基于堆叠深度卷积沙漏网络的步态识别

提出了一种基于堆叠深度卷积沙漏网络的步态识别方法。为了解决人体建模中关节点准确定位的问题，采用基于深度卷积的沙漏网络来提取步态图上的关节点坐标，并计算肘关节与膝关节的角度作为运动特征。为了解决行走速度变化带来的影响，采用动态时间规整（Dynamic Time Warping）对特征序列进行距离计算。通过最近邻分类器对结果进行准确分类。该方法在公共CASIA-B数据集与TUM-GAID数据集上进行了验证并与其他方法进行比较，结果表明该方法有较高的识别率。     

基于CNN的人体姿态识别

姿态识别是人机交互中重要的研究课题之一，随着机器学习与神经网络的发展，研究的方式和成果趋于多样化，姿态识别的应用价值也日趋广泛。本文通过构建卷积神经网络模型，该模型共有11层，在对采样的数据集中5种人体姿态进行卷积与池化操作，最后进入全连接层进行分类，从而完成对数据集的训练和识别。结果显示，相较于机器学习方法，该模型的识别性能更加优秀，且免去了复杂的特征提取方式设计，让网络自身提取特征进行识别分类，效果更好。     

Effects of incorporating roasted lupin (Lupinus angustifolius) flour on the physicochemical and sensory attributes of beef sausage

The effects of incorporating lupin flour on the physicochemical and sensory characteristics of beef sausages were explored. Lupin (Lupinus angustifolius) flour was roasted, then hydrated to match the moisture content of beef meat. The beef sausage samples were manufactured for six treatments by replacing beef with hydrated lupin flour from 0% to 36% (w/w). Proximate analysis revealed that carbohydrate (dietary fibre) level was increased from 9.62% to 19.31%, whereas fat content was decreased from 11.62% to 7.91%. Inclusion of lupin flour increased the meat emulsion stability (fluid released decreased from 9.35% to 1.53%) and decreased cooking loss from 22.70% to 14.30%. Softer texture and greater adhesiveness were observed in lupin-incorporated formulations. Sensory evaluation indicated no significant difference between control and 12% lupin-enriched beef sausage formulation in appearance, aroma, flavour and overall liking, suggesting lupin flour can be added to beef sausage at this level without compromising consumer acceptance.     

Expression of Main Toll-Like Receptors in Patients with Different Types of Colorectal Polyps and Their Relationship with Gut Microbiota

Abnormal activation of Toll-like receptor (TLRs) signaling can result in colon cancer development. The aim of this study was to investigate the expression of important TLRs in different histological types of colorectal polyps and evaluate their relationship with intestinal microbiota. The expression levels of TLR2, 3, 4, and 5 were analyzed in intestinal biopsy specimens of 21 hyperplastic polyp (HP), 16 sessile serrated adenoma (SSA), 29 tubular adenoma (TA), 21 villous/tubulovillous (VP/TVP) cases, and 31 normal controls. In addition, selected gut bacteria including Streptococcus bovis, Enterococcus faecalis, Enterotoxigenic Bacteroides fragilis (ETBF), Fusobacterium nucleatum, Porphyromonas spp., Lactobacillus spp., Roseburia spp., and Bifidobacterium spp. were quantified in fecal samples using absolute qRT PCR, and, finally, the association between TLRs and these gut microbiota- was evaluated by Spearman’s correlation coefficient. Higher expression of TLR2 and TLR4 in VP/TVP and TA, and lower expression levels of TLR3 and TLR5 in all type of polyps were observed. The differences in TLR expression patterns was not only dependent on the histology, location, size, and dysplasia grade of polyps but also related to the intestinal microbiota patterns. TLR2 and TLR4 expression was directly associated with the F. nucleatum, E. faecalis, S. bovis, Porphyromonas, and inversely to Bifidobacterium, Lactobacillus, and Roseburia quantity. Furthermore, TLR3 and TLR5 expression was directly associated with Bifidobacterium, Roseburia, and Lactobacillus quantity. Our results suggest a possible critical role of TLRs during colorectal polyp progression. An abnormal regulation of TLRs in relation to gut microbial quantity may contribute to carcinogenesis.     

