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The tower wake usually induces large-amplitude vibrations in adjacent suspenders of a long-span suspension bridge. In order to interpret the underlying mechanism of the significant wake-induced vibrations of ultra-long suspenders, the wake-induced vibration characteristics of tandem suspenders beside the bridge tower are investigated using computational fluid dynamics based simulation. First, the computational model of the bridge tower and suspenders is established. Subsequently, the effect of tower wake on suspender vibration is analyzed considering the inflow velocity and relative position between the tower and suspenders. Based on the proper orthogonal decomposition (POD), the flow characteristics behind the tower and around the suspenders are investigated, and the governing mechanism of wake-induced vibrations of the suspenders is revealed ultimately. The results indicate that the wake-induced vibration of the suspender exhibits a lock-in phenomenon with large cross-wind amplitudes at specific incoming wind velocities. The wind loads on the suspender in both along-wind and cross-wind directions exhibit components with frequencies that are multiples of the vortex shedding frequency. The flow field can be accurately constructed using the first four POD modes with the largest energy. The symmetric POD modes are the primary components that contribute to the significant wake-induced vibrations of the suspenders. [ABSTRACT FROM AUTHOR]

The task of camouflaged object segmentation (COS) is a challenging endeavor that entails the identification of objects that closely blend in with their surrounding background. Furthermore, the camouflaged object's obscure form and its subtle differentiation from the background present significant challenges during the feature extraction phase of the network. In order to extract more comprehensive information, thereby improving the accuracy of COS, we propose a diffusion model for a COS network that utilizes frequency domain information as auxiliary input, and we name it FreDiff. Firstly, we proposed a frequency auxiliary module (FAM) to extract frequency domain features. Then, we designed a Global Fusion Module (GFM) to make FreDiff pay attention to the global features. Finally, we proposed an Upsample Enhancement Module (UEM) to enhance the detailed information of the features and perform upsampling before inputting them into the diffusion model. Additionally, taking into account the specific characteristics of COS, we develop the specialized training strategy for FreDiff. We compared FreDiff with 17 COS models on the four challenging COS datasets. Experimental results showed that FreDiff outperforms or is consistent with other state-of-the-art methods under five evaluation metrics. [ABSTRACT FROM AUTHOR]

Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object's blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy. [ABSTRACT FROM AUTHOR]

The growth interruption technology is introduced to the growth of GaAsP/InAlGaAs quantum well (QW) structure using metal–organic chemical vapor deposition (MOCVD). The effect of growth interruption time (GIT) on the crystalline quality and optical properties are investigated. The two distinctive emission peaks are the transition recombination between the electron level of conduction band and the light and heavy hole level of valence band in the photoluminescence (PL) at room temperature. The PL peaks present a redshift and merge together with the increasing GIT, which is attributed to the QW energy level shift caused by the increase in arsenic concentrations in GaAsP QW, the diversified thickness of QW and the variations of indium components in the InAlGaAs barrier layer. The Gaussian deconvolution parameters in temperature-dependent PL (TDPL) show that the GaAsP/InAlGaAs QW with a GIT of 6 s has a 565.74 meV activation energy, enhancing the carrier confinement in QW and the PL intensity, while the 6 s-GIT GaAsP QW has the increasing interface roughness and the non-radiative centers at the InGaAsP intermediate layer, leading to a spectral broadening. The QW with 10 s-GIT exhibits a small full width at half maximum (FWHM) with the various temperature, indicating reduced interface roughness and excellent crystal quality. An increase in GIT may be suitable for optimizing the optical properties of GaAsP/InAlGaAs QW. [ABSTRACT FROM AUTHOR]

