Speech Encryption in Linear Canonical Transform Domain Based on Chaotic Dynamic Modulation

In order to transmit the speech information safely in the channel,a new speech encryp-tion algorithm in linear canonical transform(LCT)domain based on dynamic modulation of chaot-ic system is proposed.The algorithm first uses a chaotic system to obtain the number of sampling points of the grouped encrypted signal.Then three chaotic systems are used to modulate the corres-ponding parameters of the LCT,and each group of transform parameters corresponds to a group of encrypted signals.Thus,each group of signals is transformed by LCT with different parameters.Fi-nally,chaotic encryption is performed on the LCT domain spectrum of each group of signals,to realize the overall encryption of the speech signal.The experimental results show that the proposed algorithm is extremely sensitive to the keys and has a larger key space.Compared with the original signal,the waveform and LCT domain spectrum of obtained encrypted signal are distributed more uniformly and have less correlation,which can realize the safe transmission of speech signals.

INTERFERENCE MITIGATING BASED ON FRACTIONAL FOURIER TRANSFORM IN TRANSFORM DOMAIN COMMUNICATION SYSTEM

The method of FRactional Fourier Transform (FRFT) is introduced to Transform Domain Communication System (TDCS) for signal transforming in the paper after theoretical analysis. The method yields optimal Basis Function (BF) by FRFT with optimal transform angle. The TDCS using the proposed method has wider usable spectrum, stronger robustness and better ability of anti non-stationary jamming than using usual methods, such as Fourier Transform (FT), Auto Regressive (AR), Wavelet Transform (WT), etc. The main simulation results are as follows. First, the Bit Error Rate (BER) Pb is close to theoretical bound of no jamming no matter in single tone or in linear chirp interference. Second, the interference-to-signal ratio J /E is at least 12dB more than that of Direct Spread Spectrum System (DSSS) under the same BER if the spectrum hopping-to-signal ratio is 1:20 in chirp plus hopping interfering. Third, the Eb /N 0(when estimation difference is 90% between trans- mitter and receiver) is about 3.5dB or about 0.5dB (when estimation difference is 10% between transmitter and receiver) more than that of theoretical result when no estimation difference un-der Pb=10-2.

TRANSFORM DOMAIN SMART ANTENNAS ALGORITHM FOR MAI SUPPRESSION

Multiple Access Interference(MAI) is the major factor that degrades the performance of a CDMA system. In this paper, a novel transform domain algorithm combined with parameter estimation for MAI suppression is proposed. Compared with the method that combines an adaptive array antenna with parameter estimation for interference suppression, it converges faster with the same Bit Error Rate(BER) performance.

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.

基于双域Transformer耦合特征学习的CT截断数据重建模型

目的为解决CT扫描视野(FOV)不足导致的截断伪影和图像结构失真问题,本文提出了一种基于投影和图像双域Transformer耦合特征学习的CT截断数据重建模型(DDTrans)。方法基于Transformer网络分别构建投影域和图像域恢复模型,利用Transformer注意力模块的远距离依赖建模能力捕捉全局结构特征来恢复投影数据信息,增强重建图像。在投影域和图像域网络之间构建可微Radon反投影算子层,使得DDTrans能够进行端到端训练。此外,引入投影一致性损失来约束图像前投影结果,进一步提升图像重建的准确性。结果Mayo仿真数据实验结果表明,在部分截断和内扫描两种截断情况下,本文方法DDTrans在去除FOV边缘的截断伪影和恢复FOV外部信息等方面效果均优于对比算法。结论DDTrans模型可以有效去除CT截断伪影,确保FOV内数据的精确重建,同时实现FOV外部数据的近似重建。

基于双域交互Transformer的磁共振图像重建

对k空间数据部分采样是加速磁共振成像的主要方法.从欠采样的数据中重建出高质量的磁共振图像,在临床诊断和研究分析中有着重要的应用价值.近年来,基于深度学习的方法在磁共振重建领域取得了一些进展,然而单独面向图像域或频域的网络不能同时利用双域特征共同提升重建质量.另外,虽然已有一些双域重建方法模型,但是缺乏双域数据交互融合限制了重建性能.针对以上问题,本文提出了一种基于双域交互Transformer的磁共振图像重建网络模型,使用Transformer提取双域特征,并利用交互注意力引导双域特征融合,实现了双域特征的高效提取和交互.具体地,首先,由于频域数据每个点都对应着图像域的所有像素点,因此在k空间使用1×1的卷积提取全局特征,同时使用基于窗口的Transformer将注意力的计算限制在了窗口中,减小了计算负担,并且可以有效地对图像域特征进行表示.其次,提出了基于交互注意力的Transformer融合模块引导双域特征融合,通过挖掘双域特征的相关性,实现跨域信息融合.实验证明,在公开的数据集上,本方法相较于其他基线重建方法均能取得更为优异的重建效果.同时,消融实验证明了本文所提出的网络模块的有效性.

