Electrospinning technology has been widely used in the past few decades to prepare nanofibrous scaffolds that mimic extracellular matrices. However, traditional two‐dimensional (2D) electrospun nanofibrous mats still have some inherent disadvantages for bone tissue engineering, such as limited cell infiltration and lack of three‐dimensional (3D) structure. The development of 3D electrospun scaffolds with larger pore sizes and porosity provides new perspectives for electrospinning‐based tissue engineering scaffolds. However, there are still some challenges and areas for improvement. In this review, the applications of 3D electrospun nanofibrous scaffolds in the field of bone tissue engineering from its fabrication methods to bio‐functionalization are summarized, with the aim of providing new insights into the design of electrospinning‐based bone tissue engineering scaffolds.
No-reference image quality assessment is of great importance to numerous image processing applications, and various methods have been widely studied with promising results. These methods exploit handcrafted features in the transformation or space domain that are discriminated for image degradations. However, abundant a priori knowledge is required to extract these handcrafted features. The convolutional neural network (CNN) is recently introduced into the no-reference image quality assessment, which integrates feature learning and regression into one optimization process. Therefore, the network structure generates an effective model for estimating image quality. However, the image quality score obtained by the CNN is based on the mean of all of the image patch scores without considering the human visual system, such as edges and contour of images. In this paper, we combine the CNN and the Prewitt magnitude of segmented images and obtain the image quality score using the mean of all the products of the image patch scores and weights based on the result of segmented images. Experimental results on various image distortion types demonstrate that the proposed algorithm achieves good performance.Keywords No-reference image quality assessment · Convolutional neural networks (CNNs) · Graph-based image segmentation · Prewitt magnitude
A novel iris biometric watermarking scheme is proposed focusing on iris recognition instead of the traditional watermark for increasing the security of the digital products. The preprocess of iris image is to be done firstly, which generates the iris biometric template from person's eye images. And then the templates are to be on discrete cosine transform; the value of the discrete cosine is encoded to BCH error control coding. The host image is divided into four areas equally correspondingly. The BCH codes are embedded in the singular values of each host image's coefficients which are obtained through discrete cosine transform (DCT). Numerical results reveal that proposed method can extract the watermark effectively and illustrate its security and robustness.
As known to all, dictation method is an effective way to review and test one’s vocabulary. This paper introduces the theories that support dictation method and the effective strategies for applying it. Besides, this paper also explains how the teachers should do to help the students enhance enthusiasm and confidence for learning, stimulate learning motivation, reduce or eliminate their difficulty and take measures to improve their own language skills, so as to raise the efficiency of memorizing vocabulary and achieve the purpose of increasing vocabulary
With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance.
Aiming at high fidelity image only can be encrypted in time domain ,summarize the current all kind of encryption methods in time domain .That are only chaos permutation, only chaos encryption and hybrid encryption algorithm. Then, this paper proposed the block chaos image scrambling and chaos encryption with after effect .In the block permutation, give the formular of correlation and compare the correlation before and after permutation. After the hybrid encryption, computing the information entropy of encrypted image. At last, according to the security analysis, the image encryption algorithm demonstrates strong resistance toward exterior attacks such as statistical attacks and differential attacks.
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