一种小波域音频信息隐藏方法

提出了一种基于量化的小波域音频隐藏算法，将保密语音隐藏到载体音频中．为提高隐藏重和保密语音传输的安全性，对保密语音进行了小波域压缩编码和m序列的扩频调制，生成待隐藏的比特序列；通过量化方法，将编码和调制后的保密语音隐藏到载体音频的小波系数中；保密语音的恢复过程不需要使用原始音频、仿真结果表明，隐藏有保密语音的载体音频听觉质量没有明显下降，提取的保密语音感知质量较好；该算法对重量化、加噪、低通滤波等攻击均有良好的鲁棒性．     

机器人头部的视觉跟踪系统研究

对运动目标进行识别和跟踪是机器人视觉的一项关键技术。提出了一种可以快速跟踪运动目标的系统设计方案,采用了双计算机处理和Memolink通信方式的机器人控制结构,以图像处理为基础,在图像经过校正之后,通过区域分割和目标识别两个步骤来确定目标的位置,并控制机器人对目标进行跟踪。实验表明,该方法具有较高的速度和准确性。     

一种基于代价函数和模糊熵的图像分割方法

提出了一种基于代价函数和模糊熵的图像分割方法.该方法先用代价函数最小化法对退化的图像作预处理,之后,利用模糊熵作进一步的处理.实验结果表明:新方法和一般的阈值分割方法相比,不但分割图像的错分率较小,而且图像的视觉效果也有较大的改善.     

A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.     

An Improvement to Conformer-Based Model for High-Accuracy Speech Feature Extraction and Learning

Owing to the loss of effective information and incomplete feature extraction caused by the convolution and pooling operations in a convolution subsampling network, the accuracy and speed of current speech processing architectures based on the conformer model are influenced because the shallow features of speech signals are not completely extracted. To solve these problems, in this study, we researched a method that used a capsule network to improve the accuracy of feature extraction in a conformer-based model, and then, we proposed a new end-to-end model architecture for speech recognition. First, to improve the accuracy of speech feature extraction, a capsule network with a dynamic routing mechanism was introduced into the conformer model; thus, the structural information in speech was preserved, and it was input to the conformer blocks via sequestered vectors; the learning ability of the conformed-based model was significantly enhanced using dynamic weight updating. Second, a residual network was added to the capsule blocks, thus, the mapping ability of our model was improved and the training difficulty was reduced. Furthermore, the bi-transformer model was adopted in the decoding network to promote the consistency of the hypotheses in different directions through bidirectional modeling. Finally, the effectiveness and robustness of the proposed model were verified against different types of recognition models by performing multiple sets of experiments. The experimental results demonstrated that our speech recognition model achieved a lower word error rate without a language model because of the higher accuracy of speech feature extraction and learning using our model architecture with a capsule network. Furthermore, our model architecture benefited from the advantage of the capsule network and the conformer encoder, and also has potential for other speech-related applications.     

应用联合分析和混合回归模型进行市场细分

联合分析是一种有效地反映消费者需求差异的方法,所以被广泛地应用到市场细分的研究中。但是,传统的方法存在着一定的不足。本研究提出了一个残差分布假设不同的混合回归模型,模型估计的效率比较高,而且模型系数也必较可靠。所以不失为一个比较理想的市场细分分析工具。本文应用该模型方法对一个笔记本电脑联合分析案例进行了实证分析。     

3D Multiphase Piecewise Constant Level Set Method Based on Graph Cut Minimization

Segmentation of three-dimensional (3D) complicated structures is of great importance for many real applications. In this work we combine graph cut minimization method with a variant of the level set idea for 3D segmentation based on the Mumford-Shah model. Compared with the traditional approach for solving the Euler-Lagrange equation we do not need to solve any partial differential equations. Instead, the minimum cut on a special designed graph need to be computed. The method is tested on data with complicated structures. It is rather stable with respect to initial value and the algorithm is nearly parameter free. Experiments show that it can solve large problems much faster than traditional approaches.     

基于等阻抗差分段法的变阻抗线电路模拟

新型Z箍缩驱动源系统采用变阻抗线结构形式的径向阻抗变换器进行能量传递和变换,为了获得最大的能量效率,需要应用电路模拟方法研究变阻抗线传递功率的特性。采用了等阻抗差分段法对变阻抗线进行了建模计算,与以往的等长度分段方法比较,在相同的分段数量下,等阻抗差分段法在阻抗变化的连续性上优于等长度分段法。给出了具有指数形式的变阻抗线的算例,分别采用两种分段法进行了计算,对计算结果的比较表明:等阻抗差分段法可以使计算更快地收敛到真实解。     

A high accuracy flow segmentation method in crowded scenes based on streakline

Flow segmentation based on similar motion patterns in crowded scenes remains an open problem in computer vision due to inherent complexity and vast diversity found in such scenes. To solve this problem, the streakline framework based on Lagrangian fluid dynamics had been proposed recently. However, this framework computed optical flow field using conventional optical flow method (Lucas Kanade method) which has poor anti-interference performance, and serious deviation would be brought to the computation of optical flow field. Moreover, our experimental results show that using the formulation of streak flow similarity in this framework can result in incorrect flow segmentation. Therefore, we combine this framework with a high accurate variational model, and modify the corresponding formulation of streak flow similarity after analyzing the streakline framework in detail. Finally, an improved method is proposed to solve flow segmentation in crowded scenes. Experiments are done to compare these two methods and results verify the validity and accuracy of our method.