感知

Experimental results demonstrate that our scheme is effective and useful, guaranteeing the inaudibility and robustness of the watermark.

实验结果表明，本方法保证了水印的不可感知性以及鲁棒性，且简单易行，因此具有较高的实用价值。

Aiming at the problem which is the robustness and inaudibility of the digital audio watermark is limited for each other, so an optimization watermarking method based on Genetic Algorithm which to compute the best energy quickly is proposed in this paper.

2针对数字音频水印的鲁棒性和不可感知性两者之间是相互制约问题，提出一种采用遗传算法(Genetic Algorithms，GA)来解决最优嵌入能量的优化水印方案。

Experimental results show that the two schemes can preserve inaudibility and they are robust enough to against the different signal processing operations. In addition, very large watermarking information can be embedded into the original audio signal. Furthermore, it can resist the cropping and MP3 compression attacks.

仿真实验结果表明，两种算法都具有较强的鲁棒性和不可感知性，水印嵌入容量大，提取水印时不需要原始音频信号的参与，并且对裁剪、时间伸缩等攻击具有较强的稳健性，是可行的数字音频水印算法。

So, a novel machine learning based digital blind audio watermarking scheme in the wavelet domain is proposed in this paper, which could find a reasonable balance between the robustness and inaudibility of the watermark, then have the better robustness and inaudibility.

本文正是将小波变换应用到音频信号的数字水印系统中，提出一种小波域上基于机器学习方法的新的数字音频盲水印算法，使其能在水印的鲁棒性和不可感知性之间寻找到合理的平衡点，从而具有更良好透明性和鲁棒性，并在音频作品的版权保护中能有一定的实用性。

Thefrequency analysis of the psychoacoustic model can give the inaudible, redundanelements of audio. In this paPer we employ it to gUarantee the inaudibility of theembedded watermark.

心理声学模型的时频域分析主要用于音频数据的压缩编码，这里主要考虑将其用于音频水印算法中去，以便从理论的角度来考虑嵌入水印的不可感知性。

The experimental results show the two practical algorithms can preserve inaudibility and they are robust enough to against the different signal processing operations. In addition, the watermarking information can be embedded into the original audio signal adaptively, which also doesnt require the use of the original audio signal for detection. Furthermore, it can resist the different attacks, such as lossy compression (MP3), filtering, resampling and requantizing, etc.

仿真实验结果表明，两种方法都具有较强的鲁棒性和不可感知性，水印嵌入容量的自适应性，提取水印时不需要原始音频信号的参与，且对包括MP3有损压缩、低通滤波、重采样/重量化等多种攻击性试验具有较强的稳健性，是可行的数字音频水印算法。

Because the genetic algorithms could search the optimization of objective function randomly, which via the evolution of groups, our work is based on the embedding/detecting way of "DWT-Based Audio Watermarking Using Support Vector Regression and Subsampling". And this method provides a preliminary discussion on the application of GA, which searching the individual with the highest fitness of attack-resistance in the aggregate of the optimal embedding strength. As an optimization solution of obtaining the optimal embedding strength, it could achieve an adaptive policy, and find a reasonable balance between the robustness and inaudibility of the watermark, just for the better robustness and inaudibility in the algorithm.

遗传算法是通过群体进化来随机搜索目标函数的最优化问题，对于前述基于支持向量回归机的小波域数字音频盲水印算法，进一步地探讨采用遗传算在嵌入强度集合中搜索对抗攻击能力适应度较高的个体，从而得到最佳嵌入强度的一个优化方案，以实现自适应策略，也使本算法能在水印的鲁棒性和不可感知性之间寻找到合理的平衡点，从而具有更良好透明性和鲁棒性。

But realization of inaudibility for audios is much more difficult than realizing invisibility for images, the reason for this is that audio signals are represented by much less samples per time interval compared with images and video.

但是由于人耳听觉系统(Human Auditory System，简称HAS)比人眼视觉系统(Human Visual System，简称HVS)灵敏、以及音频作品的采样点数较少等缘故，使得音频数字水印实现不可感知性比较困难。

PubMed abstract: A preference method probed infants' perception of object motion on an inclined plane.

PubMed 摘要：探测婴儿对於斜面上物体运动之感知的一种偏好方法。

For the problem of data s mismanagement, separated usage of study tools and hard for awareness processing widely existing in the traditional collaborative learning system, the nested knowledge space model is presented in this paper, which includes the concepts of knowledge domain, inclusion relation, correlation relation, quasi-order relation and parallel relation among the knowledge domain,and knowledge space etc.

针对传统协同学习系统中普遍存在着学习材料组织混乱、学习工具隔离使用和感知处理困难等问题，本文提出了嵌套式知识空间模型，该模型包含知识域、知识域之间的包含关系、相关关系、拟序关系和并行关系以及知识空间等概念。

Do you know, i need you to come back

你知道吗，我需要你回来

Yang yinshu、Wang xiangsheng、Li decang,The first discovery of haemaphysalis conicinna.

1〕 杨银书，王祥生，李德昌。安徽省首次发现嗜群血蜱。