimal impact when phase synchronisation was applied. The pitch-to-diameter ($P/D$) ratio also influenced results: for $P/D=0.55$, reductions ranged from 13–18 dB; and for $P/D=1.0$, reductions ranged from 10–20 dB. Maximum psychoacoustic annoyance was observed when propellers were in phase ($\Delta \psi =0^{\circ }$), while annoyance decreased with increasing phase angle, confirming the effectiveness of phase control for noise mitigation. For the five-bladed configuration, the highest reduction of 15 dB occurred at $\Delta \psi ={36}^{\circ }$, with annoyance levels also decreasing with phase offset. Full article

This study presents the development and validation of an AI-based system for detecting chainsaw sounds, integrated into a fixed-wing VTOL UAV. The system employs a convolutional neural network trained on log-mel spectrograms derived from four sound classes: chainsaw, music, electric drill, and human voices. Initial validation was performed through ground testing. Acoustic data acquisition is optimized during cruise flight, when wing-mounted motors are shut down and the rear motor operates at 40–60% capacity, significantly reducing noise interference. To address residual motor noise, a preprocessing module was developed using reference recordings obtained in an anechoic chamber. Two configurations were tested to capture the motor’s acoustic profile by changing the UAV’s orientation relative to the fixed microphone. The embedded system processes incoming audio in real time, enabling low-latency classification without data transmission. Field experiments confirmed the model’s high precision and robustness under varying flight and environmental conditions. Results validate the feasibility of real-time, onboard acoustic event detection using spectrogram-based deep learning on UAV platforms, and support its applicability for scalable aerial monitoring tasks. Full article

Common ragweed (Ambrosia artemisiifolia L.) has been identified as one of the most harmful invasive weed species in Europe due to its allergenic pollen and competitive growth in diverse habitats. In the first part of this review [Common Ragweed—Ambrosia artemisiifolia L.: A Review with Special Regards to the Latest Results in Biology and Ecology], its biological characteristics and ecological behavior were described in detail. In the current paper, control strategies are summarized, focusing on integrated weed management adapted to the specific habitat where the species causes damage—arable land, semi-natural vegetation, urban areas, or along linear infrastructures. A range of management methods is reviewed, including agrotechnical, mechanical, physical, thermal, biological, and chemical approaches. Particular attention is given to the spread of herbicide resistance and the need for diversified, habitat-specific interventions. Among biological control options, the potential of Ophraella communa LeSage, a leaf beetle native to North America, is highlighted. Furthermore, innovative technologies such as UAV-assisted weed mapping, site-specific herbicide application, and autonomous weeding robots are discussed as environmentally sustainable tools. The role of legal regulations and pollen monitoring networks—particularly those implemented in Hungary—is also emphasized. By combining traditional and advanced methods within a coordinated framework, effective and ecologically sound ragweed control can be achieved. Full article

In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image. Full article

As a fundamental technology in environmental perception, sound source localization (SSL) plays a critical role in public safety, marine exploration, and smart home systems. However, traditional methods such as beamforming and time-delay estimation rely on manually designed physical models and idealized assumptions, which struggle to meet practical demands in dynamic and complex scenarios. Recent advancements in deep learning have revolutionized SSL by leveraging its end-to-end feature adaptability, cross-scenario generalization capabilities, and data-driven modeling, significantly enhancing localization robustness and accuracy in challenging environments. This review systematically examines the progress of deep learning-based SSL across three critical domains: marine environments, indoor reverberant spaces, and unmanned aerial vehicle (UAV) monitoring. In marine scenarios, complex-valued convolutional networks combined with adversarial transfer learning mitigate environmental mismatch and multipath interference through phase information fusion and domain adaptation strategies. For indoor high-reverberation conditions, attention mechanisms and multimodal fusion architectures achieve precise localization under low signal-to-noise ratios by adaptively weighting critical acoustic features. In UAV surveillance, lightweight models integrated with spatiotemporal Transformers address dynamic modeling of non-stationary noise spectra and edge computing efficiency constraints. Despite these advancements, current approaches face three core challenges: the insufficient integration of physical principles, prohibitive data annotation costs, and the trade-off between real-time performance and accuracy. Future research should prioritize physics-informed modeling to embed acoustic propagation mechanisms, unsupervised domain adaptation to reduce reliance on labeled data, and sensor-algorithm co-design to optimize hardware-software synergy. These directions aim to propel SSL toward intelligent systems characterized by high precision, strong robustness, and low power consumption. This work provides both theoretical foundations and technical references for algorithm selection and practical implementation in complex real-world scenarios. Full article

