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In order to achieve infrared aircraft detection under interference conditions, this paper proposes an infrared aircraft detection algorithm based on high-resolution feature-enhanced semantic segmentation network. Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features. Secondly, based on the idea of using dilated convolutions to expand the receptive field of feature maps, a hybrid atrous spatial pyramid pooling module is designed. By utilizing a serial structure of dilated convolutions with small dilation rates, this module addresses the issue of feature information loss when expanding the receptive field through dilated spatial pyramid pooling. It captures the contextual information of the target, further enhancing the target features. Finally, a dice loss function is introduced to calculate the overlap between the predicted results and the ground truth labels, facilitating deep excavation of foreground information for comprehensive learning of samples. This paper constructs an infrared aircraft detection algorithm based on a high-resolution feature-enhanced semantic segmentation network which combines the location attention feature fusion network, the hybrid atrous spatial pyramid pooling module, the dice loss function, and a network that maintains the resolution of feature maps. Experiments conducted on a self-built infrared dataset show that the proposed algorithm achieves a mean intersection over union (mIoU) of 92.74%, a mean pixel accuracy (mPA) of 96.34%, and a mean recall (MR) of 96.19%, all of which outperform classic segmentation algorithms such as DeepLabv3+, Segformer, HRNetv2, and DDRNet. This demonstrates that the proposed algorithm can achieve effective detection of infrared aircraft in the presence of interference. [ABSTRACT FROM AUTHOR]

Aiming at the energy inconsistency of each battery during the use of lithium‐ion batteries (LIBs), a bidirectional active equalization topology of lithium battery packs based on energy transfer was constructed, and a bivariate equalization control strategy of adjacent SOC difference and voltage is proposed according to the corresponding relationship between open circuit voltage (OCV) and state of charge (SOC). The energy transfer between the inductor and the lithium battery is realized through the combination of the main circuit and the secondary circuit. Based on the Buck–Boost equalization circuit, the pulse width modulation (PWM) drive signal duty ratio is adjusted to improve the equalization speed and efficiency. The SOC is estimated by the unscented Kalman filter (UKF) algorithm, and the SOC of each single battery is estimated and sorted. The results of charge and discharge and static simulation and test of lithium battery show that the SOC difference between each cell is controlled within the threshold value of 3%, the voltage range is controlled within the range of 0.01 V, and the equalization speed is increased by 51% compared with the traditional unidirectional transfer of inductive energy balancing method. The change trend of the test results and the simulation results show a good consistency, avoiding the overcharge and overdischarge of the battery pack, and reducing the inconsistency. This method can complete the energy balance management of the battery well, the efficiency is relatively high, and the service life of the battery is improved. [ABSTRACT FROM AUTHOR]

To address the issue of insufficient extraction of target features and the resulting impact on detection performance in long-range infrared aircraft target detection caused by small imaging area and weak radiation intensity starting from the idea of perceiving target context to enhance the features extracted by convolutional neural network, this paper proposes a detecting algorithm based on AWFGLC (adaptive weighted fusion of global–local context). Based on the mechanism of AWFGLC, the input feature map is randomly reorganized and partitioned along the channel dimension, resulting in two feature maps. One feature map is utilized by self-attention for global context modeling, establishing the correlation between target features and background features to highlight the salient features of the target, thereby enabling the detecting algorithm to better perceive the global features of the target. The other feature map is subjected to window partitioning, with max pooling and average pooling performed within each window to highlight the local features of the target. Subsequently, self-attention is applied to the pooled feature map for local context modeling, establishing the correlation between the target and its surrounding neighborhood, further enhancing the weaker parts of the target features, and enabling the detecting algorithm to better perceive the local features of the target. Based on the characteristics of the target, an adaptive weighted fusion strategy with learnable parameters is employed to aggregate the global context and local context feature maps. This results in a feature map containing more complete target information, enhancing the ability of the detection algorithm to distinguish between target and background. Finally, this paper integrates the mechanism of AWFGLC into YOLOv7 for the detection of infrared aircraft targets. The experiments indicate that the proposed algorithm achieves mAP50 scores of 97.8% and 88.7% on self-made and publicly available infrared aircraft datasets, respectively. Moreover, the mAP50:95 scores reach 65.7% and 61.2%, respectively. These results outperform those of classical target detection algorithms, indicating the effective realization of infrared aircraft target detection. [ABSTRACT FROM AUTHOR]

