Dolog Akf Software Development

• WANG Hui-li; CHEN Xi-xin; LU Qian-hao 2005-01-01 Presented here is a new adaptive state filtering algorithm for systems with multiplicative noise. This algorithm estimates the vector state of the system and the statistics of noise when all the statistics of noise are unknown. This filtering algorithm is a simple recursive structure. A simulation example is presented which demonstrates the effectiveness of this filtering algorithm. • Smati, A.; Zemmour, N. [INH, Boumerdes (Algeria) 1996-12-31 Transmission gas pipeline network consume significant amounts of energy. Then, minimizing the energy requirements is a challenging task.

Due to the nonlinearity and poor knowledge of the system states, several results, based on the optimal control theory, are obtained only for simple configurations. In this paper an optimization scheme in the face of varying demand is carried out. It is based on the use of a dynamic simulation program as a plant model and the Pareto set technique to sell out useful experiments. Experiments are used for the identification of regression models based on an original class of functions. The nonlinear programming algorithm results. Its connection with regression models permits the definition off-line, and for a long time horizon, of the optimal discharge pressure trajectory for all the compressor stations. The use of adaptive algorithms, with high frequency, permits one to cancel the effect of unknown disturbances and errors in demand forecasts.

In this way, an on-line optimization scheme using data of SCADA system is presented. • Shi Lingfeng; Guo Baolong 2006-01-01 The paper presents a new algorithm of NonLinearly Adaptive Interpolation (NLAI). NLAI is based on both the gradients and the curvature of the signals with the predicted subsection. It is characterized by adaptive nonlinear interpolation method with extracting the characteristics of signals. Experimental research testifies the validity of the algorithm using the echoes of the Ground Penetrating Radar (GPR). A comparison of this algorithm with other traditional algorithms demonstrates that it is feasible. • Wei, Jiaolong; Lei, Ling; Qian, Jingjing 2007-11-01 With more and more applications appearing and the technology developing in the Internet, only relying on terminal system can not satisfy the complicated demand of QoS network.

Towards Dependable Development Tools for Embedded Systems A Case Study in Software Verification* Uwe Petermanu Dept. Of Computer Science University of Applied Sciences Leipzig P.O.B. Amongthe supported goallanguages are widelyusedlanguages likeSTEP5, STEPT, DOLOG AKF, MEDOC-IL or FST 101.

Router mechanisms must be participated into protecting responsive flows from the non-responsive. Routers mainly use active queue management mechanism (AQM) to avoid congestion. In the point of interaction between the routers, the paper applies minority game to describe the interaction of the users and observes the affection on the length of average queue.

The parameters α, β of ARED being hard to confirm, adaptive RED based on minority game can depict the interactions of main body and amend the parameter α, β of ARED to the best. Adaptive RED based on minority game optimizes ARED and realizes the smoothness of average queue length. At the same time, this paper extends the network simulator plat - NS by adding new elements. Simulation has been implemented and the results show that new algorithm can reach the anticipative objects. • Wen Bin WEI; Yue Sheng XU; Pei Xin YE 2007-01-01 This paper deals with realizable adaptive algorithms of the nonlinear approximation with finite terms based on wavelets. We present a concrete algorithm by which we may find the required index set Am for the greedy algorithm GPm(,ψ). This makes the greedy algorithm realize the near best approximation in practice.

Moreover, we study the efficiency of the finite-term approximation of another algorithm introduced by Birge and Massart. • FAN Chen; CHEN Mei-ya; SU Li-jun; YANG Da-cheng 2006-01-01 Adaptive Modulation and Coding (AMC) has gained a lot of attentions in the research of High Speed Downlink Packet Access (HSDPA). The idea is to adapt the transmission to the fast changing channel conditions by the use of different Modulation and Coding Schemes (MCS). This paper presents an adaptive AMC algorithm and introduces a theoretical analysis model in order to to investigate its throughput and Frame Error Rate (FER). Subject to the given FER target, our numerical and link level simulation results both show that our algorithm outperforms other existing adaptive algorithms. • Cai Zejian; Ge Jianhua; Wu Guohang 2005-01-01 On the basis of the analysis of the effect of PHase Noise (PHN) and Common Phase Error (CPE) on Orthogonal Frequency Division Multiplexing (OFDM) systems, a cost function is constructed.

By the cost function and the idea of Least-Mean-Square (LMS) adaptive algorithm, the adaptive algorithm for the correction of CPE is presented. The simulations have been performed to investigate the performance for tracking PHN and estimating CPE, the results show that the algorithm performs soundly. • Liu, Yun-hui; Yang, Yu-hang 2004-11-01 The paper presents a modified least squares despread respread multitarget constant modulus algorithm (LS-DRMTCMA).

The cost function of the original algorithm was modified by the minimum bit error rate (MBER) criterion. The novel algorithm tries to optimize weight vectors by directly minimizing bit error rate (BER) of code division multiple access (CDMA) mobile communication system. In order to achieve adaptive update of weight vectors, a stochastic gradient adaptive algorithm was developed by a kernel density estimator of possibility density function based on samples. Simulation results showed that the modified algorithm remarkably improves the BER performance, capacity and near-far effect resistance of a given CDMA communication system. • Bormann, Karsten 2000-01-01 The Hierarchical Occlusion Map algorithm is combined with Frustum Slicing to give a simpler occlusion-culling algorithm that more adequately caters to large, open VEs. The algorithm adapts to the level of visual congestion and is well suited for use with large, complex models with long mean free.

