A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

The essential purpose of radar is to detect a target of interest and provide information concerning the target’s location,motion,size,and other parameters.The knowledge about the pulse trains’properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance.A low autocorrelation binary sequence(LABS)is a complex combinatorial problem.The main problems of LABS are low Merit Factor(MF)and shorter length sequences.Besides,the maximum possible MF equals 12.3248 as infinity length is unable to be achieved.Therefore,this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm(HMSCACSA)using Inverse Filtering(IF)and clipping method to achieve better results.The proposed algorithms,LABS-IF and HMSCACSA-IF,achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237,respectively,where the optimal solutions belong to the skew-symmetric sequences.The MF outperformed up to 24.335%and 2.708%against the state-of-the-art LABS heuristic algorithm,xLastovka,and Golay,respectively.These results indicated that the proposed algorithm’s simulation had quality solutions in terms of fast convergence curve with better optimal means,and standard deviation.

基于Logistic-Sine-Cosine映射的图像加密算法

为了提高数字图像加密的速度和算法的安全性,提出了基于Fridrich框架的加密算法。通过Fisher-Yates变换置乱原始图像打破像素间的强相关性,再利用滤波器在RGB平面上进行滤波得到密文。Logistic-Sine-Cosine复合混沌系统具有良好的混沌特性,且时间复杂度低。密钥由输入参数和明文的SHA-512值共同决定,对明文高度敏感。二维滤波器扩散效果良好,其滤波器模板由明文和密钥决定并引入了伪随机像素值,在提升扩散效果的同时增强了系统抗差分攻击的能力。仿真结果表明,密文图像像素分布近似均匀、像素关联性弱、密钥空间足够大、密钥敏感性高,能够有效抵抗暴力、裁剪、差分等常见攻击,具有较高的安全性,且算法时间复杂度较低。

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies,investigates,and offers solutions to language-related real-life problems.The new coronavirus disease,otherwise known as Coronavirus disease(COVID-19),has severely affected the everyday life of people all over the world.Specifically,since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection,the country has initiated the appropriate preventive measures(like lockdown,physical separation,and masking)for combating this extremely transmittable disease.So,individuals spent more time on online social media platforms(i.e.,Twitter,Facebook,Instagram,LinkedIn,and Reddit)and expressed their thoughts and feelings about coronavirus infection.Twitter has become one of the popular social media platforms and allows anyone to post tweets.This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based senti-ment analysis(SCOBGRU-SA)on COVID-19 tweets.The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic.The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this.Moreover,the BGRU model is utilized to recognise and classify sen-timents present in the tweets.Furthermore,the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter,which helps attain improved classification performance.The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset,and the results signify its promising performance compared to other DL models.

A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such challenges.Metaheuristic algorithms have received a lot of attention in recent years because of their efficient performance and simple structure.Sine Cosine Algorithm(SCA)is a recent Metaheuristic algorithm that is based on two trigonometric functions Sine&Cosine.However,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal regions.In this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive weight.The first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching capabilities.To evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are used.Moreover,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of SCA.The results show that RDSCA has competitive results with other metaheuristics algorithms.

Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm

Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.

Nyström kernel algorithm based on least logarithmic hyperbolic cosine loss

Kernel adaptive filters(KAFs)have sparked substantial attraction for online non-linear learning applications.It is noted that the effectiveness of KAFs is highly reliant on a rational learning criterion.Concerning this,the logarithmic hyperbolic cosine(lncosh)criterion with better robustness and convergence has drawn attention in recent studies.However,existing lncosh loss-based KAFs use the stochastic gradient descent(SGD)for optimization,which lack a trade-off between the convergence speed and accuracy.But recursion-based KAFs can provide more effective filtering performance.Therefore,a Nyström method-based robust sparse kernel recursive least lncosh loss algorithm is derived in this article.Experiments via measures and synthetic data against the non-Gaussian noise confirm the superiority with regard to the robustness,accuracy performance,and computational cost.

Efficient design method of non-uniform cosine modulated filter bank for digital hearing aids

To promote the performance of the traditional multichannel filter bank which leads to speech quality degradation,an efficient design method of the non-uniform cosine modulated filter bank（CMFB） based on the audiogram for digital hearing aids is proposed. First, a low-pass prototype filter is designed by the linear iterative algorithm. Secondly,the uniform CMFB is achieved on the basis of the principle formulas. Then, the adjacent channels of a uniform filter bank which have low or gradual slopes are merged according to the trend of audiogram of the hearing impaired person. Finally,the corresponding non-uniform CMFB is obtained. Simulation results show that the signal processed by the proposed filter bank is similar to the original signal in a time-domain waveform and spectrogram without significant distortion or difference. The speech quality results show that the personal evaluation of speech quality（PESQ） of non-uniform CMFB is 35% higher than that of the traditional design, and the hearing-aid speech quality index（HASQI） increases by about 40%.