In this work, we present a novel four-channel coherent optical chaotic secure communication (COCSC) system, incorporating four simultaneous photonic reservoir computers in tandem with four coherent demodulation units. We employ a quartet of photonic reservoirs that capture the chaotic dynamics of four polarization components (PCs) emitted by a driving QD spin-VCSEL. These reservoirs are realized utilizing four PCs of a corresponding reservoir QD spin-VCSEL. Through these four concurrent photonic reservoir structures, we facilitate high-quality wideband-chaos synchronization across four pairs of PCs. Leveraging wideband chaos synchronization, our COCSC system boasts a substantial 4 × 100 GHz capacity. High-quality synchronization is pivotal for the precise demasking or decoding of four distinct signal types, QPSK, 4QAM, 8QAM and 16QAM, which are concealed within disparate chaotic PCs. After initial demodulation via correlation techniques and subsequent refinement through a variety of digital signal processing methods, we successfully reconstruct four unique baseband signals that conform to the QPSK, 4QAM, 8QAM and 16QAM specifications. Careful examination of the eye diagrams, bit error rates, and temporal trajectories of the coherently demodulated baseband signals indicates that each set of baseband signals is flawlessly retrieved. This is underscored by the pronounced eye openings in the eye diagrams and a negligible bit error rate for each channel of baseband signals. Our results suggest that delay-based optical reservoir computing employing a QD spin-VCSEL is a potent approach for achieving multi-channel coherent optical secure communication with optimal performance and enhanced security. [ABSTRACT FROM AUTHOR]

Weakly supervised object detection (WSOD) is becoming increasingly important for computer vision tasks, as it alleviates the burden of manual annotation. Most WSOD techniques rely on multiple instance learning (MIL), which tends to localise the discriminative parts of salient objects instead of the whole object. In addition, network training is often supervised using simple image‐level annotations, without including object quantities or location information. However, this can lead to ambiguous differentiation of object instances, both in terms of location and semantics. To address these issues, propose an end‐to‐end triple critical feature capture network (TCFCNet) for WSOD is proposed. Specifically, a multi‐task branch, which can perform fully supervised classification and regression task, was integrated with a PCL in an end‐to‐end network for refining object locations in an online method. A cyclic parametric dropblock module (CPDM) was then designed to help the detector focus on the contextual information by using cyclic masking techniques to maximise the removal of the discriminative components of an object instance to alleviate the part domination problem. Finally, a feature decoupling module (FDM) is proposed to further reduce the ambiguous distinction of object instances by adaptively constructing robust critical features that adapt to multi‐task branch for classification and regression tasks, which contains a feature enhancement module and task‐specific polarisation functions. Comprehensive experiments are carried out on the challenging Pascal VOC 2007 and VOC 2012 datasets. The proposed method achieves a 54.6% mAP and a 44.3% mAP on the Pascal VOC 2007 and VOC 2012 datasets respectively, showed that our method outperformed existing mainstream techniques by a considerable margin. [ABSTRACT FROM AUTHOR]

A novel method for the generation of an optical frequency comb (OFC) is presented. The proposed approach uses a laser diode with optical feedback and operating at a specific nonlinear dynamic state named periodic window. In this case, the laser spectrum exhibits a feature with a series of discrete, equally spaced frequency components, and the repetition rate can be flexibly adjusted by varying the system parameters (e.g., external cavity length), which can provide many potential applications. As an application example, a dual-OFC system for distance measurement is presented. The results demonstrate the system's ability to achieve target distance detection, underscoring its potential for real-world applications in this field. [ABSTRACT FROM AUTHOR]

Fabric defect detection plays an irreplaceable role in the quality control of the textile manufacturing industry, but it is still a challenging task due to the diversity and complexity of defects and environmental factors. Visual saliency models imitating the human vision system can quickly determine the defect regions from the complex texture background. However, most visual saliency-based methods still suffer from incomplete predictions owing to the variability of fabric defects and low contrast with the background. In this paper, we develop a context-aware attention cascaded feedback network for fabric defect detection to achieve more accurate predictions, in which a parallel context extractor is designed to characterize the multi-scale contextual information. Moreover, a top-down attention cascaded feedback module was devised adaptively to select the important multi-scale complementary information and then transmit it to an adjacent shallower layer to compensate for the inconsistency of information among layers for accurate location. Finally, a multi-level loss function is applied to guide our model for generating more accurate prediction results via optimizing multiple side-output predictions. Experimental results on the two fabric datasets built under six widely used evaluation metrics demonstrate that our proposed framework outperforms state-of-the-art models remarkably. [ABSTRACT FROM AUTHOR]

