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With the increasing demand for global water resources and general deterioration of the ecological environment of the Qinghai-Tibetan Plateau, changes to the water conservation functions of ecosystems and the impact mechanisms have attracted great attention. Currently, the research on water conservation has mainly focused on a single biome type, in particular, forests. Few studies explore the differences in water conservation functions of different biome types. Research on this topic mostly utilizes field investigations and sample plot settings to explore the differences in water conservation capacity of a small number of tree species, but these methods are limited in time and space. Therefore, this study uses MODIS products to evaluate the water conservation function of different biome types in the Qinghai-Tibetan Plateau. Dynamic monitoring of the vegetation and water conservation capacity in the study area and research on the responses of the water conservation functions of different biome types were conducted. The results indicate that the vegetation of the Qinghai-Tibetan Plateau increased slightly from 2000 to 2015; however, due to the dual influence of climate and topographic factors, the water conservation capacity showed a slight decline. The water conservation service function mainly comes from grassland ecosystems, which are closely related to vegetation density and biome types. Therefore, to greatly improve the water conservation service function of the Qinghai-Tibetan Plateau, the management and planting of vegetation should be conducted according to the optimal vegetation coverage area, vegetation quantities and biome types.

An efficient, accurate and high-resolution PM

The number of end-of-life mobile phones is increasing every year, which includes parts that have high reuse values and various dangerous and toxic compounds. An intellectualized and automatic upgrade of the disassembly process of the end-of-life mobile phones would enhance the recycling value as well as efficiency. It would reduce the pollution in the environment. The detection of end-of-life mobile phone parts plays a critical role in automatic disassembly and recycling. This study offers an image processing-based approach for identifying important parts of mobile phones that are nearing the end of their useful lives. An image enhancement approach has been utilized for generating disassembly datasets of end-of-life mobile phones from several brands and models, and different retirement states. The YOLOv5m detection model is applied to train as well as validate the detection model on the customized datasets. According to the results, the proposed approach allows the intelligent detection of battery, camera, mainboard and screw. In the validation set, the Precision, Recall and mAP@.5 are 99.4%, 98.4% and 99.3%, respectively. Additionally, several path planning algorithms are utilized for the disassembly plan of screws which indicates that the genetic algorithm’s use increases the efficiency of disassembly.

Multiple vehicles tracking in the aerial video have a strong effect on the intelligent transportation system. Therefore, a fast multiple vehicles tracking method is required to meet the demand of a higher energy conversion efficiency. However, frequent and long-term occlusions in complex traffic scenes make it more difficult, and identity switches of vehicles with a similar appearance also bring challenges. Currently, vehicles tracking methods based on detection shows satisfactory performance. In the tracking-after-detection methods, data association plays the key role, and it includes two types, namely, frame-by-frame association and multi-frame association. Frame-by-frame association refers to the association between detections in the two consecutive frames, but tracking drift or failure is likely to occur when vehicles are blocked or not detected. The multi-frame association establishes a relational model by using object detection information of multiple frames instead of the previous two frames, which can effectively reduce the vehicle error association and deal with occlusions. However, the tracking will also be interrupted if the occlusion time is too long to associate the detection points before and after. Therefore, online multiple vehicles tracking in the aerial video based on fast incremental discriminative appearance learning is put forward. A fast incremental discriminative appearance learning (FIDAL) is introduced to discriminate the appearances of vehicles and adaptively update the vehicle appearance models based on the difference value between the new sample and the mean of vehicle samples to address the problem of identity switches. Experimental results on video sequences from different data sets demonstrate an average 25 percent performance improvement when using fast incremental discriminative appearance learning.

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle (UAV) is more and more utilized to capture the aerial images. Therein, the registration of the aerial infrared images play a vital role in the infrared image processing, such as object tracking, information collecting, and behavior analysis. However, due to the low contrast, low-resolution, and few texture features of the infrared images, it is really difficult to implement the image registration in the aerial infrared images. In our work, an accurate and efficient image registration algorithm in the aerial infrared images is put forward. To improve the registration accuracy and efficiency, we construct multiple screening mechanisms to screen out the incorrect and redundant feature points. Besides, to further improve the registration accuracy, the improved SIFT and convolution neural network (CNN) descriptors are combined for the feature description. Finally, the coarse-to-fine feature matching strategy is put forward via FLANN and RANSAC algorithms. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive registration results.

Due to the limited band width and congestion of communication channels in the wireless vehicle-to-vehicle (V2V) communication, time delay inevitably arises and dramatically leads to the disturbances for the automated vehicular platoon. This paper focuses on computing the exact time delay stability margin. In this study, we treat this problem as a stability issue of a consensus system with time delay, where each vehicle in the platoon is recognized as a node, and the interconnected information flow is represented as a graph. Then, the distributed controller is designed by combining the states of the vehicle itself and its neighbouring vehicles. Furthermore, the stability of the entire platoon is analysed according to the characteristic equation of the closed-loop system, and a necessary and sufficient condition for the exact time delay stability margin is obtained. Especially, for the automated vehicular platoon with undirected topology, it is revealed that exact time delay stability margin is determined by the largest eigenvalue of the augmented Laplacian matrix. Furthermore, a rapid method for finding exact time delay stability margin is proposed. Finally, numerical simulations demonstrate that this work generates exact and satisfactory time delay stability margin for the automated vehicular platoon.

