基于二元树复小波域的数字水印算法

提出了一种改进的、基于二元树复小波变换的数字水印算法.该算法对水印的嵌入和检测机制进行了改进,使用了扩频嵌入机制和纠错码技术,检测算法性能时将同样的嵌入机制应用在离散小波变换(DWT)中,并采用水印检测软件对它们进行检测对比.实验结果表明,该水印算法能够适应局部图像的变化,水印对于压缩攻击、大部分的滤波攻击都有很好的鲁棒性.     

基于双树复小波的图像边缘检测

边缘检测在图像处理中具有非常重要的作用,它可以作为图像分割、模式识别及场景分析和图像检索的基础.经典的边缘检测算法相对比较简单,但方向适应性受到一定限制,尤其针对多方向的边缘,其检测结果明显不满足要求.在充分研究小波性能特点以及边缘检测的基础上,提出了方向选择性更强的双树复小波边缘检测算法.＃实验证明:该算法边缘定位效果好,边界相对清晰而且比较连续,同时能充分检测出图像细节部分的边缘.     

Dual tree complex wavelet transform approach to copy-rotate-move forgery detection

As a common scheme of image tampering,copy-move forgery plays an important role in image forgery committee.Although several blind methods aimed at detecting replicated regions have been proposed,these methods cannot detect the rotation changes in the duplicated areas efciently.In this paper,a new forensic method is presented to detect the replicated areas rotated by arbitrary angles,even by JPEG compression.To achieve this,overlapping blocks of pixels are decomposed using dual tree complex wavelet transform(DTCWT),and then channel energies are extracted from each subband at each decomposition level using the L1norm.Finally,the anisotropic rotationally invariant features are extracted using magnitudes of discrete Fourier transform for these channel energies.The implementation procedure of the proposed method is described in detail.Extensive experimental results,including a comparative evaluation with existing methods and the special applications in practice,are also presented to demonstrate the robustness and efectiveness of the proposed method.     

尺度变换复双树小波网络隐藏信道深度检测

针对隐藏信道检测问题解决中,传统检测算法存在特定隐藏信道盲区,或对某类隐藏信道针对性过强而忽视其他隐藏信道的问题,提出一种基于复双树小波包变换的邻域和空域联合网络隐藏信道检测。首先,基于复双树小波包有限冗余变换所特有的平移不变特性,尺度间不同变换系数的相关性,以及尺度相同变换系数邻域间的相关特征,并结合信号增强机制,实现对变换系数的取舍,以达到隐藏信道信号增强效果;其次,与块阈值算法进行联合对网络时间隐藏通道特征提取,然后采用深度学习方式实现隐藏信道训练和检测。最后,通过在IPCTC、TRCTC、Jitterbug、MBCTC、FXCTC五种典型时间隐藏通道中进行实验验证,显示所提算法具有更高的特征精度和较快的计算时间。     

结合聚合通道特征和双树复小波变换的手势识别

目的 针对目前手势识别方法受环境、光线、旋转、缩放、肤色等因素的影响,导致手势识别精度下降的问题,提出一种结合聚合通道特征（ACF）的手势检测和双树复小波变换（DTCWT）的复杂背景下手势识别方法。方法 在手势图像预处理过程中引入聚合通道特征,采用Adaboost分类器和非极大值抑制算法（NMS）进行目标手势的检测;利用DTCWT对目标手势图像进行多尺度多方向分解,对高低频系数的每一块分别提取方向梯度直方图（HOG）和局部二值模式（LBP）特征;最后融合各个方向上的高低频特征并通过支持向量机（SVM）进行分类识别。结果 选取多个场景、多个对象、不同角度和距离的图像作为训练集,并标注区分前背景,对20种手势进行识别实验,并与传统的肤色检测、HOG特征手势识别、类-Hausdorff距离的手势识别算法进行了实验对比。在任意可承受范围内的光照、距离等情况下,该方法能够更准确实时地实现手势识别,平均精度达到95.1%。结论 在图像预处理的情况下,聚合通道特征的引入能够准确检测手势,同时基于DTCWT的手势图像频域特征提取和再融合的方法有效地解决了传统普通图像的单特征识别方法在光线和复杂背景下识别精度不高的问题。     

