Optimizing Connections:Applied Shortest Path Algorithms for MANETs

This study is trying to address the critical need for efficient routing in Mobile Ad Hoc Networks(MANETs)from dynamic topologies that pose great challenges because of the mobility of nodes.Themain objective was to delve into and refine the application of the Dijkstra’s algorithm in this context,a method conventionally esteemed for its efficiency in static networks.Thus,this paper has carried out a comparative theoretical analysis with the Bellman-Ford algorithm,considering adaptation to the dynamic network conditions that are typical for MANETs.This paper has shown through detailed algorithmic analysis that Dijkstra’s algorithm,when adapted for dynamic updates,yields a very workable solution to the problem of real-time routing in MANETs.The results indicate that with these changes,Dijkstra’s algorithm performs much better computationally and 30%better in routing optimization than Bellman-Ford when working with configurations of sparse networks.The theoretical framework adapted,with the adaptation of the Dijkstra’s algorithm for dynamically changing network topologies,is novel in this work and quite different from any traditional application.The adaptation should offer more efficient routing and less computational overhead,most apt in the limited resource environment of MANETs.Thus,from these findings,one may derive a conclusion that the proposed version of Dijkstra’s algorithm is the best and most feasible choice of the routing protocol for MANETs given all pertinent key performance and resource consumption indicators and further that the proposed method offers a marked improvement over traditional methods.This paper,therefore,operationalizes the theoretical model into practical scenarios and also further research with empirical simulations to understand more about its operational effectiveness.

Representation of an Integer by a Quadratic Form through the Cornacchia Algorithm

Cornachia’s algorithm can be adapted to the case of the equation x2+dy2=nand even to the case of ax2+bxy+cy2=n. For the sake of completeness, we have given modalities without proofs (the proof in the case of the equation x2+y2=n). Starting from a quadratic form with two variables f(x,y)=ax2+bxy+cy2and n an integer. We have shown that a primitive positive solution (u,v)of the equation f(x,y)=nis admissible if it is obtained in the following way: we take α modulo n such that f(α,1)≡0modn, u is the first of the remainders of Euclid’s algorithm associated with n and α that is less than 4cn/| D |) (possibly α itself) and the equation f(x,y)=n. has an integer solution u in y. At the end of our work, it also appears that the Cornacchia algorithm is good for the form n=ax2+bxy+cy2if all the primitive positive integer solutions of the equation f(x,y)=nare admissible, i.e. computable by the algorithmic process.

Time Complexity of the Oracle Phase in Grover’s Algorithm

Since Grover’s algorithm was first introduced, it has become a category of quantum algorithms that can be applied to many problems through the exploitation of quantum parallelism. The original application was the unstructured search problems with the time complexity of O(). In Grover’s algorithm, the key is Oracle and Amplitude Amplification. In this paper, our purpose is to show through examples that, in general, the time complexity of the Oracle Phase is O(N), not O(1). As a result, the time complexity of Grover’s algorithm is O(N), not O(). As a secondary purpose, we also attempt to restore the time complexity of Grover’s algorithm to its original form, O(), by introducing an O(1) parallel algorithm for unstructured search without repeated items, which will work for most cases. In the worst-case scenarios where the number of repeated items is O(N), the time complexity of the Oracle Phase is still O(N) even after additional preprocessing.

O(logN) Algorithm for Amplitude Amplification and O(logN) Algorithms for Amplitude Transfer in Grover’s Algorithm

Grovers algorithm is a category of quantum algorithms that can be applied to many problems through the exploitation of quantum parallelism. The Amplitude Amplification in Grovers algorithm is T = O(N). This paper introduces two new algorithms for Amplitude Amplification in Grovers algorithm with a time complexity of T = O(logN), aiming to improve efficiency in quantum computing. The difference between Grovers algorithm and our first algorithm is that the Amplitude Amplification ratio in Grovers algorithm is an arithmetic series and ours, a geometric one. Because our Amplitude Amplification ratios converge much faster, the time complexity is improved significantly. In our second algorithm, we introduced a new concept, Amplitude Transfer where the marked state is transferred to a new set of qubits such that the new qubit state is an eigenstate of measurable variables. When the new qubit quantum state is measured, with high probability, the correct solution will be obtained.

