An Optimized Algorithm for CR-MIMO Wireless Networks

With the rapid development of wireless communication technology, the spectrum resources are increasingly strained which needs optimal solutions. Cognitive radio (CR) is one of the key technologies to solve this problem. Spectrum sensing not only includes the precise detection of the communication signal of the primary user (PU), but also the precise identification of its modulation type, which can then determine the a priori information such as the PU’ service category, so as to use this information to make the cognitive user (CU) aware to discover and use the idle spectrum more effectively, and improve the spectrum utilization. Spectrum sensing is the primary feature and core part of CR. Classical sensing algorithms includes energy detection, cyclostationary feature detection, matched filter detection, and so on. The energy detection algorithm has a simple structure and does not require prior knowledge of the PU transmitter signal, but it is easily affected by noise and the threshold is not easy to determine. The combination of multiple-input multiple-output (MIMO) with CR improves the spectral efficiency and multi-path fading utilization. To best utilize the PU spectrum while minimizing the overall transmit power, an iterative technique based on semidefinite programming (SDP) and minimum mean squared error (MMSE) is proposed. Also, this article proposed a new method for max-min fairness beamforming. When compared to existing algorithms, the simulation results show that the proposed algorithms perform better in terms of total transmitted power and signal-to-interference plus noise ratio (SINR). Furthermore, the proposed algorithm effectively improved the system performance in terms of number of iterations, interference temperature threshold and balance SINR level which makes it superior over the conventional schemes.     

基于分数阶功率谱熵的未知水声脉冲信号检测方法

水声侦察的核心问题是在无先验知识条件下捕获其他平台发射的脉冲信号,单频（Continuous Wave,CW）信号和调频（Frequency Modulation,FM）信号是常用的水声探测脉冲。功率谱熵算法能有效检测低信噪比的CW信号,但对FM信号性能不佳,分数阶傅里叶变换（Fractional Fourier Transform,FRFT）则能聚集FM信号能量。利用FRFT的性质,结合功率谱熵算法,设计了分数阶功率谱熵检测器,可在分数阶域实施未知脉冲信号检测。理论分析了FRFT对FM信号的能量聚集作用,优化了FRFT阶数搜索方法。仿真实验以及海试数据处理结果证实检测器对FM信号性能良好,且对CW信号的侦察检测性能无影响。通过门限学习,检测器可实现对未知水声脉冲信号的统一自动检测。     

Robust energy detection in cognitive radio

The success of advanced dynamic utilisation of the scarce spectrum in cognitive radio depends upon reliable primary signal detection where accurate noise power estimation plays a critical role. However, in practical scenarios, the noise power cannot be accurately estimated, which significantly degrades the performance of primary signal detection. To avoid inaccurate noise power estimation and associated accumulated problems. A novel two-stage Bayesian estimation-based energy detection algorithm is introduced here. This algorithm, as supported by simulation results, shows two main features: (a) a superior performance of 1 dB compared with previous methods; (b) the consistency of the algorithm has been proved indicating that 100 correct primary user signal detection can be approached as the number of samples tends to infinity.     

基于神经网络的电机噪声性能在线检测技术研究

根据车用直流电机振动、噪声的特点，在自动生产线上测量电机振动，从中提取加速度均方根值、振动能量的波动度、分频段振动能量、频谱中50个最大峰值等特征指标用于电机噪声特性检测。考虑不同型号电机振动噪声特性的差异，建立了电机噪声检测的BP网络模型，用以训练噪声检测的算法和阈值。将MATLAB语言环境下的神经网络工具箱和LabVIEW虚拟仪器平台相结合，在LabVIEW平台上开发了基于振动测量的电机噪声智能检测系统，解决了电机制造厂在线检测电机噪声的难题。     

基于卡方检验的多天线认知无线网络协作频谱感知算法

研究了基于分集的多天线认知无线网络的频谱感知技术。针对天线独立性会导致已有的基于协方差矩阵的协作频谱感知算法性能急剧下降甚至失效的问题,提出了一种基于卡方拟合优度检验的多天线协作频谱感知算法。该算法将频谱感知问题转化为一个多项分布检验的问题,然后利用卡方拟合优度检验判决频谱是否空闲,从而实现频谱感知。理论分析和仿真表明,该算法的性能不受天线相关性以及噪声不确定度的影响。     

Wideband spectrum sensing in unknown white Gaussian noise

The spectrum sensing of a wideband frequency range is studied by dividing it into multiple subbands. It is assumed that in each subband either a primary user (PU) is active or absent in a additive white Gaussian noise environment with an unknown variance. It is also assumed that at least a minimum given number of subbands are vacant of PUs. In this multiple interrelated hypothesis testing problem, the noise variance is estimated and a generalised likelihood ratio detector is proposed to identify possible spectrum holes at a secondary user (SU). Provided that it is known that a specific PU can occupy a subset of subbands simultaneously, a grouping algorithm which allows faster spectrum sensing is proposed. The collaboration of multiple SUs can also be considered in order to enhance the detection performance. The collaborative algorithms are compared in terms of the required exchange information among SUs in some collaboration methods. The simulation results show that the proposed detector outperforms the energy detector in the presence of noise variance mismatch above 2.3 dB. Some involved trade-offs in the spectrum sensing using the proposed detector are discussed.     

