In light of recent advances in deep learning and high-resolution remote sensing imaging technology, there has been a growing adoption of remote sensing ship classification models that are based on deep learning methodologies. However, the efficiency of remote sensing ship classification models is affected by complex backgrounds, shooting conditions, high inter-class similarity of ship targets, and sample diversity. To tackle the challenges above, we propose a multi-scale attention-based adaptive feature fusion (AFF) network for fine-grained ship classification in remote sensing scenarios to improve the fine-grained classification ability of the model from local details. First, using the idea of information complementarity, the multi-scale feature interaction module is constructed in the multi-scale attention module. It employs bidirectional feature interaction paths to concurrently capture intricate details within both deep and shallow ship features, enhancing the interplay among different levels of information. Second, the hybrid attention module is part of the multi-scale attention module. It is designed to enhance the cross-dimensional interaction of spatial domain and channel domain information to amplify the importance of crucial feature regions and feature channels. This allows the network to pay more attention to specific areas and extract distinctive features. Finally, the AFF module is designed to automatically calibrate and fuse different levels of saliency features to obtain features with more fine-grained discrimination for model classification. In this approach, these modules synergistically collaborate and mutually reinforce each other, ultimately increasing the accuracy of ship classification tasks. We evaluated our method on three large-scale fine-grained classification benchmarks; the experimental results show that the proposed method had better fine-grained classification than other methods.
KEYWORDS: Field programmable gate arrays, Signal processing, Analog to digital converters, Data transmission, Data acquisition, Digital signal processing, Computing systems, Sensors, Data storage, Analog electronics
Signal acquisition system is widely used in various fields of reality, due to the data acquisition rate and processor performance enhancement as well as the development of sensor arrays and other technologies, the data acquisition system acquisition and transmission of the signal is exponential growth, in order to solve the high-speed signal acquisition process of the large amount of data, real-time, transmission rate and other issues, the study of high-speed acquisition of signals as well as the transmission program has an important practical significance. FPGA is very suitable to be applied in high-speed signal acquisition system because of its characteristics. In this paper, we firstly introduce the composition of a typical highspeed signal acquisition system based on FPGA, and then briefly introduce the function and development process of each module of the system and introduce the mainstream programs used in several key modules. In engineering applications, different programs should be used in different modules in conjunction with the actual.
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