Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on util...
Image recognition has become a necessary component for computer visual system and widely utilized to detect objectives for downstream tasks in realistic applications. However, existing methods are concentrated on utilizing the clustering information of image features to recognize the subjects, which are unable to dispose several high correlation subjects and cost numerous computation period. In this paper, we utilize the convolution operation for images and extract the separated features. After acquiring these features, a deep neural network is established to recognize the objectives in the input images with enough iterations training procedures. Subsequently, the trained model is evaluated through the testing data-set to measure the real performance of proposed method. From our extensive experimental results, we can conclude that our proposed model can automatically realize the recognition process for input images with reasonable accuracy and acceptable computation costs. Additionally, our experimental results also indicate that the convolutional operation is more suitable to dispose the images data-set than traditional machine learning method.
A novel miniaturized wideband high-gain palm-leaf Vivaldi array antenna is presented in this work. Firstly, a novel Vivaldi antenna element is designed. To miniaturize the element, three groups of arc-shaped slots of ...
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Automatically generating clinical texts can significantly reduce the time physicians spend on clinical data recording, which is particularly important for developing countries where physicians are extremely busy due t...
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This article aims to demonstrate a signal compression method for the wireless invasive neural recording system. A compression system with spike detection for neural signals is proposed. The input signal is firstly det...
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作者:
Tong, ZhanWu, ZhanYang, YangMao, WeilongWang, ShijieLi, YinshengChen, YangSoutheast University
Laboratory of Image Science and Technology Nanjing210096 China Southeast University
Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing210096 China Chinese Academy of Sciences
Research Center for Medical Artificial Intelligence Shenzhen Institutes of Advanced Technology Shenzhen518055 China School of Computer Science and Engineering
Key Lab. of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing The Laboratory of Image Science and Technology Nanjing210096 China
Computed Tomography (CT) is an imaging technique widely used in clinical diagnosis. However, high-attenuation metallic implants result in the obstruction of low-energy Xrays and further lead to metal artifacts in the ...
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As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges suc...
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In the issue of interference suppression, the performance of traditional adaptive methods will decrease when mainlobe interference and sidelobe interference have angle error. To this end, a robust adaptive beamforming...
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In the issue of interference suppression, the performance of traditional adaptive methods will decrease when mainlobe interference and sidelobe interference have angle error. To this end, a robust adaptive beamforming technique based on frequency diversity array (FDA) multiple-input multiple-output (MIMO) is proposed in this work. Firstly, preprocessing in data domain is adopted for mainlobe interference cancellation. Then, the sidelobe interference is suppressed in the receiving dimension. Finally, robust adaptive beamforming method is applied to suppress sidelobe interference. Simulation results show the effectiveness of the proposed algorithm.
3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with ...
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Existing cross-modal hashing still faces three challenges: (1) Most batch-based methods are unsuitable for processing large-scale and streaming data. (2) Current online methods often suffer from insufficient semantic ...
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A linearly polarized dual-band metal-only transmitarray antenna (TA) element is proposed. The TA element consists of four identical metallic layers without dielectric substrates. An air gap is present between each pai...
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