• Xin SONG; Jinkuan WANG; Yinghua HAN; Han WANG 2008-01-01 The performance of adaptive array beamform-ing algorithms substantially degrades in practice because of a slight mismatch between actual and presumed array res-ponses to the desired signal. A novel robust adaptive beam-forming algorithm based on Bayesian approach is therefore proposed.

The algorithm responds to the current envi-ronment by estimating the direction of arrival (DOA) of the actual signal from observations. Computational com-plexity of the proposed algorithm can thus be reduced com-pared with other algorithms since the recursive method is used to obtain inverse matrix. In addition, it has strong robustness to the uncertainty of actual signal DOA and makes the mean output array signal-to-interference-plus-noise ratio (SINR) consistently approach the optimum. Simulation results show that the proposed algorithm is bet-ter in performance than conventional adaptive beamform-ing algorithms.

• Qiang, Ji; Mitchell, Chad 2014-11-03 In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators.

By making all control parameters in the proposed algorithm self- adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms. • 2010-01-01 In this paper,the inter-symbol interference and eliminating method are introduced.After analyzing the principle of adaptive equalization,we designed an adaptive equalizer using the LMS algorithm,and constructed a simulation system using MATLAB.Then we analyzed the convergence speed and mean square error characteristic of the adaptive equalizer by changing the step length factor to test the performance of the algorithm. • Wang Yupeng 2015-01-01 Full Text Available Canny is a classic algorithm of edge detection which has been widely applied in various fields of image processing for years. However, the algorithm has some defects.

The most serious defect is that the traditional canny algorithm can’t set threshold adaptively. If the threshold set manually is not accurate, it will seriously affect the quality of the algorithm to detect the edge. This makes the poor adaptability of the algorithm.

This paper proposes a method which combines maximum entropy method with Otsu method to determine the high and low threshold of Canny algorithm. Experiments show that the modified algorithm has stronger robustness than traditional method. For the images which have complex distributions of grey level histogram, the modified algorithm has better performance. • MENG Dawei; FENG Zhenming; LU Mingquan 2008-01-01 The convergence rate of the power inversion (PI) algorithm is quite sensitive to the power of the interference with the used fixed parameters in the PI algorithm leading to degradation of its ability to handle interference. This paper presents a normalized PI algorithm that traces the stochastic characteristics of the interference. The algorithm adaptively adjusts the recursive step size to determine the constrained optimized parameters for the Iowpass filter. Simulations show that the normalized PI algorithm achieves faster con-vergence and produces deeper nulls.

The algorithm makes GPS receivers more robust in environments with large variations in the interference strength. • 李侃; 刘玉树 2004-01-01 A novel mercer kernel based fuzzy clustering self- adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering.

It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self- adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self- adaptive algorithm. • Manasa Ranjan Behera 2010-09-01 Full Text Available A numerical investigation has been carried out to obtain a non-dimensional grid size ( grid size/ tsunami base width for the near shore discretisation of computational domains for long wave modelling.

A 1D domain has been considered in which, the flow has been solved by 1D shallow water equations with vertically integrated flow variables. The sensitivity study of the grid size was carried out in the 1D channel with an open boundary at one end and shelf boundary at the other end.

The grid size was varied from 10 m to 1000 m and its effect on the computation of the tsunami run-up along the shoreline has been investigated. The non-dimensional grid size for the computation of run-up was optimised by comparing the non-dimensional run-up (tsunami run-up/initial tsunami height and a threshold value of 5.0e-4 was obtained. Further, the study was extended to real scenario by adopting various grids for the shelf region of northern Tamil Nadu coast, south east coast of India in 2D and a suitable grid size was obtained. • 周建强; 谢立; 等 1994-01-01 Based on the characteristics of distrubuted system and the behavior of parallel programs,this paper presents the fixed and randomized competitive memory coherence algorithms for distributed shared virtual memory.These algorithms exploit parallel programs' locality of reference and exhibit good competitive property.Our simulation shows that the fixed and randomized algorithms achieve better performance and higher stability than other strategies such as write-invalidate and write-update.

• Fei Yu 2014-01-01 CKF-SLAM and the adaptive estimator, the new ACKF-SLAM algorithm can reduce the state estimated error significantly and improve the navigation accuracy of the SLAM system effectively. The performance of this new algorithm has been examined through numerical simulations in different scenarios. The results have shown that the position error can be effectively reduced with the new adaptive CKF-SLAM algorithm. Compared with other traditional SLAM methods, the accuracy of the nonlinear SLAM system is significantly improved.

It verifies that the proposed ACKF-SLAM algorithm is valid and feasible. • Chabchoub, Yousra; Guillemin, Fabrice; Robert, Philippe 2009-01-01 We develop in this paper an adaptive algorithm based on Bloom filters in order to identify large flows.