The accurate quantification of urban anthropogenic CO₂ emissions is of paramount importance for comprehending regional carbon fluxes and supporting climate change mitigation strategies. This study explores the applicability of a cost-effective unmanned aerial vehicle (UAV)-based mass balance method for independent urban-scale emission assessments. An integrated air–ground–satellite observation framework was established by combining UAV-based vertical CO₂ profiles, ground-based observations, and ERA5 reanalysis data, and applied to quantify CO₂ emissions in Chengdu, a major city in southwestern China. The UAV-derived CO₂ concentration profiles were coupled with meteorological parameters to compute cross-sectional fluxes, yielding an annual emission estimate of 48.4 MtCO₂, which aligns well with census-based estimations. The primary uncertainty, approximately 23.61%, stems from meteorological parameter variations, highlighting the need for improved data resolution and extended observation periods. This study demonstrates that UAV-based mass balance observations can serve as an independent and verifiable approach for urban emission estimation. Beyond supplementing existing inventories, it provides a robust reference for cross-validation, contributing to the development of more accurate and adaptive emission monitoring systems for urban climate governance. Full article

Unmanned systems provide significant prospects for improving the efficiency of electromagnetic geophysical exploration in mineral prospecting and geological mapping, as they can significantly increase the productivity of field surveys by accelerating the movement of the measuring system along the site, as well as minimizing problems in cases where the pedestrian walkability of the site is a challenge. Lightweight and cheap UAV systems with a take-off weight in the low tens of kilograms are unable to carry a powerful current source; therefore, semi-airborne systems with a ground transmitter (an ungrounded loop or grounded at the ends of the line) and a measuring system towed on a UAV are becoming more and more widespread. This paper presents the results for a new generation of semi-airborne technology SibGIS UAV-TEMs belonging to the “line-loop” type and capable of realizing the transient/time-domain (TEM) electromagnetics method used for studying a uranium object of the paleovalley type. Objects of this type are characterized by a low resistivity of the ore zone located in relatively high-resistivity host rocks and, from the position of the geoelectric structure, can be considered a good benchmark for assessing the capabilities of different electrical exploration technologies in general. The aeromobile part of the geophysical system created is implemented on the basis of a hexacopter carrying a measuring system with an inductive sensor, an analog of a 50 × 50 m loop, an 18-bit ADC with satellite synchronization, and a transmitter. The ground part consists of a galvanically grounded supply line and a current source with a transmitter creating multipolar pulses of quasi-DC current in the line. The survey is carried out with a terrain drape based on a satellite digital terrain model. The article presents the results obtained from the electromagnetic soundings in comparison with the reference (drilled) profile, convincingly proving the high efficiency of UAV-TEM. This approach to pre-processing UAV–electrospecting data is described with the aim of improving data quality by taking into account the movement and swaying of the measuring system’s sensor. On the basis of the real data obtained, the sensitivity of the created semi-airborne system was modeled by solving a direct problem in the class of 3D models, which allowed us to evaluate the effectiveness of the method in relation to other geological cases. Full article

The present study evaluates the viability of fabricating unmanned aerial vehicle (UAV) propellers using fused filament fabrication (FFF), with an emphasis on low-cost, desktop-scale production. The study’s backdrop is the recent adoption of UAVs and advancements in additive manufacturing. While the scope targets accessibility for individual and small-scale users, the results have broader implications for scalable UAV propulsion systems. The research was conducted within an experimental UAV development framework aimed at optimizing propeller performance through strategic material selection, geometrical design optimization, and additive manufacturing processes. Six propeller variants were manufactured using widely available thermoplastic polymers, including polyethylene terephthalate glycol-modified (PETG) and thermoplastic polyurethane (TPU), as well as photopolymer-based propellers fabricated using vat photopolymerization, also known as digital light processing (DLP). Mechanical and aerodynamic characterizations were performed to assess the structural integrity, flexibility, and performance of each material under dynamic conditions. Two blade configurations, a toroidal propeller with anticipated aerodynamic advantages and a conventional tri-blade propeller (Gemfan 51466-3)—were comparatively analyzed. The primary contribution of this work is the systematic evaluation of performance metrics such as thrust generation, acoustic signature, mechanical strength, and thermal stress imposed on the electrical motor, thereby establishing a benchmark for polymer-based propeller fabrication via additive manufacturing. The findings underscore the potential of polymeric materials and layer-based manufacturing techniques in advancing the design and production of UAV propulsion components. Full article