基于多目标遗传算法的8×8 S盒的优化设计方法

混沌系统具有非线性、伪随机性、初始值敏感等特性,为基于动力系统构造性能良好的S盒提供了基础,进一步保证了分组加密算法安全性.目前,基于混沌构造S盒的方法大多数针对单个性能指标进行优化,难以获得全面的性能提升.针对此问题,结合混沌映射与多目标遗传算法,提出了一种新的S盒设计方法.首先,利用混沌映射的特性产生初始S盒种群;然后,以S盒的非线性度和差分均匀性为优化目标,基于遗传算法框架对上述两指标进行优化.针对S盒的特点,在优化算法中引入了交换操作,设计了新的变异操作以及非支配序集计算,有效提升了S盒的非线性度和差分均匀性.实验结果表明该算法产生的S盒其差分均匀度为6,非线性度值至少为110,有效提升了S盒的综合性能.

基于量子衍生涡流算法和T⁃S模糊推理模型的储层岩性识别

鉴于梯度下降法易陷入局部极值、普通群智能优化算法易早熟收敛,提出一种基于量子衍生涡流算法(Quantum Vortex Search Algorithm,QVSA)和T⁃S模糊推理模型的岩性识别方法,QVSA具有操作简单、收敛速度快、寻优能力强等优点,有助于T⁃S模糊推理模型获得最优参数配置,从而实现储层岩性的准确识别。首先利用具有全局搜索能力的QVSA优化T⁃S模糊推理模型的各种参数;然后利用主成分分析方法降低获取的地震属性维度;再利用优化的T⁃S模糊推理模型识别储层岩性。实验结果表明,利用反映储层特征的8个地震属性识别储层岩性时,所提方法的识别正确率达到92%,比普通BP网络方法高5.1%,同时查准率、查全率、F1分数等指标也较BP网络方法提升明显。

Smooth constraint inversion technique in genetic algorithms and its application to surface wave study in the Tibetan Plateau

Smooth constraint is important in linear inversion, but it is difficult to apply directly to model parameters in genetic algorithms. If the model parameters are smoothed in iteration, the diversity of models will be greatly suppressed and all the models in population will tend to equal in a few iterations, so the optimal solution meeting requirement can not be obtained. In this paper, an indirect smooth constraint technique is introduced to genetic inversion. In this method, the new models produced in iteration are smoothed, then used as theoretical models in calculation of misfit function, but in process of iteration only the original models are used in order to keep the diversity of models. The technique is effective in inversion of surface wave and receiver function. Using this technique, we invert the phase velocity of Raleigh wave in the Tibetan Plateau, revealing the horizontal variation of S wave velocity structure near the center of the Tibetan Plateau. The results show that the S wave velocity in the north is relatively lower than that in the south. For most paths there is a lower velocity zone with 12-25 km thick at the depth of 15-40 km. The lower velocity zone in upper mantle is located below the depth of 100 km, and the thickness is usually 40-80 km, but for a few paths reach to 100 km thick. Among the area of Ando, Maqi and Ushu stations, there is an obvious lower velocity zone with the lowest velocity of 4.2-4.3 km/s at the depth of 90-230 km. Based on the S wave velocity structures of different paths and former data, we infer that the subduction of the Indian Plate is delimited nearby the Yarlung Zangbo suture zone.

A hybrid quantum encoding algorithm of vector quantization for image compression

Many classical encoding algorithms of vector quantization （VQ） of image compression that can obtain global optimal solution have computational complexity O（N）. A pure quantum VQ encoding algorithm with probability of success near 100% has been proposed, that performs operations 45√N times approximately. In this paper, a hybrid quantum VQ encoding algorithm between the classical method and the quantum algorithm is presented. The number of its operations is less than √N for most images, and it is more efficient than the pure quantum algorithm.

