高温熔融-电感耦合等离子体原子发射光谱法测定含铁尘泥中5种有害元素

准确、快速地测定含铁尘泥中有害元素,对含铁尘泥节能降废项目研究及综合利用有着重要的指导意义。实验针对含铁尘泥试样因含碳量高,导致常规酸溶解方法如果不增加除碳步骤则很难完全分解试样的问题,采用高温熔融法将试样制成熔融液后倾倒于稀酸中溶解的方法,解决了碳含量高难溶解的问题。采用四硼酸锂-碳酸锂混合熔剂在高温下将试样熔融,熔融液高温倾倒于稀酸中制成澄清溶液,由于骤冷,试样炸裂成微小玻璃珠,无迸溅,操作简单,使得后续溶解过程快速完成,从而建立了电感耦合等离子体原子发射光谱(ICP-AES)测定P、Pb、K、Na、Zn共5种有害元素的方法。按照实验方法测定含铁尘泥中P、Pb、K、Na、Zn,测定值的相对标准偏差(RSD,n=9)在1.4%～5.7%之间,加标回收率在96%～110%之间。研究结果表明方法能够满足含铁尘泥样品中5种元素的定量分析要求。     

Level of interest sensing in spoken dialog using decision-level fusion of acoustic and lexical evidence

Automatic detection of a user's interest in spoken dialog plays an important role in many applications, such as tutoring systems and customer service systems. In this study, we propose a decision-level fusion approach using acoustic and lexical information to accurately sense a user's interest at the utterance level. Our system consists of three parts: acoustic/prosodic model, lexical model, and a model that combines their decisions for the final output. We use two different regression algorithms to complement each other for the acoustic model. For lexical information, in addition to the bag-of-words model, we propose new features including a level-of-interest value for each word, length information using the number of words, estimated speaking rate, silence in the utterance, and similarity with other utterances. We also investigate the effectiveness of using more automatic speech recognition (ASR) hypotheses (n-best lists) to extract lexical features. The outputs from the acoustic and lexical models are combined at the decision level. Our experiments show that combining acoustic evidence with lexical information improves level-of-interest detection performance, even when lexical features are extracted from ASR output with high word error rate.     

Epileptic seizure classification in EEG signals using second-order difference plot of intrinsic mode functions

Epilepsy is a neurological disorder which is characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is a commonly used signal for detection of epileptic seizures. This paper presents a new method for classification of ictal and seizure-free EEG signals. The proposed method is based on the empirical mode decomposition (EMD) and the second-order difference plot (SODP). The EMD method decomposes an EEG signal into a set of symmetric and band-limited signals termed as intrinsic mode functions (IMFs). The SODP of IMFs provides elliptical structure. The 95% confidence ellipse area measured from the SODP of IMFs has been used as a feature in order to discriminate seizure-free EEG signals from the epileptic seizure EEG signals. The feature space obtained from the ellipse area parameters of two IMFs has been used for classification of ictal and seizure-free EEG signals using the artificial neural network (ANN) classifier. It has been shown that the feature space formed using ellipse area parameters of first and second IMFs has given good classification performance. Experimental results on EEG database available by the University of Bonn, Germany, are included to illustrate the effectiveness of the proposed method.     

Feature extraction of the first difference of EMG time series for EMG pattern recognition

This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from 18 subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2–8%.     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

Chemical properties of femoral gland secretions in the desert iguana,Dipsosaurus dorsalis

This study investigates the chemistry of femoral gland secretions in the desert iguana,Dipsosaurus dorsalis (Lacertilia: Iguanidae), and discusses their possible functional significance. Electrophoretic and proton NMR studies indicated that the secretions are composed of approximately 80 % protein and 20% lipid material. Individual differences in polyacrylamide gel banding patterns of femoral gland proteins were found. Reflectance spectroscopy revealed that the secretions strongly absorb longwave ultraviolet light, a feature that may be important in the localization of secretion deposits in the environment.     

基于自适应分块的动态场景HDR图像合成算法

通过提出基于梯度上升优化处理的自适应分块算法与基于图像序列梯度域运动检测与去除相结合的方法,自适应调整分块大小及动态区域块的融合权值,最终达到混淆去除的目的.同时,利用Gaussian中心函数窗口滤波,去除在分块融合过程中引入的块边缘不连续性痕迹.实验结果表明,该方法能快速有效地增强多曝光图像融合生成HDRI并去除混淆问题.     

基于差分相关的星载AIS信号帧同步

在星载AIS接收机中,船舶信号在传输过程中产生的大范围时延和多普勒频移,给正确解码带来了困难。针对这一问题,基于AIS帧结构特点,提出一种差分相关帧同步算法。算法中将AIS帧结构中训练序列、开始标志和结束标志设计为间隔式特征序列作为同步码,通过差分相关函数来实现帧同步。仿真实验结果表明,该算法有较好的抗频偏特性,且在低信噪比下,性能要优于差分卷积法。     

基于无线传感网的智能环境监控系统设计

针对传统环境监控系统存在的缺点,设计了一种新型无线传感器网络的环境监控系统。系统采用分布式融合结构,通过终端节点的多种传感器采集环境信息,终端对信息预处理后实时将信息传输到协调器。协调器融合各终端传感器信息,提取精确信息上传至融合中心,融合中心通过分析数据发布命令给控制中心,控制中心利用单片机驱动电机可以自动或手动控制风机和水泵对环境进行调节和控制。同时,系统还具有自动预警功能。通过验证,该系统稳定、可靠,能广泛应用于不同领域的分布式多点环境监控。