Owing to the great adaptability to the weak light, the infrared camera equipped on the unmanned aerial vehicle is more and more adopted to capture the aerial images. Therefore, how to fully utilize the aerial infrared images for the vehicle detection has attracted widespread attentions. However, due to the low-resolution, low contrast, and few texture features of the infrared image, it is really difficult to implement the vehicle detection in the aerial infrared images. In our work, an efficient and accurate vehicle detection algorithm in aerial infrared images is proposed via an improved yolov3 network. To increase the detection efficiency, we construct a new structure of the improved yolov3 network with only 16 layers. Besides, we expand the anchor boxes to four scales to improve the detection accuracy of the small vehicles. Meanwhile, for the limitation of the infrared vehicle samples, the transfer learning is introduced to train the improved yolov3 network. Finally, the proposed algorithm is evaluated on the VIVID and NPU data sets. Experiments and comprehensive analyses demonstrate that the proposed algorithm generates satisfactory and competitive vehicle detection results.

Multiple vehicle tracking in aerial video has vital applications in intelligent transportation system. However, this is challenging owing to the demand for higher tracking efficiency. Moreover, it is particularly challenging to handle shadows and occlusions in complex traffic scenes. Currently, most approaches mainly address the multiple vehicle tracking problem based on consecutive frame pairs. However, they are inefficient and ineffective with respect to the tracking of vehicles that are shadowed or occluded by trees, buildings, or large vehicles. Therefore, in terms of consecutive multiframe images, we propose an efficient deshadowing approach for multiple vehicle tracking in aerial video via image segmentation and local region matching. By processing a series of frames at a time, this approach will address the occurrence of shadows and occlusions in multiple vehicle tracking. First, image segmentation and road segmentation are combined to construct the region adjacency graph within the road (RAGR) for each frame. Local region matching is then performed among the RAGRs based on the distance transform. In particular, to improve the region matching efficiency, a local matching criterion is proposed by introducing region of interest for the node of the RAGR. Finally, based on the motion dissimilarities and varying spatial distances of the background and moving vehicles, a batch-oriented foreground separation is achieved to separate vehicles (the foreground) from the background. Experiments and comparative analysis conducted on UAV123 and DARPA VIVID data sets demonstrate that the proposed approach for multiple vehicle tracking in aerial video can generate satisfactory and competitive results.

Along with the rapid development of the unmanned aerial vehicle technology, multiple vehicle tracking (MVT) in aerial video sequence has received widespread interest for providing the required traffic information. Due to the camera motion and complex background, MVT in aerial video sequence poses unique challenges. We propose an efficient MVT algorithm via driver behavior-based Kalman filter (DBKF) and an improved deterministic data association (IDDA) method. First, a hierarchical image registration method is put forward to compensate the camera motion. Afterward, to improve the accuracy of the state estimation, we propose the DBKF module by incorporating the driver behavior into the Kalman filter, where artificial potential field is introduced to reflect the driver behavior. Then, to implement the data association, a local optimization method is designed instead of global optimization. By introducing the adaptive operating strategy, the proposed IDDA method can also deal with the situation in which the vehicles suddenly appear or disappear. Finally, comprehensive experiments on the DARPA VIVID data set and KIT AIS data set demonstrate that the proposed algorithm can generate satisfactory and superior results.

Improving seismic resolution is essential for obtaining more detailed structural and stratigraphic information. Seismic wave propagating in the non-complete elastic underground medium will occur the absorption attenuation, which is related to the frequency and the travel time. So the absorption attenuation influences the resolution of seismic data as well as showing the nonstationary characteristics. In view of this, this paper proposes the nonstationary compensation method of the absorption attenuation of the seismic wave. Firstly, we use the matching pursuit algorithm to decompose the seismic signal into different matching wavelets; then, make the product of the travel time and central frequency be the horizontal coordinate and the corresponding amplitude be the vertical coordinate of every matching wavelet putting them in the coordinate plane, the amplitude attenuation function that is changing with the product is established in the coordinate plane, so amplitudes of the matching wavelets can be compensated through the amplitude attenuation function, respectively; finally, the seismic signal is reconstructed with the matching wavelets after compensation. The analysis of the synthetic seismic signal and the real field data shows that the deep energy of seismogram after nonstationary compensation is effectively restored. What’s more, the resolution of the seismic data in deep and middle is improved greatly.

Vehicle detection in high-resolution aerial images has received widespread interests when it comes to providing the required information for traffic management and urban planning. It is challenging due to the relatively small size of the vehicles and the complex background. Furthermore, it is particularly challenging if the higher detection efficiency is required. Therefore, an urban vehicle detection algorithm is proposed via improved entropy rate clustering (IERC) and correlation-based sequential dictionary learning (CSDL). First, to enhance the detection accuracy, IERC is designed to generate more regular superpixels. It aims to avoid the situation that one superpixel sometimes straddles multiple vehicles. The generated superpixels are then treated as the seeds for the training sample selection. Then, CSDL is constructed to achieve a fast sequential training and updating of the dictionary. In CSDL, only the atoms correlated with the sparse representation of the new training data are inferred. Finally, comprehensive analyses and comparisons on two data sets demonstrate that the proposed method generates satisfactory and competitive results.