Development of a Post Quantum Encryption Key Generation Algorithm Using Electromagnetic Wave Propagation Theory

In today’s rapid widespread of digital technologies into all live aspects to enhance efficiency and productivity on the one hand and on the other hand ensure customer engagement, personal data counterfeiting has become a major concern for businesses and end-users. One solution to ensure data security is encryption, where keys are central. There is therefore a need to find robusts key generation implementation that is effective, inexpensive and non-invasive for protecting and preventing data counterfeiting. In this paper, we use the theory of electromagnetic wave propagation to generate encryption keys.

Age and Gender Classification Using Backpropagation and Bagging Algorithms

Voice classification is important in creating more intelligent systems that help with student exams,identifying criminals,and security systems.The main aim of the research is to develop a system able to predicate and classify gender,age,and accent.So,a newsystem calledClassifyingVoice Gender,Age,and Accent(CVGAA)is proposed.Backpropagation and bagging algorithms are designed to improve voice recognition systems that incorporate sensory voice features such as rhythm-based features used to train the device to distinguish between the two gender categories.It has high precision compared to other algorithms used in this problem,as the adaptive backpropagation algorithm had an accuracy of 98%and the Bagging algorithm had an accuracy of 98.10%in the gender identification data.Bagging has the best accuracy among all algorithms,with 55.39%accuracy in the voice common dataset and age classification and accent accuracy in a speech accent of 78.94%.

Neutrino Oscillations and Superluminal Propagation,in OPERA or Otherwise

We digress on the implications of recent claims of superluminal neutrino propagation. No matter how we turn it around such behaviour is very odd and sits uncomfortably even within “far-fetched” theories. In the context of non-linear realizations of the Lorentz group (where superluminal misbehaviour is run of the mill) one has to accept rather contrived constructions to predict superluminal properties for the neutrino. The simplest explanation is to require that at least one of the mass states be tachyonic. We show that due to neutrino mixing, the flavor energy does not suffer from the usual runaway pathologies of tachyons. For non-tachyonic mass states the theories become more speculative. A neutrino specific dispersion relation is exhibited, rendering the amplitude of the effect reasonable for a standard Planck energy. This uses the fact that the beam energy is close to the geometrical average of the neutrino and Planck mass;or, seen in another way, the beam energy is unexceptional but its gamma factor is very large. A dispersion relation crossing over from a low energy bradyonic branch to a high energy tachyonic one is also considered. We comment on consistency with SN 1987Awithin these models.

Intensified Eastward and Northward Propagation of Tropical Intraseasonal Oscillation over the Equatorial Indian Ocean in a Global Warming Scenario

Northward propagation in summer and eastward propagation in winter are two distinguished features of tropical intraseasonal oscillation （TISO） over the equatorial Indian Ocean. According to numerical modeling results, under a global warming scenario, both propagations were intensified. The enhanced northward propagation in summer can be attributed to the enhanced atmosphere-ocean interaction and the strengthened mean southerly wind; and the intensified eastward propagation in winter is associated with the enhanced convection-wind coupling process and the strengthened equatorial Kevin wave. Future changes of TISO propagations need to be explored in more climate models.

基于不同算法优化的back propagation神经网络在三元乙丙橡胶混炼胶门尼黏度预测中的应用

分别采用遗传算法(GA)和粒子群算法(PSO)优化的back propagation(BP)神经网络建立了三元乙丙橡胶(EPDM)混炼胶门尼黏度的预测模型,并对预测结果的误差进行了对比分析。结果表明,两种算法优化后的BP神经网络模型的预测值与实测值均保持较高的拟合度和相关性;相比单一的BP神经网络,GA优化后BP神经网络模型的精度提高了58.9%,PSO优化后BP神经网络模型的精度提高了3.57%,说明两种算法优化后的预测模型,特别是GA优化的BP神经网络预测模型对EPDM混炼胶门尼黏度的预测精度改善明显。

An Integrated Approach to Oscillation Propagation Identification and Source Locating in Process Multi-loop Systems

An integrated method for identifying the propagation of multi-loop process oscillations and their source location is proposed in this paper. Oscillatory process loop variables are automatically selected based on the component-related ratio index and a mixing matrix, both of which are obtained in data preprocessing by spectral independent component analysis. The complex causality among oscillatory process variables is then revealed by Granger causality test and is visualized in the form of causality diagram. The simplification of causal connectivity in the diagram is performed according to the understanding of process knowledge and the final simplest causality diagram, which represents the main oscillation propagation paths, is achieved by the automated cutting-off thresh-old search, with which less significant causality pathways are filtered out. The source of the oscillation disturbance can be identified intuitively through the final causality diagram. Both simulated and real plant data tests are presented to demonstrate the effectiveness and feasibility of the proposed method.