In recent years, UAV technology has developed rapidly and has been widely applied across various fields. However, as the adoption of civilian UAVs continues to grow, there has been a corresponding rise in the number of black flights by UAVs, which may cause criminal activities and privacy and security issues, so it has become necessary to recognize UAVs in the airspace in order to deal with potential threats. This study recognizes UAVs based on the acoustic signals of UAV flights. Since there are various acoustic interferences in the real environment, more efficient acoustic recognition techniques are needed to meet the recognition needs in complex environments. Aiming at the recognition difficulties caused by the overlap of UAV sound and the background noise spectrum in low signal-to-noise ratio environments, this study proposes an improved lightweight ResNet10_CBAM deep learning model. The optimal performance of MFCC in low SNR environments is verified by comparing three feature extraction methods, Spectrogram, Fbank, and MFCC. The enhanced ResNet10_CBAM model, with fewer layers and integrated channel and spatial attention mechanisms, significantly improved feature extraction in low SNR conditions while reducing model parameters. The experimental results show that the model improves the average accuracy by 14.52%, 17.53%, and 20.71% compared with ResNet18 under the low SNR conditions of −20 dB, −25 dB, and −30 dB, respectively, and the F1 score reaches 94.30%. The study verifies the effectiveness of lightweight design and attention mechanisms in complex acoustic environments. Full article

The use of insect sex pheromones as an alternative technology for pest control in agriculture and forestry offers a promising solution. The development of a novel technology for the biological production of pheromones through yeast fermentation significantly lowers production costs, enabling the adoption of sustainable pest control practices in field crops, a strategy previously reserved for high-value crops. Over three years of monitoring and mating disruption trials in Greek cotton fields, focusing on the cotton bollworm Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae), it was confirmed that yeast-derived pheromones exhibit equal efficacy compared to their chemically synthesized counterparts. For the mating disruption of H. armigera, a biodegradable, flowable, and paraffin-based matrix was developed. The matrix adheres to plants, protects the labile pheromone molecules (Z)-11-hexadecenal and (Z)-9-hexadecenal, and controls their gradual release into the environment. These biodegradable polymer blobs act as non-retrievable dispensers and can be deployed manually or via unmanned aerial vehicles (UAVs), ensuring efficient and accurate application. This precise, time-efficient, and economically sound technology aligns with European Commission initiatives, such as the Green Deal’s Farm to Fork Strategy and the Biodiversity Strategy, contributing to food sustainability while respecting biodiversity. Full article

This article presents UaVirBASE, a publicly available dataset for the sound source localization (SSL) of unmanned aerial vehicles (UAVs). The dataset contains synchronized multi-microphone recordings captured under controlled conditions, featuring variations in UAV distances, altitudes, azimuths, and orientations relative to a fixed microphone array. UAV orientations include front, back, left, and right-facing configurations. UaVirBASE addresses the growing need for standardized SSL datasets tailored for UAV applications, filling a gap left behind by existing databases that often lack such specific variations. Additionally, we describe the software and hardware employed for data acquisition and annotation alongside an analysis of the dataset’s structure. With its well-annotated and diverse data, UaVirBASE is ideally suited for applications in artificial intelligence, particularly in developing and benchmarking machine learning and deep learning models for SSL. Controlling the dataset’s variations enables the training of AI systems capable of adapting to complex UAV-based scenarios. We also demonstrate the architecture and results of the deep neural network (DNN) trained on this dataset, evaluating model performance across different features. Our results show an average Mean Absolute Error (MAE) of 0.5 m for distance and height, an average azimuth error of around 1 degree, and side errors under 10 degrees. UaVirBASE serves as a valuable resource to support reproducible research and foster innovation in UAV-based acoustic signal processing by addressing the need for a standardized and versatile UAV SSL dataset. Full article

