Unmanned Aerial Vehicle (UAV), when equipped as communication relays, offer a flexible solution to extend Vehicle-to-Vehicle (V2V) communications beyond fixed infrastructure and Non-Line-of-Sight constraints. In this setting, the allocation of radio resources, across time, frequency and space through beamforming, is challenged by the mobility of Connected and Autonomous Vehicles (CAVs) and their temporal dependencies, as access opportunities depend on prior transmission outcomes such as queue backlog or failed attempts. This paper proposes a Radio Resource Assignment (RRA) framework for UAV-aided V2V networks with beamforming-capable UAV relays. The model discretizes time and space to account for mobility and to track the movement of groups of CAVs across beam segments. The model also incorporates Time Division Multiple Access (TDMA)-based scheduling, beam activation constraints, and realistic traffic generation patterns. Analytical expressions are derived for per-user success probability and system throughput under both, ideal and realistic conditions, and they are validated against simulations, confirming the accuracy of the proposed approximations. Numerical results highlight trade-offs involving UAV altitude and resource allocation interval, while a heuristic beam-activation optimization strategy is shown to further enhance performance, achieving up to 12% throughput gain over uniform activation.
Accurate finger force estimation is critical for next-generation human-machine interfaces. Traditional electromyography (EMG)-based decoding methods using deep learning require large datasets and high computational resources, limiting their use in real-time, embedded systems. Here, we propose a novel approach that performs finger force regression using spike trains from individual motor neurons, extracted from high-density EMG. These biologically grounded signals drive a spiking neural network implemented on a mixed-signal neuromorphic processor. Unlike prior work that encodes EMG into events, our method exploits spike timing on motor units to perform low-power, real-time inference. This is the first demonstration of motor neuron-based continuous regression computed directly on neuromorphic hardware. Our results confirm accurate finger-specific force prediction with minimal energy use, opening new possibilities for embedded decoding in prosthetics and wearable neurotechnology.
Subramaniam Chembai Ganesh, Jessica S. Rosenberg, Jeffrey F. Morris
et al.
We analyze the interaction between a self-diffusiophoretic spherical Janus motor and an inert spherical cargo particle in an axisymmetric configuration in the Stokes regime. To study the different configurations of the two spheres and their motions, we develop an analog to the twin multipole approach to numerically determine the axisymmetric stream function for the flow field. We verify the validity and accuracy of this approach using existing literature and COMSOL Multiphysics. We study the effects of the size of the Janus cap, the relative ratio of sizes of the two spheres, and their separation distance on their interactions. For the case of a stationary cargo, we identify the existence of a distinct regime where the Janus motor hovers at a finite separation distance from the cargo and summarize the results using a phase diagram. In the presence of a freely moving cargo, we analyze the steady terminal velocities of the Janus motor and the cargo to identify distinct conditions at which the two spheres can translate with equal velocities while maintaining a finite separation distance.
Magnetically controlled welding technology， as an efficient and high-quality metal welding method， possesses broad application prospects in the field of aerospace material processing. This paper provides a comprehensive review on the basic principles， technical classifications， applications in aerospace materials， as well as the current research status and development trends of magnetically controlled welding. By analyzing the impact of magnetically controlled welding on arc morphology， droplet transfer， molten pool flow， and solidification processes， the advantages of this technology in improving weld bead formation quality and enhancing mechanical properties are explored. Furthermore， with specific case studies and data analysis， the future development of magnetically controlled welding technology in the aerospace industry is identified.
Shape memory polymer composite (SMPC) hinges have been researched as deployable structures in space missions due to their stable and controllable shape recovery behaviors. The elastic energy of the fabrics plays a dominant role in predicting the recovered shape of the hinges, as it strongly drives shape restoration. In this research, the shape recovery behaviors of SMPC hinges are numerically investigated by applying an equation that accounts for the hysteresis characteristics of the fabric reinforcement. The constitutive equation integrates the Mooney–Rivlin model, a viscoelastic, stored energy model, to characterize the hyperelastic properties varying with time, temperature, and shape recovery behaviors of the SMP matrix. Additionally, polynomial functions are introduced to represent the hysteresis effects and energy dissipation behavior of the fabrics. Since the elasticity of fabrics significantly affects the shape recovery of SMPCs, the developed constitutive equation enables accurate prediction of the recovered configuration. Finite element method analysis is performed based on this model and validated through comparison with experimental results. Finally, the constitutive equation is applied to investigate the shape memory response of SMPC hinges. The simulations present the significant design factors to increase the shape recovery ratio of the SMPC hinges.
