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41.
As early intervention is highly effective for young children with autism spectrum disorder (ASD), it is imperative to make accurate diagnosis as early as possible. ASD has often been associated with atypical visual attention and eye gaze data can be collected at a very early age. An automatic screening tool based on eye gaze data that could identify ASD risk offers the opportunity for intervention before the full set of symptoms is present. In this paper, we propose two machine learning methods, synthetic saccade approach and image based approach, to automatically classify ASD given children’s eye gaze data collected from free-viewing tasks of natural images. The first approach uses a generative model of synthetic saccade patterns to represent the baseline scan-path from a typical non-ASD individual and combines it with the real scan-path as well as other auxiliary data as inputs to a deep learning classifier. The second approach adopts a more holistic image-based approach by feeding the input image and a sequence of fixation maps into a convolutional or recurrent neural network. Using a publicly-accessible collection of children’s gaze data, our experiments indicate that the ASD prediction accuracy reaches 67.23% accuracy on the validation dataset and 62.13% accuracy on the test dataset.  相似文献   
42.
周蓉  胡思福  张庆中 《微电子学》2000,30(2):100-102
提出了一种能同时提高硅双极器件频率和功率的新技术-具有深阱终端结构的新型梳状深阱结构技术。采用MEDICI模拟分析表明,该技术可交双极器件的击穿电压BVCB0提高到平行平面结的90%以上;可减小寄生效应和漏电流,有助于提高小电流β0;可适当的增加集电区掺杂浓度,减小τd,同时提高ICM和Po。  相似文献   
43.
深亚微米光学光刻工艺技术   总被引:1,自引:0,他引:1  
光学光刻的生命力仍然在不断延续 ,即使在 0 13 μm及 0 13 μm以下集成电路制造水平上 ,光学光刻仍然是一个非常重要的候选者。深亚微米光学光刻工艺技术目前面临着越来越严重的挑战。对深亚微米光学光刻中的一些关键工艺技术如移相光刻、光学邻近效应校正、远紫外光刻胶、套刻对准误差等进行了论述。  相似文献   
44.
Image completion is a challenging task which aims to fill the missing or masked regions in images with plausibly synthesized contents. In this paper, we focus on face image inpainting tasks, aiming at reconstructing missing or damaged regions of an incomplete face image given the context information. We specially design the U-Net architecture to tackle the problem. The proposed U-Net based method combines Hybrid Dilated Convolution (HDC) and spectral normalization to fill in missing regions of any shape with sharp structures and fine-detailed textures. We perform both qualitative and quantitative evaluation on two challenging face datasets. Experimental results demonstrate that our method outperforms previous learning-based inpainting methods. The proposed method can generate realistic and semantically plausible images.  相似文献   
45.
Hyperspectral image quality assessment (HIQA) is an indispensable technique in both academic and industry domain However, HIQA is still a challenging task since those fine-grained and quality-aware visual details are difficult to be captured. Compared with the conventional low-level features, mid-level features usually contain more semantic and quality clues and exhibit higher discriminant ability. Thus, we aim to leverage the mid-level features for HIQA. More specifically, three-scale superpixel mosaics are generated from the input image pre-processed by PCA. Each superpixel scale corresponds to various homogeneousobject parts. Subsequently, three mid-level visual features (fisher vector, combined mean features, reconstructed image matrix) as well as deep features of hyperspectral images are calculated with three-scale superpixel images to constitute multiple kernels. Afterwards, we integrate these kernels into a multimodal one, which is further integrated into a feature vector by row-wise stacking. The image quality evaluation can be calculated based on the designed similarity metric. Comprehensive experiments have demonstrated the effectiveness of our proposed HIQA algorithm.  相似文献   
46.
深层发酵灵芝生物工程技术与产品研究   总被引:7,自引:0,他引:7  
研究了深层发酵灵芝(Ganoderma Lucidum)工艺条件;基质中强化锌(Zn)、硒(Se)和测试中试菌种质量指标、最佳发酵条件:温度28℃±1℃、时间72h、基质WG、pH值(最终)3.8左右、锌(Zn)、硒(Se)。加入量分别为8mg/L、4mg/L  相似文献   
47.
Automatic image annotation is one of the most important challenges in computer vision, which is critical to many real-world researches and applications. In this paper, we focus on the issue of large scale image annotation with deep learning. Firstly, considering the existing image data, especially the network images, most of the labels of themselves are inaccurate or imprecise. We propose a Multitask Voting (MV) method, which can improve the accuracy of original annotation to a certain extent, thereby enhancing the training effect of the model. Secondly, the MV method can also achieve the adaptive label, whereas most existing methods pre-specify the number of tags to be selected. Additionally, based on convolutional neural network, a large scale image annotation model MVAIACNN is constructed. Finally, we evaluate the performance with experiments on the MIRFlickr25K and NUS-WIDE datasets, and compare with other methods, demonstrating the effectiveness of the MVAIACNN.  相似文献   
48.
From the temperature dependence of the hole concentration in unirradiated lightly Al-doped 4H-SiC epilayers, an Al acceptor with EV + 0.2 eV, which is an Al atom (AlSi) at a Si sublattice site, and an unknown deep acceptor with EV + 0.35 eV are found, where EV is the top of the valence band. Both the densities are similar. With irradiation of 0.2 MeV electrons the Al acceptor density is reduced, while the unknown deep acceptor density is increased. Judging from the minimum electron energy required to displace a substitutional C atom (Cs) or the AlSi, the bond between the AlSi and its nearest neighbor Cs is broken due to the displacement of the Cs by this irradiation. Moreover, the displacement of the Cs results in the creation of a complex (AlSi-VC) of AlSi and a carbon vacancy (VC), indicating that the possible origin of the deep acceptor with EV + 0.35 eV is AlSi-VC.  相似文献   
49.
Learning-based shadow detection methods have achieved an impressive performance, while these works still struggle on complex scenes, especially ambiguous soft shadows. To tackle this issue, this work proposes an efficient shadow detection network (ESDNet) and then applies uncertainty analysis and graph convolutional networks for detection refinement. Specifically, we first aggregate global information from high-level features and harvest shadow details in low-level features for obtaining an initial prediction. Secondly, we analyze the uncertainty of our ESDNet for an input shadow image and then take its intensity, expectation, and entropy into account to formulate a semi-supervised graph learning problem. Finally, we solve this problem by training a graph convolution network to obtain the refined detection result for every training image. To evaluate our method, we conduct extensive experiments on several benchmark datasets, i.e., SBU, UCF, ISTD, and even on soft shadow scenes. Experimental results demonstrate that our strategy can improve shadow detection performance by suppressing the uncertainties of false positive and false negative regions, achieving state-of-the-art results.  相似文献   
50.
一种Deep Web数据源质量评估模型   总被引:2,自引:1,他引:2  
分析了影响Deep Web数据源质量评估的若干因素,提出了一种Deep Web数据源质量评估模型.该模型从浏览器、Web数据库、Web服务器和用户四个方面对数据源进行质量评估.通过在真实的Deep Web数据源上进行实验验证,说明该方法是有效和可行的.  相似文献   
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