A Discrete Cosine Adaptive Harmonic Wavelet Packet and Its Application to Signal Compression

A new adaptive Packet algorithm based on Discrete Cosine harmonic wavelet transform (DCHWT), (DCAHWP) has been proposed. This is realized by the Discrete Cosine Harmonic Wavelet transform (DCHTWT) which exploits the good properties of DCT viz., energy compaction (low leakage), frequency resolution and computational simplicity due its real nature, compared to those of DFT and its harmonic wavelet version. Hence the proposed wavelet packet is advantageous both in terms of performance and computational efficiency compared to those of existing DFT harmonic wavelet packet. Further, the new DCAHWP also enjoys the desirable properties of a Harmonic wavelet transform over the time domain WT, viz., built in decimation without any explicit antialiasing filtering and easy interpolation by mere concatenation of different scales in frequency (DCT) domain with out any image rejection filter and with out laborious delay compensation required. Further, the compression by the proposed DCAHWP is much better compared to that by adaptive WP based on Daubechies-2 wavelet (DBAWP). For a compression factor (CF) of 1/8, the ratio of the percentage error energy by proposed DCAHWP to that by DBAWP is about 1/8 and 1/5 for considered 1-D signal and speech signal, respectively. Its compression performance is better than that of DCHWT, both for 1-D and 2-D signals. The improvement is more significant for signals with abrupt changes or images with rapid variations (textures). For compression factor of 1/8, the ratio of the percentage error energy by DCAHWP to that by DCHWT, is about 1/3 and 1/2, for the considered 1-D signal and speech signal, respectively. This factor for an image considered is 2/3 and in particular for a textural image it is 1/5.

Correction of cosine oscillation to the improved correlation method of estimating the amplitude of gravitational background signal

In the measurement of G with the angular acceleration method,the improved correlation method developed by Wu et al.（Wu W H,Tian Y,Luo J,Shao C G,Xu J H and Wang DH 2016 Rev.Sci.Instrum.87 094501） is used to accurately estimate the amplitudes of the prominent harmonic components of the gravitational background signal with time-varying frequency.Except the quadratic slow drift,the angular frequency of the gravitational background signal also includes a cosine oscillation coming from the useful angular acceleration signal,which leads to a deviation from the estimated amplitude.We calculate the correction of the cosine oscillation to the amplitude estimation.The result shows that the corrections of the cosine oscillation to the amplitudes of the fundamental frequency and second harmonic components obtained by the improved correlation method are within respective errors.

Modeling a Periodic Signal Using Fourier Series

This paper covers the concept of Fourier series and its application for a periodic signal. A periodic signal is a signal that repeats its pattern over time at regular intervals. The idea inspiring is to approximate a regular periodic signal, under Dirichlet conditions, via a linear superposition of trigonometric functions, thus Fourier polynomials are constructed. The Dirichlet conditions, are a set of mathematical conditions, providing a foundational framework for the validity of the Fourier series representation. By understanding and applying these conditions, we can accurately represent and process periodic signals, leading to advancements in various areas of signal processing. The resulting Fourier approximation allows complex periodic signals to be expressed as a sum of simpler sinusoidal functions, making it easier to analyze and manipulate such signals.

A Multiobjective Optimization Method for Designing <i>M</i>-Channel NPR Cosine Modulated Filter Bank for Image Compression

This paper proposes a method to design multichannel cosine modulated filter bank for image compression using multiobjective optimization technique. The design problem is a combination of stopband residual energy, least square error of the overall transfer function of the filter bank, coding gain with dc leakage free condition as constraint. The proposed algorithm uses Non-dominated Sorting Genetic Algorithm (NSGA) to minimize the mutually contradictory objective function by minimizing filter tap weights of prototype filter. The algorithm solves this problem by searching solutions that achieve the best compromise between the different objectives criteria. The performance of this algorithm is evaluated in terms of coding gain and peak signal to noise ratio (PSNR). Simulation results on different images are included to illustrate the effectiveness of the proposed algorithm for image compression application.

基于伪相关函数的cosine-BOC调制信号无模糊跟踪算法

针对余弦二进制偏移载波(cosine-BOC)调制信号自相关函数的多峰特性会导致导航接收机同步阶段产生跟踪模糊度问题,提出了一种基于伪相关函数(PCF)的cosine-BOC调制信号无模糊跟踪算法。该算法通过分析cosine-BOC信号的时域模型,在本地设计两路辅助信号波形,与接收到的cosine-BOC信号进行相关运算,再将得到的两互相关函数利用伪相关函数表达式进行组合,从而消除cosine-BOC信号自相关函数的边峰。仿真结果表明,该算法能够消除cosine-BOC信号鉴相曲线中存在的误锁点,实现伪码无模糊跟踪,提高跟踪可靠性,且该算法适用于任意阶数的cosineBOC信号。