While most algorithms proposed so far in the technical literature rely on a periodic erasure of the Bloom filter, we propose in this paper to progressively decrement the various counters of the filter according to some overload criteria. When tested against real traffic traces, the proposed algorithm performs well in the sense that a high percentage of large flows in traffic are detected by the algorithm. In order to improve the accuracy of the algorithm, we introduce a shadow Bloom filter, which is less frequently decremented so that elephants have more chance of being identified. Since elephant detection issue is very close to flood attack detection, we adapt the proposed algorithm in order to detect SYN and volume flood attack in Internet traffic. The attack detection algorithm is tested against traffic traces from France Telecom collect and transit networks.

Some performance issues are finally discussed. • Ghanshyam G. Tejani 2016-07-01 Full Text Available The symbiotic organisms search (SOS algorithm is an effective metaheuristic developed in 2014, which mimics the symbiotic relationship among the living beings, such as mutualism, commensalism, and parasitism, to survive in the ecosystem. In this study, three modified versions of the SOS algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency. The basic SOS algorithm only considers benefit factors, whereas the proposed variants of the SOS algorithm, consider effective combinations of adaptive benefit factors and benefit factors to study their competence to lay down a good balance between exploration and exploitation of the search space. The proposed algorithms are tested to suit its applications to the engineering structures subjected to dynamic excitation, which may lead to undesirable vibrations.

Structure optimization problems become more challenging if the shape and size variables are taken into account along with the frequency. To check the feasibility and effectiveness of the proposed algorithms, six different planar and space trusses are subjected to experimental analysis.

The results obtained using the proposed methods are compared with those obtained using other optimization methods well established in the literature. The results reveal that the adaptive SOS algorithm is more reliable and efficient than the basic SOS algorithm and other state-of-the-art algorithms. Radhika 2016-04-01 Full Text Available Maximum correntropy criterion (MCC based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.

• Xiu, Dongbin [Univ. Of Utah, Salt Lake City, UT (United States) 2017-03-03 The focus of the project is the development of mathematical methods and high-performance computational tools for stochastic simulations, with a particular emphasis on computations on extreme scales.

The core of the project revolves around the design of highly efficient and scalable numerical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities. 96 Hours Taken 2 German Download here. • Christensen, Anders; Ravn, Ole 1988-01-01 SLLAB is a MATLAB-family software package for solving control and identification problems. This paper concerns the planning of a general-purpose subroutine structure for solving identification and adaptive control problems.

A general-purpose identification algorithm is suggested, which allows a t. • Stotsky, Alexander A.

2008-01-01 Many event detection mechanisms in spark ignition automotive engines are based on the comparison of the engine signals to the detection threshold values. Different signal qualities for new and aged engines necessitate the development of an adaptation algorithm for the detection thresholds. • Pogrebnyak, Oleksiy B.; Ramirez, Pablo M.; Acevedo Mosqueda, Marco Antonio 2004-11-01 A new algorithm of locally adaptive wavelet transform based on the modified lifting scheme is presented. It performs an adaptation of the wavelet high-pass filter at the prediction stage to the local image data activity. The proposed algorithm uses the generalized framework for the lifting scheme that permits to obtain easily different wavelet filter coefficients in the case of the (~N, N) lifting. Changing wavelet filter order and different control parameters, one can obtain the desired filter frequency response.

It is proposed to perform the hard switching between different wavelet lifting filter outputs according to the local data activity estimate. The proposed adaptive transform possesses a good energy compaction. The designed algorithm was tested on different images. The obtained simulation results show that the visual and quantitative quality of the restored images is high. The distortions are less in the vicinity of high spatial activity details comparing to the non- adaptive transform, which introduces ringing artifacts.

The designed algorithm can be used for lossy image compression and in the noise suppression applications. • Guangwei Zhou; Albert Gan; L. David Shen 2007-01-01 Optimization of adaptive traffic signal timing is one of the most complex problems in traffic control systems. This paper presents an adaptive transit signal priority (TSP) strategy that applies the parallel genetic algorithm (PGA) to optimize adaptive traffic signal control in the presence of TSP. The method can optimize the phase plan, cycle length, and green splits at isolated intersections with consideration for the performance of both the transit and the general vehicles. A VISSlM (VISual SIMulation) simulation testbed was developed to evaluate the performance of the proposed PGA-based adaptive traffic signal control with TSP.The simulation results show that the PGA-based optimizer for adaptive TSP outperformed the fully actuated NEMA control in all test cases.

The results also show that the PGA-based optimizer can produce TSP timing plans that benefit the transit vehicles while minimizing the impact of TSP on the general vehicles. • Huang, Jen-Chao 2016-01-01 We present a novel automatic adaptive aperture photometry algorithm for measuring the total magnitudes of merging galaxies with irregular shapes.

First, we use a morphological pattern recognition routine for identifying the shape of an irregular source in a background-subtracted image. Then, we extend the shape of the source by using the Dilation image operation to obtain an aperture that is quasi-homomorphic to the shape of the irregular source. The magnitude measured from the homomorphic aperture would thus have minimal contamination from the nearby background. As a test of our algorithm, we applied our technique to the merging galaxies observed by the Sloan Digital Sky Survey (SDSS) and the Canada-France-Hawaii Telescope (CFHT). Our results suggest that the adaptive homomorphic aperture algorithm can be very useful for investigating extended sources with irregular shapes and sources in crowded regions. C.; Hwang, C.