Optimization of Process Parameters for Cracking Prevention of UHSS in Hot Stamping Based on Hammersley Sequence Sampling and Back Propagation Neural Network-Genetic Algorithm Mixed Methods

In order to prevent cracking appeared in the work-piece during the hot stamping operation,this paper proposes a hybrid optimization method based on Hammersley sequence sampling( HSS),finite analysis,backpropagation( BP) neural network and genetic algorithm( GA). The mechanical properties of high strength boron steel are characterized on the basis of uniaxial tensile test at elevated temperatures. The samples of process parameters are chosen via the HSS that encourages the exploration throughout the design space and hence achieves better discovery of possible global optimum in the solution space. Meanwhile, numerical simulation is carried out to predict the forming quality for the optimized design. A BP neural network model is developed to obtain the mathematical relationship between optimization goal and design variables,and genetic algorithm is used to optimize the process parameters. Finally,the results of numerical simulation are compared with those of production experiment to demonstrate that the optimization strategy proposed in the paper is feasible.

Comparison of Microwave Imaging Algorithms for Short-Range Scenarios

Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MBPA)is accordingly proposed and four imaging algorithms are used for comparison,back-projection method(BP),back-projection one in time domain(BP-TD),modified back-projection one and fast Fourier transform(FFT)-based MIMO range migration algorithm(FFT-based MIMO RMA).All of the algorithms have been implemented in practical application scenarios by use of the proposed imaging system.Back to the practical applications,MIMO array-based imaging system with wide-bandwidth properties provides an efficient tool to detect objects hidden behind a wall.An MIMO imaging radar system,composed of a vector network analyzer(VNA),a set of switches,and an array of Vivaldi antennas,have been designed,fabricated,and tested.Then,these algorithms have been applied to measured data collected in different scenarios constituted by five metallic spheres in the absence and in the presence of a wall between the antennas and the targets in simulation and pliers in free space for experimental test.Finally,the focusing properties and time consumption of the above algorithms are compared.

RELATIONSHIP BETWEEN SUMMER LOW-FREQUENCY RAINFALL OVER SOUTHERN CHINA AND PROPAGATION OF TROPICAL INTRASEASONAL OSCILLATION

This study investigates the relationship between summer low-frequency rainfall over southern China and tropical intraseasonal oscillation(ISO) in the atmosphere by examining systematically the propagation features of the tropical ISO in terms of focusing on five large-scale low-frequency rainfall regimes in summer over southern China. It is demonstrated that there is a close linkage between the five rainfall regimes over southern China and the northward propagation of the tropical ISO. The moist ISO signals, which influence the low-frequency rainfall events in different regions of southern China, mainly propagate northwestward from the tropical ocean to the southeast of China. The southeast China rainfall regime is intimately associated with the moist ISO signals propagating northwestward from the equatorial mid-western Pacific Ocean. For both the Yangtze River regime and South of Yangtze River regime, the moist ISO signals over the northern South China Sea show an evident northward propagation towards the Yangtze River region, and then propagate westward. It is further found that the interaction between the northward propagation of low-latitude ISO signals and the southward propagation of high-latitude ISO signals can also make a clear influence on the low-frequency rainfall in southern China. For the Southern China regime, the moist ISO signals show a significant northward propagation from the Philippines. Moreover, for the rainless regime, southern China is under dry ISO signals' control, and the latter shows no clear propagation to southern China. This study may provide insights for the extended-range forecasting of summer rainfall in southern China.

