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The paper focuses on modelling the heat flux through the isothermal wall boundary in premixed turbulent combustion. In a statistically stationary configuration, the distorted flames, advected by the incoming flow toward the wall, can be either head-on or entrained. The wall heat flux model with respect to the head-on and entrained parts are constructed differently to describe their distinct behaviours. In model validation, to exclude the interference from various inaccuracies, either numerically or experimentally, the filtered three-dimensional direct numerical simulation (DNS) fields are adopted as benchmark. Results justify that the newly proposed model is able to capture the wall heat flux statistics. The predictions are consistently satisfactory at different filter widths, performing clearly better than direct evaluation from the filtered temperature gradient and effective diffusivity. In addition, it is also straightforward to integrate this model together with existing flow solvers. [ABSTRACT FROM AUTHOR]

Image stitching aims to construct a wide field of view with high spatial resolution, which cannot be achieved in a single exposure. Typically, conventional image stitching techniques, other than deep learning, require complex computation and are thus computationally expensive, especially for stitching large raw images. In this study, inspired by the multiscale feature of fluid turbulence, we developed a fast feature point detection algorithm named local-peak scale-invariant feature transform (LP-SIFT), based on the multiscale local peaks and scale-invariant feature transform method. By combining LP-SIFT and RANSAC in image stitching, the stitching speed can be improved by orders compared with the original SIFT method. Benefiting from the adjustable size of the interrogation window, the LP-SIFT algorithm demonstrates comparable or even less stitching time than the other commonly used algorithms, while achieving comparable or even better stitching results. Nine large images (over 2600 × 1600 pixels), arranged randomly without prior knowledge, can be stitched within 158.94 s. The algorithm is highly practical for applications requiring a wide field of view in diverse application scenes, e.g., terrain mapping, biological analysis, and even criminal investigation. [ABSTRACT FROM AUTHOR]

Detecting abnormal human behaviors in surveillance videos is crucial for various domains, including security and public safety. Many successful detection techniques based on deep learning models have been introduced. However, the scarcity of labeled abnormal behavior data poses significant challenges for developing effective detection systems. This paper presents a comprehensive survey of deep learning techniques for detecting abnormal human behaviors in surveillance video streams. We categorize the existing techniques into three approaches: unsupervised, partially supervised, and fully supervised. Each approach is examined in terms of its underlying conceptual framework, strengths, and drawbacks. Additionally, we provide an extensive comparison of these approaches using popular datasets frequently used in the prior research, highlighting their performance across different scenarios. We summarize the advantages and disadvantages of each approach for abnormal human behavior detection. We also discuss open research issues identified through our survey, including enhancing robustness to environmental variations through diverse datasets, formulating strategies for contextual abnormal behavior detection. Finally, we outline potential directions for future development to pave the way for more effective abnormal behavior detection systems. [ABSTRACT FROM AUTHOR]

For continuum fields such as turbulence, analyses of the field structure offer insights into their kinematic and dynamic properties. To ensure the analyses are quantitative rather than merely illustrative, two conditions are essential: space-filling and structure quantification. A pertinent example is the dissipation element (DE) structure, which is however susceptible to noisy interference, rendering it inefficient for extracting the large-scale features of the field. In this study, the multi-level DE structure is proposed based on the multi-level extremal point concept. At a given scale level, the entire field can be decomposed into the corresponding space-filling and non-overlapping DEs, each characterized by its length scale l and the scalar difference Δ ϕ between its two extremal points. We will first elaborate on the fundamental principles of this method. Results from an artificially constructed two-scale field indicate that the decomposed units adequately represent the geometry of the original field. In examining the fractal Brownian motion, a structure function equivalent ⟨ Δ ϕ | l ⟩ and an energy spectrum equivalent are introduced. The scaling relation derived from ⟨ Δ ϕ | l ⟩ corresponds with the Hurst number. Furthermore, the multi-level DE structure distinctly reveals the two different inertial ranges in two-dimensional turbulence. Overall, this novel structure identification approach holds significant potential for complex analyses concerning the field geometry. [ABSTRACT FROM AUTHOR]