2017-03-01 We present a novel automatic adaptive aperture photometry algorithm for measuring the total magnitudes of merging galaxies with irregular shapes. First, we use a morphological pattern recognition routine for identifying the shape of an irregular source in a background-subtracted image.

Then, we extend the shape of the source by using the Dilation image operation to obtain an aperture that is quasi-homomorphic to the shape of the irregular source. The magnitude measured from the homomorphic aperture would thus have minimal contamination from the nearby background. As a test of our algorithm, we applied our technique to the merging galaxies observed by the Sloan Digital Sky Survey and the Canada–France–Hawaii Telescope. Our results suggest that the adaptive homomorphic aperture algorithm can be very useful for investigating extended sources with irregular shapes and sources in crowded regions. • Xuan Huang 2014-03-01 Full Text Available As with the development of computer technology and informatization, network technique, sensor technique and communication technology become three necessary components of information industry. As the core technique of sensor application, signal processing mainly determines the sensor performances. For this reason, study on signal processing mode is very important to sensors and the application of sensor network.

In this paper, we introduce a new sensor coarse signal processing mode based on adaptive genetic algorithm. This algorithm selects crossover, mutation probability adaptively and compensates multiple operators commutatively to optimize the search process, so that we can obtain the global optimum solution. Based on the proposed algorithm, using auto-correlative characteristic parameter extraction method, it achieves smaller test error in sensor coarse signal processing mode of processing interference signal. We evaluate the proposed approach on a set of data.

The experimental results show that, the proposed approach is able to improve the performance in different experimental setting • Li, Ying; Bai, Bendu; Zhang, Yanning An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise. • Zhang, L.; Bhatnagar, S.; Rau, U.; Zhang, M.

2016-08-01 Context. Most popular algorithms in use to remove the effects of a telescope's point spread function (PSF) in radio astronomy are variants of the CLEAN algorithm. Most of these algorithms model the sky brightness using the delta-function basis, which results in undesired artefacts when used to image extended emission.

The adaptive scale pixel decomposition (Asp-Clean) algorithm models the sky brightness on a scale-sensitive basis and thus gives a significantly better imaging performance when imaging fields that contain both resolved and unresolved emission. Aims: However, the runtime cost of Asp-Clean is higher than that of scale-insensitive algorithms. In this paper, we identify the most expensive step in the original Asp-Clean algorithm and present an efficient implementation of it, which significantly reduces the computational cost while keeping the imaging performance comparable to the original algorithm. The PSF sidelobe levels of modern wide-band telescopes are significantly reduced, allowing us to make approximations to reduce the computational cost, which in turn allows for the deconvolution of larger images on reasonable timescales. Methods: As in the original algorithm, scales in the image are estimated through function fitting. Here we introduce an analytical method to model extended emission, and a modified method for estimating the initial values used for the fitting procedure, which ultimately leads to a lower computational cost. Results: The new implementation was tested with simulated EVLA data and the imaging performance compared well with the original Asp-Clean algorithm.

Tests show that the current algorithm can recover features at different scales with lower computational cost. • 无 2008-01-01 In wireless communication environment, the time-varying channel and angular spreads caused by multipath fading and the mobility of Mobile Stations (MS) degrade the performance of the conventional Direction-Of-Arrival (DOA) tracking algorithms. On the other hand, although the DOA estimation methods based on the Maximum Likelihood (ML) principle have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. This letter first proposes a new suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algorithms, and then proposes a Kalman filtering based tracking algorithm to model the dynamic property of directional changes for mobile terminals in such a way that the association between the estimates made at different time points is maintained. At each stage during tracking process, the current suboptimal estimates of DOA are treated as measurements, predicted and updated via a Kalman state equation, hence adaptive tracking of moving MS can be carried out without the need to perform unduly heavy computations. Computer simulation results show that this proposed algorithm has better performance of DOA estimation and tracking of MS than the conventional ML or subspace based algorithms in terms of accuracy and robustness.

• Liming Chen 2012-09-01 Full Text Available Nowadays, congestion in communication networks has been more intractable than ever before due to the explosive growth of network scale and multimedia traffic. Active queue management (AQM algorithms had been proposed to alleviate congestion to improve quality of service (QoS, but existing algorithms often suffer from some flaws in one aspect or another.

In this paper, a novel AQM algorithm with adaptive reference queue threshold (ARTAQM is proposed of which the main innovative contributions are recounted as follows. First, traffic is predicted to calculate the packet loss ratio (PLR and the traffic rate based on traffic prediction algorithm. Second, by means of periodical measurements, a weighted PLR is obtained to dynamically adjust packet dropping probability in ARTAQM algorithm. Third, ARTAQM algorithm runs in both coarse and fine granularities. In coarse granularity, the mismatch of the predicted traffic rate and link capacity can adjusts the reference queue length in every period, while in fine granularity, reference queue remains fixed and the instantaneous queue is adjusted packet by packet in one period. Simulation results indicate that ARTAQM algorithm not only maintains stable queue and fast response speed, but has lower PLR and higher link utilization as well.

