面向长距离量子通信的双光子关联成像技术

量子时域鬼成像利用光子对的时间-频率关联性实现了50km光纤链路上的图像传送,有望应用于量子通信的协议中。然而,现有方案的成像质量较差,成像速度较慢,因此其应用性在一定程度上受到了限制。文章对已有成像方案进行了较大的改进,利用可编程滤波器数字设定待成像信息,进而实现了二维图像信息的自动化实时传送。实验结果证明,改进方案的成像质量和速度均有显著改善,有利于推动量子关联成像在量子通信领域的应用。     

基于多尺度结构特征的快速异源图像匹配

针对异源图像提出一种基于多尺度密集结构特征的快速匹配算法。算法首先利用Gabor滤波器逐像素提取图像中的结构响应,再根据主方向响应对多尺度结构特征融合,然后使用快速傅里叶变换在频域计算各特征分量图像之间的卷积,最后将卷积生成的系数矩阵求和计算出图像之间的相似性并选择相似性最大位置作为匹配结果输出。本文算法能有效适应异源图像间的非线性灰度变化和噪声干扰问题。测试使用可见光、红外、雷达图像组成的异源图像数据集对本文算法和现有算法进行测试比较,结果表明:本文算法的平均误匹配率最低,并且计算速度有明显优势。     

滚动轴承振动加速度信号积分测试技术研究

滚动轴承出厂时需要根据国家标准进行振动检测。对滚动轴承的振动进行测量的方法主要是采用速度型测振和加速度型测振。由于速度型测振仪和加速度型测振仪所采用的传感器不同,两者的测量结果有相关性,但测量结果并不一致。传统的轴承测振仪采用模拟电路,对加速度信号进行准确积分,难度较大;随着计算机测量技术的迅速发展,运用数字量测试和积分技术,可以采用对加速度信号进行积分得到速度的方法进行测试。通过对轴承的加速度信号进行积分,进行振动速度测量,可以在同样的测试条件下对轴承的振动加速度和振动速度进行比较,具有实际应用价值。本文采用数字量测试技术,以深沟球轴承为研究对象,对加速度积分算法进行研究,利用MATLAB和Lab VIEW软件编写了数字滤波器和时域积分算法,减去了时域积分产生的趋势项,降低了趋势项误差。通过实际测量实验,所得结果与轴承行业所用的标准精密模拟积分器所得结果具有较好的一致性,可以满足轴承振动速度测量的需要。     

Improving oxidation resistance of porous FeAl-based intermetallics with high boron/yttrium alloying

The coalloying with high contents of chromium (Cr), boron (B) and yttrium (Y) for porous B2-structured FeAl intermetallics fabricated through reactive synthesis was conducted. The oxidation behaviors of porous FeAl-based materials were investigated by evaluating the pore-structure evolution, oxidation kinetics and oxide-scale configuration. The results show that with the coalloying of high contents of Cr, B and Y, the oxidation mass gains of porous FeAl materials at 600?800 °C are significantly reduced. The combination of B enriched on the surface of oxide scales and Y located at the scale?metal interface promotes the formation of thin protective nodular α-Al₂O₃ oxide scales. It is indicated that introducing relatively high contents of reactive elements such as B and Y can benefit the selective growth of α-Al₂O₃ scales at relatively low temperatures without pre-treatment.     

Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles of patients. There are two possible vein structures in each image: a cross-section (circular) and a tube (non-circular) structure. This proposed system identifies dilated (varicocele) veins of these structures in ultrasound images in three stages: preprocessing, processing, and detection and measurement. These three stages are applied in three different color modes: Grayscale, Red-Green-Blue (RGB), and Hue, Saturation, and Value (HSV). In the preprocessing stage, the region of interest enclosing the pampiniform plexus area is extracted using a median filter and threshold segmentation. Then, the processing stage employs different filters to perform image denoising. Finally, edge detection is applied in multiple steps and the detected veins are measured to determine if dilated veins exist. Overall implementation results showed the proposed system is faster and more effective than the previous work.     

Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings

Rolling element bearings (REBs) play an essential role in modern machinery and their condition monitoring is significant in predictive maintenance. Due to the harsh operating conditions, multi-fault may co-exist in one bearing and vibration signal always exhibits low signal-to-noise ratio (SNR), which causes difficulties in detecting fault. In the previous studies, maximum correlated kurtosis deconvolution (MCKD) has been validated as an efficient method to extract fault feature in the fault signals. Nonetheless, there are still some challenges when MCKD is applied to fault detection owing to the rigorous requirements of multiple input parameters. To overcome limitation, a multi-objective iterative optimization algorithm (MOIOA) for multi-fault diagnosis is proposed. In this method, correlated kurtosis (CK) is taken as a criterion to select optimal Morlet wavelet filter using the whale optimization algorithm (WOA). Meanwhile, to further eliminate the effect of the inaccurate period on CK, the update process of period is incorporated. After that, the simulated and experimental signals are utilized to testify the validity and superiority of the MOIOA for multiple faults detection by the comparison with MCKD. The results indicate that MOIOA is efficient to extract weak fault features even with heavy noise and harmonic interferences.     

融合重检测机制的卷积回归网络目标跟踪算法

针对基于人工特征的背景感知相关滤波（CACF）算法在形变、运动模糊、低分辨率情形跟踪效果较差以及跟踪器遇到严重遮挡等情形容易陷入局部最优而导致跟踪失败的问题，提出一种融合重检测机制的卷积回归网络（CRN）目标跟踪算法。在训练阶段，将相关滤波作为CRN层融入进深度神经网络，使网络成为一个整体进行端到端训练；在跟踪阶段，通过残差连接融合不同网络层及其响应值，同时引入重检测机制使算法从潜在的跟踪失败中恢复，当响应值低于给定阈值时激活检测器。在数据集OTB-2013上的实验表明，所提算法在50个视频序列上精确度达到88.1%，相比原始CACF算法提高9.7个百分点，在具有形变、运动模糊等属性的视频序列上相比原始算法表现更优秀。