A new dual‐band single‐ended‐to‐balanced filtering power divider

This article proposes a new dual‐band single‐ended‐to‐balanced (SETB) filtering power divider (FPD), which shows the excellent characteristics of wideband common‐mode (CM) suppression and good selectivity. By employing the structure of double‐sided parallel‐strip line with a mid‐inserted conductor and a T‐shaped defected ground structure etched in the mid‐inserted conductor, out‐of‐phase behavior and high CM suppression can be achieved successfully. Besides, to realize dual‐wideband filtering performance and high selectivity, two pairs of step impedance stubs (SIS) loaded quarter‐wavelength central line‐terminal‐shorted three parallel‐coupled microstrip lines structure are adopted. Meanwhile, two pairs of resistors are introduced so as to realize excellent isolation. To verify effectiveness of the design method, a prototype of dual‐band SETB FPD which operates at 3.2 and 4.9 GHz is designed, fabricated, and tested. Final results exhibit that the new dual‐band SETB FPD possess high selective dual‐band differential mode response, wideband CM suppression, and excellent isolation between the balanced output ports.     

A novel symmetrical Wilkinson power divider for dual‐band application

A symmetrical two‐way Wilkinson power divider with shifted output ports, much wide bandwidth and large frequency‐ratio is proposed for dual‐band application. The corresponding transcendental design equations are derived by using the even‐ and odd‐mode analysis. Moreover, the closed‐form scattering parameter expressions are derived. Transcendental design equations are solved and accurate numerical design parameters along with different frequency ratios are obtained. Finally, the proposed structure and design method are validated by simulated and experimental results of typical microstrip planar power dividers, the performance is clearly observed for the input and output matching, isolation and transmission characteristic very well at the two band frequencies. More specifically, the measured transmission characteristics of the divider are 3.11 dB/3.58 dB at the 1.0 GHz/3.5 GHz, respectively. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:102–108, 2014.     

Dynamic deviation reduction‐based concurrent dual‐band digital predistortion

This article introduces the concurrent dual‐band digital predistortion (DPD) architecture with only one upconvertion unit, which is suitable for the linearization of wideband power amplifiers (PAs) excited by concurrent dual‐band signals. By extending the conventional dynamic deviation reduction (DDR) model to the concurrent dual‐band mode, we propose two DDR‐based concurrent dual‐band models, the dual‐band DDR (DB‐DDR) model and the simplified dual‐band DDR (SDB‐DDR) model. The performance of these two models is experimentally assessed with two types of wideband PAs (a GaN Class F PA and a GaN Doherty PA) driven by the concurrent dual‐band signal, and compared with the prior two‐dimensional digital predistortion (2D‐DPD) model and the two‐dimensional modified memory polynomial (2D‐MMP) model. The results prove the good DPD performance and low computational complexity of the proposed models. © 2013 Wiley Periodicals, Inc. Int J RF and Microwave CAE 24:401–411, 2014.     

ANN‐based modeling of compact impedance‐varying transmission lines with applications to ultra‐wideband Wilkinson power dividers

The modeling of the physical and electrical characteristics of microstrip non‐uniform transmission lines (NTLs) utilizing artificial neural networks (ANNs) is investigated. The fundamental equations and constraints for designing variable impedance transmission lines are first presented. Then, a proof‐of‐concept example of a compact non‐uniform matching transformer and the counterpart modeled version is elaborated for source and load impedances Z_s and Z_l, respectively, at 0.5 GHz. For comparison purposes, weights and biases of the proposed ANN are established with three different training techniques; namely: backpropagation (BP), Quasi‐Newton (QN), and conjugate gradient (CG); at which the ABCD matrix, impedance variations, input port matching (S₁₁), and transmission parameter (S₂₁) are set as benchmarks to examine the validity of the trained model. The concept is then extended to model a NTL ultrawideband (UWB) Wilkinson power divider (WPD) with three resistors for improved isolation. S‐parameters derived from the trained ANN outputs are close to those obtained by the traditional time‐consuming optimization procedure, and show input and output ports matching and isolation of below ?10 dB, and acceptable values of transmission parameters over the 3.1 GHz to 10.6 GHz band. The resulting models outperform traditional optimizations in terms of simulation time and reserved resources with comparable accuracy. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:563–572, 2015.     

基于小波与神经网络的GPS周跳探测与修复*

为了得到精密的GPS定位结果,必须对载波相位中的周跳进行有效地探测与修复.分析了周跳发生的原因及其特性,提出了一种周跳探测与修复的二步法.首先对信号进行小波多分辨率分析,根据小波系数的模极大值点的位置准确探测出周跳发生的历元;然后对周跳两侧的信号采用神经网络进行分别预测,通过对比预测值的不同来确定出周跳的大小,从而实现了周跳的修复.文末,利用实测的相位数据,验证了方法的可行性与有效性.     