The spray flame in a laminar counterflow is influenced by various setup conditions, for instance the strain rate and liquid droplet parameters, including the initial temperature, size distribution, hydrodynamics and the vaporisation history. With some reasonable simplification, the two phase governing equations can be reformulated as equations in the single gas phase with reconstructed spray-related source terms. Numerical solutions assume interesting similarity features, such as the (almost) independence of the evaporation path after mapping onto a newly defined quantity R, constructed from sensible enthalpy and mixture fraction. In this regard, the dimensionality of free parameters describing the structure of the spray flame can be hopefully reduced, which then provides a different scenario to understand the spray combustion physics. The flame regime diagram is also elaborated in the present framework. [ABSTRACT FROM AUTHOR]

A premixed H 2 /air flame impinging onto a flat surface in statistically stationary state is studied for both reactive and inert wall cases to gain insights into the effects of the heterogeneous surface reactions. Direct numerical simulation (DNS) results with detailed gas-phase chemistry and surface adsorption and desorption mechanisms indicate differences in the flame front topology and near-wall flame dynamics between these two cases. In the reactive surface case, gas-phase free radicals are inclined to be adsorbed with much reduced near-wall concentration. Consequently, the gas-phase heat release rate (HRR) close to the wall decreases as well because of the low availability of free radicals. However, extra heat released from the reactive surface partially compensates for such difference. Moreover, wall reactions will intensify the turbulent fluctuations of the wall temperature and wall heat flux, while for these two cases the mean temperature profile along the flame propagating direction remains similar. [ABSTRACT FROM AUTHOR]

Acoustic resolution photoacoustic micros- copy (AR-PAM) can achieve deeper imaging depth in biological tissue, with the sacrifice of imaging resolution compared with optical resolution photoacoustic microscopy (OR-PAM). Here we aim to enhance the AR-PAM image quality towards OR-PAM image, which specifically includes the enhancement of imaging resolution, restoration of micro-vasculatures, and reduction of artifacts. To address this issue, a network (MultiResU-Net) is first trained as generative model with simulated AR-OR image pairs, which are synthesized with physical transducer model. Moderate enhancement results can already be obtained when applying this model to in vivo AR imaging data. Nevertheless, the perceptual quality is unsatisfactory due to domain shift. Further, domain transfer learning technique under generative adversarial network (GAN) framework is proposed to drive the enhanced image’s manifold towards that of real OR image. In this way, perceptually convincing AR to OR enhancement result is obtained, which can also be supported by quantitative analysis. Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) values are significantly increased from 14.74 dB to 19.01 dB and from 0.1974 to 0.2937, respectively, validating the improvement of reconstruction correctness and overall perceptual quality. The proposed algorithm has also been validated across different imaging depths with experiments conducted in both shallow and deep tissue. The above AR to OR domain transfer learning with GAN (AODTL-GAN) framework has enabled the enhancement target with limited amount of matched in vivo AR-OR imaging data. [ABSTRACT FROM AUTHOR]

Deep learning for electroencephalogram-based classification is confronted with data scarcity, due to the time-consuming and expensive data collection procedure. Data augmentation has been shown as an effective way to improve data efficiency. In addition, contrastive learning has recently been shown to hold great promise in learning effective representations without human supervision, which has the potential to improve the electroencephalogram-based recognition performance with limited labeled data. However, heavy data augmentation is a key ingredient of contrastive learning. In view of the limited number of sample-based data augmentation in electroencephalogram processing, three methods, performance-measure-based time warp, frequency noise addition and frequency masking, are proposed based on the characteristics of electroencephalogram signal. These methods are parameter learning free, easy to implement, and can be applied to individual samples. In the experiment, the proposed data augmentation methods are evaluated on three electroencephalogram-based classification tasks, including situation awareness recognition, motor imagery classification and brain-computer interface steady-state visually evoked potentials speller system. Results demonstrated that the convolutional models trained with the proposed data augmentation methods yielded significantly improved performance over baselines. In overall, this work provides more potential methods to cope with the problem of limited data and boost the classification performance in electroencephalogram processing. [ABSTRACT FROM AUTHOR]