声压振速信息联合处理应用于环境噪声分析

本文将声压、振速信息联合处理应用于环境噪声分析,分析了利用新型的组合传感器采集的湖试环境噪声样本。将环境噪声场分解为不相干的各向同性分量和相干的各向异性分量,可进一步分析环境噪声场的构成及各组成分量的特性     

环境噪声学进展

本文简要地回顾了30年来中国环境噪声学的发展，并从飞机噪声、地面运输噪声、环境噪声影响与评价以及工业噪声控制四个方面介绍了近年来环境噪声学的进展。     

噪声对特征综合法语音识别性能的影响

本文考查了噪声对特征综合法语音识别的影响，实验表明在不同环境噪声情况，文献1提出的通过特征综合提高语音识别性能的三种方法仍然能够提高识别率7％左右。     

利用环境噪声进行安静目标探测*

该文回顾了近年来该课题组利用环境噪声互相关方法和环境噪声自相关方法进行安静目标探测的主要工作。首先简单回顾了利用环境噪声互相关方法和环境噪声自相关方法进行安静目标探测的理论,然后介绍了两次利用海浪噪声进行安静目标探测的实验。通过海浪噪声实验验证了利用环境噪声互相关和自相关进行安静目标探测的可行性。最后对利用环境噪声互相关进行安静目标探测所需要的累积时间进行了讨论,并给出了累积时间的下界。     

利用环境噪声互相关实现散射体无源成像

最近研究表明利用环境噪声的互相关可以恢复两点之间的时域格林函数(声脉冲响应),这一原理在文献中被称为格林函数恢复。基于此原理,通过对多个传声器所接收的环境噪声进行互相关处理,获取与散射体相关联的散射波的到达时延信息,结合最小二乘反演算法和改进克希霍夫移位算法,分别获得道路交通噪声场中石柱以及海浪噪声场中塑料桶的空间位置,且其估计结果与实际测量相一致。实验结果表明将环境噪声作为探测信号进行散射体无源成像是可行的。这为设计室内无源声监测系统以及通过海洋环境噪声实现对水中静默目标成像提供了新思路和有益参考。      

公园环境噪声的主观反应及烦恼度阈值

本文用模糊数学处理游客对公园环境噪声主观反应的所有信息,给出各中心声级下的烦恼概率,并以此加权平均,得出公园不同区域环境噪声烦恼度的阈值;草坪休息区等效声级63.3dBA,游览观赏区63.6dBA,茶室餐饮区70.9dBA.本文所采用的方法比心理物理法计算得出烦恼度阈值更为理想.     

基于Hilbert-Huang变换的水声信号特征提取及分类技术

水下目标噪声信号是一种典型的非线性非平稳随机信号，Hilbert-Huang变换较传统方法在处理此类信号时具有很大优势，据此本文提出了一种基于Hilbert-Huang变换的水下目标噪声特征提取方法。本文将环境噪声当作一类目标，利用上述方法对涉及两种航行船舶、海洋生物和海洋环境噪声的四类目标信号的特征进行了详细分析，最后对各类目标噪声信号进行了分类识别实验。实验结果表明基于Hilbert-Huang变换提取的特征对各类目标具有较好的可分性，达到了较高的识别率。     

一种海洋环境噪声分步反演地声参数方法

分析不同海底参数对环境噪声垂向空间特性的敏感度,根据海底密度对环境噪声垂直指向性的小掠射角部分不敏感,而对等效海底损失的大掠射角部分相对敏感的特点,提出了一种海洋环境噪声分步反演地声参数方法:先用环境噪声垂直指向性小掠射角部分反演海底声速、衰减;之后利用大掠射角部分来反演海底密度。仿真算例和海上实验数据处理结果验证了该方法的有效性。结果表明:分步反演加强了匹配物理量对海底参数敏感部分的关注,在反演结果精度(海底密度)以及反演效率上都有所改善,具有一定可行性。      

警惕室内环境中的噪声污染

《中华人民共和国环境噪声污染防治法》中把超过国家规定的环境噪声排放标准，并干扰他人正常生活、工作和学习的现象称为环境噪声污染．按照国家标准规定，住宅区的噪音，白天不能超过50分贝，夜间应低于45分贝，超过标准，会对人体造成危害．1室内噪声的主要来源 1．1交通运输噪声 随着城乡车辆的增加，公路和铁路交通干线的增多，机车和机动车辆的噪声已成了交通噪声的元凶，占城市噪声的75％．统计表明，北京是世界有名的噪声污染城市．虽然城市车辆不及日本的十分之一，噪声程度却比日本高出1倍．临街建筑，受害极重．     