• Smith, J E 2012-01-01 Among the most promising and active research areas in heuristic optimisation is the field of adaptive memetic algorithms (AMAs). These gain much of their reported robustness by adapting the probability with which each of a set of local improvement operators is applied, according to an estimate of their current value to the search process. This paper addresses the issue of how the current value should be estimated.

Assuming the estimate occurs over several applications of a meme, we consider whether the extreme or mean improvements should be used, and whether this aggregation should be global, or local to some part of the solution space. To investigate these issues, we use the well-established COMA framework that coevolves the specification of a population of memes (representing different local search algorithms) alongside a population of candidate solutions to the problem at hand. Two very different memetic algorithms are considered: the first using adaptive operator pursuit to adjust the probabilities of applying a fixed set of memes, and a second which applies genetic operators to dynamically adapt and create memes and their functional definitions. For the latter, especially on combinatorial problems, credit assignment mechanisms based on historical records, or on notions of landscape locality, will have limited application, and it is necessary to estimate the value of a meme via some form of sampling.

The results on a set of binary encoded combinatorial problems show that both methods are very effective, and that for some problems it is necessary to use thousands of variables in order to tease apart the differences between different reward schemes. However, for both memetic algorithms, a significant pattern emerges that reward based on mean improvement is better than that based on extreme improvement. This contradicts recent findings from adapting the parameters of operators involved in global evolutionary search.

The results also show that local reward schemes • B. Sharmila 2012-02-01 Full Text Available Steganography is the art of hiding the fact that communication is taking place, by hiding information in other medium.

Many different carrier file formats can be used, but digital images are the most popular because of their frequent use on the Internet. For hiding secret information in images, there exists a large variety of steganographic techniques. The Least Significant Bit (LSB based approach is a simplest type of steganographic algorithm. In all the existing approaches, the decision of choosing the region within a cover image is performed without considering the relationship between image content and the size of secret message. Thus, the plain regions in the cover will be ruin after data hiding even at a low data rate. Hence choosing the edge region for data hiding will be a solution. Many algorithms are deal with edges in images for data hiding.

The Paper 'Edge adaptive image steganography based on LSBMR algorithm' is a LSB steganography presented the results of algorithms on gray-scale images only. This paper presents the results of analyzing the performance of edge adaptive steganography for colored images (JPEG.

The algorithms have been slightly modified for colored image implementation and are compared on the basis of evaluation parameters like peak signal noise ratio (PSNR and mean square error (MSE. This method can select the edge region depending on the length of secret message and difference between two consecutive bits in the cover image.

For length of message is short, only small edge regions are utilized while on leaving other region as such. When the data rate increases, more regions can be used adaptively for data hiding by adjusting the parameters.

Besides this, the message is encrypted using efficient cryptographic algorithm which further increases the security. • Christensen, Anders; Ravn, Ole 1988-01-01 SLLAB is a MATLAB-family software package for solving control and identification problems. This paper concerns the planning of a general-purpose subroutine structure for solving identification and adaptive control problems. A general-purpose identification algorithm is suggested, which allows. A total redesign of the system within each sample. The necessary design parameters are evaluated and a decision vector is defined, from which the identification algorithm can be generated by the program.

Using the decision vector, a decision-node tree structure is built up, where the nodes define. • Kaiping Luo 2013-01-01 Full Text Available The harmony search algorithm is a music-inspired optimization technology and has been successfully applied to diverse scientific and engineering problems. However, like other metaheuristic algorithms, it still faces two difficulties: parameter setting and finding the optimal balance between diversity and intensity in searching. This paper proposes a novel, self- adaptive search mechanism for optimization problems with continuous variables. This new variant can automatically configure the evolutionary parameters in accordance with problem characteristics, such as the scale and the boundaries, and dynamically select evolutionary strategies in accordance with its search performance.

The new variant simplifies the parameter setting and efficiently solves all types of optimization problems with continuous variables. Statistical test results show that this variant is considerably robust and outperforms the original harmony search (HS, improved harmony search (IHS, and other self- adaptive variants for large-scale optimization problems and constrained problems. • Gimeno-Martin, A.; Pardo-Martin, J.; Ortega-Gonzalez, F. 2010-01-01 An adaptive algorithm to correct phase misalignments in Cartesian feedback linearization loops for power amplifiers has been presented. It yields an error smaller than 0.035 rad between forward and feedback loop signals once convergence is reached. Because this algorithm enables a feedback system to process forward and feedback samples belonging to almost the same algorithm iteration, it is suitable to improve the performance not only of power amplifiers but also any other digital feedback system for communications systems and circuits such as all digital phase locked loops. Synchronizing forward and feedback paths of Cartesian feedback loops takes a small period of time after the system starts up.

The phase alignment algorithm needs to converge before the feedback Cartesian loop can start its ideal behavior. However, once the steady state is reached, both paths can be considered synchronized, and the Cartesian feedback loop will only depend on the loop parameters (open-loop gain, loop bandwidth, etc.). It means that the linearization process will also depend only on these parameters since the misalignment effect disappears.

Therefore, this algorithm relieves the power amplifier linearizer circuit design of any task required for solving phase misalignment effects inherent to Cartesian feedback systems. Furthermore, when a feedback Cartesian loop has to be designed, the designer can consider that forward and feedback paths are synchronized, since the phase alignment algorithm will do this task. This will reduce the simulation complexity. Then, all efforts are applied to determining the suitable loop parameters that will make the linearization process more efficient.

• Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States) 2016-06-21 The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities. • 郭雁文 2014-01-01 New adaptive preprocessing algorithms based on the polar coordinate system were put forward to get high-precision corneal topography calculation results. Adaptive locating algorithms of concentric circle center were created to accurately capture the circle center of original Placido-based image, expand the image into matrix centered around the circle center, and convert the matrix into the polar coordinate system with the circle center as pole.

Adaptive image smoothing treatment was followed and the characteristics of useful circles were extracted via horizontal edge detection, based on useful circles presenting approximate horizontal lines while noise signals presenting vertical lines or different angles. Effective combination of different operators of morphology were designed to remedy data loss caused by noise disturbances, get complete image about circle edge detection to satisfy the requests of precise calculation on follow-up parameters. The experimental data show that the algorithms meet the requirements of practical detection with characteristics of less data loss, higher data accuracy and easier availability. Raida 1994-09-01 Full Text Available The paper presents the Simple Kalman filter (SKF that has been designed for the control of digital adaptive antenna arrays. The SKF has been applied to the pilot signal system and the steering vector one. The above systems based on the SKF are compared with adaptive antenna arrays controlled by the classical LMS and the Variable Step Size (VSS LMS algorithms and by the pure Kalman filter. It is shown that the pure Kalman filter is the most convenient for the control of the adaptive arrays because it does not require any a priori information about noise statistics and excels in high rate of convergence and low misadjustment.

Extremely high computational requirements are drawback of this filter. Hence, if low computational power of signal processors is at the disposal, the SKF is recommended to be used. Computational requirements of the SKF are of the same order as the classical LMS algorithm exhibits.

On the other hand, all the important features of the pure Kalman filter are inherited by the SKF. The paper shows that presented Kalman filters can be regarded as special gradient algorithms. That is why they can be compared with the LMS family. • FaliangGui; TiesongHu 2004-01-01 Optimising both qualitative and quantitative factors is a key challenge in solving construction finance decisions. The semi-structured nature of construction finance optimisation problems precludes conventional optimisation techniques. With a desire to improve the performance of the canonical genetic algorithm (CCA) which is characterised by static crossover and mutation probability, and to provide contractors with a profit-risk trade-off curve and cash flow prediction, an adaptive genetic algorithm (AGA) model is developed.

Ten projects being undertaken by a major construction firm in Hong Kong were used as case studies to evaluate the performance of the genetic algorithm (CA). The results of case study reveal that the ACA outperformed the CGA both in terms of its quality of solutions and the computational time required for a certain level of accuracy. The results also indicate that there is a potential for using the GA for modelling financial decisions should both quantitative and qualitative factors be optimised simultaneously. • Hittinger, J A F 2012-01-01 Direct discretization of continuum kinetic equations, like the Vlasov equation, are under-utilized because the distribution function generally exists in a high-dimensional (>3D) space and computational cost increases geometrically with dimension. We propose to use high-order finite-volume techniques with block-structured adaptive mesh refinement (AMR) to reduce the computational cost. The primary complication comes from a solution state comprised of variables of different dimensions. We develop the algorithms required to extend standard single-dimension block structured AMR to the multi-dimension case.

Specifically, algorithms for reduction and injection operations that transfer data between mesh hierarchies of different dimensions are explained in detail. In addition, modifications to the basic AMR algorithm that enable the use of high-order spatial and temporal discretizations are discussed. Preliminary results for a standard 1D+1V Vlasov-Poisson test problem are presented. Results indicate that there is po. • Adwan Yasin 2016-04-01 Full Text Available In this paper we present a new algorithm for clustering MANET by considering several parameters. This is a new adaptive load balancing technique for clustering out Mobile Ad-hoc Networks (MANET.

MANET is special kind of wireless networks where no central management exits and the nodes in the network cooperatively manage itself and maintains connectivity. The algorithm takes into account the local capabilities of each node, the remaining battery power, degree of connectivity and finally the power consumption based on the average distance between nodes and candidate cluster head. The proposed algorithm efficiently decreases the overhead in the network that enhances the overall MANET performance. Reducing the maintenance time of broken routes makes the network more stable, reliable. Saving the power of the nodes also guarantee consistent and reliable network. • Hadei, Sayed A 2011-01-01 Recently a framework has been introduced within which a large number of classical and modern adaptive filter algorithms can be viewed as special cases. Variable Step-Size (VSS) normalized least mean square (VSSNLMS) and VSS Affine Projection Algorithms (VSSAPA) are two particular examples of the adaptive algorithms that can be covered by this generic adaptive filter.

In this paper, we introduce a new VSS Partial Rank (VSSPR) adaptive algorithm based on the generic VSS adaptive filter and use it for channel equalization. The proposed algorithm performs very well in attenuating noise and inter-symbol interference (ISI) in comparison with the standard NLMS and the recently introduced AP algorithms. • Polak, Elijah; Wetter, Michael 2003-05-14 In the literature on generalized pattern search algorithms, convergence to a stationary point of a once continuously differentiable cost function is established under the assumption that the cost function can be evaluated exactly. However, there is a large class of engineering problems where the numerical evaluation of the cost function involves the solution of systems of differential algebraic equations. Since the termination criteria of the numerical solvers often depend on the design parameters, computer code for solving these systems usually defines a numerical approximation to the cost function that is discontinuous with respect to the design parameters.

