Synthesis of amino acid-containing polyacids and their application in self-cured glass-ionomer cement

Six methacrylate or acrylate derivatives of natural amino acids were synthesized and characterized. Based upon these monomers, six terpolymers [poly(acrylic) acid-co-itaconic acid-co-amino acid] were prepared and characterized. The synthesized polymers were used to formulate glass-ionomer cements (GICs) using Fuji II glass filler. The effects of the molecular weight (MW) and powder/liquid (P/L) ratio were evaluated. Scanning electron microscopy (SEM) was used to examine the fracture surfaces of the selected cement specimens. Results show that all the amino acid modified GICs exhibited higher compressive strengths (CS, 193-236 MPa) and much higher flexural strengths (FS, 55-71 MPa) as compared to commercial Fuji II GIC (191 in CS and 16 in FS). Both MW and P/L ratio affected the strength of the formed cement. It was important to find the optimal MW and P/L ratio to obtain the highest FS. In this study, optimized MW (number average) of the polyacids and P/L ratio were around 50,000 and 2.7/1, respectively. The microstructures of the fracture surfaces helped to explain the strength differences among the materials tested in the study. SEM analysis suggests that more integrated microstructures and fewer defects can lead to higher FS.     

A Noise-Aware Real-Time Processing Approach for Electroencephalogram Signal Classification

Electroencephalogram (EEG) signal processing has emerged as a critical problem for biometric applications due to its real-time requirement. While compressive sensing is an efficient method for signal compression, its application in EEG signal processing is limited due to its noise unawareness during transmission and time-consuming reconstruction procedure. In this paper, we propose a noise-aware sparse Bayesian learning approach with block structure (NA-BSBL) to achieve higher efficiency on data compression, reconstruction and classification. By applying novel structure for parameter and introducing the Mahalanobis Distance, our approach achieves an almost 20% reconstruction performance lift and 10% accuracy lift under noise condition. For further application of reconstructed EEG signal, we extract both the spatial and frequency domain features for classification. Experimental results show that the proposed approach can achieve 94% classification accuracy with 16% speed up compared with the conventional approach.     

Compressively sampled light field reconstruction using orthogonal frequency selection and refinement

This paper considers the compressive sensing framework as a way of overcoming the spatio-angular trade-off inherent to light field acquisition devices. We present a novel method to reconstruct a full 4D light field from a sparse set of data samples or measurements. The approach relies on the assumption that sparse models in the 4D Fourier domain can efficiently represent light fields. The proposed algorithm reconstructs light fields by selecting the frequencies of the Fourier basis functions that best approximate the available samples in 4D hyper-blocks. The performance of the reconstruction algorithm is further improved by enforcing orthogonality of the approximation residue at each iteration, i.e. for each selected basis function. Since sparsity is better preserved in the continuous Fourier domain, we propose to refine the selected frequencies by searching for neighboring non-integer frequency values. Experiments show that the proposed algorithm yields performance improvements of more than 1 dB compared to state-of-the-art compressive light field reconstruction methods. The frequency refinement step also significantly enhances the visual quality of reconstruction results of our method by a 1.8 dB average.     

Strain rate effects on compressive behavior of covalently bonded CNT networks

In this study, strain rate effects on the compressive mechanical properties of randomly structured carbon nanotube (CNT) networks were examined. For this purpose, three-dimensional atomistic models of CNT networks with covalently-bonded junctions were generated. After that, molecular dynamics (MD) simulations of compressive loading were performed at five different strain rates to investigate the basic deformation characteristic mechanisms of CNT networks and determine the effect of strain rate on stress–strain curves. The simulation results showed that the strain rate of compressive loading increases, so that a higher resistance of specimens to deformation is observed. Furthermore, the local deformation characteristics of CNT segments, which are mainly driven by bending and buckling modes, and their prevalence are strongly affected by the deformation rate. It was also observed that CNT networks have superior features to metal foams such as metal matrix syntactic foams (MMSFs) and porous sintered fiber metals (PSFMs) in terms of energy absorbing capabilities.     