In off-axis quantitative phase imaging, existing methods of suppressing the zero-frequency component (ZFC) always cause the loss of high-frequency phase information, thus degrading the accuracy of phase reconstruction. To overcome these problems, this paper proposes to preserve the high-frequency information by filtering the intrinsic mode function. In this method, empirical mode decomposition is employed to decompose the interferometric image into a series of intrinsic mode function (IMF) components from high to low frequencies. The decomposed low-frequency IMF components are processed by Gaussian high-pass filters for ZFC suppression, and the high-frequency IMF components and the filtered low-frequency IMF components are combined to obtain the reconstructed hologram. Hilbert transform is then performed on the reconstructed hologram to filter out the conjugate image, leaving only the original image. In order to verify the performance of our proposed method, the phase maps processed by our proposed method are compared with those processed by the Fourier filtering method, wavelet transform-based method and Laplace operator method. The experimental results show that the proposed method not only suppresses ZFC but also achieves higher accuracy in phase reconstruction. [ABSTRACT FROM AUTHOR]

In three-dimensional (3D) shape measurement based on fringe projection, various factors can degrade the quality of the point cloud. Existing point cloud filtering methods involve analyzing the geometric relationship between 3D space and point cloud, which poses challenges such as complex calculation and low efficiency. To improve the accuracy and speed of point cloud filtering, this paper proposes a new point cloud filtering method based on image segmentation and the absolute phase for the 3D imaging obtained by fringe projection. Firstly, a two-dimensional (2D) point cloud mapping image is established based on the 3D point cloud obtained from fringe projection. Secondly, threshold segmentation and region growing methods are used to segment the 2D point cloud mapping image, followed by recording and removal of the segmented noise region. Using the relationship between the noise point cloud and the absolute phase noise point in fringe projection, a reference noise-free point is established, and the absolute phase line segment is restored to obtain the absolute phase of the noise-free point. Finally, a new 2D point cloud mapping image is reconstructed in 3D space to obtain a point cloud with noise removed. Experimental results show that the point cloud denoising accuracy calculated by this method can reach up to 99.974%, and the running time is 0.954 s. The proposed method can effectively remove point cloud noise and avoid complex calculations in 3D space. This method can not only remove the noise of the 3D point cloud but also can restore the partly removed noise point cloud into a noise-free 3D point cloud, which can improve the accuracy of the 3D point cloud. [ABSTRACT FROM AUTHOR]

Hadamard single-pixel imaging (SPI) employs the differential measurement strategy to eliminate the effect of negative value of Hadamard basis patterns but leads to doubling the number of measurements. To reduce the number of measurements, a Hadamard SPI method based on positive patterns is proposed. In this method, only the positive patterns are used to acquire measurement values and reconstruct images, so the number of measurements will be reduced by 1/2. Combined with the intensity correlation theory of ghost imaging, the average value of the acquired measures is found; this average value is subtracted from all the measurement values to obtain the spectral coefficients, thus the background noise is eliminated to ensure the imaging quality. Simulation and experimental results show that the proposed method has good noise robustness and can efficiently reconstruct high quality images. [ABSTRACT FROM AUTHOR]

In a recent work, a technique was proposed to recover the absolute phase maps of fringe patterns with two or three selected fringe wavelengths with the aim to improve anti-error capability. To further increase the anti-error capability associated with the phase unwrapping operation, multiple fringe patterns with different wavelengths may also be used. In this paper, we present a general rule to select the wavelengths and the strategy to increase phase error tolerance bound. Based on this rule, it is concluded that to maximize the error tolerance, four-wavelength method should be selected, where the maximum upper bound of the tolerable phase error is π/6. To minimize the required number of fringe patterns, three-wavelength method with three-step PSP should be selected, that is exactly the case the previous paper has demonstrated. These theoretical conclusions are verified by experimental results. [ABSTRACT FROM AUTHOR]

Imaging the interaction between the laser pulse and photoresist mixture on the ultrafast time scale can track the path of the light pulse and reveal the procedure of the microstructure machining. However, most existing imaging technologies suffer from problems such as requiring multiple repeated shots or a limited time resolution. To overcome these problems, we propose to capture the motion of laser pulses in a photoresist mixture by using compressed ultrafast photography (CUP). In this method, we can recover the motion process of non-repeatable events with a single shot at the time-resolution of about 1. 54 × 10 11 fps, where the depth of the imaging sequence reaches hundreds of frames. To verify the effectiveness of the proposed method, we estimate the speed of the laser pulse in a photoresist mixture and evaluate the similarity between the image captured by a streak camera and our reconstructed ultrafast sequence; the results validate the reliability of our proposed method. [ABSTRACT FROM AUTHOR]