基于DSP的矿井光纤直放系统的设计

根据煤矿井下巷道狭长、拐点多、分支多的特点,针对井下环境中移动通信系统通话质量差、可靠性低等问题,提出了一种基于DSP的矿井光纤直放系统的设计方案,详细介绍了系统基于DSP的自动监控单元和FIR数字滤波器的设计及语音激活技术在直放系统中的应用。该光纤直放系统解决了井下广域范围移动通信问题,采用DSP对系统实施自动监控,为系统稳定运行提供了可靠保障;数字滤波器的引入、话音激活技术的应用使井下通信系统性能更稳定、话音质量更好、抗干扰性更强,为矿井移动通信的最佳实现奠定基础。     

A hybrid data-fusion system using modal data and probabilistic neural network for damage detection

This paper addresses a novel hybrid data-fusion system for damage detection by integrating the data fusion technique, probabilistic neural network (PNN) models and measured modal data. The hybrid system proposed consists of three models, i.e. a feature-level fusion model, a decision-level fusion model and a single PNN classifier model without data fusion. Underlying this system is the idea that we can choose any of these models for damage detection under different circumstances, i.e. the feature-level model is preferable to other models when enormous data are made available through multi-sensors, whereas the confidence level for each of multi-sensors must be determined (as a prerequisite) before the adoption of the decision-level model, and lastly, the single model is applicable only when data collected is somehow limited as in the cases when few sensors have been installed or are known to be functioning properly. The hybrid system is suitable for damage detection and identification of a complex structure, especially when a huge volume of measured data, often with uncertainties, are involved, such as the data available from a large-scale structural health monitoring system. The numerical simulations conducted by applying the proposed system to detect both single- and multi-damage patterns of a 7-storey steel frame show that the hybrid data-fusion system cannot only reliably identify damage with different noise levels, but also have excellent anti-noise capability and robustness.     

基于感应无线的高分辨率位置检测系统研究

为了提高移动机车位置检测精度,提出了一种感应无线位置的检测方法.该方法采用一条独特的编码电缆,利用电磁感应原理,建立了发送线圈与编码电缆之间的信息传输系统,并通过检测感应电动势的相位和幅度,得到移动机车的位置.分析了感应无线检测的原理和方法,推导出了位置检测公式,并进行相关试验.试验结果表明,感应无线位置检测分辨率为2mm,检测精度达到了国际先进水平,可广泛应用于移动机车自动定位中.     

A new strategy for defect inspection by the virtual inspection in semiconductor wafer fabrication

The defect of process equipments is a major factor that impairs the yields in the mass production of semiconductor wafer fabrication and it is a main supervision means to use high-resolution defect inspection tools to detect and monitor the defect damage. Due to the high investment costs of these inspection tools and the resulting decrease in the throughput, how to improve the sampling rate is an important issue for the associated inspection strategy. This paper proposes a new concept and implementation of virtual inspection (VI) to enhance the detection and monitoring of defect in semiconductor production process. The underlying theory of the VI concept is that the state variables identifications (SVIDs) of process equipments can reflect the process quality effectively and loyally. The approach of VI is to combine the application of the fault detection and classification (FDC), and the defect library and the re-engineering of inspection procedure to reach the full-scope of strategic objective. VI enables the defect monitoring to enter a new era by promoting the monitoring level of defect inspection from the previous lot-sampling basis to the wafer-sampling level, and hence upgrades the sampling strategy from random-sampling to full and right-sampling. In this study, various typical defect cases are utilized to illustrate how to create VI models and verify the reliability of the proposed approach. Furthermore, a feasible architecture of the VI implementation for mass production in semiconductor factory is presented in the paper.     

PIRF-Nav 2.0: Fast and online incremental appearance-based loop-closure detection in an indoor environment

This paper presents a fast and online incremental solution for an appearance-based loop-closure detection problem in a dynamic indoor environment. Closing the loop in a dynamic environment has been an important topic in robotics for decades. Recently, PIRF-Nav has been reported as being successful in achieving high recall rate at precision 1. However, PIRF-Nav has three main disadvantages: (i) the computational expense of PIRF-Nav is beyond real-time, (ii) it utilizes a large amount of memory in the redundant process of keeping signatures of places, and (iii) it is ill-suited to an indoor environment. These factors hinder the use of PIRF-Nav in a general environment for long-term, high-speed mobile robotic applications. Therefore, this paper proposes two techniques: (i) new modified PIRF extraction that makes the system more suitable for an indoor environment and (ii) new dictionary management that can eliminate redundant searching and conserve memory consumption. The results show that our proposed method can complete tasks up to 12 times faster than PIRF-Nav with only a slight percentage decline in recall. In addition, we collected additional data from a university canteen crowded during lunch time. Even in this crowded indoor environment, our proposed method has better real-time processing performance compared with other methods.     

Automatic detection of breast cancers in mammograms using structured support vector machines

Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.     

A novel, electrically protein-manipulated microcantilever biosensor for enhancement of capture antibody immobilization

This study demonstrated a microcantilever biosensor for enhancement of capture antibody immobilization. The electrically protein-manipulated, microcantilever biosensor is featured with enhanced capture antibody immobilization, miniaturization, and high sensitivity. Thanks to the electric property of biological substances in a real environment, given charged proteins can be manipulated with attraction in solution under an electric field. It is evident that higher amount of capture antibody molecules immobilized onto sensing surfaces captures or detects specific molecules, indicating greater deflection and stresses as well. This however leads to significant cost in biosensors. With the merit of MEMS technique that allows highly fabrication-compatible integration into microcantilever biosensors, sparsely distributed antibody molecules in solution are attracted in focus onto a sensing solid surface under electric fields. As the sensing element of the gold-coated, V-shaped silicon nitride microcantilever also serves as an electrode, the electric fields are applied in a channel of flowing microfluidics by locally in-plane electrodes or by a top electrode arranged for three-dimensional fields. As expected, most charged proteins distributed in solution are effectively attracted onto the sensing area within the electric fields. This improves the efficiency of capture antibody immobilization and achieves an eight-fold reduction over the necessary amount of capture antibodies without applying electric fields. With such a successful manipulation of charged proteins, the novel microcantilever biosensor exhibits efficient use of capture antibodies in solution.     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

一种基于W7100A的实验室仪器信息采集系统

针对实验室仪器信息统计中的准确性、完整性欠缺及低效率等问题,设计了一种全新的实验室仪器信息采集系统,以低功耗网络微处理器W7100A为系统的主控制器,通过SHT11温湿度传感模块和ZLG500AT射频IC模块直接采集仪器的环境参数等信息,通过用户与上位机客户端软件交互的方式采集仪器的工作内容等信息,相较于传统的手工记录方式,信息采集的及时性、完整性、可靠性方面都有了极大的提高.