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The thin film narrow band-pass filters are widely used in precision optical systems such as gas detection, fiber optic communication and optical analysis instruments due to their unique optical characteristics. However, as the applications become more widespread, the problems of regulating the half bandwidth and cut-off bandwidth of the thin film narrow band-pass filters based on the Fabry-Perot (F-P) principle are becoming more apparent. In this work, we took the thin film narrow band-pass filter with a center wavelength of 450 nm as the design target, and comprehensively optimized the film system based on standard F-P principle to obtain the film systems with different half bandwidths and their systematic adjustment methods. The half bandwidth can be adjusted effectively by using the conventional half bandwidth control methods such as increasing the interference orders and the number of mirror layers or the cavities, but it is quite difficult to accurately obtain the filters with various half bandwidths since the adjustment range is too large. Based on the principle of equivalent refractive index, the optical thicknesses of the high and low refractive index film layers of the mirror layers were modified in an unconventional way, and thus the half bandwidth can be flexibly adjusted. Moreover, the standard F-P narrow band-pass filter film system was also modified by the principle of induced transmission film system to achieve a significant broadening of the cut-off bandwidth.

Aiming at the problems of high specificity and low efficiency of vegetable transplanting machine, a substrate block seedling transplanter was designed. Through theoretical calculation and force analysis, the structural parameters of two key components of automatic seedling separation device and planting device are determined. Taking cabbage seedlings with 40 × 40 × 40mm substrate block size as the experimental object, the effects of forward speed, planting frequency, front conveyor belt speed on lodging rate, missing rate and qualified rate were studied by single factor test and three factor three-level orthogonal test. Based on the analysis of the significance and interaction of the experimental data, the best combination of the forward speed of 1.1km/h, the planting frequency of 55 plants / min and the front conveyor belt speed of 0.5km/h was obtained. The verification test of the best combination showed that the average qualified rate of planting was 93.31%, which met the relevant industry standards.

In indoor ultra-wideband (UWB) positioning systems, positioning accuracy can be improved by determining the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. The existing methods, such as support vector machine (SVM), decision tree (DT), k-Nearest Neighbor (KNN), identify LOS/NLOS mainly using time-domain characteristics. However, using only time-domain characteristics cannot achieve satisfactory performance. In this letter, we propose a LOS/NLOS identification method based on Morlet wave transform and convolutional neural networks (MWT-CNN). MWT-CNN is capable of identifying LOS/NLOS in the time-frequency domain. Our simulations are based on the 802.15.4a UWB model and an open-source dataset. The simulation results show that MWT-CNN achieves an accuracy of 100% in the office scenario, 99.89% in the industrial scenario, 96.10% in the residential scenario, and 98.84% in a real experimental scenario. Further simulation results show that MWT-CNN is more suitable to be deployed in static scenarios.

This letter studies an unmanned aerial vehicle (UAV)-enabled uplink communication system with turning and climbing angle constraints, and the communication process suffers from the attack from one ground jammer with imperfect location information. Under such setup, we aim to maximize the uplink throughput by jointly considering the UAV’s three dimensions (3D) trajectory design and the ground node (GN)’s transmit power control. However, the optimization problem is non-convex and is hard to be tackled. To overcome this difficulty, we develop an alternating algorithm by leveraging the block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and ${\mathcal {S}}$ -procedure to acquire the suboptimal solution. Simulation results show that our proposed algorithm significantly improves the uplink throughput compared with the benchmark algorithms.

The accuracy of positioning in ultra-wide band (UWB) systems can be improved by identifying the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. At present, machine learning methods have already been extensively applied to NLOS identification. Unfortunately, those traditional methods are incapable of performing well in the cases of small samples and class-imbalance samples. In this letter, a novel method is proposed to identify LOS and NLOS components by utilizing Capsule Networks (CapsNet). As indicated by simulation results, the CapsNet-based method allows LOS and NLOS measurements to be classified with 94.63% accuracy, 95.58% precision, 94.74% recall rate, and a 0.9490 F2-score. In addition, CapsNet is compared against decision tree (DT), least squares vector machine (LS-SVM) and K nearest neighbor (KNN) based on experimental data. The comparison results show that the proposed CapsNet outperforms benchmark methods in the cases of both small samples and class-imbalance samples.

This study addresses the problem of non-perspective pose estimation from line correspondences in the traffic scenarios. A coarse-to-fine 3D road registration method is proposed for this problem in two stages. Firstly, the iterative closest point algorithm is exploited to estimate the pose coarsely. An objective function is then established to incorporate the feature correspondences for refining the coarse pose. Besides, the framework including road registration is employed for traffic video augmentation. The framework begins with the inputs of traffic videos, road information from Geographic Information Systems and 3D models of traffic elements (e.g. vehicles, pedestrians). Subsequently, 3D road model generation and point-to-line correspondence establishment are achieved in the preprossessing stage. After road and viewpoint registration, the 3D graphic engine is employed to simulate the traffic scene with the road, viewpoints and traffic elements. The augmented videos are generated by fusing the original frames and newly projected traffic elements. The authors demonstrate the superiority of the proposed registration method by the comparison to state-of-the-arts in both quantitative and qualitative experiments. In addition, the frames of the augmented videos validate the proposed method in the application.