Unmanned aerial vehicles (UAVs) are emerging as mobile aerial platforms for radio frequency (RF) spectrum sensing, enabling dynamic monitoring of the spectrum occupancy of cellular systems at different altitudes. The impact of UAV receiver antenna configurations, particularly with respect to altitude, is critical in determining occupancy performance. In this paper, we present a height-dependent analytical framework for UAV-based spectrum occupancy, focusing on how different receiver antenna configurations affect the sensed signal power. We consider two types of 3D antenna patterns: a typical dipole antenna and a downward directional antenna. Using a stochastic geometry-based approach, we derive closed-form expressions for the altitude-dependent sensed power under both antenna configurations. Moreover, we execute ray tracing-based analysis with a real-world 3-D map and realistic antenna patterns. Monte Carlo simulations are conducted to validate the analytical results, revealing that both altitude and antenna directivity critically affect occupancy accuracy and coverage.
Unmanned Aerial Vehicles (UAVs) are playing an increasingly crucial role in large-scale Wireless Sensor Networks (WSNs) due to their high mobility and flexible deployment capabilities. To enhance network sustainability and profitability, this paper proposes a coordinated charging and data-collection system that integrates a green energy base station, Wireless Charging Vehicles (WCVs), and UAVs, ensuring full coverage of all sensor nodes in the target region. On the other hand, the economic feasibility of charging strategies is an essential factor, which is usually neglected. Thus, we further design a joint optimization algorithm to simultaneously maximize system profit and node survivability. To this end, we design a cylindrical-sector-based charging sequence for WCVs. In particular, we develop a dynamic cluster head selection algorithm that accounts for buffer size, residual energy, and inter-node distance. This scheme prevents cluster-head running out of energy before the charging devices arrive, thereby ensuring reliable data transmission. Simulation results demonstrate that the proposed strategy not only maximizes overall profit but also significantly improves node survivability and enhances the sustainability of the wireless sensor network.
The coordination problem of multi-vehicle systems is of great interests in the area of autonomous driving and multi-vehicle control. This work mainly focuses on multi-task coordination problem of a group of vehicles with a bicycle model and some specific control objectives, including collision avoidance, connectivity maintenance and convergence to desired destinations. The basic idea is to develop a proper Lyapunov-like barrier function for all tasks and a distributed controller could be built in the presence of misbehaving vehicles. Control protocols are provided for both leader vehicle and follower vehicles. The simulation results demonstrate the effectiveness of proposed method.
Many production processes require the cooperation of various resources. Especially when using expensive machines, their utilization plays a decisive role in efficient production. In agricultural production or civil construction processes, e.g., harvesting or road building, the machines are typically mobile, and synchronization of different machine types is required to perform operations. In addition, the productivity of one type often depends on the availability of another type. In this paper, we consider two types of vehicles, called primary and support vehicles. Primary vehicles perform operations and are assisted by at least one support vehicle, with more support vehicles resulting in faster service times for primary vehicles. We call this practical problem the vehicle routing and scheduling problem with support vehicle-dependent service times and introduce two mixed-integer linear programming models. The first represents each support vehicle individually with binary decision variables, while the second considers the cumulative flow of support vehicles with integer decision variables. Furthermore, the models are defined on a graph that allows easy transformation into multiple variants. These variants are based on allowing or prohibiting switching support vehicles between primary vehicles and splitting services among primary vehicles. We show in our extensive computational experiments that: i) the integer representation of support vehicles is superior to the binary representation, ii) the benefit of additional vehicles is subject to saturation effects and depends on the ratio of support and primary vehicles, and iii) switching and splitting lead to problems that are more difficult to solve, but also result in better solutions with higher primary vehicle utilization.
Ernesto Vazquez-Sanchez, Jaime Gomez-Gil, Jose-Carlos Gamazo-Real
et al.
Currently, for many applications, it is necessary to know the speed and position of motors. This can be achieved using mechanical sensors coupled to the motor shaft or using sensorless techniques. The sensorless techniques in brushed dc motors can be classified into two types: 1) techniques based on the dynamic brushed dc motor model and 2) techniques based on the ripple component of the current. This paper presents a new method, based on the ripple component, for speed and position estimation in brushed dc motors, using support vector machines. The proposed method only measures the current and detects the pulses in this signal. The motor speed is estimated by using the inverse distance between the detected pulses, and the position is estimated by counting all detected pulses. The ability to detect ghost pulses and to discard false pulses is the main advantage of this method over other sensorless methods. The performed tests on two fractional horsepower brushed dc motors indicate that the method works correctly in a wide range of speeds and situations, in which the speed is constant or varies dynamically.