Hydraulic Optimization of a Double-channel Pump's Impeller Based on Multi-objective Genetic Algorithm

Computational fluid dynamics（CFD） can give a lot of potentially very useful information for hydraulic optimization design of pumps, however, it cannot directly state what kind of modification should be made to improve such hydrodynamic performance. In this paper, a more convenient and effective approach is proposed by combined using of CFD, multi-objective genetic algorithm（MOGA） and artificial neural networks（ANN） for a double-channel pump＇s impeller, with maximum head and efficiency set as optimization objectives, four key geometrical parameters including inlet diameter, outlet diameter, exit width and midline wrap angle chosen as optimization parameters. Firstly, a multi-fidelity fitness assignment system in which fitness of impellers serving as training and comparison samples for ANN is evaluated by CFD, meanwhile fitness of impellers generated by MOGA is evaluated by ANN, is established and dramatically reduces the computational expense. Then, a modified MOGA optimization process, in which selection is performed independently in two sub-populations according to two optimization objectives, crossover and mutation is performed afterword in the merged population, is developed to ensure the global optimal solution to be found. Finally, Pareto optimal frontier is found after 500 steps of iterations, and two optimal design schemes are chosen according to the design requirements. The preliminary and optimal design schemes are compared, and the comparing results show that hydraulic performances of both pumps 1 and 2 are improved, with the head and efficiency of pump 1 increased by 5.7% and 5.2%, respectively in the design working conditions, meanwhile shaft power decreased in all working conditions, the head and efficiency of pump 2 increased by 11.7% and 5.9%, respectively while shaft power increased by 5.5%. Inner flow field analyses also show that the backflow phenomenon significantly diminishes at the entrance of the optimal impellers 1 and 2, both the area of vortex and intensity of vortex decreases in the whole flow channel. This paper provides a promising tool to solve the hydraulic optimization problem of pumps＇ impellers.

DETERMINING THE STRUCTURES AND PARAMETERS OF RADIAL BASIS FUNCTION NEURAL NETWORKS USING IMPROVED GENETIC ALGORITHMS

The method of determining the structures and parameters of radial basis function neural networks(RBFNNs) using improved genetic algorithms is proposed. Akaike′s information criterion (AIC) with generalization error term is used as the best criterion of optimizing the structures and parameters of networks. It is shown from the simulation results that the method not only improves the approximation and generalization capability of RBFNNs ,but also obtain the optimal or suboptimal structures of networks.

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

A quantum search algorithm of two entangled registers to realize quantum discrete Fourier transform of signal processing

The discrete Fourier transform （DFT） is the base of modern signal processing. 1-dimensional fast Fourier transform （1D FFT） and 2D FFT have time complexity O（N log N） and O（N^2 log N） respectively. Since 1965, there has been no more essential breakthrough for the design of fast DFT algorithm. DFT has two properties. One property is that DFT is energy conservation transform. The other property is that many DFT coefficients are close to zero. The basic idea of this paper is that the generalized Grover＇s iteration can perform the computation of DFT which acts on the entangled states to search the big DFT coefficients until these big coefficients contain nearly all energy. One-dimensional quantum DFT （1D QDFT） and two-dimensional quantum DFT （2D QDFT） are presented in this paper. The quantum algorithm for convolution estimation is also presented in this paper. Compared with FFT, 1D and 2D QDFT have time complexity O（v/N） and O（N） respectively. QDFT and quantum convolution demonstrate that quantum computation to process classical signal is possible.