Polarization‐division multiplexing is crucial for increasing channel capacity and spectral efficiency in communications. A key component is the polarization multiplexer, which combines or separates signals with orthogonal polarizations. Existing planar multiplexers lack an ultrawide band for terahertz communications. While microwave‐inspired orthomode transducers (OMTs) provide broadband operation, they suffer from high ohmic loss and bulkiness at terahertz frequencies. Optical integrated polarization beam splitters (PBSs) offer low loss and good integrability but have narrow bandwidths. This work presents a substrateless all‐silicon polarization multiplexer based on tapered directional couplers and air‐silicon effective mediums, integrated monolithically on a compact footprint. The device demonstrates a 37.8% fractional bandwidth, an average insertion loss of ≈$\approx$1 dB, and a polarization extinction ratio above 20 dB over 225–330 GHz. This superior performance is enabled by the anisotropy of the effective medium claddings, affecting the two orthogonal guided modes differently. The multiplexer enables simultaneous two‐channel terahertz fiber communications with polarization diversity.   It supports aggregated data rates up to 155 and 190 Gbit s−1${\rm s}^{-1}$ with bit error rates below hard‐ and soft‐decision forward‐error‐correction limits, respectively. This advancement holds promise for 6G terahertz integrated systems with enhanced channel capacities. [ABSTRACT FROM AUTHOR]

Recently, researchers have achieved the goal of using unified architecture to achieve multiple modulation modes under specific conditions. However, existing research still suffers from the problem of large resource and time overhead. This paper proposes an adaptive modem based on a vector quantization (VQ) long short-term memory autoencoder (LSTM-AE), designed to implement modulation and demodulation of signals from the second to thirty-second order in a lightweight way. By leveraging the memory capacity of the LSTM module and the compression capability of the autoencoder, the model is able to support multi-order modulation methods. This study used the Adam optimizer for training and testing on a simple dataset extended by adding AWGN noise only and made modifications based on the MSE loss function to balance the accuracy and training speed of each part of the model. Experiments demonstrate that the proposed method not only achieves comparable modem performance to existing frameworks for second to thirty-second order signals, but also significantly reduces the number of parameters and training time. Experimental results indicate that the proposed methodology not only matches the performance of existing frameworks for signals ranging from the second to the thirty-second order, but also employs merely 79.6% of the average parameter count and a mere 7.4% of the average training duration. This represents a substantial reduction in resource expenditure. [ABSTRACT FROM AUTHOR]

Deep learning models are highly susceptible to adversarial samples, and even minuscule image perturbations that are not perceptible to the naked eye can disable well-trained deep learning models. Recent research indicates that these perturbations can exist in the physical world. This paper provides insight into physical adversarial attacks on deep learning object detection models, clarifying the concept of physical adversarial attack and outlining the general process of such attacks on object detection. According to the different attack tasks, a series of physical adversarial attack methods against object detection networks in recent years are reviewed from vehicle detection and pedestrian detection. Other attacks against target detection models, other attack tasks and other attack methods are briefly introduced. The current challenges of physical adversarial attack are discussed, the limitations of adversarial training are leaded out, and future development directions and application prospect are suggested. [ABSTRACT FROM AUTHOR]

Karst rocky desertification (KRD) has become the most serious ecological disaster in the southwest of China and is a major obstacle to the sustainable development of the karst region in the southwest. Remarkably, scientific understanding of the spatial-temporal evolution of rocky desertification and the corresponding driving mechanism is the primary prerequisite crucial to controlling rocky desertification. Hence, the typical rocky desertification area of Qujing City, located in the upper reaches of the Yangtze River, was selected as the research object. On the basis of the Google Earth Engine (GEE) cloud platform and decision tree classification, the spatial-temporal evolution process of rocky desertification in Qujing City from 1990 to 2020 was investigated, and the driving factors of rocky desertification were explored in terms of the natural environment and socio-economic aspects. Consequently, over this period, the area of rocky desertification had decreased by 1728.38 km2, while the no rocky desertification area had increased by 1936.61 km2. Notably, the major driving factors of rocky desertification were fractional vegetation cover (FVC) (q = 0.41), land use type (q = 0.26), slope (q = 0.21), and land reclamation rate (q = 0.21). Typically, rocky desertification is likely to occur in areas with moderate or low FVC (<0.7), a low slope (0–8°) or high slope (35°–80°), a land type of cultivated-land or grassland, and a land reclamation rate of 10–70%. In addition, all two-factor interactions acted as drivers that exacerbate rocky desertification. Furthermore, FVC ∩ slope (q = 0.79) and slope ∩ land use type (q = 0.56) were two interacting drivers that promote rocky desertification strongly. [ABSTRACT FROM AUTHOR]