一种恒虚警线谱检测算法研究

提出基于高低分辨率功率谱匹配度估计的线谱检测算法,该算法对信号分别进行高分辨率功率谱估计和多段低分辨率功率谱估计,然后以高分辨率功率谱估计为模板,用多段低分辨率功率谱估计进行校验的方式进行线谱检测。基于单频信号加高斯白噪声假设条件,理论上导出该检测方法的检测率和虚警率,并进行了仿真验证。与传统的线谱检测方法相比,该线谱检测算法不需要预知噪声功率谱,且具有恒虚警的特点。在车辆目标远距离声学检测应用中,该检测算法具有较好的检测性能。     

一种低信噪比环境下的语音端点检测算法

端点检测技术是语音信号处理的关键技术之一，为提高低信噪比环境下端点检测的准确率和稳健性，提出了一种非平稳噪声抑制和调制域谱减结合功率归一化倒谱距离的端点检测算法。该算法首先通过抑制非平稳噪声再采用调制域谱减消除残余噪声来提升信噪比，减少语音失真。然后再提取每帧信号的功率归一化倒谱系数，计算每帧信号与背景噪声的功率归一化倒谱距离。最后将该倒谱距离作为检测参数，采用双门限判决方法进行端点检测。实验结果表明，该端点检测算法对语音帧和噪声帧具有较好的区分性。此外，在低信噪比环境下，所提出的算法对于不同类型的噪声都具有较好的稳健性。     

Approximate performance of the nonlinear joint transform correlator in signallike noise

We present a statistical-analysis technique for a nonlinear joint transform correlator (JTC) based on two assumptions: the noise and the signal spectra are identical, and the signal energy is small relative to the noise energy. The first assumption, while admittedly convenient, is also defensible in that it is a worst case and in that image and scene noise can be similar in texture. The second is also reasonable, given that even a clearly visible signal may have small energy compared with the scene noise if it is of limited extent; in any case, the results appear moderately faithful even for the case that signal and noise energies are equal. We discover that the optimal Fourier-plane transformation is spatially variant and tends to remove the Fourier amplitudes of the input image, and indeed functions in a way very similar to the spatially variant binary JTC. We also see that the classic (or spatially invariant linear) JTC is a very inferior technique for signallike noise, that the best spatially variant binary JTC uses a threshold proportional to the noise power spectrum, and that, if a spatially invariant binary-thresholded JTC is desired, then the median Fourier-plane value is an excellent choice of threshold. The performance predictions are verified by simulation and appear to be reasonable even for the highly nonlinear binary schemes.     

基于排气噪声的柴油机失火故障诊断

通过计算机和声传感器采集排气噪声,研究九种工况下柴油机排气噪声的频谱规律。通过对柴油机排气噪声信号功率谱分析,提取二个参数来表征排气噪声的频谱规律。对这两个参数在各工况下的值进行了详细的分析后,发现这两个参数在失火工况下有较大的变化,故对这两个参数确定相应的阈值来诊断失火故障。通过编写相应的故障诊断程序,输入排气噪声样本和对应的转速后即可判断出失火故障。这种方法具有非接触,操作简单,成本低的特点。     

基于临界带功率谱方差的端点检测

端点检测作为语音信号处理的关键技术,其准确性直接影响到语音识别系统的计算复杂度和识别能力。在人耳听觉特性理论研究的基础上,利用语音段和背景噪声段临界带功率谱上的差异,提出了一种基于临界带功率谱方差的端点检测方法。通过自适应门限值的选取,该方法对背景噪声具有良好的跟踪性能。在不同的信噪比条件下,进行了端点检测实验。结果表明：该方法与传统的短时能量和短时平均过零率方法、谱熵方法相比,可以有效降低背景噪声的影响,具有更好的鲁棒性和正确率。     

基于小波奇异性理论的水轮机空化检测

空化噪声谱分析中常采用的功率谱分析在研究空化噪声这种具有很强突变性的信号时具有明显的缺陷,文中提出了一种基于小波奇异性理论的水轮机空化检测方法,并提出了小波基选择方案、突变点检测条件和最佳检测阈值。进行了混流式水轮机的模型转轮试验,观测了模型转轮额定工况下的涡带形态和空化发展情况,并采集存储了大量空化噪声数据。对照观测结果分析实际的检测数据,结果表明该方法的有效性,并很好的检测出空化初生和空化形态转变。     

An improved method for R-peak detection by using Shannon energy envelope

R-peaks in electrocardiogram (ECG) play a vital role in diagnosis of heart rhythm irregularities and also estimating heart rate variability. However, almost all existing R-peak detectors suffer from the non-stationary of both QRS morphology and noise. To overcome these difficulties, we propose a four-stage improved method to detect R-peak using Shannon energy envelope. In the first stage, noise is suppressed and QRS complex is enhanced by using band pass filter, first order differentiation, and amplitude normalization. In the second stage, Shannon energy envelope is extracted. In the third stage, peak is estimated without considering any threshold amplitude. In the final stage, true R-peaks are detected. Our proposed R-peak detection method is validated using 48 first channel ECG records of the MIT-BIH arrhythmia database with the accuracy of 99.84%, sensitivity of 99.95% and positive predictability of 99.88%. Our proposed method outperforms other well-known methods in case of pathological ECG signals.     