高效跨域的Transformer小样本语义分割网络

小样本语义分割旨在仅使用数个标注样本学习目标类别特征并完成分割任务。主流研究存在的主要问题是:训练效率低下,训练和测试在同一数据域。为此构建了一种基于Transformer的高效、跨域的小样本语义分割网络SGFNet。在编码层,使用共享权重的MixVisionTransformer构建孪生网络,用于提取支持集和查询集的图像特征;在关系计算层,通过计算支持集图像特征向量与其对应mask的哈达玛积,提取目标类别的高维特征,并与查询集图像特征进行关系计算;在解码层,改进基于MLP的解码器,提出了残差解码器,将不同层级的特征解码得到最终分割结果。实验表明,该模型只需要在FSS-1000数据集上使用单张3090 GPU训练1.5~4.0 h,即可在FSS-1000数据集上获得最优结果1-shot mIoU 87.0%,在PASCAL-5i和COCO-20i数据集进行跨域测试达到非跨域的效果,1-shot mIoU分别为60.4%和33.0%,证明了该模型高效且跨域。

基于复数域Transformer-Unet混合模型的PolSAR地物分类

传统的基于深度学习的极化合成孔径雷达(Polarimetric Synthetic Aperture Radar,PolSAR)地物分类方法,通过堆叠卷积层提取图像局部特征,难以建立长距离依赖关系。基于自注意力机制的深度学习模型Transformer (变换)在图像分类任务中取得了成功,其自注意力机制能够捕获全局像素之间的关联性,同时PolSAR地物分类任务被证实:相比于实数域,其在复数域上表现出更好的分类效果。因此,本文将Transformer引入到复数域中,提出了一种基于复数域的Transformer和Unet (语义分割网络)混合模型(CT-Unet)用于PolSAR地物分类,将Transformer与CNN相结合,对复数类型的PolSAR数据进行特征提取,使用西安数据集和德国数据集进行PolSAR地物分类的实验结果表明:提出的模型能够有效提高PolSAR地物分类的准确性,Transformer有望在PolSAR地物分类任务中弥补卷积神经网络的不足。

Transformer在域适应中的应用研究综述

作为迁移学习的重要分支,域适应旨在解决传统机器学习算法在训练样本和测试样本服从不同数据分布时性能急剧下降的问题。Transformer是基于自注意力机制的深度学习框架,具有强大的全局特征提取能力和建模能力,近年来Transformer与域适应相结合也成为研究的热点。虽然已有大量相关方法问世,但Transformer应用在域适应的综述却未见报道。为了填补这个领域的空白,为相关研究提供借鉴和参考,对近年来出现的一些基于Transformer的典型域适应方法进行归纳总结与分析,概述域适应的相关概念与Transformer的基本结构,从图像分类、图像语义分割、目标检测、医学图像分析四个应用梳理了各种基于Transformer的域适应方法,对图像分类下的域适应方法进行比较,总结当前域适应Transformer模型存在的挑战并探讨未来可行的研究方向。

小波分频自注意力Transformer图像去雨网络

针对视觉Transformer对高频信息捕捉能力弱以及目前许多图像去雨方法易丢失细节的问题,提出小波分频自注意力Transformer图像去雨网络(WFDST-Net)。小波分频自注意力Transformer(WFDST)作为WFDST-Net的主要模块,其利用不可分提升小波变换获取特征图的低频分量和高频分量,分别在低频和高频中进行自注意力交互,使模块从低频中学习恢复全局结构的能力,在高频中强化捕捉雨纹等线条细节的能力,增强对不同频域特征的建模能力。WFDST-Net采用U形架构并通过不可分提升小波变换获取多尺度特征,可在捕获不同形状高频雨纹的同时保证信息的完整性。相比其他图像去雨相关的Transformer,WFDST-Net具有更低的参数量。此外,提出VOCRain250数据集用于联合图像去雨和语义分割任务,该数据集比目前广泛使用的BDD150更具优势。实验表明,所提方法增强了视觉Transformer对不同频域信息的捕获能力,并在合成和真实数据集以及VOCRain250中的表现优于目前先进的去雨方法,能有效去除复杂雨纹并保留更多细节特征。

