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.     

基于灰度变换的红外图像实时分割算法

针对高灰度等级红外数字图像提出了一种基于灰度变换的实时分割算法.通过实时分析原始图像中几个小区域的灰度直方图,自动确定分割阈值,将原始图像通过灰度变换实时转换为低灰度等级图像.该算法能够将高灰度等级的红外数字图像实时显示在普通显示器上,并且能够有效抑制背景并保留目标灰度信息.由于变换后的图像灰度等级较低而且背景及噪音信息较弱,极大地提高了红外跟踪器的性能.     

Blind Speech Watermarking Method with Frame Self-Synchronization Based on Spread-Spectrum Using Linear Prediction Residue

State-of-the-art speech watermarking techniques enable speech signals to be authenticated and protected against any malicious attack to ensure secure speech communication. In general, reliable speech watermarking methods must satisfy four requirements: inaudibility, robustness, blind-detectability, and confidentiality. We previously proposed a method of non-blind speech watermarking based on direct spread spectrum (DSS) using a linear prediction (LP) scheme to solve the first two issues (inaudibility and robustness) due to distortion by spread spectrum. This method not only effectively embeds watermarks with small distortion but also has the same robustness as the DSS method. There are, however, two remaining issues with blind-detectability and confidentiality. In this work, we attempt to resolve these issues by developing an approach called the LP-DSS scheme, which takes two forms of data embedding for blind detection and frame synchronization. We incorporate blind detection with frame synchronization into the scheme to satisfy blind-detectability and incorporate two forms of data embedding process, front-side and back-side embedding for blind detection and frame synchronization, to satisfy confidentiality. We evaluated these improved processes by carrying out four objective tests (PESQ, LSD, Bit-error-rate, and accuracy of frame synchronization) to determine whether inaudibility and blind-detectability could be satisfied. We also evaluated all combinations with the two forms of data embedding for blind detection with frame synchronization by carrying out BER tests to determine whether confidentiality could be satisfied. Finally, we comparatively evaluated the proposed method by carrying out ten robustness tests against various processing and attacks. Our findings showed that an inaudible, robust, blindly detectable, and confidential speech watermarking method based on the proposed LP-DSS scheme could be achieved.     

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。     

结合隐藏技术的新颖语音检索算法

采用一种新颖的结合信息隐藏技术的语音检索算法,该算法将保密语音和伪语音分割成多段,利用经典的LSB算法分别隐藏到不同的音频,并存储在磁盘空间中．提取语音码流时,利用小波变换第一级细节分量系数能量的大小找到相应的载体音频,提取其中的语音码流,并建立语音库．利用特定人语音检索技术,检出相应的语音码流,合并语音码流得到真正的保密语音．研究表明：新算法的查准率和查全率可以达到很高的水平,为信息隐藏技术的发展开辟了一条新的研究道路,同样扩大了语音检索技术的应用范围。     

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.     

基于Faster-RCNN和Level-Set的桥小脑角区肿瘤自动精准分割

桥小脑角区（CPA）肿瘤的精准分割在手术治疗、放疗中有重要影响,本文结合更快速区域卷积神经网络（Faster-RCNN）和水平集（Level-Set）方法对CPA肿瘤的自动分割进行了研究.首先,采集317名CPA肿瘤患者的T₁WI-SE序列磁共振图像,使用基于Faster-RCNN主干网络VGG16提取特征,结合区域建议网络（RPN）进行学习训练,建立带有CPA肿瘤位置信息的定位模型,再应用Level-Set对肿瘤进行精准分割.本文对比了不同CPA肿瘤区域勾画范围对分割结果产生的影响,并以精确率、召回率、均值平均精度值（mAP）和戴斯系数（Dice系数）等指标评估了模型定位和分割的性能.实验结果表明,结合Faster-RCNN和Level-Set建立的模型能更有效对CPA肿瘤进行精准分割,减轻临床医生的负担,并提升治疗效果.     

基于改进U-Net的量子点STM图像分割

为提升量子点图像分割精度,降低特征识别误差,提出一种基于改进U-Net的量子点图像分割方法.首先,在预处理阶段,设计了以色彩通道为权值的灰度化算法,以提升后续分割效果.其次,在STM图像分割部分,在原始U-Net结构上引入中间过渡层以均衡网络各层特征.而后,建立数据集,并通过实验对比不同分割算法的精确度、召回率、F-measure.最后,将分割算法应用于量子点的特征识别,并测试了不同分割方式对应用的影响.实验结果显示,改进灰度化方法保留细节信息丰富,明显提升了量子点分割精度;改进U-Net的平均精确率、召回率、F-measure相较原始网络分别提升了13.83%、2.16%、8.13%.同时,实验数据表明由于分割精度的提升,量子点数量、纵横比等特征参数的识别更加精确.