Standard generalized pattern search algorithms have been applied heuristically to such problems, but no convergence properties have been stated. In this paper we extend a class of generalized pattern search algorithms to a form that uses adaptive precision approximations to the cost function.

These numerical approximations need not define a continuous function. Our algorithms can be used for solving linearly constrained problems with cost functions that are at least locally Lipschitz continuous. Assuming that the cost function is smooth, we prove that our algorithms converge to a stationary point. Under the weaker assumption that the cost function is only locally Lipschitz continuous, we show that our algorithms converge to points at which the Clarke generalized directional derivatives are nonnegative in predefined directions. An important feature of our adaptive precision scheme is the use of coarse approximations in the early iterations, with the approximation precision controlled by a test. Such an approach leads to substantial time savings in minimizing computationally expensive functions. • Kong Haipeng 2015-02-01 Full Text Available Optimization problems are often highly constrained and evolutionary algorithms (EAs are effective methods to tackle this kind of problems.

To further improve search efficiency and convergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm (ADCQGA for solving constrained optimization problems. ADCQGA makes use of double-individuals to represent solutions that are classified as feasible and infeasible solutions. Fitness (or evaluation functions are defined for both types of solutions. Based on the fitness function, three types of step evolution (SE are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions.

To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process (AEP, adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability.

Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems. Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem. • Kong Haipeng; Li Ni; Shen Yuzhong 2015-01-01 Optimization problems are often highly constrained and evolutionary algorithms (EAs) are effective methods to tackle this kind of problems. Free Download Program Sergio Mendes Arara RARE more. To further improve search efficiency and con-vergence rate of EAs, this paper presents an adaptive double chain quantum genetic algorithm (ADCQGA) for solving constrained optimization problems.

ADCQGA makes use of double-individuals to represent solutions that are classified as feasible and infeasible solutions. Fitness (or evaluation) functions are defined for both types of solutions. Based on the fitness function, three types of step evolution (SE) are defined and utilized for judging evolutionary individuals. An adaptive rotation is proposed and used to facilitate updating individuals in different solutions. To further improve the search capability and convergence rate, ADCQGA utilizes an adaptive evolution process (AEP), adaptive mutation and replacement techniques. ADCQGA was first tested on a widely used benchmark function to illustrate the relationship between initial parameter values and the convergence rate/search capability. Then the proposed ADCQGA is successfully applied to solve other twelve benchmark functions and five well-known constrained engineering design problems.

Multi-aircraft cooperative target allocation problem is a typical constrained optimization problem and requires efficient methods to tackle. Finally, ADCQGA is successfully applied to solving the target allocation problem. • YUPeng; WANGZuoying 2003-01-01 Spatial structure information, i.e., the rel-ative position information of phonetic states in the feature space, is long to be carefully researched yet.

In this pa-per, a new model named “Distance Field” is proposed to describe the spatial structure information. Based on this model, a modified MAP adaptation algorithm named dis-tance constrained maximum a poateriori (DCMAP) is in-troduced. The distance field model gives large penalty when the spatial structure is destroyed. As a result the DCMAP reserves the spatial structure information in adaptation process. Experiments show the Distance Field Model improves the performance of MAP adapta-tion. Further results show DCMAP has strong cross-state estimation ability, which is used to train a well-performed speaker-dependent model by data from only part of pho- • Rogers, David 1991-01-01 G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search.

The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered. • Zhu, Jun; Yan, Xuefeng; Zhao, Weixiang 2013-10-01 To solve chemical process dynamic optimization problems, a differential evolution algorithm integrated with adaptive scheduling mutation strategy (ASDE) is proposed. According to the evolution feedback information, ASDE, with adaptive control parameters, adopts the round-robin scheduling algorithm to adaptively schedule different mutation strategies. By employing an adaptive mutation strategy and control parameters, the real-time optimal control parameters and mutation strategy are obtained to improve the optimization performance. The performance of ASDE is evaluated using a suite of 14 benchmark functions. The results demonstrate that ASDE performs better than four conventional differential evolution (DE) algorithm variants with different mutation strategies, and that the whole performance of ASDE is equivalent to a self- adaptive DE algorithm variant and better than five conventional DE algorithm variants. Furthermore, ASDE was applied to solve a typical dynamic optimization problem of a chemical process.

The obtained results indicate that ASDE is a feasible and competitive optimizer for this kind of problem. • WU Ji; WANG Zuoying 2001-01-01 Gaussian Similarity Analysis (GSA) algorithm can be used to estimate the similarity between two Gaussian distributed variables with full covariance matrix. Based on this algorithm, we propose a method in speaker adaptation of covariance.

It is different from the traditional algorithms, which mainly focus on the adaptation of mean vector of state observation probability density. A binary decision tree is constructed offline with the similarity measure and the adaptation procedure is data-driven. It can be shown from the experiments that we can get a significant further improvement over the mean vectors adaptation.

