A novel integrated silicon capacitive microphone-floating electrode“electret” microphone (FEEM)

A novel principle “electret” microphone, i.e., floating electrode electret microphone, is proposed and implemented in this study. Single-chip fabrication and corrugation technique are used in the design and fabrication of the microphone. The floating electrode is encapsulated by highly insulated materials to ensure that there is no electric-leakage passage between the floating electrode and the electrodes of the microphone. Net-free electronic charges (not “bonded” charges as in traditional electret) in the floating electrode can excite the electric field, which is similar to that of the traditional electret. The floating electrode can be easily charged by use of the “hot” electron technique, available using the avalanche breakdown of the p⁺-n junction. Therefore, the electret microphone is rechargeable, which can greatly increase the lifetime of the device. The preamplifier has been on-chip integrated in a junction-field-effect transistor (JFET) source-follower type with resistors by use of ion implantation. Electret charges are banded in a deep potential trap, thus, this microphone can operate at a high temperature (as high as 300°C) and has high stability and reliability. Experiments show that the prototype has a 3-mV/Pa sensitivity and a larger than 21-kHz frequency bandwidth in a 1 mm ×1-mm diaphragm area. Microphone performance can be further improved by optimized process and design. The fabrication is completely integrated-circuit (IC) compatible, hence, the microphone shows promise in integrated acoustic systems     

The super-connected property of recursive circulant graphs

In a graph G, a k-container C_k(u,v) is a set of k disjoint paths joining u and v. A k-container C_k(u,v) is k∗-container if every vertex of G is passed by some path in C_k(u,v). A graph G is k∗-connected if there exists a k∗-container between any two vertices. An m-regular graph G is super-connected if G is k∗-connected for any k with 1?k?m. In this paper, we prove that the recursive circulant graphs G(2^m,4), proposed by Park and Chwa [Theoret. Comput. Sci. 244 (2000) 35-62], are super-connected if and only if m≠2.     

网络化测控设备间的时间同步

为使广域网络范围内的测控设备同步工作，必须准确的计算出网络数据传输的时延。依据排队论，本文提出并证明了准确的网络节点模型和数据延时分布统计模型，以此为基础计算出数据报在互联网上传输的精确时延并给出时延修正方法。实验数据论证了模型的正确性。最后提出了广域自动测试系统内设备的同步解决方案，时间同步精度在10～50μs范围内。     

有色噪声激发下结构随机振动离散分析方法

本文以线性结构体系在白噪声激发下随机振动离散分析方法为基础,对线性结构体系在有色噪声激发下的随机振动离散分析方法进行了研究。给出均值、均方响应的离散递推式.并为了能在计算机上进行实际运算,给出了实用化计算公式,最后用该方法对一结构进行了实际计算,分析了计算结果。     

随机振动离散分析方法的实用化处理

通过作者前文[2]的讨论,我们得到了随机振动离散分析方法的一个具有无条件稳定性和高精确度的递推公式—β递推格式。但由于在这个递推格式中存在一个增广矩阵求逆的问题,这就给它在实际应用中带来了一定的困难。本文通过等效变换,成功地克服了这一困难,避免了这个增广矩阵求逆,并完全应用了原动力体系质量阵、阻尼阵和刚度阵的对称、带状等特点,大大减少了计算所需的内存和工作量,从而使该方法很实用。     

电动缸式钢包长水口机械手的技术改造

介绍了宝钢5号连铸机钢包长水口机械手的设备构成及技术性能,阐述设备长时间运行所存在的问题,针对问题提出了设备改进的措施。     

氧化锰矿直接浸出制备硫酸锰的几个影响因素

根据云南建水锰矿一年多来的生产实践，就氧化锰矿直接浸出制备硫酸锰工业生产的几个影响因素，进行了探讨并提出了提高浸出回收率的措施。     

多波束条带测深中的声线跟踪技术

海洋环境里声速随着盐度、温度和深度以及其他因素的变化而变化，导致声波并不沿直线传播，利用声波到达海底某区域的往返时间和声线离换能器的出射角计算该区域的海底深度时，如果采用固定声速，则必然带来误差。为提高测深精度，有必要进行声速补偿，为此，研究一种在深度方向上采用声线跟踪技术进行补偿以提高海底深度测量精度的方法，叙述了射线跟踪的基本原理，进行了计算机仿真，并利用海试数据进行了相应的处理。结果表明，依据声速的变化进行声线跟踪可以大大提高测深的精度。     

A general stochastic clustering method for automatic cluster discovery

Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the number of natural clusters hidden in the dataset. The problem is exacerbated when there is little or no additional information except the data itself. This paper proposes a general stochastic clustering method that is a simplification of nature-inspired ant-based clustering approach. It begins with a basic solution and then performs stochastic search to incrementally improve the solution until the underlying clusters emerge, resulting in automatic cluster discovery in datasets. This method differs from several recent methods in that it does not require users to input the number of clusters and it makes no explicit assumption about the underlying distribution of a dataset. Our experimental results show that the proposed method performs better than several existing methods in terms of clustering accuracy and efficiency in majority of the datasets used in this study. Our theoretical analysis shows that the proposed method has linear time and space complexities, and our empirical study shows that it can accurately and efficiently discover clusters in large datasets in which many existing methods fail to run.