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.

Cosine Modulated Non-Uniform Filter Banks

Traditional designs for non-uniform filter bank (NUFB) are usually complex;involve complicated nonlinear optimization with a large number of parameters and lack of linear phase ([LP) property. In this paper, we describe a simple design method for multirate near perfect reconstruction (NPR) cosine modulated filter banks with non-uniform frequency spacing and linear phase property that involves optimization of only single parameter. It is derived from the uniform cosine modulated filter bank (CMFB) by merging some relevant band pass filters. The design procedure and the structure of the uniform CMFB are mostly preserved in the non-uniform implementation. Compared to other design methods our method provides very good design and converges very rapidly but the method is applicable, only if the upper band edge frequency of each non-uniform filter is an integral multiple of the bandwidth of the corresponding band. The design examples are presented to show the superiority of the proposed method over existing one.

基于分数阶Cosine和Sine变换说话人证实特征的提取

提出了基于分数阶Cosine变换和分数阶Sine变换用于说话人证实的新颖特征,即广义Bark平均能量差和主、从分数阶域间广义Bark平均能量差。为考核这些特征的实用性能,完成了一系列说话人证实实验。虽然上述特征的单一应用并未体现出极佳的准确率,但它们经过合理组合后的性能同原单一特征相比更能良好地描述说话人声音个性。

级联COSINE滤波器在抽取滤波中的研究

为了解决高速抽取滤波器系统中传统CIC滤波器旁瓣抑制不够的问题,通过对级联COSINE抽取滤波器和传统CIC抽取滤波器的原理推导进行对比,分析出级联COSINE滤波器在幅频特性上同CIC滤波器具有很大相似之处,且在满足高速抽取滤波器的情况下,同时具备很好的低通特性和硬件实现性。通过MATLAB仿真实验得到,级联COSINE滤波器在进行32倍整数抽取时,第一旁瓣衰减约是传统CIC滤波器的2倍,进而说明相对于传统CIC滤波器,级联COSINE滤波器具有更好的旁瓣抑制性能。

求广义KP方程精确行波解的sine-cosine方法和推广的tanh方法

运用sine-cosine方法和推广的tanh方法求解广义KP方程,以获得其类孤立波解和紧孤子解,这2类精确解的主要特点是具有超强的稳定性,从而对非线性偏微分方程的研究具有重要的意义,sine-cosine方法和推广的tanh方法为众多的非线性偏微分方程的求解提供了有效的数学工具。

一种基于Logistic-Sine-Cosine映射的彩色图像加密算法

科技的飞速发展为拍摄和分享图像带来了便利,但随着图像数据的急剧增多,泄露和篡改等安全问题也频频发生,图像加密技术的应用迫在眉睫,尤其是彩色图像的加密急需改进与发展。传统的加密技术主要是针对数据流加密,其效率低、计算量大,存在一定的局限性。基于变换域加密将图像从空域变换到频域进行加密再变换到空域,属于一种有损加密。基于混沌的加密,密钥空间大,实现简单,加密速度快,一般需要采用多个混沌系统来增强加密的安全性。为此,文中设计了一种针对彩色图像RGB三通道的简单安全的空域加密算法,先由Logistic-Sine-Cosine映射生成较为安全的混沌序列,接着利用混沌序列设计4轮置乱和扩散像素,最后,在一系列安全性分析实验中验证了基于Logistic-Sine-Cosine映射的彩色图像加密算法的安全性和有效性。

Logarithmic Sine and Cosine Transforms and Their Applications to Boundary-Value Problems Connected with Sectionally-Harmonic Functions

Let stand for the polar coordinates in R2, ?be a given constant while satisfies the Laplace equation in the wedge-shaped domain or . Here αj(j = 1,2,...,n + 1) denote certain angles such that αj αj(j = 1,2,...,n + 1). It is known that if r = a satisfies homogeneous boundary conditions on all boundary lines ?in addition to non-homogeneous ones on the circular boundary , then an explicit expression of in terms of eigen-functions can be found through the classical method of separation of variables. But when the boundary?condition given on the circular boundary r = a is homogeneous, it is not possible to define a discrete set of eigen-functions. In this paper one shows that if the homogeneous condition in question is of the Dirichlet (or Neumann) type, then the logarithmic sine transform (or logarithmic cosine transform) defined by (or ) may be effective in solving the problem. The inverses of these transformations are expressed through the same kernels on or . Some properties of these transforms are also given in four theorems. An illustrative example, connected with the heat transfer in a two-part wedge domain, shows their effectiveness in getting exact solution. In the example in question the lateral boundaries are assumed to be non-conducting, which are expressed through Neumann type boundary conditions. The application of the method gives also the necessary condition for the solvability of the problem (the already known existence condition!). This kind of problems arise in various domain of applications such as electrostatics, magneto-statics, hydrostatics, heat transfer, mass transfer, acoustics, elasticity, etc.