不同温度下电阻热噪声测量方案与电磁屏蔽的改进

电阻热噪声测量需要隔离环境噪声对测量的干扰。通过远程控制扫频噪声测量发现教学实验室即使在无人时仍有明显特征的环境噪声；另一方面，位于电阻旁边的热电偶即使在非测量状态下也会引入噪声。为此，高屏蔽能力的变温样品盒成为变温测量电阻热噪声的关键。本文研制一款用于实验教学的样品盒，测温热电偶设置在样品盒外，但与电阻保持良好热接触；进一步结合锁相放大器A-B共模输入和输入带宽修正，获得较好的实验效果。     

Noise from opencast coal mining sites

The principal sources of noise in opencast coal mining operations are described. Currently available information on the assessment, prediction, measurement and control of this type of noise is considered. Some of the problems associated with the setting up of a scheme for the control of noise from opencast sites which are discussed are found to apply to other sources of environmental noise. It is concluded that at present there is insufficient information available to enable a scheme covering all aspects of noise control to be produced. Suggestions are made for further research.     

Time history speckle pattern under statistical view

The dynamic speckle analysis has been done because speckle interference began to be evaluated as an important source of information, especially those related to biological samples under laser beam. The time history speckle pattern, a THSP image, is an approach to analyze, which allows a summary to represent the activity monitored. THSP adoption has compelled the research to evaluate the information and the reliability of its inner pattern. This study presents a statistical approach to analyze the data using white noise tests and cross-spectrum analysis. The data analyzed was a set of THSP from animal sperm samples. The evaluation of white noise among lines was conducted using the Fischer test over eight THSP and the cross-spectral approach has been conducted by comparing the information in each THSP. The results presented that the THSP of the sperm did not behave as a white noise in a global evaluation, and specifically in the Gaussian white noise test over 4096 lines; only six lines presented the behavior of a white noise pattern. The strong rejection of white noise test confirms that the THSP pattern allows reliable information. The results obtained with the cross-spectral analysis presented differences between lines within the same THSP, showing that the information of an inner pattern varies in relation to space, which is against the findings in literature.     

Removal of stripe noise with spatially adaptive unidirectional total variation

Multi-detectors imaging system often suffers from the problem of the stripe noise, which greatly degrades the quality of the resulting images. To better remove stripe noise and preserve the edge and texture information, a robust destriping algorithm with spatially adaptive unidirectional total variation (SAUTV) model is introduced. The spatial information of the striping noise is detected by using the stripe indicator called difference eigenvalue, and a weighted parameter determined by the difference eigenvalue information is added to constrain the regularization strength of the UTV regularization. The proposed algorithm can effectively remove the stripe noise and preserve the edge and detailed information. Moreover, it becomes more robust with the change of the regularization parameter. Split Bregman method is utilized to efficiently solve the resulting minimization problem. Comparative results on simulated and real striped images taken with two kinds of imaging systems are reported.     

Overview: Unsolved problems of noise and fluctuations

Noise and fluctuations are at the seat of all physical phenomena. It is well known that, in linear systems, noise plays a destructive role. However, an emerging paradigm for nonlinear systems is that noise can play a constructive role-in some cases information transfer can be optimized at nonzero noise levels. Another use of noise is that its measured characteristics can tell us useful information about the system itself. Problems associated with fluctuations have been studied since 1826 and this Focus Issue brings together a collection of articles that highlight some of the emerging hot unsolved noise problems to point the way for future research. (c) 2001 American Institute of Physics.     

Bridging rate coding and temporal spike coding by effect of noise

It is controversial whether temporal spike coding or rate coding is dominant in the information processing of the brain. We show by a two-layered neural network model with noise that, when noise is small, cortical neurons fire synchronously and intervals of synchronous firing robustly encode the signal information, but that the neurons desynchronize with moderately strong noise to encode waveforms of the signal more accurately. Further increase of noise just deteriorates the encoding. A positive role of noise in the brain is suggested in a meaning different from stochastic resonance, coherence resonance, and deterministic chaos.     

Biologically-inspired stochastic vector matching for noise-robust information processing

“End of Moore’s Law” has recently become a topic. Keeping the signal-to-noise ratio (SNR) at the same level in the future will surely increase the energy density of smaller-sized transistors. Lowering the operating voltage will prevent this, but the SNR would inevitably degrade. Meanwhile, biological systems such as cells and brains possess robustness against noise in their information processing in spite of the strong influence of stochastic thermal noise. Inspired by the information processing of organisms, we propose a stochastic computing model to acquire information from noisy signals. Our model is based on vector matching, in which the similarities between the input vector carrying external noisy signals and the reference vectors prepared in advance as memorized templates are evaluated in a stochastic manner. This model exhibited robustness against the noise strength and its performance was improved by addition of noise with an appropriate strength, which is similar to a phenomenon observed in stochastic resonance. Because the stochastic vector matching we propose here has robustness against noise, it is a candidate for noisy information processing that is driven by stochastically-operating devices with low energy consumption in future. Moreover, the stochastic vector matching may be applied to memory-based information processing like that of the brain.     

