针对语音变换的语音篡改检测

针对使用语音变换技术的语音篡改,提出一种自动检测方法。在分析语音变换基本模型和变换语音失真的基础上,提取语音信号的声道参数以及相关的信号统计量,并通过支持向量机递归特征消除法,选择出对语音变换比较敏感的特征作为分类特征,使用支持向量机进行语音变换检测和变换语音的说话人性别判别。对于一种语音变换软件的实验结果表明,该方法具有较高的检测准确率,其中语音变换检测的平均准确率为94.90%,变换语音的说话人性别判别平均准确率为92.09%。     

基于LabVIEW的超声信号小波阈值去噪方法

图形化编程软件LabVIEW具有强大的信号采集功能,但对于需要进行大量数据运算处理的复杂应用就显得力不从心。数学工具软件MATLAB强大的计算功能及较高的编程效率可以弥补LabVIEW开发的不足。文中结合两者软件的优点,采用混合编程的方法进行实际工程的去噪处理,同时针对超声缺陷回波信号信噪比低、易于被噪声淹没的特点,选取小波阈值去噪的方法,通过调用LabVIEW里的MATLAB Script节点对信号进行仿真实验,仿真结果表明采用stein的无偏风险阈值去噪的方法消噪效果最为明显,得到较高的信噪比。     

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.     

LearningADD: Machine learning based acoustic defect detection in factory automation

Defect inspection of glass bottles in the beverage industrial is of significance to prevent unexpected losses caused by the damage of bottles during manufacturing and transporting. The commonly used manual methods suffer from inefficiency, excessive space consumption, and beverage wastes after filling. To replace the manual operations in the pre-filling detection with improved efficiency and reduced costs, this paper proposes a machine learning based Acoustic Defect Detection (LearningADD) system. Moreover, to realize scalable deployment on edge and cloud computing platforms, deployment strategies especially partitioning and allocation of functionalities need to be compared and optimized under realistic constraints such as latency, complexity, and capacity of the platforms. In particular, to distinguish the defects in glass bottles efficiently, the improved Hilbert-Huang transform (HHT) is employed to extend the extracted feature sets, and then Shuffled Frog Leaping Algorithm (SFLA) based feature selection is applied to optimize the feature sets. Five deployment strategies are quantitatively compared to optimize real-time performances based on the constraints measured from a real edge and cloud environment. The LearningADD algorithms are validated by the datasets from a real-life beverage factory, and the F-measure of the system reaches 98.48 %. The proposed deployment strategies are verified by experiments on private cloud platforms, which shows that the Distributed Heavy Edge deployment outperforms other strategies, benefited from the parallel computing and edge computing, where the Defect Detection Time for one bottle is less than 2.061 s in 99 % probability.     

基于SPOT-5多光谱影像的矿区塌塘水体提取方法研究

针对基于遥感影像的水体提取方法存在水体提取不完整和误提的现象,提出了一种基于SPOT-5多光谱影像的矿区塌塘水体提取方法。在利用波段合成增加一个可用波段的基础上对已有的水体提取方法进行适当的改进,并基于决策树分类器和改进后的方法进行矿区水体的四级提取,保证了水体提取的完整性,同时减少了误提率;最后利用实测数据对水体提取的精度进行了评定。试验结果表明,基于决策树分类器的水体提取方法具有较高的精度,能满足矿区实际应用的需要。     

矿用隔爆兼本安型计算机信号隔离器设计

针对煤矿井下电气设备要求本质安全的特点,设计了一种矿用隔爆兼本安型计算机信号隔离器,主要介绍了该隔离器的硬件电路设计。该隔离器主要用于矿井中本质安全型计算机主机与外部输入输出设备的连接,能够实时地传送键盘输入的数据,具有良好的信号隔离功能和抗干扰性能。     

基于CPLD的某型导弹测角仪视频信号模拟

针对导弹检测系统的训练和计量,采用CPLD技术设计了某型导弹电视测角仪视频信号模拟系统;介绍了测角仪的视频显示方式和模拟信号产生原理,并给出了CPLD主要的内部逻辑电路和VHDL语言描述;将视频信号分解为同步信号、固定图像和可控实心圆,以行计数器和列计数器构成图像的时间基准,通过对行、列计数器的编码实现同步信号和固定图像,通过对可逆计数器及行、列计数器的运算实现可控实心圆,并对可逆计数器的数据进行编码构成变焦数据,最终精确地实现了测角仪视频信号及其变焦数据输出的模拟。     

机载塔康设备综合检查仪设计

使用市场上成熟的模块和技术研制机载塔康设备综合检查仪,以降低成本和提高设备的可靠性;根据设备的测试需求,选用嵌入式工业平板电脑、USB-ARINC429通讯卡、USB-数字IO卡、串行接口数字表和自研的塔康信号模拟器等构成检查仪硬件,使用CVI9.0编写了测试程序;应用表明:检查仪集成度高,使用方便,能够完成对塔康设备的自动及手动测试。     

Blind estimation of the PN sequence in lower SNR DS-SS signals with residual carrier

This paper presents a method of singular value decomposition (SVD) plus digital phase lock loop (DPLL) to solve the difficult problem of blind pseudo-noise (PN) sequence estimation in low signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS, DS) signals with residual carrier. Of course, the method needs to know the parameters of DS signal, such as the period and code rate of PN sequence. Firstly, the received signal is sampled and divided into non-overlapping signal vectors according to a temporal window, whose duration is two periods of PN sequence. Then, an autocorrelation matrix is computed and accumulated by the signal vectors one by one. The PN sequence with residual carrier can be estimated by the principal eigenvector of this autocorrelation matrix. Furthermore, a DPLL is used to deal with the estimated PN sequence with residual carrier, it estimates and tracks the residual carrier, removes the residual carrier in the end. Theory analysis and computer simulation results show that this method can effectively realize the PN sequence estimation from the input DS signals with residual carrier in lower SNR.     

Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.