Ultrasound-Directed Symmetry Breaking and Spin Filtering of Supramolecular Assemblies from only Achiral Building Blocks

Herein we describe the self-assembly of an achiral molecule into macroscopic helicity as well as the emergent chiral-selective spin-filtering effect. It was found that a benzene-1,3,5-tricarboxamide (BTA) motif with an aminopyridine group in each arm could coordinate with Ag^I and self-assemble into nanospheres. Upon sonication, symmetry breaking occurred and the nanospheres transferred into helical nanofibers with strong CD signals. Although the sign of the CD signals appeared randomly, it could be controlled by using the as-made chiral assemblies as a seed. Furthermore, it was found that the charge transport of the helical nanofibers was highly selective with a spin-polarization transport of up to 45 %, although the chiral nanofibers are composed exclusively from achiral building blocks. This work demonstrates symmetry breaking under sonication and the chiral-selective spin-filtering effect.     

Poly(N-isopropylacrylamide)-grafted dual stimuli-responsive filter paper for protein separation

Thermal and salt dual stimuli-responsive filter-paper-based membranes were prepared by UV-induced grafting of NIPAM-based polymers on paper surface. The grafting ratio could be controlled by monomer concentration during grafting polymerization. The results from pressure drop measurement of the mobile phase flowed cross the membrane demonstrate that an appropriate grafting ratio would be 8%-10%. Protein adsorption on the membrane through hydrophobic interaction could be promoted by increasing temperature and lyotropic salt concentration. The effect of grafted polymer structure on protein binding performance was studied. Filter paper grafted with NIPAM-based branched copolymer consisting of hydrophobic monomer moieties shows ten times higher protein binding capacity than that of the original filter paper. The separation of plasma proteins using the dual stimuli-responsive membrane was examined to demonstrate feasible application for hydrophobic interaction chromatographic separation of proteins.     

Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification

Naturally inspired evolutionary algorithms prove effectiveness when used for solving feature selection and classification problems. Artificial Bee Colony (ABC) is a relatively new swarm intelligence method. In this paper, we propose a new hybrid gene selection method, namely Genetic Bee Colony (GBC) algorithm. The proposed algorithm combines the used of a Genetic Algorithm (GA) along with Artificial Bee Colony (ABC) algorithm. The goal is to integrate the advantages of both algorithms. The proposed algorithm is applied to a microarray gene expression profile in order to select the most predictive and informative genes for cancer classification. In order to test the accuracy performance of the proposed algorithm, extensive experiments were conducted. Three binary microarray datasets are use, which include: colon, leukemia, and lung. In addition, another three multi-class microarray datasets are used, which are: SRBCT, lymphoma, and leukemia. Results of the GBC algorithm are compared with our recently proposed technique: mRMR when combined with the Artificial Bee Colony algorithm (mRMR-ABC). We also compared the combination of mRMR with GA (mRMR-GA) and Particle Swarm Optimization (mRMR-PSO) algorithms. In addition, we compared the GBC algorithm with other related algorithms that have been recently published in the literature, using all benchmark datasets. The GBC algorithm shows superior performance as it achieved the highest classification accuracy along with the lowest average number of selected genes. This proves that the GBC algorithm is a promising approach for solving the gene selection problem in both binary and multi-class cancer classification.     

Independent continuous mapping for topological optimization of frame structures

Based on the Independent Continuous Mapping method (ICM), a topological optimization model with continuous topological variables is built by introducing three filter functions for element weight, element allowable stress and element stiffness, which transform the 0-1 type discrete topological variables into continuous topological variables between 0 and 1. Two methods for the filter functions are adopted to avoid the structural singularity and recover falsely deleted elements: the weak material element method and the tiny section element method. Three criteria (no structural singularity, no violated constraints and no change of structural weight) are introduced to judge iteration convergence. These criteria allow finding an appropriate threshold by adjusting a discount factor in the iteration procedure. To improve the efficiency, the original optimization model is transformed into a dual problem according to the dual theory and solved in its dual space. By using MSC/Nastran as the structural solver and MSC/Patran as the developing platform, a topological optimization software of frame structures is accomplished. Numerical examples show that the ICM method is very efficient for the topological optimization of frame structures.The project supported by the National Natural Science Foundation of China (10472003) and Beijing Natural Science Foundation (3042002) The English text was polished by Yunming Chen.     