Underwater acoustic signal denoising model based on secondary variational mode decomposition

Due to the complexity of marine environment,underwater acoustic signal will be affected by complex background noise during transmission.Underwater acoustic signal denoising is always a difficult problem in underwater acoustic signal processing.To obtain a better denoising effect,a new denoising method of underwater acoustic signal based on optimized variational mode decomposition by black widow optimization algorithm(BVMD),fluctuation-based dispersion entropy threshold improved by Otsu method(OFDE),cosine similarity stationary threshold(CSST),BVMD,fluctuation-based dispersion entropy(FDE),named BVMD-OFDE-CSST-BVMD-FDE,is proposed.In the first place,decompose the original signal into a series of intrinsic mode functions(IMFs)by BVMD.Afterwards,distinguish pure IMFs,mixed IMFs and noise IMFs by OFDE and CSST,and reconstruct pure IMFs and mixed IMFs to obtain primary denoised signal.In the end,decompose primary denoising signal into IMFs by BVMD again,use the FDE value to distinguish noise IMFs and pure IMFs,and reconstruct pure IMFs to obtain the final denoised signal.The proposed mothod has three advantages:(i)BVMD can adaptively select the decomposition layer and penalty factor of VMD.(ii)FDE and CS are used as double criteria to distinguish noise IMFs from useful IMFs,and Otsu algorithm and CSST algorithm can effectively avoid the error caused by manually selecting thresholds.(iii)Secondary decomposition can make up for the deficiency of primary decomposition and further remove a small amount of noise.The chaotic signal and real ship signal are denoised.The experiment result shows that the proposed method can effectively denoise.It improves the denoising effect after primary decomposition,and has good practical value.

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.

A method to compress vibration signals using wavelet packet transformation combined with sub-band vector quantization

A novel compression method for mechanical vibrating signals,binding with sub-band vector quantization(SVQ) by wavelet packet transformation(WPT) and discrete cosine transformation(DCT) is proposed.Firstly,the vibrating signal is decomposed into sub-bands by WPT.Then DCT and adaptive bit allocation are done per sub-band and SVQ is performed in each sub-band.It is noted that,after DCT,we only need to code the first components whose numbers are determined by the bits allocated to that sub-band.Through an actual signal,our algorithm is proven to improve the signal-to-noise ratio(SNR) of the reconstructed signal effectively,especially in the situation of lowrate transmission.

海量多媒体图像压缩中的冗余零系数消除算法设计

为了提高海量多媒体图像压缩的效果,提出一种针对海量多媒体图像压缩中的冗余零系数消除算法。计算图像信息熵,并构建金字塔模型,以提取多媒体图像的信息特征。将图像压缩分为一维离散余弦变换(DCT)压缩与二维DCT压缩,确保图像压缩后结构不被破坏。结合零系数数量变换曲线,确定最佳聚焦位置,建立冗余零系数消除模型,即可完成压缩图像冗余零系数消除。试验结果表明,所提方法可提高海量多媒体图像压缩的峰值信噪比,并且缩小图像所占用的空间。所提方法能够有效提高图像压缩的质量,使图像结构更加完整,故实际应用价值更高。

一种基于高阶累积量和谱特征结合的卫星信号调制识别方法

为了提升卫星通信系统的频带利用率,卫星通信信号常采用升余弦、高斯等信号成形方法。由于信号高阶累积量的理论值以矩形成形为基础,未考虑其他成形方式,采用上述理论值作为特征参量将导致信号调制识别性能受限。针对这一问题,仿真了升余弦成形后的BPSK、QPSK、8PSK、OQPSK、pi/4-QPSK、16QAM和高斯成形后的GMSK等7种信号的高阶累积量并提取特征,同时基于信号包络四次方谱提取GMSK信号的识别特征。在提取上述两类特征和信号四次方谱特征的基础上,使用决策树分类器对上述7种常见卫星信号进行调制识别。仿真结果表明,在信噪比为3 dB下,所提算法对7种信号的调制识别概率可以达到95%。

