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The advancements in droplet microfluidics have made it an eminent technology for high-throughput biomedical applications involving biofluids such as blood, saliva, tear film, and synovial fluid, all characterized by their non-Newtonian/viscoelastic properties. However, most existing models for the design and optimization of droplet microfluidic systems are primarily based on Newtonian fluids. Therefore, studies that systematically explore the dynamics underlying biofluid droplet formation and manipulation are critically important, both fundamentally and practically. This study uses artificial tear films to experimentally investigate the effects of operational parameters such as geometric conditions (width ratio Λ = w d w c and aspect ratio h * = h w c ), fluid properties (viscosity ratio η = μ d μ c ), and flow rate ratios (ϕ = Q d Q c ) on filament formation during droplet generation in a T-junction geometry. All the experiments were conducted at a constant capillary number of the continuous phase (Ca = μ c Q c w c h γ ). The results indicate that filament growth and rupture characteristics are determined by the predominance of two distinct regions: pre-stretch, governed by the interplay between inertial and capillary forces, and elastocapillarity, regulated by elastic and capillary forces. Specifically, filament rupture is influenced by inertia–capillary effects at low flow rates and viscosity ratios. In contrast, the domination of elastic effects is more pronounced at higher flow rates and viscosity ratios. Additionally, reductions in the width (Λ) and aspect ( h * ) ratio when Λ ≥ 0.5 and h * ≥ 0.4 increase the elastic effects within the filaments while inertial effects dominate for Λ < 0.5 and h * < 0.4. The relaxation times (λ) are obtained from the exponential thinning curve, with longer times observed for cases with high elastic effects. Finally, the filament collapses into satellite droplets, and their monodispersity increases with elasticity. [ABSTRACT FROM AUTHOR]

In the application of rotorcraft atmospheric environment detection, to reflect the distribution of atmospheric pollutants more realistically and completely, the sampling points must be spread throughout the entire three-dimensional space, and the cooperation of multiple unmanned aerial vehicles (multi-UAVs) can ensure real-time performance and increase operational efficiency. In view of the problem of coordinated detection by multi-UAVs, the region division and global coverage path planning of the stereo space to be detected are studied. A whale optimization algorithm based on the simulated annealing-whale optimization algorithm (SA-WOA) is proposed, which introduces adaptive weights with the Levy flight mechanism, improves the metropolis criterion, and introduces an adaptive tempering mechanism in the SA stage. Path smoothing is subsequently performed with the help of nonuniform rational B-spline (NURBS) curves. The comparison of algorithms using the eil76 dataset shows that the path length planned by the SA-WOA algorithm in this paper is 10.15% shorter than that of the WOA algorithm, 13.25% shorter than the SA planning result, and only 0.95% difference from the optimal path length in the dataset. From the perspective of planning time, its speed is similar to WOA, with a relative speed increase of 27.15% compared to SA, proving that the algorithm proposed in this paper has good planning performance. A hardware system platform is designed and built, and environmental gas measurement experiments were conducted. The experimental results indicate that the multi-UAV collaborative environment detection task planning method proposed in this paper has certain practical value in the field of atmospheric environment detection. [ABSTRACT FROM AUTHOR]

Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques. Our design features a detection AE that harnesses reconstruction errors and convolutional neural networks to discern deep features, employing thresholds from reconstruction error and Kullback–Leibler divergence to identify adversarial samples and their origin mechanisms. Additionally, a restoration AE with a multi-layered structure effectively restores adversarial samples generated via optimization methods, ensuring accurate classification. Tested rigorously on the RML2016.10a dataset, our framework proves robust against adversarial threats, presenting a versatile defense solution compatible with various deep learning models. [ABSTRACT FROM AUTHOR]

In this article, an effective algorithm is proposed for direction‐of‐arrival (DOA) estimation of coherent signals using the l2‐norm with moving coprime arrays. The proposed algorithm exhibits high accuracy and robustness in decorrelation of coherent signals compared to existing methods. Firstly, the received signal is filtered on the moving coprime array and a signal model is constructed that is compatible with the moving coprime array. Next, virtual sensors is inserted into the holes of the moving coprime array and the received signals are initialised. Then, the Toeplitz matrix is reconstructed based on the covariance matrix of the initial signal and perform low‐rank matrix recovery to eliminate signal coherence by compensating the matrix for zero. Finally, the DOA estimation for the signal is performed using spectral peak search. Simulation results verify that the estimation accuracy of our proposed algorithm outperforms various existing algorithms. [ABSTRACT FROM AUTHOR]

In this letter, a localization algorithm for mixed‐field sources based on fourth‐order cumulant matrix rank reduction is proposed. First, a cumulant matrix is constructed, and a spectral peak search function associated with the direction‐of‐arrival (DOA) of the sources only is constructed using the rank reduction principle to achieve DOA estimation. Then the range estimation is also obtained by a spectral peak search. Compared to existing algorithms, the algorithm is able to utilize all the elements of the cumulant matrix and has a higher accuracy and degrees‐of‐freedom. The simulation results verify the excellence of the algorithm. [ABSTRACT FROM AUTHOR]

Joint direction of arrival (DOA) and time delay (TD) estimation of wideband signals such as WIFI and mobile communication signals has to face the challenges aroused by multipath effect in indoor environments. New methods that can deal with coherent wideband signals with high estimation accuracy are necessary. To achieve localization when signals are coherent, this paper proposes a new joint estimation method which fully utilizes the subcarriers of wideband signals. This method could greatly extend the array aperture and accomplish the joint estimation through UCA-ESPRIT and one-dimensional (1-D) search. Simulation experiments are conducted to show the validity and efficiency of this method.