基于复小波和支持向量机的纹理分类法*

针对图像纹理分类问题,提出了一种将二元树复小波变换与支持向量机相结合的分类方法,通过二元树复小波变换对纹理图像进行四层分解,提取各子频带小波系数模的均值和标准方差组成特征向量,利用支持向量机作为分类器实现纹理图像分类。对20类Brodatz纹理图像的分类实验表明,提出的方法具有较高的分类精度,在有限训练样本的情况下比传统的分类算法平均正确率有10%左右的提高,体现了该方法的有效性和良好的泛化能力。     

Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients

Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. As EEG signals are non-stationary, the conventional method of frequency analysis is not highly successful in recognition of alertness level. This paper deals with a novel method of analysis of EEG signals using wavelet transform, and classification using ANN. EEG signals were decomposed into the frequency sub-bands using wavelet transform and a set of statistical features was extracted from the sub-bands to represent the distribution of wavelet coefficients. Then these statistical features were used as an input to an ANN with three discrete outputs: alert, drowsy and sleep. The error back-propagation neural network is selected as a classifier to discriminate the alertness level of a subject. EEG signals were obtained from 30 healthy subjects. The group consisted of 14 females and 16 males with ages ranging from 18 to 65 years and a mean age of 33.5 years, and a Body Mass Index (BMI) of 32.4±7.3 kg/m². Alertness level and classification properties of ANN were tested using the data recorded in 12 healthy subjects, whereby the EEG recordings were not used to train the ANN. The statistics were used as a measure of potential applicability of the ANN. The accuracy of the ANN was 95±3% alert, 93±4% drowsy and 92±5% sleep.     

Volterra series‐based model for concurrent dual‐band power amplifier using dynamic memory depth

In this article, a dynamic dual‐band baseband equivalent Volterra (DDBE) model is proposed to compensate the nonlinear distortions of the concurrent dual‐band power amplifier (PA). The DDBE model is obtained by improving the discretized dual‐band baseband equivalent Volterra model which can describe the output characteristics of concurrent dual‐band PA completely in theory but is lack of practicability. Three simplification rules are proposed in the article, and the relevance between in‐band intermodulation and in‐band cross modulation is employed to simplify the establishment complexity of the model. Then the dynamic kernels are categorized into three groups, and based on this, DDBE models with different level dynamical kernels are derived. In addition, draw on the experience of single‐band PA behavioral model, even‐order kernels are introduced into DDBE models. Digital predistortion performances of a wideband PA, which works in concurrent dual‐band mode, are evaluated to verify the feasibility and advantages of the proposed model.     

Behavioral modeling for concurrent dual‐band power amplifiers using 2D hammerstein/wiener models

Behavioral modeling for the concurrent dual‐band power amplifier (PA) is a critical problem in practical applications. The nonlinear distortion in the concurrent dual‐band PA is quite different from that in the conventional single‐band PA. This article analyzes the nonlinearities in the concurrent dual‐band PA and reveals that both input signals in the dual bands are important for the behavioral modeling. The 2D Hammerstein model and 2D Wiener model are proposed for the first time for the concurrent dual‐band PA. They are extended versions of conventional Hammerstein and Wiener structures used in the single‐band PA by including the cross‐band intermodulation in the static nonlinearity block. The proposed 2D models require much less coefficients than the original work of the 2D‐DPD model. Experiments were carried out for an 880 MHz/1960 MHz concurrent dual‐band Doherty PA to demonstrate the effectiveness of the proposed models. The results clearly show that less than ?40 dB normalized mean square errors (NMSEs) are obtained in the dual bands in the behavioral modeling. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 23: 646–654, 2013.     

基于替换规则的对偶树复小波变换遥感影像融合*

针对对偶树复小波变换(DT-CWT)比离散小波变换具有更好的方向选择性、平移不变性,能更好地表征影像特征的优点,提出了基于替换规则的对偶树复小波变换遥感多光谱和全色影像融合算法。通过对IKONOS影像的实验表明,本方法比基于实数小波变换的融合方法和IHS变换法有优势,在保留了光谱特征的基础上,更好地提高了融合影像的清晰度,且方法简单高效。     

