Stereo audio source separation based on time–frequency masking and multilevel thresholding

Source separation and up-mixing in real commercial music recordings is a challenging problem. In the last few years, some algorithms have provided interesting results, but the problem remains unsolved. In this paper we describe a method for separating the sources present in a two channel mixture based on the panning coefficients used in the stereo mixdown. The sources are separated by estimating time–frequency masks using the multilevel extension of the Otsu thresholding algorithm used in image segmentation. A refinement step is also carried out for extraction and reassignment of inter-source residuals. Examples of application and performance evaluation are also discussed.     

The need for considering multilevel analysis in CSCL research—An appeal for the use of more advanced statistical methods

Per definition, CSCL research deals with the data of individuals nested in groups, and the influence of a specific learning setting on the collaborative process of learning. Most well-established statistical methods are not able to analyze such nested data adequately. This article describes the problems which arise when standard methods are applied and introduces multilevel modelling (MLM) as an alternative and adequate statistical approach in CSCL research. MLM enables testing interactional effects of predictor variables varying within groups (for example, the activity of group members in a chat) and predictors varying between groups (for example, the group homogeneity created by group members’ prior knowledge). So it allows taking into account that an instruction, tool or learning environment has different but systematic effects on the members within the groups on the one hand and on the groups on the other hand. The underlying statistical model of MLM is described using an example from CSCL. Attention is drawn to the fact that MLM requires large sample sizes which are not provided in most CSCL research. A proposal is made for the use of some analyses which are useful.     

阈值分割与颜色聚类相结合的色度学图象分割方法

根据真彩色图象象素在3D彩色空间呈现聚类分布的特点,可利用基于直方图的多维空间滤波技术得到目标图象,但会产生丢失目标信息的过度分割现象。采用颜色聚类方法改善此现象,在保证算法速度的基础上,大大提高了真彩色图象的分割效果,并对不同彩色模型和聚类中心有较好的鲁棒性,在医学染色图象处理中得到很好应用。     

基于多层快速多极子和有限元混合求解的飞行器天线布局设计

设计一种微带天线,以该天线为基础进行飞行器天线布局设计,并考虑遮挡的判断问题。模型仿真采用多层快速多极子和有限元的方法混合求解,设置混合场积分方法提高收敛性,解决了电大尺寸模型复杂电磁场的计算问题,兼顾运算速度和精度,得出并分析飞行器上3天线、4天线、5天线以不同方式布局的远场方向图仿真结果,并提出一种方向图评估法则,对飞行器天线布局有一定参考意义。     

Harmonic elimination in diode-clamped multilevel inverter using evolutionary algorithms

This paper describes two evolutionary algorithms for the optimized harmonic stepped–waveform technique. Genetic algorithms and particle swarm optimization are applied to compute the switching angles in a three-phase seven-level inverter to produce the required fundamental voltage while, at the same time, specified harmonics are eliminated. Furthermore, these algorithms are also used to solve the starting point problem of the Newton-Raphson conventional method. This combination provides a very effective method for the harmonic elimination technique. This strategy is useful for different structures of seven-level inverters. The diode-clamped topology is considered in this study.     

一种具有自修复功能的多电平变换器拓朴

从提高复杂多电平变换器的可靠性出发，提出了一种具有类似生物系统自修复功能的多电平变换器拓扑。电路拓扑工作原理的分析结果表明，该拓扑在正常工作情况下所有开关管均承受单位电平的电压应力，并且以较少的飞跨电容实现每个电压电平的自动平衡。该拓扑不需要任何复杂的中点电位平衡电路或控制措施。文中详细分析了电路中开关管故障对多电平变换器系统运行和器件电压应力的影响。分析结果表明，当某些开关管出现短路或断路故障时，通过简单的软件重构改变驱动信号，利用冗余开关状态组合，电路仍能得到所有电平的正常输出和直流侧电容的电压自平衡，真正实现了原有功能的自修复。整个多电平变换器系统的可靠性由此提高。文中以五电平变换器为例进行了分析研究，并通过仿真实验进行验证。     

一种新型单相不对称五电平逆变器

该文对一种新型单相不对称五电平逆变器进行研究，这种新型逆变器是通过将半桥二极管钳位逆变器和传统两电平半桥逆变器共享同一直流母线而获得的。针对这种新型拓扑结构该文分析了一种方波合成与消谐波三角载波PWM相结合的控制方法，在这种控制方法下，新型逆变器允许开关速度较快的器件和耐压值较高的器件工作在一起。文中将这种逆变器与其它三种单相五电平逆变器进行了比较。最后本文对一个单相五电平不对称逆变器电路进行了实验验证，并给出相关结论。     

High-quality low-complexity wavelet-based compression algorithm for audio signals

Wavelets have recently emerged as a powerful tool for signal compression, particularly in the areas of image, video, and audio compression. In this paper, we present a low-complexity wavelet-based audio compression algorithm that is capable of handling fairly arbitrary audio sources. The algorithm transforms the incoming audio data into the wavelet domain, and compresses data by exploring redundancy in the wavelet coefficients and exploiting the large runs of zeros in the transformed signal. Also there is a possibility of applying a threshold to the non-zero coefficients, thus a further increase in the number of zeros is expected. The audio signal is first preprocessed to scale down the wavelet coefficients. Then the preprocessed signal is wavelet transformed using a bi-orthogonal discrete wavelet transform (DWT) and threshold by applying energy compaction strategy. Encoding represents the threshold coefficients in compact form. A new encoding technique that is easy to implement, and that provides a reasonable compression ratio for a certain acceptable distortion level has been developed to encode the threshold DWT. So, a bit rate can be controlled such that the algorithm operates at virtually any pre-selected bit rate. The motivation of using the bi-orthogonal wavelet transform is that it permits the use of a much broader class of filters, and this class includes symmetric linear phase filters. The superior performance of this algorithm is also demonstrated by comparing it with two other popular audio compression techniques and this meets the requirements of multimedia computing.