Compact Triple-Band Antenna Employing Simplified MTLs for Wireless Applications

A compact triple-band asymmetric coplanar waveguide- (ACPW-) fed antenna based on simplified metamaterial transmission lines (SMTLs) is proposed in this paper. The antenna consists of two SMTL unit cells of the same dimension. Three operating bands, which cover UMTS in the 1.76 GHz band and WLAN in the 5.2 GHz and 5.8 GHz, are achieved when the zeroth-order and first-positive-order modes appear. The characteristics of the proposed transmission line metamaterial structure are studied in detail by circuit analysis and dispersion analysis. The working mechanism and radiation performances of the antenna are examined and illustrated at the three operating bands, respectively. A prototype designed on FR4 substrate with dielectric constant 4.3 occupies an overall size of 12.55 × 22.7 × 1.6 mm3 and is constructed and successfully measured

Histogram of visual words based on locally adaptive regression kernels descriptors for image feature extraction

Image feature extraction is one of the most important problems for image recognition system. We tackle this by combing the locally adaptive regression kernel descriptors (LARK), bag-of-visual-words and sparse representation. Specifically, this paper makes two main contributions: (1) we introduce a novel method called histogram of visual words based on locally adaptive regression kernels descriptors (HWLD) for image feature extraction. LARK is used to describe the image local information and build the visual vocabulary. Each pixel of an image is assigned to the visual words and gets the corresponding weights. Image feature vector is obtained by subdividing the image and computing the accumulative weight histograms of visual words in these sub-blocks. (2) The K nearest neighbor based sparse representation (KNN-SR) is presented for assigning the visual words. Compared with nearest neighbors based method, KNN-SR has stronger discriminant power to identify different patches in the image. Experimental results on the AR face image set, the CMU-PIE face image set, the ETH80 object image set and the Nister image set demonstrate that our method is more effective than some state-of-the-art feature extraction methods

Dual collaborative representation based discriminant projection for face recognition

Collaborative representation based techniques have shown promising results for face recognition; however, most of them code the samples by taking the overall samples as a dictionary, which may contain much noise information. To tackle this problem, a new face recognition algorithm, namely dual collaborative representation based discriminant projection (DCRDP), is proposed in this paper. In DCRDP, each training sample is reconstructed via dual collaborative representation to enhance the robustness to noise information: the first collaborative representation is used to choose an appropriate dictionary with respect to the training sample, while the second collaborative representation is used to find collaborative representation relationships between samples. After dual collaborative representation, DCRDP constructs two adjacency graphs to model the similarity and dissimilarity between samples, and then finds the optimal projection matrix for dimension reduction. Experiments on ExtYaleB, AR and CMU PIE face datasets verify the superiority of DCRDP to some other state-of-the-art approaches.This research was supported by the National Natural Science Foundation of China (Grant Nos. 62172229 and 61876213), Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant Nos. SJCX21_0887 and SJCX22_0994), and the Natural Science Fund of Jiangsu Province (Grants Nos. BK20201397, BK20221349 and BK20211295)

Membership-Degree Preserving Discriminant Analysis with Applications to Face Recognition

In pattern recognition, feature extraction techniques have been widely employed to reduce the dimensionality of high-dimensional data. In this paper, we propose a novel feature extraction algorithm called membership-degree preserving discriminant analysis (MPDA) based on the fisher criterion and fuzzy set theory for face recognition. In the proposed algorithm, the membership degree of each sample to particular classes is firstly calculated by the fuzzy k-nearest neighbor (FKNN) algorithm to characterize the similarity between each sample and class centers, and then the membership degree is incorporated into the definition of the between-class scatter and the within-class scatter. The feature extraction criterion via maximizing the ratio of the between-class scatter to the within-class scatter is applied. Experimental results on the ORL, Yale, and FERET face databases demonstrate the effectiveness of the proposed algorithm

Identifying Wood Based on Near-Infrared Spectra and Four Gray-Level Co-Occurrence Matrix Texture Features