High-quality semen with high viability is critical to improving the in-vitro fertilization efficiency. This study aimed to understand the effect of ambient temperature and humidity on semen quality and seminal plasma biochemical parameters of Mediterranean buffalo in March and July. The metabolites of seminal plasma in two seasons were detected using the UPLC-MS/MS method. The results showed that temperature and humidity index (THI) in March were 66.86 ± 2.98, and 82.94 ± 3.52 in July. Compared with in March, breath frequency, rectal temperature, and heat shock protein 70 expressions of seminal plasma were significantly increased in July (

This study proposes a vision-based method for traffic sign attribute estimation, i.e. 3D position and pose, from image sequences by binocular or monocular cameras. The method starts with acquiring robust feature correspondences based on homography constraints from image pairs. Then the objective function is designed to integrate the feature correspondences to optimise the parameters of the traffic sign plane in the 3D coordinate. Finally, the sign plane is utilised for attribute estimation. In addition, the authors provide an extension for the raw KITTI dataset, which can be utilised for 3D tasks of traffic sign localisation and pose estimation. In the experiments, three popular methods are employed for comparisons based on the publicly available BelgiumTS and KITTI datasets. The results show that the authors’ method based on SIFT and SURF features can locate the traffic signs with a mean error of ∼0.44 and 0.51 m in the BelgiumTS and KITTI datasets, respectively, and estimate the pose with a mean error of ∼14.45° in the KITTI dataset.

As a representative single component epoxy resin, diglycidyl ether of bisphenol A resin (DGEBA) is the most widely used matrix in adhesives. However, challenges are still remained including high curing temperature and short shelf life. Herein, bisphenol A (BPA), Tetrachlorobisphenol A (TCBPA), and Tetrabromobisphenol A (TBBPA) as latent curing agents and imidazole as accelerator were used in adhesives. Curing kinetics was studied by non-isothermal DSC, using a model-fitting Malek approach and a model-free advanced isoconversional method of Vyazovkin. Results indicated that curing temperature of EP-BPA, EP-TCBPA, and EP-TBBPA was reduced to 126 ℃, 94 ℃, and 106 ℃, respectively. Storage stability evaluated by DSC indicated the shelf life was up to five months. After curing, molecular weights of the as-prepared linear polymers (EP-BPA, EP-TCBPA, and EP-TBBPA) were 33,822 g/mol, 14,913 g/mol, and 18,846 g/mol, and their tensile strength and shear strength were more than 40.06 MPa and 13.64 MPa, respectively.

This paper studies the anti-jamming issue for cognitive unmanned aerial vehicle (UAV) communication networks. We aim to maximize the throughput of information collection in sensitive areas or unmanned areas of military borders by optimizing the UAV trajectory and the secondary transmitters (STs)' transmit power, in the presence of malicious jammers with imperfect location information. However, the optimization problem is challenging to solve due to the non-convexity. To overcome this difficulty, we propose a throughput maximization algorithm with the aid of block coordinate descent (BCD) method, successive convex approximation (SCA) technique, and $S$ -procedure. Numerical results show that our proposed algorithm significantly improves the system throughput compared with the benchmark algorithms, and simultaneously achieves effective anti-jamming.

In this paper, a non-iterative solution to the non-perspective pose estimation from line correspondences was proposed. Specifically, the proposed method uses an intermediate camera frame and an intermediate world frame, which simplifies the expression of rotation matrix by reducing to the two freedoms from three in the rotation matrix R. Then formulate the pose estimation problem into an optimal problem. Our method solve the parameters of rotation matrix by building the fifteenth-order and fourth-order univariate polynomial. The proposed method can be applied into the pose estimation of the perspective camera. We utilize both the simulated data and real data to conduct the comparative experiments. The experimental results show that the proposed method is comparable or better than existing methods in the aspects of accuracy, stability and efficiency.

In ultra-wideband (UWB) positioning systems, positioning accuracy can be improved by determining the conditions of line-of-sight (LOS) and non-line-of-sight (NLOS) propagation and taking appropriate measures. The machine learning-based LOS/NLOS identification methods have been widely used due to its advantages of eliminating the need for prior knowledge. Unfortunately, traditional identification methods, such as LS-SVM, DT, and KTT, decrease significantly in other scenarios. Fortunately, Capsule Networks (CapsNet) showed its advantages of high accuracy and robustness in image classification as one of the latest machine learning achievements. Recently, some researchers noticed CapsNet and extended its application to LOS/NLOS identification. However, the generation ability and robustness of the capsule network needs to be further studied. In this paper, we verify the generalization ability and robustness of CapsNet based on the IEEE 802.15.4a channel model and experimental data. Experimental results show that the generation ability and robustness of the CapsNet-based method is better than the benchmark methods.

This paper investigates the influence of uncertainty of the jammers’ positions and the UAV’s turning angle constraint jointly by studying a UAV-enabled wireless communication system, where the UAV is dispatched to collect information from a group of ground terminals (GTs) in the presence of potential jammers. We aim to maximize the average throughput of all GTs by jointly optimizing the UAV’s trajectory and the GTs’ scheduling. The formulated problem is shown to be a mixed integer non-convex optimization problem that is intractable to work out. Thus, we develop a robust alternating algorithm by utilizing the block coordinate descent method, successive convex approximation technique, and $\mathcal{S}$-procedure to acquire the suboptimal solution. Simulation results show that our proposed design offers a substantial throughput gain compared with the benchmark algorithm.

X-ray security automatic screening has attracted a broad attention for providing auxiliary support to eliminate potential threat in public. However, recent automatic detection algorithms may not perform quite well when a great number of dangerous articles are overlapping and compact in the X-ray images. In order to deal with it, this paper takes the segmentation scheme and attention network into consideration. Based on Mask R-CNN, we propose a two-stage deep CNN model called SAN to solve the performance degradation caused by the dense distribution of objects in X-ray images. The model contains a semantic segmentation network block to generate soft attention masks for each category and an attention network to combine each of the attention masks with the ROIs. In the experiment, the proposed SAN achieves 86% AP on our dataset, and brings an obvious improvement compared with recent detector algorithms on complicated X-ray dataset.