ON DOUBLE SINE AND COSINE TRANSFORMS,LIPSCHITZ AND ZYGMUND CLASSES

We consider complex-valued functions f ∈ L^1 （R^2＋）, where R＋ ：= [0,∞）, and prove sufficient conditions under which the double sine Fourier transform fss and the double cosine Fourier transform fcc belong to one of the two-dimensional Lipschitz classes Lip（a,β） for some 0 〈 α,β ≤ 1; or to one of the Zygmund classes Zyg（α,β） for some 0 〈 α,β ≤ 2. These sufficient conditions are best possible in the sense that they are also necessary for nonnegative-valued functions f ∈ L^1 （R^2＋）.

Hybrid Sine Cosine and Stochastic Fractal Search for Hemoglobin Estimation

The sample’s hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it.Hemoglobin(HGB)is a critical component of the human body because it transports oxygen from the lungs to the body’s tissues and returns carbon dioxide from the tissues to the lungs.Calculating the HGB level is a critical step in any blood analysis job.TheHGBlevels often indicate whether a person is anemic or polycythemia vera.Constructing ensemble models by combining two or more base machine learning(ML)models can help create a more improved model.The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels.An optimization method is utilized to get the ensemble’s optimum weights.The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search(SCSFS).The proposed SCSFS ensemble is compared toDecision Tree,Multilayer perceptron(MLP),Support Vector Regression(SVR)and Random Forest Regressors as model-based approaches and the average ensemble model.The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.

Discrete Convolution Associated with Fractional Cosine and Sine Series

Fractional sine series(FRSS)and fractional cosine series(FRCS)are the discrete form of the fractional cosine transform(FRCT)and fractional sine transform(FRST).The recent stud-ies have shown that discrete convolution is widely used in optics,signal processing and applied mathematics.In this paper,firstly,the definitions of fractional sine series(FRSS)and fractional co-sine series(FRCS)are presented.Secondly,the discrete convolution operations and convolution theorems for fractional sine and cosine series are given.The relationship of two convolution opera-tions is presented.Lastly,the discrete Young’s type inequality is established.The proposed theory plays an important role in digital filtering and the solution of differential and integral equations.

Debiased conversion measurements based target tracking with direction cosine measurements

Phased array radar’s measurements include two direction cosine and range measurements,which can be obtained in the direction cosine coordinates.State equation of the target is nonlinear with the measurements and in order to solve the nonlinear problem,debiased conversion measurements based target tracking with direction cosine and range measurements in direction cosine coordinates named DCMKFPreDcos is proposed first in this paper,where the predicted information is introduced to calculate the converted measurement errors’statistical characteristics to eliminate the correlation between measurement noise and the converted measurement errors covariance.When range rate information can be obtained further,based on the above DCMKF-PreDcos’filtering result,the sequential filtering is adopted to process the additional range rate measurement and the DCMKF-PreDcos algorithm with extra range rate information is given.The predicted information is also introduced to calculate the involved statistical characteristics of converted measurements.The effectiveness of the proposed algorithms is shown in simulation results.

DCT domain filtering method for multi-antenna code acquisition

For global navigation satellite system （GNSS） signals in Gaussian and Rayleigh fading channel, a novel signal detection algorithm is proposed. Under the low frequency uncertainty case, after performing discrete cosine transform （DCT） to the outputs of the partial matched filter （PMF） for every antenna, the high order com- ponents in the transforming domain will be filtered, then the equalgain （EG） combination for the inverse discrete cosine transform （IDCT） reconstructed signal would be done subsequently. Thus, due to the different frequency distribution characteristics between the noise and signals, after EG combination, the energy of signals has almost no loss and the noise energy is greatly reduced. The theoretical analysis and simulation results show that the detection algorithm can effectively improve the signal-to-noise ratio of the captured signal and increase the probability of detection under the same false alarm probability. In addition, it should be pointed out that this method can also be applied to Rayleigh fading channels with moving antenna.

正余弦函数法结合sine-Gordon方程的解求BBM方程的新行波解

为研究BBM方程的解析解法和行波解。首先用试探函数法获得了sine-Gordon方程及约化方程的精确解,然后采用sine-Gordon方程的约化方程和精确解构造了正余弦函数法,最后利用正余弦函数法找到了BBM方程的许多新显式行波解。