Oscillating Propagation of Kink in Nondissipative Frenkel-Kontorova Chain Due to External DC Force

We report the oscillating propagation of kink in a nondissipative Frenkel-Kontorova （FK） chain driven by external DC force, which is different from the usual propagation of localized modes with equal speed. When the kink moves in the opposite direction of the external DC force, the kink will be accelerated and the potential of the FK chain in the external force field is transformed to be the kinetic energy of the kink. If the kink reaches the boundary of the FK chain, the kink will be bounced back and moves in the opposite direction, then the kink will be decelerated gradually and the kinetic energy of the kink ＆ transformed to be the potential of the FK chain in the external force field. If the speed of the kink reaches zero, the kink will move in the opposite direction again driven by the external DC force, and a new oscillating cycle begins. Simulation result demonstrates exactly the transformation between the kinetic energy of the kink and the potential of the FK chain in the external force field. The interesting energy exchange is induced by the special topology of kinks, and other localized modes, such as breathers and envelope solitons, have no the interesting phenomenon.

Dynamics of a Quantum Dissipative System Coupled with an Oscillator

We study the dynamics of a quantum dissipative system. Besides its linear coupling with a harmonic bath modelling the dissipation, we suppose that it is coupled with an oscillator with an interaction of the form s 2 x 2 . In our study, we integrate over the bath and the oscillator, extract the corresponding influence functionals and then solve the system’s sign problem. We apply the theory to the case of a double well and study the time evolution of the expectation value of the position.

Improving the accuracy of heart disease diagnosis with an augmented back propagation algorithm

A multilayer perceptron neural network system is established to support the diagnosis for five most common heart diseases (coronary heart disease, rheumatic valvular heart disease, hypertension, chronic cor pulmonale and congenital heart disease). Momentum term, adaptive learning rate, the forgetting mechanics, and conjugate gradients method are introduced to improve the basic BP algorithm aiming to speed up the convergence of the BP algorithm and enhance the accuracy for diagnosis. A heart disease database consisting of 352 samples is applied to the training and testing courses of the system. The performance of the system is assessed by cross-validation method. It is found that as the basic BP algorithm is improved step by step, the convergence speed and the classification accuracy of the network are enhanced, and the system has great application prospect in supporting heart diseases diagnosis.

A Third-Order Accurate Wave Propagation Algorithm for Hyperbolic Partial Differential Equations

We extend LeVeque's wave propagation algorithm,a widely used finite volume method for hyperbolic partial differential equations,to a third-order accurate method.The resulting scheme shares main properties with the original method,i.e.,it is based on a wave decomposition at grid cell interfaces,it can be used to approximate hyperbolic problems in divergence form as well as in quasilinear form and limiting is introduced in the form of a wave limiter.

分组衰落信道下LDPC的一种带信道估计的改进Belief-Propagation算法

该文首先通过仿真证明了LDPC(Low Density Parity Check code)在分组衰落信道下,以通常的 Belief-propagation算法译码,具有较好性能。然后基于算法的特殊迭代特性,提出分组衰落信道下,在每一迭代步 骤中结合信道估计的改进的Belief-propagation算法。仿真证明,该算法可以有效地减少译码迭代次数。

FORCE RIPPLE SUPPRESSION TECHNOLOGY FOR LINEAR MOTORS BASED ON BACK PROPAGATION NEURAL NETWORK

Various force disturbances influence the thrust force of linear motors when a linear motor （LM） is running. Among all of force disturbances, the force ripple is the dominant while a linear motor runs in low speed. In order to suppress the force ripple, back propagation（BP） neural network is proposed to learn the function of the force ripple of linear motors, and the acquisition method of training samples is proposed based on a disturbance observer. An off-line BP neural network is used mainly because of its high running efficiency and the real-time requirement of the servo control system of a linear motor. By using the function, the force ripple is on-line compensated according to the position of the LM. The experimental results show that the force ripple is effectively suppressed by the compensation of the BP neural network.

Detecting community structure using label propagation with consensus weight in complex network

Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm （LPA） is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight （LPAcw）, and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions.