Agnese Grison, Irene Mendez Guerra, Alexander Kenneth Clarke
et al.
The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for investigating the neural control of movement, providing insights into motor neuron recruitment and discharge behavior. However, current algorithms, while very effective under certain conditions, face significant challenges in complex scenarios, as their accuracy and motor unit yield are highly dependent on anatomical differences among individuals. This can limit the number of decomposed motor units, particularly in challenging conditions. To address this issue, we recently introduced Swarm-Contrastive Decomposition (SCD), which dynamically adjusts the separation function based on the distribution of the data and prevents convergence to the same source. Initially applied to intramuscular EMG signals, SCD is here adapted for HD-sEMG signals. We demonstrated its ability to address key challenges faced by existing methods, particularly in identifying low-amplitude motor unit action potentials and effectively handling complex decomposition scenarios, like high-interference signals. We extensively validated SCD using simulated and experimental HD-sEMG recordings and compared it with current state-of-the-art decomposition methods under varying conditions, including different excitation levels, noise intensities, force profiles, sexes, and muscle groups. The proposed method consistently outperformed existing techniques in both the quantity of decoded motor units and the precision of their firing time identification. For instance, under certain experimental conditions, SCD detected more than three times as many motor units compared to previous methods, while also significantly improving accuracy. These advancements represent a major step forward in non-invasive EMG technology for studying motor unit activity in complex scenarios.
David Sládek, Blanka Chalupníková, Alžběta Schneidrová
et al.
Terminal Aerodrome Forecasts (TAFs) are essential components of aviation meteorology, providing critical information for flight safety and operational decision-making. This study conducts a comprehensive analysis of TAF for European airports during the years 2022 and 2023, leveraging Python functions accessible via a dedicated GitHub repository. The complexity inherent in TAF, characterized by diverse change groups, header formats, and regional variations, presents challenges for accurate interpretation. The analysis focuses on key parameters within TAF, including the count of corrected messages and the frequency and types of change groups. The count of corrected messages serves as a metric for evaluating the quality of service provided, while the examination of change group utilization reveals distinct patterns and tendencies specific to each airport. The findings underscore the significance of regional regulations, meteorologist decision-making, and adherence to International Civil Aviation Organization (ICAO) standards in shaping TAF. The GitHub repository and associated Python functions presented in this study provide valuable resources for meteorologists, researchers, and aviation personnel to conduct in-depth analyses and derive insights from TAF. Ultimately, this study identifies local differences and inconsistencies in the publication of TAF, laying the groundwork for enhancing their consistency and uniformity.
María Barroso, José M. Bossio, Carlos M. Alaíz
et al.
The implementation of strategies for fault detection and diagnosis on rotating electrical machines is crucial for the reliability and safety of modern industrial systems. The contribution of this work is a methodology that combines conventional strategy of Motor Current Signature Analysis with functional dimensionality reduction methods, namely Functional Principal Components Analysis and Functional Diffusion Maps, for detecting and classifying fault conditions in induction motors. The results obtained from the proposed scheme are very encouraging, revealing a potential use in the future not only for real-time detection of the presence of a fault in an induction motor, but also in the identification of a greater number of types of faults present through an offline analysis.
Uncertainty information is often limited and has great discreteness, how to ensure the structure having good robustness under epistemic uncertainty becomes a technical problem for multi-objective topology optimization design. In this paper, the evidence theory is used to quantify the epistemic uncertainty; the plausibility measurement expressing upper limit of failure probability is applied to be a new evaluation of reliability. The total weight and the reliability of structures are taken as the optimization objectives, and a multi-objective robust topology optimization design model based on evidence theory is proposed. Parallelization technique based differential evolution for multi-objective optimization (DEMO) is preferable to search above robust Pareto front due to its merits of non-requirement of any gradient and superior mechanisms of non-dominate strategy. In order to verify the effectiveness of the proposed method, the multi-objective reliability optimization of a wood truss structure is implemented with considering elastic modulus and structural load as uncertain variables. According to the optimization results, six same truss specimens are made for the static random loading test. The failure probability of the truss structure is judged by the stress and node displacement obtained from the test, so as to verify the feasibility of the reliability optimization method based on evidence theory. The experimental results also indicate that the proposed method can avoid the deviation of the optimization results caused by the fluctuation of epistemic uncertainty, and provide a new method for designers to make the optimization results robust even when the data information is insufficient and the cognitive level is limited.
Abel Ortego, Sofia Russo, Marta Iglesias-Émbil
et al.
