<p>Detailed description for General Chemistry, Cell culture, Antibodies, Plasmid constructs, Establishment of oncogenic kinase transformed Ba/F3 cells, Generation of BRK, EGFR, HER2 knockdown cell lines, Anti-proliferation assay and enzymatic kinase assay, Immunoblotting and immunoprecipitation , Clonogenic assay for the cell-survival experiment, In vivo tumor models, Histological analysis, TUNEL staining, Cell apoptosis assay, Drug combination studies.</p>
This paper proposes a dense network composed of an improved Transformer network, which successfully restores low-light images to high-quality normal-light images, alleviating issues such as low brightness, high noise, and missing critical information in low-light images. The entire network architecture is based on the improved Transformer network and builds a dense network with a combination of long and short connections. While retaining the self-attention mechanism of the Transformer network, it achieves multi-level fusion and utilization of shallow and deep features, providing the network with rich image features and enabling the restoration of low-light images to high-quality normal-light images. Additionally, a spatial-domain and frequency-domain combined loss function is designed, considering both pixel-level and frequency domain losses, effectively constraining the image restoration process and avoiding spectral biases. Lastly, a multi-scale hybrid gate feedforward network is designed to replace the traditional feedforward network in the Transformer, facilitating feature selection and forward propagation. Experimental results on various typical image enhancement datasets demonstrate that our approach outperforms the current state-of-the-art networks in both qualitative and quantitative evaluations.
The Bi 2 O 2 Se/MoO 3 heterojunction has the characteristics of high stability and detection in the optical communication spectrum, which provides a simple and effective method to fabricate large-scale, fast response, broadband flexible array optoelectronic devices.
Automatic and periodic recompiling of building databases with up-to-date high-resolution images has become a critical requirement for rapidly developing urban environments. However, the architecture of most existing approaches for change extraction attempts to learn features related to changes but ignores objectives related to buildings. This inevitably leads to the generation of significant pseudo-changes, due to factors such as seasonal changes in images and the inclination of building fa\c{c}ades. To alleviate the above-mentioned problems, we developed a contrastive learning approach by validating historical building footprints against single up-to-date remotely sensed images. This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings. In addition, to reduce the effects of inconsistencies between historical building polygons and buildings in up-to-date images, we employed a deformable convolutional neural network to learn offsets intuitively. In summary, we formulated a multi-branch building extraction method that identifies newly constructed and removed buildings, respectively. To validate our method, we conducted comparative experiments using the public Wuhan University building change detection dataset and a more practical dataset named SI-BU that we established. Our method achieved F1 scores of 93.99% and 70.74% on the above datasets, respectively. Moreover, when the data of the public dataset were divided in the same manner as in previous related studies, our method achieved an F1 score of 94.63%, which surpasses that of the state-of-the-art method.
Visual servoing is a method to achieve precise positioning and motion control of objects by visual feedback, and it is widely applied in the fields of robotics and unmanned aerial vehicles (UAVs) in recent years. This paper presents a novel image-based visual servoing (IBVS) control method for UAVs based on fuzzy logic to effectively solve the problem under field of view constraint and improve the control efficiency. In this paper, a fuzzy logic of servo gain is designed for the control input of visual servoing, which solves the problem of feature loss in IBVS and improves the efficiency. Meanwhile, a deep computing method based on known data is proposed to solve the unknown depth of Jacobian matrix, which makes the control easier to converge. The effectiveness of the proposed method is verified by the simulation of a quadrotor UAV equipped with a monocular camera.
With the rapid development of information technology, various products used in information technology are also constantly optimized. Among them, the task and path planning of UAV in the high-end robot industry has always been the focus of relevant researchers. In the high-end robot industry, in addition to the research and development of UAVs, they also continue to learn and strengthen the task and path planning of UAVs. Nowadays, using unmanned aerial vehicles for real-time shooting has become the trend of this era. Drones have brought great convenience to people’s lives, and more and more people are willing to use drones. Based on the above situation, this paper studies the task and path planning of UAV based on reinforcement learning in dynamic environment. In the case of unpredictable scene parameters, reinforcement learning method can be established by value function. Thus, a more reasonable path can be given to realize the reconnaissance and detection of points of interest. MATLAB simulation experiments show that the algorithm can effectively detect targets in complex terrain composed of terrain restricted areas, and return to the designated end point to complete communication. Firstly, the development of unmanned aerial vehicles in various countries and the social status of unmanned aerial vehicles are discussed. By making UAV build threat model and task allocation in dynamic environment. The path planning and optimization of UAV in dynamic environment is studied, and the path planning algorithm and Hungarian algorithm are added. The optimized UAV has the fastest data transmission and calculation speed, while the other two types of UAVs have slower data transmission and calculation speed. In particular, ordinary UAVs also have data transmission failures, resulting in incomplete experimental results. The results show that the optimized UAV system is better in data calculation and transmission, which also shows that the UAV can quickly plan and process flight paths, which is suitable for practical applications.
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