退火温度对6.5%Si极薄带织构及磁性能的影响

采用单辊甩带法制备了厚度为0.03 mm的6.5%Si极薄带,并在真空炉中进行了880 ℃和920 ℃的退火处理。利用XRD和EBSD研究了其在退火后的宏观和微观织构演变规律。结果表明:经单辊甩带制得的6.5%Si极薄带织构主要由{001}<100>立方织构、{110}<001> Goss织构和{112}<111>织构组成。随着退火温度的升高,λ纤维仍占主导地位,且晶粒尺寸逐渐增大。特别是在920 ℃退火时,{001}<120>和{001}<100>晶粒的尺寸优势逐渐增大,不利织构{111}<110>和{111}<112>逐渐减弱,且最终的磁感应强度B₅₀=1.644 T,铁损耗P_10/400=5.896 W/kg。     

An Integrated Flexible All-Nanowire Infrared Sensing System with Record Photosensitivity

Infrared (IR) photodetectors are a key optoelectronic device and have thus attracted considerable research attention in recent years. Photosensitivity is an increasingly important device performance parameter for nanoscale photodetectors and image sensors, as it determines the ultimate imaging quality and contrast. However, photosensitivities of state-of-the-art low-dimensional nanostructure-based IR detectors are considerably low, limiting their practical applications. Herein, a biomimetic IR detection amplification (IRDA) system that boosts photosensitivity by several orders of magnitude by introducting nanowire field effect transistors (FETs), resulting in a peak photosensitivity of 7.6 × 10⁴ under an illumination of 1342 nm, is presented. Consequently, high-contrast imaging of IR light is obtained on the flexible IRDA arrays. The image information can be then trained and recognized by an artificial neural network for higher image-recognition efficiency. This work provides a new perspective for developing high-performance IR imaging systems, and is expected to undoubtedly enlighten future work on artificial intelligence and biorobotic systems.     

ZerNet: Convolutional Neural Networks on Arbitrary Surfaces Via Zernike Local Tangent Space Estimation

In this paper, we propose a novel formulation extending convolutional neural networks (CNN) to arbitrary two-dimensional manifolds using orthogonal basis functions called Zernike polynomials. In many areas, geometric features play a key role in understanding scientific trends and phenomena, where accurate numerical quantification of geometric features is critical. Recently, CNNs have demonstrated a substantial improvement in extracting and codifying geometric features. However, the progress is mostly centred around computer vision and its applications where an inherent grid-like data representation is naturally present. In contrast, many geometry processing problems deal with curved surfaces and the application of CNNs is not trivial due to the lack of canonical grid-like representation, the absence of globally consistent orientation and the incompatible local discretizations. In this paper, we show that the Zernike polynomials allow rigourous yet practical mathematical generalization of CNNs to arbitrary surfaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike coefficients and the rotation of a convolution kernel is essentially a set of 2 × 2 rotation matrices applied to the coefficients. The key contribution of this work is in such a computationally efficient but rigorous generalization of the major CNN building blocks.     

基于信息熵与形态学的鸟鸣数据处理

为了探索一种减少训练量并提高精度,且适应于卷积神经网络的预处理方法,以识别鸟鸣为例,基于信息熵以及形态学在图像处理上的应用,根据鸟鸣的间隔性特点,提出新的处理方案。利用形态学将音频片段归类为有效和噪声信号两类;使用加权的方法利用信息熵预测有效数据的分布。仿真结果表明,形态学使数据量减少且信息熵处理使单个数据稀疏化并起到滤波作用,在保持精度甚至精度提高的情况下缩短了训练时间,为卷积神经网络的数据预处理的简化提供了方向。