Matched Filter Based Spectrum Sensing When Primary User Has Multiple Power Levels

In this paper,we investigate the matched filter based spectrum sensing in a more reasonable cognitive radio(CR) scenario when the primary user(PU) has more than one transmit power levels,as regulated in most standards,i.e.,IEEE 802.11 Series,GSM,LTE,LTE-A,etc.This new multiple primary transmit power(MPTP) scenario is specialized by two different targets:detecting the presence of PU and identifying the power level.Compared to the traditional binary sensing where only the presence of PU is checked,SU may attain more information about the primary network(making CR more "intelligent") and design the subsequent optimization strategy.The key technology is the multiple hypothesis testing as opposed to the traditional binary hypothesis testing.We discuss two situations under whether the channel phase is known or not,and we derive the closed form solutions for decision regions and several performance metrics,from which some interesting phenomenons are observed and the related discussions are presented.Numerical examples are provided to corroborate the proposed studies.

Instantaneous spectrum estimation of earthquake ground motions based on unscented Kalman filter method

Representing earthquake ground： motion as time varying ARMA model, the instantaneous spectrum can only be determined by the time varying coefficients of the corresponding ARMA model. In this paper, unscented Kalman filter is applied to estimate the time varying coefficients. The comparison between the estimation results of unscented Kalman filter and Kalman filter methods shows that unscented Kalman filter can more precisely represent the distribution of the spectral peaks in time-frequency plane than Kalman filter, and its time and frequency resolution is finer which ensures its better ability to track the local properties of earthquake ground motions and to identify the systems with nonlinearity or abruptness. Moreover, the estimation results of ARMA models with different orders indicate that the theoretical frequency resolving power of ARMA model which was usually ignored in former studies has great effect on the estimation precision of instantaneous spectrum and it should be taken as one of the key factors in order selection of ARMA model.

Novel matched filtering method and its application in radar system

The method of using a narrowband filter to realize matched filtering is derived.A novel method of using spectrum sampling to realize matched filtering is presented,and the method can conquer the disadvantages that the narrowband filter cannot adopt the adaptive scheduling of phased array radars and realize matched filtering for several waveforms.A novel error extraction method is proposed,which uses a time division multipath method to realize the intermediate frequency extraction.This method can save lots of space for vehicle-born radar systems,reduce the influence of amplitude and phase distortion caused by devices,and enhance the system reliability.Simulation results show that the spectrum sampling method is applicable,and the implementation of frequency spectrum sampling is elaborated.

Sequential Filtering for Surface Wind Speed Estimation from Ambient Noise Measurement

Many research results show that ocean ambient noise and wind speed are highly relevant, and the surface wind speed can be effectively inverted using ocean noise data. In most deep-sea cases, the ambient noise of medium frequency is mainly determined by the surface wind, and there is a conventional relationship between them. This paper gives an equation which shows this relationship firstly, and then a surface-wind inversion method is proposed. An efficient particle filter is used to estimate the speed distribution, and the results exhibit more focused close to the actual wind speed. The method is verified by the measured noise data, and analysis results showed that this approach can accurately give the trend of sea surface wind speed.

Auditory-Spectrum Quantization Based Speech Recognition

Ａｕｄｉｔｏｒｙ┐ＳｐｅｃｔｒｕｍＱｕａｎｔｉｚａｔｉｏｎＢａｓｅｄＳｐｅｅｃｈＲｅｃｏｇｎｉｔｉｏｎＷｕＹｕａｎｑｉｎｇＨａｏＪｉｅＬｕＤａｊｉｎＬｉＸｉｎｇＺｈｕＸｕｅｌｏｎｇ（ＤｅｐａｒｔｍｅｎｔｏｆＥｌｅｃｔｒｏｎｉｃＥｎｇｉｎｅｅｒｉｎｇ，...