量子BB84协议在联合旋转噪音信道上的安全性分析

多数在理想条件下设计的量子密码协议没有考虑实际通信中噪音的影响,可能造成机密信息不能被准确传输,或可能存在窃听隐藏在噪音中的风险,因此分析噪音条件下量子密码协议的安全性具有重要的意义.为了分析量子BB84协议在联合旋转噪音信道上的安全性,本文采用粒子偏转模型,对量子信道中的联合噪音进行建模,定量地区分量子信道中噪音和窃听干扰;并且采用冯·诺依曼熵理论建立窃听者能窃取的信息量与量子比特误码率、噪音水平三者之间的函数关系,定量地分析噪音条件下量子信道的安全性;最后根据联合噪音模型及窃听者能窃取的信息量与量子比特误码率、噪音水平三者之间的关系,定量地分析了量子BB84协议在联合噪音条件下的安全性并计算噪音临界点.通过分析可知,在已有噪音水平条件下,窃听者最多能够从通信双方窃取25%的密钥,但是Eve的窃听行为会被检测出来,这样Alice和Bob会放弃当前协商的密钥,重新进行密钥协商,直至确认没有Eve的窃听为止.这个结果说明量子BB84协议在联合旋转噪音信道下的通信是安全的.     

利用自相关函数和双耳自相关函数分析对交通噪声特征参量的提取~

对实地双通道测量获得的道路交通噪声和铁路噪声信号样本进行了自相关函数和双耳自相关函数(Interaural CrossCorrelation Function)的分析。进而通过对噪声样本时间因子和空间因子的相关性分析、主成分分析和主观评价实验,得到了3个铁路噪声源特征参量物理因子和4个道路交通噪声源特征参量物理因子。发现与传统的声压级测量相比,表征声音信号时间特性和空间特性的这7个物理量可以更全面、准确地表征交通噪声的特性。在对道路噪声进行测量或分析时,掌握与声源视觉宽度和音调感相对应的物理因子以及双耳时延和初始能量,就可获悉与人的主观评价相一致的道路交通噪声特征信息;对铁路噪声而言,掌握与声源视觉宽度相对应的物理因子以及双耳时延和声音的重复性特征,就可以得到与入主观评价相一致的铁路噪声特征信息。综合道路噪声特征参量和铁路噪声特征参量可以发现,双耳时延和与声源视觉宽度相对应的物理因子是与人的主观反应最为一致的主成分指标,说明噪声中决定人的评价的最主要的因素是代表空间特征的信号因子。      

Noise-enhanced classical and quantum capacities in communication networks

The unavoidable presence of noise is thought to be one of the major problems to solve in order to pave the way for implementing quantum information technologies in realistic physical platforms. However, here we show a clear example in which noise, in terms of dephasing, may enhance the capability of transmitting not only classical but also quantum information, encoded in quantum systems, through communication networks. In particular, we find analytically and numerically the quantum and classical capacities for a large family of quantum channels and show that these information transmission rates can be strongly enhanced by introducing dephasing noise in the complex network dynamics.     

一种基于信息保留网络的图像去噪算法

由于成像设备等各种因素影响, 图像在成像或传感过程中会受到噪声干扰。图像去噪旨在减少或消除噪声对图像的影响, 这一过程往往会导致高频信息的丢失。为了在去除图像噪声的同时保护图像的边缘信息与纹理细节, 文章提出了一种计算复杂度相对较低的含有信息保留模块的卷积神经网络, 直接对含噪声图像进行降噪。信息保留模块通过残差学习提取局部长路径和局部短路径的混合特征信息。该文采用峰值信噪比(PSNR/dB)和结构相似性(SSIM)两项评价指标对实验结果进行量化, 这两项指标值越大, 说明去噪效果越好。实验结果表明, 在峰值信噪比和结构相似性2项评价指标的均值可达到30.36 dB和0.828 0, 相比其他对比算法, 2项评价指标分别平均提升了2.15 dB和0.072 9。该算法对不同种类、不同水平的噪声都具有良好的去噪效果, 且速度优于所对比的一般算法, 对基于卷积神经网络的去噪工作的进一步发展有一定的作用。