Petrova 2016-05-01 Full Text Available Subject of Research.We propose to modify the EA+RL method, which increases efficiency of evolutionary algorithms by means of auxiliary objectives. The proposed modification is compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method a reinforcement learning algorithm is used to select an objective – the target objective or one of the auxiliary objectives – at each iteration of the single-objective evolutionary algorithm.The proposed modification of the EA+RL method adopts this approach for the usage with a multiobjective evolutionary algorithm. As opposed to theEA+RL method, in this modification one of the auxiliary objectives is selected by reinforcement learning and optimized together with the target objective at each step of the multiobjective evolutionary algorithm. Main Results.The proposed modification of the EA+RL method was compared to the existing objective selection methods on the example of travelling salesman problem. In the EA+RL method and its proposed modification reinforcement learning algorithms for stationary and non-stationary environment were used.

The proposed modification of the EA+RL method applied with reinforcement learning for non-stationary environment outperformed the considered objective selection algorithms on the most problem instances. Practical Significance. The proposed approach increases efficiency of evolutionary algorithms, which may be used for solving discrete NP-hard optimization problems. They are, in particular, combinatorial path search problems and scheduling problems. • Sun Liping; Hu Guangrui 2004-01-01 Blind Adaptive Step-size Constant Modulus Algorithm (AS-CMA) for multiuser detection in DS-CDMA systems is presented. It combines the CMA and the concept of variable step-size, uses a second LMS algorithm for the step size. It adjusts the step-size according to the minimum output-energy principle within a specified range, thus overcomes the problems of bad effect of fixed step-size LMS algorithm.

Compared with Adaptive Step-size LMS (AS-LMS) algorithm, through simulations, this algorithm can adapt the changes of the environment, suppress multiple access interference in the dynamic environment and the stability of Signal to Interference Ratio (SIR) is superior to that of AS-LMS. Raida 2007-09-01 Full Text Available In the paper, a novel instance of the real-coding steady-state genetic algorithm, called the Mean- adaptive real-coding genetic algorithm, is put forward.

In this instance, three novel implementations of evolution operators are incorporated. Those are a recombination and two mutation operators. All of the evolution operators are designed with the aim of possessing a big explorative power. Moreover, one of the mutation operators exhibits self- adaptive behavior and the other exhibits adaptive behavior, thereby allowing the algorithm to self-control its own mutability as the search advances. This algorithm also takes advantage of population-elitist selection, acting as a replacement policy, being adopted from evolution strategies.

The purpose of this paper (i.e., the first part is to provide theoretical foundations of a robust and advanced instance of the real-coding genetic algorithm having the big potential of being successfully applied to electromagnetic optimization. • Wujian Yang; Yining Cheng; Jie He; Wenqiong Hu; Xiaojia Lin 2016-01-01 As there are many researches about traditional Tang poetry, among which automatically generated Tang poetry has arouse great concern in recent years.

This study presents a community-based competition and adaptive genetic algorithm for automatically generating Tang poetry. The improved algorithm with community-based competition that has been added aims to maintain the diversity of genes during evolution; meanwhile, the adaptation means that the probabilities of crossover and mutation are varie. • TAN Xinglong 2015-04-01 Full Text Available The predicted residual vectors should be zero-mean Gaussian white noise, which is the precondition for multiple fading factors adaptive filtering algorithm based on statistical information in GPS/INS integration system. However the abnormalities in observations will affect the distribution of the residual vectors. In this paper, a neural network aided adaptive unscented Kalman filter (UKF algorithm with multiple fading factors based on singular value decomposition(SVD is proposed. The algorithm uses the neural network algorithm to weaken the influence of the observed abnormalities on the residual vectors.

Singular value decomposition instead of unscented transformation is adopted to suppress negative definite variation in priori covariance matrix of UKF. Since single fading factor in poor tracking of multiple variables has the limitation, multiple fading factors to adjust the predicted-state covariance matrix are constructed with better robustness so that each filter channel has different adjustability.

Finally, vehicle measurement data are collected to validate the proposed algorithm. It shows that the neural network algorithm can prevent the observed abnormalities from affecting the distribution of the residual vectors, expanding the applied range of the adaptive algorithm. The neural network algorithm aided SVD-UKF algorithm with multiple fading factors is able to remove influences of state anomalies on condition of the observed abnormalities.

The accuracy and reliability of the navigation solution can be improved by this algorithm. • LIU Long; HAN Chongzhao; BAI Yan 2005-01-01 In the motion vector field adaptive search technique (MVFAST) and the predictive motion vector field adaptive search technique (PMVFAST), the size of the largest motion vector from the three adjacent blocks (left, top, top-right) is compared with the threshold to select different search scheme.

But a suitable search center and search pattern will not be selected in the adaptive search technique when the adjacent motion vectors are not coherent in local region. This paper presents an efficient adaptive search algorithm. The motion vector variation degree (MVVD) is considered a reasonable factor for adaptive search selection. By the relationship between local motion similarity degree (LMSD) and the variation degree of motion vector (MVVD), the motion vectors are classified as three categories according to corresponding LMSD; then different proposed search schemes are adopted for motion estimation. The experimental results show that the proposed algorithm has a significant computational speedup compared with MVFAST and PMVFAST algorithms, and offers a similar, even better performance.