Visual signal detection with two-component noise: low-pass spectrum effects

Detection of signals in natural images and scenes is limited by both noise and structure. The purpose of this study is to investigate phenomenological issues of signal detection in two-component noise. One component had a broadband (white) spectrum designed to simulate image noise. The other component was filtered to simulate two classes of low-pass background structure spectra: Gaussian-filtered noise and power-law noise. Measurements of human and model observer performance are reported for several aperiodic signals and both classes of background spectra. Human results are compared with two classes of observer models and are fitted very well by suboptimal prewhitening matched filter models. The nonprewhitening model with an eye filter does not agree with human results when background-noise-component power spectrum bandwidths are less than signal energy bandwidths.     

Energy loss and ionization of fast charged particles in a 20 μm silicon detector

The calculation of straggling functions (i.e. energy loss spectra) for fast charged particles with the method of convolutions of the collision spectrum is compared with the Laplace transform methods. Reasons to explain the differences between the straggling functions obtained with the two methods are presented.The major differences between the energy loss spectrum of charged particles and the related ionization spectrum observed in a silicon detector are described. Finally, a calculated ionization spectrum is compared with measurements in a thin detector. It is possible to determine the thickness of the detector with an uncertainty of no more than 10%.     

基于神经网络和功率谱的水中目标信号检测方法

将功率谱和神经网络相结合，应用于高海况、低信噪比条件下，水中目标信号的特征提取中．文中首先对信号进行功率谱估计，利用目标信号功率主要集中在低频部分的特点，提取低频信号的能量作为特征，然后利用人工神经网络对目标信号进行检测．利用不同浪级情况下海洋水压场的仿真信号数据，对某型目标舰船的水压信号进行了检测计算，验证了该方法的有效性，尤其是达到了在高海况、低信噪比条件下，对目标信号检测率比较高、虚警率比较低的效果．     

Photovoltaic–Pyroelectric Coupled Effect Induced Electricity for Self‐Powered Photodetector System

Ferroelectric materials have demonstrated novel photovoltaic effect to scavenge solar energy. However, most of the ferroelectric materials with wide bandgaps (2.7–4 eV) suffer from low power conversion efficiency of less than 0.5% due to absorbing only 8–20% of solar spectrum. Instead of harvesting solar energy, these ferroelectric materials can be well suited for photodetector applications, especially for sensing near‐UV irradiations. Here, a ferroelectric BaTiO₃ film‐based photodetector is demonstrated that can be operated without using any external power source and a fast sensing of 405 nm light illumination is enabled. As compared with photovoltaic effect, both the responsivity and the specific detectivity of the photodetector can be dramatically enhanced by larger than 260% due to the light‐induced photovoltaic–pyroelectric coupled effect. A self‐powered photodetector array system can be utilized to achieve spatially resolved light intensity detection by recording the output voltage signals as a mapping figure.     

二元联合变换相关器的实现方法研究

采用中值取阈，局部取阈，参考图像功率谱取阈和联合功率谱（JPS）取阈四种方法对传统联合变换相关器的功率谱二值化，从而将振幅调制的功率谱转化为脉冲调制的功率谱。计算机模拟结果表明二值化可以获得更清晰更尖锐的相关峰，其中联合功率谱取阈法的识别效果最佳。     

一种基于DOA分布信息熵加权的线谱目标检测方法

针对常规宽带能量检测方法对低信噪比线谱目标检测性能较差的不足,文章在分析目标线谱波达方向（Direction of Arrival,DOA）估计分布信息熵的基础上,提出一种基于DOA分布信息熵加权的线谱目标检测方法。通过仿真对比分析了该方法的检测性能,并利用海上实验数据验证了其有效性。结果表明,当目标方位较为稳定时,该方法可有效检测低信噪比线谱目标,并可适用存在多个同频线谱目标的情况。     

强干扰背景下的鱼雷辐射噪声信号检测方法

常规被动声纳利用宽带波束能量进行目标检测,在低信噪比、强目标干扰情况下,宽带能量检测的性能迅速降低。利用鱼雷辐射噪声信号中含有丰富线谱成分的特点,提出了一种针对鱼雷辐射噪声信号的窄带和宽带联合检测方法。通过对预成波束方向信号的窄带处理和线谱提取,利用特定频段的线谱能量对宽带波束输出进行加权,提高了线谱目标的检测能力。该方法能够有效地抑制非线谱强干扰目标,提高低信噪比信号的检测能力。仿真和海试数据处理结果验证了该方法的有效性。