抑郁症静息脑电的小波包节点功率谱熵分析

头皮脑电(EEG)信号反映了大脑皮层神经元细胞群自发性节律性的电生理活动,含有丰富的生理与病理信息,是临床脑神经与精神疾病诊断的重要依据.针对抑郁症的研究和诊断中缺少客观有效的量化参数和指标的状况,提出一种基于小波包分解节点重构信号的功率谱熵值(记为W值)的脑电信号分析方法,并利用此方法对静息态的脑电信号进行计算和分析.实验和分析结果表明:抑郁症患者脑电信号S32节点(频率24~32 Hz)的熵值(置信区间[0.0129,0.0176])在部分脑区显著大于正常健康人(置信区间[0.0246,0.0303]),显示抑郁症病人快波节律的能量分布存在弥散性,符合现在关于抑郁症患者自我调节能力减弱的发病机制.对结果进行了T检验统计分析,证明了这种辨别方法的准确性和可行性,将为抑郁症疾病检测诊断提供有效的量化物理指标.     

A compact omnidirectional dual‐circinal rectenna for 2.45 GHz wireless power transfer

A compact dual‐circinal rectenna with omnidirectional characteristic is designed for microwave wireless power transmission at 2.45 GHz. A novel dual‐circinal receiving antenna with the reflection coefficient of ?32.5 dB is proposed which is formed by expanding folded curves. By designing an impedance matching network with a 60 ° radial stub and a single stub, a rectifier is presented with the maximum efficiency of 55.6 % and the output dc voltage of 1.19 V under the input power of 0 dBm. Simulation and measurements have been carried out for the antenna and the rectifier. The measured results agree well with the simulated value. The results of rectenna experiment show that the maximum conversion efficiency is 51% at 2.45 GHz when the input power is 0 dBm. The proposed rectenna has the characteristics of compact size and omnidirectional harvesting which are advantageous in RF energy harvesting applications.     

A neuro‐based approach to designing a Wilkinson power divider

In this paper, a new neuro‐based approach using a feed‐forward neural network is presented to design a Wilkinson power divider. The proposed power divider is composed of symmetrical modified T‐shaped resonators, which are a replacement for quarter‐wave transmission lines in the conventional structure. The proposed technique reduces the size of the power divider by 45% and suppresses unwanted bands up to the fifth harmonics. To verify the concept, a prototype of the power divider has been fabricated and tested, exhibiting good agreement between the predicted and measured results. The results show that the insertion loss and the isolation at the center frequency are about 3.3 ± 0.1 dB and 23 dB, respectively.     

Automated emotion recognition employing a novel modified binary quantum‐behaved gravitational search algorithm with differential mutation

The present paper proposes a supervised learning based automated human facial emotion recognition strategy with a feature selection scheme employing a novel variation of the gravitational search algorithm (GSA). The initial feature set is generated from the facial images by using the 2‐D discrete cosine transform (DCT) and then the proposed modified binary quantum GSA with differential mutation (MBQGSA‐DM) is utilized to select a sub‐set of features with high discriminative power. This is achieved by minimising the cost function formulated as the ratio of the within class and interclass distances. The overall system performs its final classification task based on selected feature inputs, utilising a back propagation based artificial neural network (ANN). Extensive experimental evaluations are carried out utilising a standard, benchmark emotion database, that is, Japanese Female Facial Expresssion (JAFFE) database and the results clearly indicate that the proposed method outperforms several existing techniques, already known in literature for solving similar problems. Further validation has also been carried out on a facial expression database developed at Jadavpur University, Kolkata, India and the results obtained further strengthen the notion of superiority of the proposed method.     

A Case‐Based Reasoning system for complex medical diagnosis

A Case‐Based Reasoning (CBR) system for medical diagnosis mimics the way doctors make a diagnosis. Given a new case, its accuracy in practice depends on successful retrieval of similar cases. CBR systems have had some success in dealing with simple diseases because of the robustness of their case base. However, their diagnostic accuracy suffers when dealing with complex diseases particularly those that involve multiple domains in medicine. An example of such a condition is Premenstrual syndrome (PMS) as it falls under both gynaecology and psychiatry. To address this issue, the paper proposes a CBR‐based expert system that uses the K‐nearest neighbour (KNN) algorithm to search k similar cases based on the Euclidean distance measure. The novelty of the system is in the design of a flexible auto‐set tolerance (T), which serves as a threshold to extract cases for which similarities are greater than the assigned value of T. A prototype software tool with a menu‐driven Graphical User Interface (GUI) has been developed for case input, analysis of results, and case adaptation within the system. Finally, the performance of the tool has been checked on a set of real‐world PMS cases.