红外仿真中的海面发射率模型

海面作为舰船的主要背景,其红外辐射特性对于舰船红外仿真研究具有重大意义。介绍了三种海面发射率的计算模型：灰体模型、经验模型和理论模型,并针对红外仿真中舰船辐射场和海面背景红外热像模拟两方面的计算要求,分析比较了它们的计算结果。结果显示,对于舰船辐射场计算而言,使用经验模型已经具有足够的精度;而在模拟海面背景红外热像时,在天顶角较大的情况下必须使用理论模型。     

Deep learning methods for fingerprint-based indoor positioning: a review

ABSTRACT

Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. This paper provides a comprehensive review of deep learning methods in indoor positioning. First, the advantages and disadvantages of various fingerprint types for indoor positioning are discussed. The solutions proposed in the literature are then analyzed, categorized, and compared against various performance evaluation metrics. Since data is key in fingerprinting, a detailed review of publicly available indoor positioning datasets is presented. While incorporating deep learning into fingerprinting has resulted in significant improvements, doing so, has also introduced new challenges. These challenges along with the common implementation pitfalls are discussed. Finally, the paper is concluded with some remarks as well as future research trends.     

海杂波数据的采集与分析

介绍了海杂波数据的采集环境和采集方法,以及对海杂波数据幅度统计特性和谱特性的分析方法和分析结果。     

Deep Neural Architecture for Recovering Dropped Pronouns in Korean

Pronouns are frequently dropped in Korean sentences, especially in text messages in the mobile phone environment. Restoring dropped pronouns can be a beneficial preprocessing task for machine translation, information extraction, spoken dialog systems, and many other applications. In this work, we address the problem of dropped pronoun recovery by resolving two simultaneous subtasks: detecting zero-pronoun sentences and determining the type of dropped pronouns. The problems are statistically modeled by encoding the sentence and classifying types of dropped pronouns using a recurrent neural network (RNN) architecture. Various RNN-based encoding architectures were investigated, and the stacked RNN was shown to be the best model for Korean zero-pronoun recovery. The proposed method does not require any manual features to be implemented; nevertheless, it shows good performance.     

Predicting ASD diagnosis in children with synthetic and image-based eye gaze data

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.     

一种提高硅双极器件频率和功率的新技术

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。     

深亚微米光学光刻工艺技术

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。     

Face completion with Hybrid Dilated Convolution

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.     

Hyperspectral remote sensing IQA via learning multiple kernels from mid-level features

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.     

深层发酵灵芝生物工程技术与产品研究

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