Efficient reconstruction scheme for transform domain Wyner-Ziv video coding

To minimize the errors of the reconstructed values and improve the quality of decoded image, an efficient reconstruction scheme for transform domain Wyner-Ziv （WZ） video coding is proposed. The reconstruction scheme exploits temporal correlation of the coefficient bands, the WZ decoded bits stream and the side information efficiently. When side information is outside the decoded quantization bin, the reconstructed value is derived using expectation of the WZ decoded bit stream and the side information. When side information is within the decoded quantization bin, the reconstructed value is derived using the biased predictor. Simulation results show that the proposed reconstruction scheme gains up to 1.32 dB compared with the commonly used boundary reconstruction scheme at the same bit rates and similar computation cost.

Post-Mesozoic Transformation of Tectonic Domain in Southeastern China and Its Geodynamic Mechanism

Since the Mesozoic and Cenozoic, a transformation from a Tethyan Himalayan tectonic domain into a circum Pacific tectonic domain from Indosinian to Yanshanian is indicated in this paper, resulting in conspicuous changes in geophysics, tectono magmatic distribution, lithofacies and paleo geography, tectonic system in southeastern China. Tectonic analysis shows that the tectonic framework resulted from the compounding, transforming and superimposing of the two tectonic domains. The geodynamic mechanism of the transformation is mainly shown as the transverse and longitudinal heterogeneity of lithosphere, and the exchange between the crust and the mantle.

基于多尺度Transformer融合多域信息的伪造人脸检测

当前,基于Deepfakes等深度伪造技术生成的“换脸”类伪造视频泛滥,给公民个人隐私和国家政治安全带来巨大威胁,为此,研究视频中深度伪造人脸检测技术具有重要意义。针对已有伪造人脸检测方法存在的面部特征提取不充分、泛化能力弱等不足,提出一种基于多尺度Transformer对多域信息进行融合的伪造人脸检测方法。基于多域特征融合的思路,同时从视频帧的频域与RGB域进行特征提取,提高模型的泛化性;联合EfficientNet和多尺度Transformer,设计多层级的特征提取网络以提取更精细的伪造特征。在开源数据集上的测试结果表明,相比已有方法,所提方法具有更好的检测效果;同时在跨数据集上的实验结果证明了所提模型具有较好的泛化性能。

Power System Harmonic Detection Using Frequency-Domain Interpolation Wavelet Transform

Aiming at harmonic detection, fast Fourier transform can only detect integer harmonics precisely, short time Fourier transform can detect non-integer harmonics with low resolution, and some former wavelet based methods have no aliasing-reduction scheme which result in low measurement precision and poor robustness. A frequency-domain interpolation algorithm to detect harmonics is proposed by choosing Shannon wavelet. Shannon wavelet is an orthogonal wavelet possessing best ideal frequency domain localization ability, it can restrict wavelet abasing but bring about Gibbs oscillation phenomenon simultaneously. An interpolation algorithm is developed to overcome this problem. Simulation reveals that the proposed method can effectively cancel aliasing, spectral leakage and Gibbs phenomenon, so it provides an effective means for power system harmonic analysis.

基于频域Transformer的对抗生成网络去运动模糊算法

对于图像不均匀的运动模糊问题,模型感受野的大小相当重要,基于自注意力机制的Transformer的感受野远大于传统CNN模型。并且引入了高效的频域处理模块,以往结合频域处理模糊的方法忽略了运动模糊在频域图中呈现出来的方向性特征,根据这种特征将其分解处理可以高效地恢复图像。实验中与目前较经典的几个方法进行了对比,模型在GoPro数据集上PSNR得分为32.24 dB,在RealBlur-J数据集上的得分为29.38 dB,效果相较于其他方法在两个数据集上分别提高了0.72 dB和0.83 dB。

TIME DOMAIN ANALYSIS OF TRANSIENT PROPAGATION IN INHOMOGENEOUS MAGNETIZED PLASMA USING Z-TRANSFORMS

The electromagnetic propagation through an inhomogeneous magnetized plasma slab is studied using the Z-transform formulation of the Finite-Difference Time-Domain(FDTD) method. The direction of electromagnetic propagation is parallel to the biasing magnetic filed. To validate the Z-transform algorithm, the reflection and transmission coefficients for the right-hand circularly polarized wave of the homogeneous magnetized plasma slab are computed by means of discrete Fourier transform. The comparison between the reflection and transmission coefficients of the homogeneous plasma slab and analytical values indicates that Z-transform algorithm is very accurate. When the plasma frequency varies according to the square root and parabolic relations, the reflection and transmission coefficients of the inhomogeneous magnetized plasma slab are computed.