Light-duty vehicles are increasingly incorporating plastic materials to reduce production costs and achieve lightweight designs. On average, a conventional car utilizes over 200 kg of plastic, comprising more than 23 different types, which often present challenges for recycling due to their incompatibility. Consequently, the focus on plastic recycling in end-of-life vehicles has intensified. This study aims to analyze critical car parts based on the plastics used, employing a novel thermodynamic approach that examines the embodied exergy (EE) of different plastics. Six vehicles from various segments, years, and equipment levels were assessed to understand their plastic compositions. The findings reveal that, on average, a vehicle contains 222 kg of plastic, accounting for 17.7% of its total weight. Among these plastics, 47.5% (105 kg) are utilized in car parts weighing over 1 kg, with plastics comprising over 80% of the part’s weight. The identified critical car parts include the front door trim panel, front and rear covers, fuel tank, floor covering, front lighting, dashboard, rear door trim panel, plastic front end, backrest pad, door trim panel pocket, plastic foam rear seat, rear lighting, window guide, molded headliner, bulkhead sound insulation, foam seat part, and wheel trim. Regarding their contribution to EE, the plastics with the highest shares are polypropylene—PP (24.5%), polypropylene and ethylene blends—E/P (20.3%), and polyurethane- PU (15.3%). Understanding the criticality of these car parts and their associated plastics enables targeted efforts in design, material selection, and end-of-life management to enhance recycling and promote circularity within the automotive industry.
Mechanical engineering and machinery, Machine design and drawing
Post-disaster search and rescue is critical to disaster response and recovery efforts and is often conducted in hazardous and challenging environments. However, the existing post-disaster search and rescue operations have problems such as low efficiency, limited search range, difficulty in identifying the nature of the target, and wrong target location. Therefore, this study develops an air–ground integrated intelligent cognition visual enhancement system based on a UAV (VisionICE). The technique combines a portable AR display device, a camera-equipped helmet, and a quadcopter UAV for efficient patrols over a wide area. First, the system utilizes wireless image sensors on the UAV and helmet to capture images from the air and ground views. Using the YOLOv7 algorithm, the cloud server calculates and analyzes these visual data to accurately identify and detect targets. Lastly, the AR display device obtains real-time intelligent cognitive results. The system allows personnel to simultaneously acquire air and ground dual views and achieve brilliant cognitive results and immersive visual experiences in real time. The findings indicate that the system demonstrates significant recognition accuracy and mobility. In contrast to conventional post-disaster search and rescue operations, the system can autonomously identify and track targets of interest, addressing the difficulty of a person needing help to conduct field inspections in particular environments. At the same time, the system can issue potential threat or anomaly alerts to searchers, significantly enhancing their situational awareness capabilities.
We consider the dynamics of a bio-filament under the collective drive of motor proteins. They are attached irreversibly to a substrate and undergo stochastic attachment-detachment with the filament to produce a directed force on it. We establish the dependence of the mean directed force and force correlations on the parameters describing the individual motor proteins using analytical theory and direct numerical simulations. The effective Langevin description for the filament motion gives mean-squared displacement, asymptotic diffusion constant, and mobility leading to an effective temperature. Finally, we show how competition between motor protein extensions generates a self-load, describable in terms of the effective temperature, affecting the filament motion.
In this paper, an improved reduced-order average modeling method for the bidirectional interleaved boost with coupled inductors (BIBCI) converter is proposed, based on the PWM and phase-shift dual-degree-of-freedom modulation and traditional reduced-order average model. Considering the power loss of the coupled inductor, the core loss, and the parasitic parameters of the inductors, capacitors, and switches in the circuit topology, the new model reflects the dynamic performance of the converter in a wide frequency domain more accurately than the traditional model. The small-signal model and transfer function are further deduced to provide a basis for the design of closed-loop controllers and have good engineering practicability. According to voltage source load or resistive load, the double-loop or triple-loop controller is designed correspondingly. The two models are theoretically analyzed and compared, and the proposed controller is verified by a 1.5 kW prototype.
Considering that the terminal impact angle constraint can improve the interception performance of hypersonic target, a novel particle swarm optimization guidance (NPSOG) algorithm is proposed to satisfy the impact angle constraint. Two-dimensional dynamics engagement mode for hypersonic target interception is formulated. The performance index is positively correlated with the line-of-sight (LOS), LOS rate, and the relative distance between missile and target. The weight coefficients among the three are adaptively adjusted by the fuzzy logic controller. The particle swarm optimization (PSO) algorithm is utilized to generate the guidance commands. Numerical examples are given to verify the performance of the proposed guidance law in various engagement scenarios, and the performance of the algorithm is validated comparing with several heuristic guidance methods and nonheuristic guidance methods.