Identifying wood accurately and rapidly is one of the best ways to prevent wood product fakes and adulterants in forestry products. Wood identification traditionally relies heavily on special experts that spend extensive time in the laboratory. A new method is proposed that uses near-infrared (NIR) spectra at a wavelength of 780–2300 nm incorporated with the gray-level co-occurrence (GLCM) texture feature to accurately and rapidly identify timbers. The NIR spectral features were determined by principal component analysis (PCA), and the digital image features extracted with the GLCM were used to create a support vector machine (SVM) model to identify the timbers. The results from fusion features of raw spectra and four GLCM features of 25 timbers showed that identification accuracy by the model was 99.43%. A sample anisotropy and heterogeneity comparative analysis revealed that the wood identification information from the transverse surface had more characteristics than that from the tangential and radial surfaces. Furthermore, short-wavelength pre-processed NIR bands of 780–1100 nm and 1100–2300 nm realized high identification accuracy of 99.43% and 100%, respectively. The four GLCM features were effective for improving identification accuracy by improving the data spatial clustering features

Emerin anchors Msx1 and its protein partners at the nuclear periphery to inhibit myogenesis

Abstract Background Previous studies have shown that in myogenic precursors, the homeoprotein Msx1 and its protein partners, histone methyltransferases and repressive histone marks, tend to be enriched on target myogenic regulatory genes at the nuclear periphery. The nuclear periphery localization of Msx1 and its protein partners is required for Msx1’s function of preventing myogenic precursors from pre-maturation through repressing target myogenic regulatory genes. However, the mechanisms underlying the maintenance of Msx1 and its protein partners’ nuclear periphery localization are unknown. Results We show that an inner nuclear membrane protein, Emerin, performs as an anchor settled at the inner nuclear membrane to keep Msx1 and its protein partners Ezh2, H3K27me3 enriching at the nuclear periphery, and participates in inhibition of myogenesis mediated by Msx1. Msx1 interacts with Emerin both in C2C12 myoblasts and mouse developing limbs, which is the prerequisite for Emerin mediating the precise location of Msx1, Ezh2, and H3K27me3. The deficiency of Emerin in C2C12 myoblasts disturbs the nuclear periphery localization of Msx1, Ezh2, and H3K27me3, directly indicating Emerin functioning as an anchor. Furthermore, Emerin cooperates with Msx1 to repress target myogenic regulatory genes, and assists Msx1 with inhibition of myogenesis. Conclusions Emerin cooperates with Msx1 to inhibit myogenesis through maintaining the nuclear periphery localization of Msx1 and Msx1’s protein partners

Multistate structures in a hydrogen-bonded polycatenation non-covalent organic framework with diverse resistive switching behaviors

Abstract The inherent structural flexibility and reversibility of non-covalent organic frameworks have enabled them to exhibit switchable multistate structures under external stimuli, providing great potential in the field of resistive switching (RS), but not well explored yet. Herein, we report the 0D+1D hydrogen-bonded polycatenation non-covalent organic framework (HOF-FJU-52), exhibiting diverse and reversible RS behaviors with the high performance. Triggered by the external stimulus of electrical field E at room temperature, HOF-FJU-52 has excellent resistive random-access memory (RRAM) behaviors, comparable to the state-of-the-art materials. When cooling down below 200 K, it was transferred to write-once-read-many-times memory (WORM) behaviors. The two memory behaviors exhibit reversibility on a single crystal device through the temperature changes. The RS mechanism of this non-covalent organic framework has been deciphered at the atomic level by the detailed single-crystal X-ray diffraction analyses, demonstrating that the structural dual-flexibility both in the asymmetric hydrogen bonded dimers within the 0D loops and in the infinite π–π stacking column between the loops and chains contribute to reversible structure transformations between multi-states and thus to its dual RS behaviors

A cationic microporous metal–organic framework for highly selective separation of small hydrocarbons at room temperature

A new three-dimensional cationic metal–organic framework Zn8O(EDDA)4(ad)4•(HEDDA)2•6DMF•27H2O (ZJU-48; H2EDDA = (E)-4,4′-(ethene-1,2-diyl)dibenzoic acid; ad = adenine) was solvothermally synthesized and structurally characterized. ZJU-48 features a three-dimensional structure with a cationic skeleton and has one-dimensional pores along the c axis of about 9.1 × 9.1 Å2. The activated ZJU-48a exhibits a BET surface area of 1450 m2 g−1. The structural features of the charged skeleton of ZJU-48a have enabled its stronger charge-induced interaction with C2 hydrocarbons than with C1 methane, resulting in highly selective gas sorption of C2 hydrocarbons over CH4 with the adsorption selectivity over 6 at 298 K. The separation feasibility has been further established by the simulated breakthrough and pulse chromatographic experiments, thus methane can be readily separated from their quaternary mixtures at room temperature