Multi-level basis selection of wavelet packet decomposition tree for heart sound classification

Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.     

A method of tumor classification based on wavelet packet transforms and neighborhood rough set

Tumor classification is an important application domain of gene expression data. Because of its characteristics of high dimensionality and small sample size (SSS), and a great number of redundant genes not related to tumor phenotypes, various feature extraction or gene selection methods have been applied to gene expression data analysis. Wavelet packet transforms (WPT) and neighborhood rough sets (NRS) are effective tools to extract and select features. In this paper, a novel approach of tumor classification is proposed based on WPT and NRS. First the classification features are extracted by WPT and the decision tables are formed, then the attributes of the decision tables are reduced by NRS. Thirdly, a feature subset with few attributes and high classification ability is obtained. The experimental results on three gene expression datasets demonstrate that the proposed method is effective and feasible.     

Matrix Metamaterial Shielding Design for Wireless Power Transfer to Control the Magnetic Field

A wireless power transfer (WPT) system can bring convenience to human life, while a leakage magnetic field around the system can be harmful to humans or the environment. Due to application limitations of aluminum and ferrite materials, it is urgent to find a new type of shielding material. This paper first proposes a detailed model and analysis method of the matrix shielding metamaterial (MSM), which is applied to the low-frequency WPT system in an electric vehicle (EV). The MSM is placed on the top and side of the EV system to shield the magnetic field from all positions. To explore its function, a theoretical analysis of the MSM is proposed to prove the shielding performance. The simulation modeling and the design procedure of the MSM are introduced. Moreover, the prototype model of the WPT system with the MSM is established. The experimental results indicate that the magnetic field is controlled when the MSM is applied on the top or side of the EV-WPT system. The proposed MSM has been successfully proven to effectively shield the leakage magnetic field in the WPT system, which is suitable for the kHz range frequency.     

Recent advances in brain tumor-targeted nano-drug delivery systems

Introduction: Brain tumors represent one of the most challenging and difficult areas in unmet medical needs. Fortunately, the past decade has seen momentous developments in brain tumor research in terms of brain tumor-targeted novel nano-drug delivery systems with significant important superiority over conventional formulations with respect to decreased toxicity and improved pharmacokinetics/pharmacodynamics.

Area covered: This review first introduces the characteristics of the two major obstacles in brain-tumor targeted delivery, blood–brain barrier (BBB) and blood–brain tumor barrier (BBTB), and then reviews recent advances in brain tumor-targeted novel nano-drug delivery systems according to their targeting strategies aimed at different stages of brain tumor development and growth.

Expert opinion: Based on continuously changing vascular characteristics of brain tumors at different development and growth stages, we propose the concept of ‘whole-process targeting’ for brain tumor for nano-drug delivery systems, referring to a series of overall targeted drug delivery strategies aimed at key points during the whole development of brain tumors.     

基于小波包多尺度分析的感康近红外光谱快速分析方法的研究

目的 利用小波包多尺度分析去噪感康近红外光谱,建立有效测定感康中对乙酰氨基酚、盐酸金刚烷胺的近红外光谱分析模型.方法 使用最优分解方法和合适的阈值处理得到重构光谱的小波包分解系数,达到降噪的目的.结合偏最小二乘法(PLS)建立不同尺度分解重构光谱的感康定量分析模型.结果 模型的校正和预测精度在小波包尺度为3的时候最好,模型的相关系数对对乙酰氨基酚和盐酸金刚烷胺分别为0.995 95和0.913 07.交互验证均方与误差(RM-SECV)分别为0.005 62和0.006 86,预测均方与误差(RMSEP)分别为从0.005 79和0.007 88减小为0.004 78和0.00603,与原始光谱相比模型的稳定性和预测性能都得到了优化.结论 使用小波包变换的多尺度分析能够消除原始光谱的噪声,使建立定量分析的PLS模型具有更好的稳健性和预测精度.