基于RSSI序列特性的RFID多标签相对定位方法

室内多目标的高精度定位技术是实现定制化智能服务的关键。当前,基于射频识别技术(Radio Frequency Identification,RFID)的室内定位技术因其成本低、易于部署和多目标感知等优势,受到了学术界和产业界的广泛关注。然而,传统的基于RFID的多目标相对定位系统需要使用多组接收天线进行数据收发,这导致系统的部署成本高昂,同时接收信号强度指示(Received Signal Strength Indication,RSSI)序列还会出现数据中断的问题。为解决这些问题,提出了一种基于RSSI序列特性的RFID多标签相对定位方法。该方法首先采用匀速移动天线的方式来获取多目标标签的接收RSSI信号序列;然后,对接收RSSI数据进行预处理来填充缺失数据,并构建基于余弦相似度的序列相似度量表;最后,从多个组维度设计不同的标签分组算法,以实现RFID多标签的相对定位。通过对典型室内多组RFID标签阵列进行大量相对定位测试,实验结果表明,所提方法的RFID标签相对定位平均准确率超过92%,对5*5的天线阵列平均定位计算时长小于1 s,相比其他工作计算效率提高了近10倍。

基于余弦函数的核脉冲信号数字成形方法研究

核脉冲信号的数字余弦函数(cos)成形,因成形简单、可操作性强和灵活性高等优势被用于核脉冲信号的数字成形处理中。本文从单指数衰减脉冲信号和cos脉冲信号的数学模型出发,推导了对称cos成形、零面积cos成形与对称零面积cos成形三种成形方法在Z域中的传递函数和级联公式,分析了成形参数对数字cos成形结果的影响。针对仿真核信号和实际采样核信号,分别实现了三种数字cos成形;基于现场可编程逻辑门阵列(Field Programmable Gate Array,FPGA)系统,实现了核信号的数字cos成形、幅度提取、能谱构建等功能。通过对137Cs(NaI(Tl)探测器)γ能谱的测试,结果表明三种数字cos成形方法在能量分辨率和计数方面均表现良好,且对称零面积cos成形性能指标更为优异,具有较大的应用前景。

基于傅里叶系数实部的脉冲流信号欠Nyquist采样方法

有限新息率(FRI)采样理论可以远低于信号Nyquist频率的采样速率实现对脉冲流信号的欠采样。经典的FRI重构算法大多基于傅里叶系数进行运算,其中存在大量的对复数矩阵的奇异值分解,降低了算法的执行效率。针对该问题,该文提出基于傅里叶系数实部的脉冲流信号FRI采样及重构方法。首先利用离散余弦变换从脉冲流信号的低速采样值中获取其傅里叶系数实部信息,并在重构算法中使用实部的Toeplitz矩阵以提高奇异值分解(SVD)的效率;其次,为了提升经典的零化滤波器算法的鲁棒性,该文从傅里叶系数实部协方差矩阵的旋转不变特性以及零空间特性出发,提出基于离散余弦变换的协方差矩阵分解算法以及基于离散余弦变换的零空间搜索算法来估计脉冲流信号的特征参数,并针对出现的共轭根问题,提出基于交替方向乘子法的去共轭算法。仿真结果表明:在信号新息率较高的情况下,使用傅里叶系数实部信息会极大提高算法的执行效率,同时保证参数估计的准确性。

基于改进1DCNN的煤岩识别模型研究

随着煤矿智能化建设的加速推进,煤岩高效识别已成为煤炭智能化开采亟待解决的技术难题。针对复杂煤矿地质条件下现有煤岩识别方法精度低、通用性差且难以工程应用等问题,提出了一种基于改进一维卷积神经网络(1DCNN)的煤岩识别模型。以1DCNN为基础,使用多个连续卷积层提取一维振动信号特征,通过全局均值池化(GAP)层代替全连接层,以减少模型训练参数,节省计算资源,同时采用带有线性热启动的余弦退火衰减方法优化学习率,以避免模型训练陷入局部极小值区域,提升训练质量。为直观描述改进1DCNN模型对煤岩截割振动数据的特征提取过程和分类能力,采用t-分布随机近邻嵌入(t-SNE)流形学习算法对模型的特征学习过程进行可视化分析,结果表明,改进1DCNN模型通过逐层特征学习,很好地实现了对煤岩截割状态的识别。以陕西某矿MG650/1590-WD型采煤机截割煤岩时的实测振动数据为样本进行模型训练,结果表明,改进1DCNN模型在训练集上的准确率为99.91%,在测试集上的准确率为99.32%,可直接用于采煤机截割煤岩时的原始振动信号分类,并能够有效识别煤岩截割状态。与传统机器学习、集成学习及未改进的1DCNN模型相比,改进1DCNN模型具有明显优势,平均识别准确率达99.56%,同时大大节约了计算成本,提高了模型识别速度。

根升余弦脉冲成型正交相移键控信号关键参数估计

针对非合作通信中根升余弦脉冲成型正交相移键控(QPSK)信号关键参数估计难度较大的问题,利用估计出的功率谱及其直方图实现对载频、符号周期的快速粗估计,在此基础上根据包络谱特点利用Chirp-Z变换估计得到符号周期的精确估计值,引入奇异谱分析方法在信号去噪的基础上实现载频精确估计,以载频及符号周期精确估计值为基础,实现对根升余弦脉冲滚降系数的估计,并给出了基于子空间分解对信噪比和信号幅度进行估计的方法。仿真表明,该方法在较低信噪比条件下仍能达到较好的估计精度。