Relation between interfacial shear strength and compressive strength of carbon fiber/epoxy resin composite strands

The mechanism with which the fiber-matrix interfacial strength exerts its influence on the compressive strength of fiber reinforced composites has been studied by measuring the axial compressive strength of carbon fiber/epoxy resin unidirectional composite strands having different levels of interfacial shear strength. The composite strands are used for experiments in order to investigate the compressive strength which is not affected by the delamination taking place at a weak interlayer of the laminated composites. The interfacial strength is varied by applying various degrees of liquid-phase surface treatment to the fibers. The efficiency of the compressive strength of the fibers utilized in the strength of the composite strands is estimated by measuring the compressive strength of the single carbon filaments with a micro-compression test. The compressive strength of the composite strands does not increase monotonically with increasing interfacial shear strength but showes lower values at higher interfacial shear strengths. With increasing interfacial shear strength, the suppression of the interfacial failure in the misaligned fiber region increases the compressive strength, while at higher interfacial shear strengths, the enhancement of the crack sensitivity decreases the compressive strength.     

确定性矩阵可分离压缩成像

针对可分离压缩传感使用的可分离随机正交矩阵在处理大尺度图像等高维信号感知时难度太大或成本过高的问题,引入确定性测量矩阵,提出确定性矩阵可分离压缩传感,可将如托普利兹矩阵及循环矩阵等具有确定性结构的矩阵作为可分离压缩传感的左、右可分离矩阵.该方案可以降低独立元素的数目,从而显著降低前端物理实现的难度与成本.数值模拟实验分别评估了该方法在不同采样率及不同图像尺寸下的压缩重建性能,结果表明该方法在独立元素非常少的情形下得到与原随机正交矩阵相近的重建质量,证明了其可行性.     

基于Laplace先验的Bayes压缩感知波达方向估计

基于多任务贝叶斯压缩感知(BCS)理论,该文提出一种使用Laplace先验的目标到达角(DOA)估计算法。该算法利用阵元输出为观测值,将DOA估计转化为Laplace先验约束下的BCS求解稀疏信号问题,使用Laplace先验获得比传统BCS更好的稀疏性。该算法不需要信源个数的先验信息和进行特征值分解,能够适应相干信源场景,仿真结果表明该算法具有比传统BCS方法和经典MUSIC算法更好的DOA估计性能。     

1-Bit压缩感知盲重构算法

1-Bit压缩感知(CS)是压缩感知理论的一个重要分支。该领域中二进制迭代硬阈值(BIHT)算法重构精度高且一致性好,是一种有效的重构算法。该文针对BIHT算法重构过程需要信号稀疏度为先验信息的问题,提出一种稀疏度自适应二进制迭代硬阈值算法,简称为SABIHT算法。该算法修正了BIHT算法,首先通过自适应过程自动调节硬阈值参数,然后利用测试条件估计信号的稀疏度,最终实现不需要确切信号稀疏度的1-Bit压缩感知盲重构。理论分析和仿真结果表明,该算法较好地实现了未知信号稀疏度的精确重建,并且与BIHT算法相比重构精度及算法复杂度均相当。     

基于压缩感知及光学理论的图像信息加密

针对信息加密系统中信息安全性不理想的问题,提出一种基于压缩感知的光学图像信息加密方法.在发送端,自然图像经稀疏表示、随机投影实现图像信息加密;然后将降维后的观测值通过4F双随机相位编码光学系统进行二次加密并将其融入宿主图像,实现信息加密及隐藏.在接收端,图像信息经双随机相位编码技术解码,通过正交匹配追踪算法实现原始图像信息重构.该系统能有效降低数据传输量、减小随机相位板大小.且收发方只需按照规则生成密钥而不需传输密钥,保证了密钥的安全性.仿真结果表明:解密恢复图像质量理想,峰值信噪比为30.899 1dB,且系统能较好地抵抗裁剪、噪音污染、高通滤波、旋转等攻击,鲁棒性强,安全性高.