Convergence Rate Analysis of Gaussian Belief Propagation for Markov Networks

Gaussian belief propagation algorithm(GaBP) is one of the most important distributed algorithms in signal processing and statistical learning involving Markov networks. It is well known that the algorithm correctly computes marginal density functions from a high dimensional joint density function over a Markov network in a finite number of iterations when the underlying Gaussian graph is acyclic. It is also known more recently that the algorithm produces correct marginal means asymptotically for cyclic Gaussian graphs under the condition of walk summability(or generalised diagonal dominance). This paper extends this convergence result further by showing that the convergence is exponential under the generalised diagonal dominance condition,and provides a simple bound for the convergence rate. Our results are derived by combining the known walk summability approach for asymptotic convergence analysis with the control systems approach for stability analysis.

Full-vectorial finite-difference beam propagation method based on the modified alternating direction implicit method

A modified alternating direction implicit algorithm is proposed to solve the full-vectorial finite-difference beam propagation method formulation based on H fields. The cross-coupling terms are neglected in the first sub-step, but evaluated and doubly used in the second sub-step. The order of two sub-steps is reversed for each transverse magnetic field component so that the cross-coupling terms are always expressed in implicit form, thus the calculation is very efficient and stable. Moreover, an improved six-point finite-difference scheme with high accuracy independent of specific structures of waveguide is also constructed to approximate the cross-coupling terms along the transverse directions. The imaginary-distance procedure is used to assess the validity and utility of the present method. The field patterns and the normalized propagation constants of the fundamental mode for a buried rectangular waveguide and a rib waveguide are presented. Solutions are in excellent agreement with the benchmark results from the modal transverse resonance method.

IMPACT OF TROPICAL INTRASEASONAL OSCILLATIONS ON THE PRECIPITATION OF GUANGDONG IN JUNES

The impact of tropical intraseasonal oscillations on the precipitation of Guangdong in Junes and its physical mechanism are analyzed using 30-yr(1979 to 2008), 86-station observational daily precipitation of Guangdong and daily atmospheric data from NCEP-DOE Reanalysis. It is found that during the annually first rainy season(April to June),the modulating effect of the activity of intraseasonal oscillations propagating eastward along the equator(MJO) on the June precipitation in Guangdong is different from that in other months. The most indicative effect of MJO on positive(negative) anomalous precipitation over the whole or most of the province is phase 3(phase 6) of strong MJO events in Junes. A Northwest Pacific subtropical high intensifies and extends westward during phase 3. Water vapor transporting along the edge of the subtropical high from Western Pacific enhances significantly the water vapor flux over Guangdong, resulting in the enhancement of the precipitation. The condition is reverse during phase 6. The mechanism for which the subtropical high intensifies and extends westward during phase 3 is related to the atmospheric response to the asymmetric heating over the eastern Indian Ocean. Analyses of two cases of sustained strong rainfall of Guangdong in June 2010 showed that both of them are closely linked with a MJO state which is both strong and in phase 3, besides the effect from a westerly trough. It is argued further that the MJO activity is indicative of strong rainfall of Guangdong in June. The results in the present work are helpful in developing strategies for forecasting severe rainfall in Guangdong and extending, combined with the outputs of dynamic forecast models, the period of forecasting validity.

Combinatorial Optimization Based Analog Circuit Fault Diagnosis with Back Propagation Neural Network

Electronic components' reliability has become the key of the complex system mission execution. Analog circuit is an important part of electronic components. Its fault diagnosis is far more challenging than that of digital circuit. Simulations and applications have shown that the methods based on BP neural network are effective in analog circuit fault diagnosis. Aiming at the tolerance of analog circuit,a combinatorial optimization diagnosis scheme was proposed with back propagation( BP) neural network( BPNN).The main contributions of this scheme included two parts:( 1) the random tolerance samples were added into the nominal training samples to establish new training samples,which were used to train the BP neural network based diagnosis model;( 2) the initial weights of the BP neural network were optimized by genetic algorithm( GA) to avoid local minima,and the BP neural network was tuned with Levenberg-Marquardt algorithm( LMA) in the local solution space to look for the optimum solution or approximate optimal solutions. The experimental results show preliminarily that the scheme substantially improves the whole learning process approximation and generalization ability,and effectively promotes analog circuit fault diagnosis performance based on BPNN.