时间域单频干扰波的压制

在野外地震数据采集过程中 ,地震测线上方如果有高压输电线通过 ,在地震记录中就存在一个 50 Hz左右的强单频干扰波。这种干扰波在地震记录的整个时间段上具有很强的能量 ,并且其频率、相位和振幅可认为基本保持不变。采用频率域压制方法仅仅是对噪声和信号同时压制一定的倍数 ,并没有完全消除这种干扰 ,即没有改善单频分量频率附近的信噪比。因此 ,我们尝试在时间域内采用余弦波来逼近这种干扰波 ,并从地震记录中减去它 ,以实现压制这种干扰波的目的。本方法的最大优点是只压制单频干扰波 ,不伤害有效信号 ,从而使单频频率分量附近信噪比得到提高。通过理论和实际数据的试算 ,说明了本方法的有效性和可行性。

过渡内蕴模态函数对经验模态分解去噪结果的影响研究及改进算法

经验模态分解(Empirical Mode Decomposition,EMD)是一种具有较大应用潜力的去噪算法.目前,该算法存在的一个较大问题是过渡内蕴模态函数(Intrinsic Mode Function,IMF)中混叠噪声不能有效处理.过渡内蕴模态函数中混叠噪声不易剔除,限制了该算法的应用.本文针对此问题,通过研究过渡IMF的特点,首次提出一种有效去除过渡IMF中混叠噪声的方法.该方法首先对原信号进行一次EMD处理,得到包含过渡IMF的初步去噪结果,并将其与合适的余弦信号结合,改变其包络分布,然后对其结果再次进行EMD处理,仿真实验表明该方法在保留有效信号的同时,可以有效的去除过渡IMF中混叠的噪声,并将该方法用于实际地震资料随机噪声压制,处理效果令人满意.

一种利用分布式传声器阵列的声源三维定位方法

为了提高噪声和混响条件下分布式传声器阵列进行声源定位的性能,提出一种利用空间稀疏性和压缩感知原理的声源三维定位方法。该方法首先通过两次离散余弦变换方式提取出声音信号特征,并用该特征来构建稀疏定位模型,以便能够综合利用语音信号的短时和长时特性,同时降低模型维数;然后利用在线字典学习技术动态调整字典,克服稀疏模型与实际信号之间的失配问题,增强稀疏定位模型的鲁棒性;进而提出一种改进的平滑l_0范数稀疏重构算法来进行声源位置解算,以提高低信噪比条件下的重构精度。仿真结果表明该方法不仅可以实现多目标定位,而且具有较强的抗噪声和抗混响能力.

离散分数余弦变换在碰摩声发射信号降噪中的应用

声发射信号经常淹没在各种复杂的非平稳噪声中,使得对有用的声发射的识别非常困难,因此,在声发射识别时进行信号增强成为诸多研究者的关注热点。离散余弦变换是声信号增强的有效方法。基于态函数,给出了分数余弦变换的新定义,提出三周期离散分数余弦变换方法,介绍了基于三周期离散分数余弦变换的声发射信号增强算法和改进算法的推导过程。实验数据为在转子实验台上采集的碰摩声发射信号,通过在该信号上叠加高斯白噪声和非平稳噪声来获得模拟的严重噪声污染的声发射信号,然后用增强算法及改进算法对该信号进行降噪处理和声发射信号识别。实验结果表明:两种算法对多种非平稳噪声环境下的碰摩声发射信号均能取得较好的降噪效果,且优于离散余弦变换,是声发射信号识别的有效途径。

形态分量分析框架下基于DCT与曲波字典组合的地震信号重建

针对单一型数学变换或字典不能有效刻画地震信号的形态特征多样性这一问题,在形态分量分析(MCA)框架下,提出了一种基于离散余弦变换(DCT)与曲波字典组合的地震信号重建方法。该方法首先建立MCA框架下的地震信号重建模型,依托模型将信号分解成局部奇异形态分量以及平滑与线状形态分量。然后采用DCT字典表示局部奇异分量,采用曲波字典表示平滑与线状分量。再以迭代求解方式逐一重建各分量,最后将重建后的分量合并。人工合成地震信号和二维叠前及叠后实际地震信号重建实验结果表明,该方法能很好完成信号重建,重建精度不仅要高于非抽样小波变换(UDWT)与曲波字典组合、曲波与曲波字典组合、余弦调制滤波器组与曲波字典组合,而且更高于DCT,UDWT,或曲波等单一型字典。