基于XGBoost算法的v_(P)/v_(S)预测及其在储层检测中的应用

鄂尔多斯盆地碳酸盐岩地层蕴含着丰富的油气资源。在勘探实践中发现,大牛地气田马家沟组断层发育、断距小,类型多样且成因复杂,给勘探、开发带来了较多挑战。为了应对这些挑战,提高储层预测的精度变得至关重要。在分析大牛地气田敏感弹性参数的基础上,建立地震属性与储层纵横波速度比(v_(P)/v_(S))的关系,提出一种基于XGBoost算法的地震多属性v_(P)/v_(S)预测方法。为了进一步提升XGBoost算法的预测精度和泛化能力,采用贝叶斯算法对XGBoost算法的超参数进行优化,从而找到最佳的超参数组合,以确保模型在训练集和测试集上的性能均能得到提升。将XGBoost算法应用于Marmousi 2模型进行横波速度预测,预测值与实际值相关系数超过0.88,而均方误差、平均绝对百分比误差分别低于6.55×10^(-7)和4%,验证了该方法的准确性和可靠性。在鄂尔多斯盆地大牛地气田,应用该方法获得的v_(P)/v_(S)成功识别出含气储层,结果与实际钻井数据一致。理论模型和实际数据应用结果表明,XGBoost作为一种强大的机器学习算法预测精度较高,为直接由叠后地震属性预测v_(P)/v_(S)提供了一种有效的途径。

Comparative analysis of various modularization algorithms and species specific study of VEGF signaling pathways

In biology, signal transduction refers to a process by which a cell converts one kind of signal or stimulus into another. It involves ordered sequences of biochemical reactions inside the cell. These cascades of reactions are carried out by enzymes and activated by second messengers. Signal transduction pathways are complex in nature. Each pathway is responsible for tuning one or more biological functions in the intracellular environment as well as more than one pathway interact among themselves to carry forward a single biological function. Such kind of behavior of these pathways makes understanding difficult. Hence, for the sake of simplicity, they need to be partitioned into smaller modules and then analyzed. We took VEGF signaling pathway, which is responsible for angiogenesis for this kind of modularized study. Modules were obtained by applying the algorithm of Nayak and De (Nayak and De, 2007) for different complexity values. These sets of modules were compared among themselves to get the best set of modules for an optimal complexity value. The best set of modules compared with four different partitioning algorithms namely, Farhat’s (Farhat, 1998), Greedy (Chartrand and Oellermann, 1993), Kernighan-Lin’s (Kernighan and Lin, 1970) and Newman’s community finding algorithm (Newman, 2006). These comparisons enabled us to decide which of the aforementioned algorithms was the best one to create partitions from human VEGF signaling pathway. The optimal complexity value, on which the best set of modules was obtained, was used to get modules from different species for comparative study. Comparison among these modules would shed light on the trend of development of VEGF signaling pathway over these species.

Routing Protocol Based on Grover’s Searching Algorithm for Mobile Ad-hoc Networks

In Mobile Ad-hoc Networks (MANETs), routing protocols directly affect various indices of network Quality of Service (QoS), so they play an important role in network performance. To address the drawbacks associated with traditional routing protocols in MANETs, such as poor anti-fading performance and slow convergence rate, for basic Dynamic Source Routing (DSR), we propose a new routing model based on Grover's searching algorithm. With this new routing model, each node maintains a node vector function, and all the nodes can obtain a node probability vector using Grover's algorithm, and then select an optimal routing according to node probability. Simulation results show that compared with DSR, this new routing protocol can effectively extend the network lifetime, as well as reduce the network delay and the number of routing hops. It can also significantly improve the anti-jamming capability of the network.

Design of quantum VQ iteration and quantum VQ encoding algorithm taking O（√N） steps for data compression

Vector quantization （VQ） is an important data compression method. The key of the encoding of VQ is to find the closest vector among N vectors for a feature vector. Many classical linear search algorithms take O（N） steps of distance computing between two vectors. The quantum VQ iteration and corresponding quantum VQ encoding algorithm that takes O（√N） steps are presented in this paper. The unitary operation of distance computing can be performed on a number of vectors simultaneously because the quantum state exists in a superposition of states. The quantum VQ iteration comprises three oracles, by contrast many quantum algorithms have only one oracle, such as Shor＇s factorization algorithm and Grover＇s algorithm. Entanglement state is generated and used, by contrast the state in Grover＇s algorithm is not an entanglement state. The quantum VQ iteration is a rotation over subspace, by contrast the Grover iteration is a rotation over global space. The quantum VQ iteration extends the Grover iteration to the more complex search that requires more oracles. The method of the quantum VQ iteration is universal.