基于Transformer和多特征融合的DGA域名检测方法

针对域名生成算法生成的恶意域名隐蔽性高,现有方法在恶意域名检测上准确率不高的问题,提出一种基于Transformer和多特征融合的DGA域名检测方法。该方法使用Transformer编码器捕获域名字符的全局信息,通过并行深度卷积神经网络获取不同粒度的长距离上下文特征,同时引入双向长短期记忆网络BiLSTM和自注意力机制Self-Attention结合浅层CNN得到浅层时空特征,融合长距离上下文特征和浅层时空特征进行DGA域名检测。实验结果表明,所提方法在恶意域名检测方法上有更好的性能。相对于CNN、LSTM、L-PCAL和SW-DRN,所提方法在二分类实验中准确率分别提升了1.72%,1.10%,0.75%和0.34%;在多分类实验中准确率分别提升了1.75%,1.29%,0.88%和0.83%。

基于深度学习和双域融合的红外成像制导系统复杂背景噪声去除方法

红外成像制导系统受到严苛运行环境影响,其成像过程伴随复杂背景噪声干扰,严重影响系统制导跟踪精度。为减少复合噪声对红外成像效果的影响,在分析多种常见噪声的成因和特性的基础上,提出一种基于加性成分和乘性成分的噪声特性先验设定,结合空间域和变换域的双域融合去噪思想,设计了一种基于深度卷积神经网络的多类型噪声去除方法。该方法将富梯度流卷积模块引入UNet++结构以缩减梯度信息冗余并提升多感受野特征提取能力;针对噪声形态特性提出维度注意力机制以实现双域噪声估计;引入高阶双树复小波变换作为域变换方法,提升在不同尺度和方向上对噪声成分的识别能力。通过消融实验验证了噪声先验设定以及双域融合去噪思想的有效性和优越性,通过对比实验证明了所提方法对多种类型噪声均具有优秀的去噪能力。所提方法对高斯噪声的去噪峰值信噪比和结构相似度指标分别达到29.57和0.85,优于其他典型噪声抑制方法;对多类型混合噪声则分别达到27.84和0.82,达到良好的去噪水平。此外,也验证了所提方法对真实图像噪声具有优秀的去噪能力。

Mechatronic Modeling and Domain Transformation of Multi-physics Systems

The enhanced definition of Mechatronics involves the four underlying characteristics of integrated,unified,unique,and systematic approaches.In this realm,Mechatronics is not limited to electro-mechanical systems,in the multi-physics sense,but involves other physical domains such as fluid and thermal.This paper summarizes the mechatronic approach to modeling.Linear graphs facilitate the development of state-space models of mechatronic systems,through this approach.The use of linear graphs in mechatronic modeling is outlined and an illustrative example of sound system modeling is given.Both time-domain and frequency-domain approaches are presented for the use of linear graphs.A mechatronic model of a multi-physics system may be simplified by converting all the physical domains into an equivalent single-domain system that is entirely in the output domain of the system.This approach of converting(transforming)physical domains is presented.An illustrative example of a pressure-controlled hydraulic actuator system that operates a mechanical load is given.

基于离散余弦变换与Transformer的语音增强

为更有效提取低信噪比下含噪语音中的语音信息,受到基于多头自注意力机制的两阶段Transformer的启发,提出结合离散余弦变换与Transformer的语音增强算法。语音信号经离散余弦变换后的频谱为实数谱,将其作为深度神经网络的输入时,无需考虑实部、虚部的计算问题。使用两阶段Transformer提取局部和全局特征,在此基础上,通过编解码器网络得到增强后的语音。实验结果表明,所提方法与各参考方法相比,在-9到5的信噪比下,PESQ指标平均提升了9.92%,STOI指标平均提升了10.19%,在较低信噪比下具有良好的降噪能力。