Fingerprint image enhancement using data driven Directional Filter Bank

A new method based upon data driven tool, principal component analysis (PCA), for fingerprint enhancement is proposed in this paper. PCA is a very useful statistical technique that has found application in many different fields like image compression, face recognition and is commonly used for finding patterns in data of high dimension. In the proposed method, the input image is first decomposed into directional images using decimation free Directional Filter Bank (DDFB). Then these directional images are normalized. A data driven technique PCA is applied to these normalized directional fingerprint images, which gives the PCA filtered images. These are basically directional images. Then these directional images are reconstructed into one image which is the enhanced one. Simulation results are included illustrating the capability of the proposed method.     

Plate/shell topological optimization subjected to linear buckling constraints by adopting composite exponential filtering function

In this paper, a model of topology optimization with linear buckling constraints is established based on an independent and continuous mapping method to minimize the plate/shell structure weight. A composite exponential function (CEF) is selected as filtering functions for element weight, the element stiffness matrix and the element geomet-ric stiffness matrix, which recognize the design variables, and to implement the changing process of design variables from“discrete”to“continuous”and back to“discrete”. The buck-ling constraints are approximated as explicit formulations based on the Taylor expansion and the filtering function. The optimization model is transformed to dual programming and solved by the dual sequence quadratic programming algo-rithm. Finally, three numerical examples with power function and CEF as filter function are analyzed and discussed to demonstrate the feasibility and efficiency of the proposed method.     

Implementation of a nonlinear filter for online nuclear counting

Nuclear counting is a challenging task for nuclear instrumentation because of the stochastic nature of radioactivity. Event counting has to be processed and filtered to determine a stable count rate value and perform variation monitoring of the measured event. An innovative approach for nuclear counting is presented in this study, improving response time and maintaining count rate stability. Some nonlinear filters providing a local maximum likelihood estimation of the signal have been recently developed, which have been tested and compared with conventional linear filters. A nonlinear filter thus developed shows significant performance in terms of response time and measurement precision. The filter also presents the specificity of easy embedment into digital signal processor (DSP) electronics based on field-programmable gate arrays (FPGA) or microcontrollers, compatible with real-time requirements. © 2001 Elsevier Science. All rights reserved.     

Multiple carrier frequency offsets and multipath channels estimation for coordinated multi-point FBMC systems

Filter bank multicarrier (FBMC) systems are active candidates for the physical layer of the fifth generation of wireless communication networks (5G). The combination of FBMC system with the coordinated multipoint (CoMP) has been proposed to improve the performance of mobile terminals (MT) located at the cell edge. Accurate estimation of carrier frequency offset (CFO) and channel has great importance in communication systems. In this paper, a data-aided joint maximum likelihood (ML) CFO and channel estimation is proposed for the downlink of CoMP. Notably choosing the proper training sequences from Hadamard matrix for different base stations (BSs) reduces the complexity of estimator. Additionally joint CFO correction and channel equalization has been studied at the MT. Finally, we evaluate the efficacy of the presented algorithms using simulation.     

一种在色噪声环境下的信号检测方法

为了抑制蛟龙号载人潜水器水声通信受母船色噪声干扰的影响,本文提出了一种统计匹配滤波器在色噪声环境下使用的新方法,它通过将接收信号映射到一个生成子空间中进行信号检测,可提高输出水声通信信号的信噪比。受客观条件限制,向阳红09船——蛟龙号载人潜水器的试验母船噪声环境比较恶劣,对水声通信产生了较强的干扰。如何在向阳红09船的这种强、色噪声环境下,提升水声同步信号的检测能力,对提高水声通信的可靠性是非常有帮助的。实际采集试验数据分析结果验证了该检测算法比传统匹配滤波器有更强的信号检测能力。