SINGLE PROJECTION ALGORITHM FOR VARIATIONAL INEQUALITIES IN BANACH SPACES WITH APPLICATION TO CONTACT PROBLEM

We study the single projection algorithm of Tseng for solving a variational inequality problem in a 2-uniformly convex Banach space.The underline cost function of the variational inequality is assumed to be monotone and Lipschitz continuous.A weak convergence result is obtained under reasonable assumptions on the variable step-sizes.We also give the strong convergence result for when the underline cost function is strongly monotone and Lipchitz continuous.For this strong convergence case,the proposed method does not require prior knowledge of the modulus of strong monotonicity and the Lipschitz constant of the cost function as input parameters,rather,the variable step-sizes are diminishing and non-summable.The asymptotic estimate of the convergence rate for the strong convergence case is also given.For completeness,we give another strong convergence result using the idea of Halpern iteration when the cost function is monotone and Lipschitz continuous and the variable step-sizes are bounded by the inverse of the Lipschitz constant of the cost function.Finally,we give an example of a contact problem where our proposed method can be applied.

A Modified Genetic Algorithm for Product Family Optimization with Platform Specified by Information Theoretical Approach

Many existing product family design methods assume a given platform, However, it is not an in-tuitive task to select the platform and unique variable within a product family. Meanwhile, most approaches are single-platform methods, in which design variables are either shared across all product variants or not at all. While in multiple-platform design, platform variables can have special value with regard to a subset of product variants within the product family, and offer opportunities for superior overall design. An information theoretical approach incorporating fuzzy clustering and Shannon's entropy was proposed for platform variables selection in multiple-platform product family. A 2-level chromosome genetic algorithm (2LCGA) was proposed and developed for optimizing the corresponding product family in a single stage, simultaneously determining the optimal settings for the product platform and unique variables. The single-stage approach can yield im-provements in the overall performance of the product family compared with two-stage approaches, in which the first stage involves determining the best settings for the platform and values of unique variables are found for each product in the second stage. An example of design of a family of universal motors was used to verify the proposed method.

A Note on Shor’s Quantum Algorithm

Shor proposed a polynomial time algorithm for computing the order of one element in a multiplicative group using a quantum computer. Based on Miller’s randomization, he then gave a factorization algorithm. But the algorithm has two shortcomings, the order must be even and the output might be a trivial factor. Actually, these drawbacks can be overcome if the number is an RSA modulus. Applying the special structure of the RSA modulus, an algorithm is presented to overcome the two shortcomings. The new algorithm improves Shor’s algorithm for factoring RSA modulus. The cost of the factorization algorithm almost depends on the calculation of the order of 2 in the multiplication group.

Applying and Comparison of Chaotic-Based Permutation Algorithms for Audio Encryption

This research presents,and claries the application of two permutation algorithms,based on chaotic map systems,and applied to a le of speech signals.They are the Arnold cat map-based permutation algorithm,and the Baker’s chaotic map-based permutation algorithm.Both algorithms are implemented on the same speech signal sample.Then,both the premier and the encrypted le histograms are documented and plotted.The speech signal amplitude values with time signals of the original le are recorded and plotted against the encrypted and decrypted les.Furthermore,the original le is plotted against the encrypted le,using the spectrogram frequencies of speech signals with the signal duration.These permutation algorithms are used to shufe the positions of the speech les signals’values without any changes,to produce an encrypted speech le.A comparative analysis is introduced by using some of sundry statistical and experimental analyses for the procedures of encryption and decryption,e.g.,the time of both procedures,the encrypted audio signals histogram,the correlation coefcient between specimens in the premier and encrypted signals,a test of the Spectral Distortion(SD),and the Log-Likelihood Ratio(LLR)measures.The outcomes of the different experimental and comparative studies demonstrate that the two permutation algorithms(Baker and Arnold)are sufcient for providing an efcient and reliable voice signal encryption solution.However,the Arnold’s algorithm gives better results in most cases as compared to the results of Baker’s algorithm.