The unmanned aerial vehicle (UAV) industry is developing rapidly, and the application of UAVs is becoming increasingly widespread. Due to the lowering of the threshold for using UAVs, the random flight of UAVs poses safety hazards. In response to the safety risks associated with the unauthorized operation of UAVs, research on anti-UAV technology has become imperative. This study proposes an improved sound feature extraction method that utilizes the frequency distribution features of UAV sounds. By analyzing the spectrogram of UAV sounds, it was found that the classic Mel Frequency Cepstral Coefficients (MFCC) feature extraction method does not match the frequency bands of UAV sounds. Based on the MFCC feature extraction algorithm framework, an improved frequency band feature extraction method was proposed. This method replaces the Mel filter in the classic algorithm with a piecewise linear function with the frequency band weight as the slope, which can effectively suppress the influence of low- and high-frequency noise and fully focus on the different frequency band feature data of UAV sounds. In this study, the actual flight sounds of UAVs were collected, and the sound feature matrix of UAVs was extracted using the frequency band feature extraction method. The sound features were classified and recognized using a Convolutional Neural Network (CNN). The experimental results show that the frequency band feature extraction method has a better recognition effect compared to the classic MFCC feature extraction method. Full article

This work introduces a broad framework for the enhanced nonlinearity assessment of PAs for 5G FR1 in UAV–RIS networks with integration. This study employs LabVIEW-based double-frequency IMD3 testing methodology for a fast automatic test bed to quantify the nonlinearity and its EMI effect on the UAV–RIS system. The analysis includes the evaluation of the EMI effect on the TxRx link using the SNR model. In order to enhance the efficiency of the system, a 16 QAM modulator is incorporated into the system in order to modulate and demodulate the signal. Additionally, the system includes a Deep Q agent based on the DRL model that enhances the controllability and efficiency of amplifiers under the dynamic environment typical for UAV–RIS networks. This work offers a framework for enhancing amplifier design for EMI reduction and supports the sound engineering of future UAV–RIS-based communication networks. The outcomes prove that signal integrity and nonlinearity characterization are enhanced significantly, providing important information for future 5G and beyond wireless communication systems. Full article

This study examines noise reduction strategies for unmanned aerial vehicles (UAVs) in urban air mobility applications, with a particular focus on package delivery. By employing a combination of low-, mid- and high-fidelity aerodynamic and aeroacoustic analyses, this research investigates the impact of rotor design parameters, including blade spacing and rotational speed, on noise emissions. The results demonstrate that an increase in rotor spacing results in a reduction in noise emissions. By adjusting the blade chord and twist within an optimization loop, it was possible to decrease tonal noise, yielding a Sound Pressure Level (SPL) reduction of about 3.51 dB while improving propulsive efficiency by 39%. These findings highlight the importance of rotor geometry optimisation during the early design stages in order to meet both performance and noise requirements. Full article

The characterization of dimensional stone deposits is essential for quarry assessment and design. However, uncertainties in mapping and designing pose significant challenges. To address this issue, an innovative approach is initiated to develop a virtual reality model by integrating unmanned aerial vehicle (UAV) photogrammetry for surface modeling and Electric Resistivity Tomography (ERT) for subsurface deposit imaging. This strategy offers a cost-effective, time-efficient, and safer alternative to traditional surveying methods for challenging mountainous terrain. UAV methodology involved data collection using a DJI Mavic 2 Pro (20 MP camera) with 4 K resolution images captured at 221 m altitude and 80 min flight duration. Images were taken with 75% frontal and 70% side overlaps. The Structure from Motion (SfM) processing chain generated high-resolution outputs, including point clouds, Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and orthophotos. To ensure accuracy, five ground control points (GCPs) were established by a Real-Time Kinematic Global Navigation Satellite System (RTK GNSS). An ERT method known as vertical electric sounding (VES) revealed subsurface anomalies like solid rock mass, fractured zones and areas of iron leaching within marble deposits. Three Schlumberger (VES-1, 2, 3) and two parallel Wenner (VES-4, 5) arrays to a depth of 60 m were employed. The resistivity signature acquired by PASI RM1 was analyzed using 1D inversion technique software (ZondP1D). The integrated outputs of photogrammetry and subsurface imaging were used to design an optimized quarry with bench heights of 30 feet and widths of 50 feet, utilizing open-source 3D software (Blender, BIM, and InfraWorks). This integrated approach provides a comprehensive understanding of deposit surface and subsurface characteristics, facilitating optimized and sustainable quarry design and extraction. This research demonstrates the value of an innovative approach in synergistic integration of UAV photogrammetry and ERT, which are often used separately, for enhanced characterization, decision-making and promoting sustainable practices in dimensional stone deposits. Full article