Electronic Nose Technology Based on Quantum Dot Filters

The electronic nose with chemical dyes as sensor can react with target gas and have specific color changes. In general, RGB camera collects a group of images to record these changes used for pattern recognition. RGB filters are not sensitive to the slight color changes, which limits the performance of this kind of electronic nose. This paper demonstrates using quantum dot spec-troscopy technology to solve this problem. Multiple quantum dot filters are placed on the surface of image sensor. When capturing images, there are more response channels of the same incident light than RGB filters. Simulation and experiment both prove that quantum dot filters with appropriate processing are more sensitive to color changes than RGB filters.

Rejection of Narow─Band Interference in DS Spread Spectrum Systems Using Recurent Neural Networks

Adaptive whitening filters have been proven to be powerful to eliminate narrow band interference in spread spectrum (SS) communication systems. However digital implementation of such kind adaptive filters is difficult for applications with chip rate as

A Spectrum Sensing Framework for UWB-Cognitive Network

Due to that the Ultra Wide Band (UWB) technology has some attractive features like robustness to multipath fading, high data rate, low cost and low power consumption, it is widely use to implement cognitive radio network. Intuitively, one of the most important tasks required for cognitive network is the spectrum sensing. A framework for implementing spectrum sensing for UWB-Cognitive Network will be presented in this paper. Since the information about primary licensed users are known to the cognitive radios then the best spectrum sensing scheme for UWB-cognitive network is the matched filter detection scheme. Simulation results verified and demonstrated the using of matched filter spectrum sensing in cognitive radio network with UWB and proved that the bit error rate for this detection scheme can be considered acceptable.

Improvement of digital S-K filter and its application in nuclear signal processing

On the basis of preliminary studies,a novel duo-parameter model consisting of amplitude filter factor and frequency filter factor for low-pass S-K filter is presented in this paper.The model is established by applying numerical differentiation method.Some simulation experiments and real data tests are carried out to verify the feasibility and superiority of the new algorithm.The results show that this duo-parameter model of low-pass S-K filter can be used to achieve high performance in signal processing and nuclear spectrum smoothing.

Crystal Growth and Characterization of Potassium Manganese Nickel Sulphate Hexahydrate—A New UV Filter

Potassium Manganese Nickel Sulphate Hexahydrate (KMNSH) crystals have been successfully grown by using traditional slow evaporation method. The empirical formula of KMNNSH is K2Mn0.1Ni0.9 (SO4)2?6H2O with formula weight 430.7698. KMNNSH crystal revealed monoclinic space group P2(1)/c, a = 6.12(4) ?, b = 12.19(9) ?, c = 8.96(7) ?, α = γ = 90?, β = 105.3(2), V = 645(1)?3, Z = 2, Dc = 2.2281 g/cm3 with deep green color. IR confirms that there is strong interaction between free water molecules. The thermal analysis indicates that the water molecules are present in the KMNNSH crystals. The transmission spectrometry of KMNNSH in the range from UV to near IR wavelengths is reported. Atomic Absorption Spectroscopy also have been studied and discussed.

空间热光伏发电技术进展与展望

空间同位素电源是通过热电转换技术将衰变热能转换为电能的空间电源装置,广泛应用于深空探测任务中太阳能电池无法使用的任务场景,其热电转换方式包括温差、热光伏等静态转换方式以及斯特林、布雷顿等动态转换方式。空间同位素电源普遍使用钚238核源,其制备生产困难,价格高昂,在全球范围内产能受限,同时,长周期的深空探测任务对同位素电源寿命也提出较高要求,因此热电转换效率高、易于实现长寿命的空间热光伏发电技术成为开展深空探测任务的优选技术路线。近年来,近场热光伏、基于光谱调控的热光伏技术是研究重点,结构热控、辐射器、滤光器、电池晶元等关键器件的理论研究与技术验证也取得了显著的进展。对热光伏空间同位素电源技术方案、关键器件研究情况进行了综述,从电源系统总体设计的角度对各器件的发展现状与未来技术的发展重点方向进行了研判,可为进一步推动热光伏同位素电源系统的工程化研制提供参考。

A Method for Incipient Fault Diagnosis of Roller Bearings Based on the Wavelet Transform Correlation Filter and Hilbert Transform

Noise is the biggest obstacle that makes the incipient fault diagnosis results of roller bearings uncorrected; a new method for diagnosing incipient fault of roller bearings based on the Wavelet Transform Correlation Filter and Hilbert Transform was proposed. First, the weak fault information features are picked up from the roller bearings fault vibration signals by use of a de-noising characteristic of the Wavelet Transform Correlation Filter as the preprocessing of the Hilbert Envelope Analysis. Then, in order to get fault features frequency, de-noised wavelet coefficients of high scales which represent high frequency signal were analyzed by Hilbert Envelope Spectrum Analysis. The simulation signals and diagnosing examples analysis results reveal that the proposed method is more effective than the method of direct wavelet coefficients-Hilbert Transform in de-noising and clarifying roller bearing incipient fault.

LAPPED TRANSFORM DOMAIN INTERFERENCE EXCISION USING MODIFIED MEDIAN FILTER

A novel communication receiver which uses lapped transform(LT) incorporating modified median filter(MMF) algorithm was designed for narrow band interference(NBI) excision.Comparing to traditional Fourier Transform,LT has longer basis vectors,less spectral leakage,thus better frequency resolution.The LT domain MMF algorithm takes full advantages of the direct sequence spread spectrum signal,as well as the characteristics of LT,performs the transform domain filtering twice.The first filtering locates the position of interference and mitigates most of them.The second filtering is performed in a small neighborhood of the located interference.So LT domain MMF algorithm can completely mitigate the interference without distorting the desired signal.The simulation results demonstrate the improved BER(Bit Error Rate)performance and increased robustness of our receiver.

Stochastic Response Analysis of Piled Offshore Platform Excited by Stationary Filtered White Noise

In this paper, the analysis method of stochastic response of piled offshore platform excited by stationary filtered white noise is presented. With this method, the strong ground motion is considered as three direction stationary filtered white noise process, the theoretic solutions of three special integration equations are derived with the residue theorem, and the expression of response nodal displacements and member forces of offshore platform excited by the stationary filtered white noise is put forward. The stochastic response of a piled offshore platform excited by the stationary filtered white noise, which is located 114.3 m in water depth, is computed. The results are compared with those obtained with the response spectrum analysis method and the stationary white noise model analysis method, and the corresponding conclusion is drawn.

A Two Step Secure Spectrum Sensing Algorithm Using Fuzzy Logic for Cognitive Radio Networks

In this paper, a two step secure spectrum sensing algorithm is proposed for cognitive radio networks. In this algorithm, the sensing results of secondary users are pre-filtered and applying fuzzy logic, so, the overall sensing performance of the network is improved. To determine pre-filter parameters, statistical parameters of the sensing results are used to remove those sensing results which are far from the majority sensing results. However, to obtain a better performance in the spectrum sensing, we propose a fuzzy logic to nullify the effects of malicious users who transmit false sensing data to the fusion center. We further propose a Fuzzy Trust Level for each user as to weight the sensing result of the corresponding user before combining all sensing results in the fusion center. Simulation results demonstrate that our proposed algorithm yield significant improvement in the performance of the spectrum sensing and identifying malicious users.

增强组合差分乘积形态学滤波的轴承故障特征提取方法

针对滚动轴承故障信号的非线性、非平稳、强噪声特性导致的常规时频域特征提取方法受限问题,提出一种增强组合差分乘积形态学滤波的轴承故障特征提取方法。在分析数学形态学4种基本运算的正、负冲击脉冲提取特性的基础上,运用级联、差分、乘积构造的一种新的组合差分乘积算子(combination difference multiply operator,CDMO)具备了同时提取正、负冲击脉冲的能力,并发挥梯度乘积运算对脉冲提取更敏感的优势,实现故障信息的充分提取。引入故障特征频率比指标优化CDMO结构元素参数,修正待处理信号的几何特征,提取与结构元素相匹配的信号特征信息。在CDMO滤波的基础上,借助三阶累积量切片谱技术能够抑制高斯噪声、突出二次耦合分量的优势,准确提取故障特征频率及其倍频,增强轴承故障特征提取能力并抑制噪声干扰。依托2种不同来源的工程实际信号并与经典故障特征提取方法对比分析,验证了所提方法的有效性。

多窗谱小波阈值估计的维纳滤波语音增强算法

提出一种多窗谱小波阈值估计的维纳滤波语音增强算法.该算法采用多窗谱估计噪声功率谱,结合改进的小波阈值法减小其误差,再计算先验信噪比.改进后的阈值函数具有连续性,增加了小波系数的平滑度,实现了信噪比的平滑.与传统软、硬阈值函数算法相比,文中算法具有更高的信噪比,能较好地抑制噪声,有效提高语音清晰度.

低计数特征的自然伽马能谱测井数据滤波方法研究

岩石天然放射性强度较弱,自然伽马能谱测井所测得的能谱计数较低,低计数特征使得地层衰变随机误差影响被放大,造成测量能谱质量差、测井曲线统计噪声大的情况。该文通过对比多种能谱滤波结果,使单深度点能谱最大程度还原准确能谱信息;利用曲线形态,针对求解的U、Th、K含量曲线进行深度域的滤波处理,综合从高噪声、低计数的数据中准确提取地层信息;利用中国石油集团测井有限公司的过钻具自然伽马能谱仪器的实测数据验证该方法的有效性。结果表明:对于计数低、谱形差的自然伽马能谱,高斯滤波、卷积滤波效果相对较好,该文提出结合这2种方法的复合滤波可以准确地还原出高保真的能谱形状;深度域的滤波,卡尔曼滤波相对自适应滤波方法有一定优势;依次经过能谱滤波和深度滤波后,结果的准确性和重复性有了大幅提升。该文所介绍的针对低计数特征的自然伽马能谱测井滤波方法,可以有效提高U、Th、K的测量精度。

数字全息显微信号项频谱定位及干扰项抑制

在离轴数字全息显微和非相干光成像研究中,空间频域滤波是重建目标物体的振幅和相位信息的关键步骤。信号项频谱选取及中心定位对滤波后重建相位的质量有着极大影响,即使像素级的微小差异也会导致重建相位的严重失真。为了解决这个问题,文章提出了一种数字全息显微信号项频谱定位及干扰项抑制的方法。方法根据全息图频谱的直方图分布特性,自适应确定滤波形状和大小,同时,采用质心法来快速准确的确定信号项频谱的中心,并在频域中运用形态学处理来消除信号项频谱周围的干扰项。实现了信号项频谱自适应滤波,从而提高了相位重建的质量。文章使用二维山形图模拟相位进行了仿真验证,并搭建光路拍摄USAF1951标定板上的数字区域和洋葱表皮细胞进行了实验验证。仿真和实验结果表明,算法可以实现高质量的相位重建,并对干扰项进行抑制,提高了离轴数字全息显微系统的成像质量。

基于CEEMDAN-WP-SSA的GPS/BDS-RTK多路径噪声抑制方法

针对GNSS-RTK技术在复杂环境下的多路径效应误差和随机噪声难以消除的问题,提出一种自适应完备集合经验模态分解(CEEMDAN)、小波包(WP)去噪和奇异谱分析(SSA)结合的联合去噪方法,来消除基线坐标序列的噪声影响,在此基础上构建适用于GPS/BDS组合的恒星日滤波模型。首先采用CEEMDAN方法将原始信号分解成若干个特征模态函数(IMF),使用排列熵区分高频和低频分量,然后分别利用WP和SSA对高频信号和低频信号进行去噪,最后重构去噪后信号并通过恒星日滤波削弱后续坐标序列里的多路径误差。实验结果表明,基于CEEMDAN-WP-SSA构建的恒星日滤波模型能够很好的去除随机噪声以及削弱多路径误差影响,与CEEMDAN和CEEMDAN-WP方法相比,文中方法东(E)、北(N)、高程(U)3个方向的定位精度分别提升约17%、23.6%、13.6%和17%、19.8%、10%。