Search

In the robotized production one of the more important think is to choose the optimal solution to use the robots with respect an objective function which represents, for example, minimum time of motion during a application, or minimum consumption of energy, or maximum precision, or combination of these. Some objective functions could results from the specificity of the application like is the case of casting of forging, where the minimum of the accumulation of heat could be one of the optimization criteria. In the controlling of the space movement of the end effecter and the robot’s joints of the all robots from the applications, one of the most important think is to know, with the extreme precision, the joints relative displacements of all robots. One of the most precise method to solve the inverse kinematics problem in the robots with redundant chain is the complex coupled method of the neural network with Iterative Pseudo Inverse Jacobian Matrix Method. In this paper was used the proper coupled method Iterative Pseudo Inverse Jacobian Matrix Method (IPIJMM) with Sigmoid Bipolar Hyperbolic Tangent Neural Network with Time Delay and Recurrent Links (SBHTNN-TDRL) to establish the optimal position of the application point of the robot's base with respect simultaneously two objective functions: the extreme precision and the minimum of the movements time. The paper shown how can be changed the multi robots application in to one application with parallel robot structure with three independent robots, all of them with optimal location point with respect the obiective function. The presented method and the virtual instrumentations (VI) are generally and they can be used in all other robots application and for all other conventional and unconventional space curves.

This paper describes a simple, reliable and swift load-flow solution method with a wide range of practical application. It is attractive for accurate or approximate off-and on-line calculations for routine and contingency purposes. It is applicable for networks of any size and can be executed effectively on computers. The method is a development on conventional load flow principle and its precise algorithm form has been determined to bring improvement to the conventional techniques. This paper presents a comparative study of the new constant Jacobian matrix load flow method built based on several conventional NR load flow methods. Assumptions are made so as to make the matrix constant, thus eliminating the need of calculating the matrix in every iteration. The proposed method exhibits better computation speed.

Traffic flow video processing is an important direction of the development of intelligent transportation, Gaussian mixture background modeling is one of commonly algorithms to build background modeling that is widely used in image processing field. In order to analysis the algorithm comprehensively ,in this paper, it calculus on formula as well as tests and compares some traffic flow videos, the solution based on the K-means, EM parameters model are presented, it gets the mathematical meaning and evolution process of the algorithm clearly by calculating formulas to provide theoretical guidance for practical application; the traffic flow videos processing can get the treatment ability of this algorithm according to the different situation of videos, analyze the mixed Gauss background modeling for time migration, the light changing, moving object shadow processing ability. In this paper, the algorithm of mixed Gauss background modeling is analyzed from two aspects of theory and application.

With more and more researches about improving BP algorithm, there are more improvement methods. The paper researches two improvement algorithms based on quasi-Newton method, DFP algorithm and L-BFGS algorithm. After fully analyzing the features of quasi-Newton methods, the paper improves BP neural network algorithm. And the adjustment is made for the problems in the improvement process. The paper makes empirical analysis and proves the effectiveness of BP neural network algorithm based on quasi-Newton method. The improved algorithms are compared with the traditional BP algorithm, which indicates that the improved BP algorithm is better.

In the three-order polynomial phase signal, HAF can be used to reduce phase order to achieve the phase parameter estimation. The loss of signal to noise ratio in the order reduction processing is serious. In order to reduce the SNR loss, this paper uses CPF to reduce phase order. It requiers two one-dimensional searches to estimate the rate of acceleration and acceleration. The estimated motion parameters are used to compensate the target echo doppler and doppler spectrum expansion can be suppressed. This paper proposes a new maneuvering target detection algorithm with low non-linearity. The algorithm makes use of the cubic phase function (CPF) to estimate target motion parameters and reduce the number of phase order. Simulation demonstrates the effectiveness of the proposed algorithm and the adaptability to low signal noise ratio.

The traditional Otsu threshold algorithm is not a good method for processing the real images because of complex shape and unbalanced distribution. To solve this problem, the paper uses the thinking of Otsu’s method for reference, introduces a threshold segmentation algorithm based on histogram statistical property. In addition, the paper draws a comparison between the new algorithm and Otsu’s algorithm. Experimental results show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximatively, and the target region is less than the background region.

Traditional power quality signal samples are based on the Nyquist sampling theory. Because of the existence of disturbance signal for the presence of power, it requires two times higher than the sampling frequency of the original signal, resulting in many problems, such as a high cost of hardware. Compressed sensing algorithm abandoned the characteristics of Shannon theorem, using a lower sampling frequency and the less amount of the signal to reconstruct the signal, with the method of a loss compression, which can effectively solve this problem. A team in Beijing University of Chemical Technology has done a deep research in this direction and proposed the total variation gradient reduction algorithm, which has good effects on reduction. But the algorithm runs slower and needs higher sample volumes of signal. Therefore, this paper presents a modified algorithm based on Nesta algorithm to reduce the amount of data sampled of power quality signal, the complexity of the algorithm to improve the algorithm’s speed. The modified algorithm has a very important value in practical applications. This paper has carried out simulations in matlab, the results of the simulation show that this method is accurate and applied.

Spatial distorted target is very hard to be recognized for complexity and variety of targets, which has restricted the development of pattern recognition technology to a great extent. Joint transform correlator is one of the key equipments to detect and recognize distorted targets. The appearance and development of maximum average correlation height (MACH) algorithm is introduced in this paper. Based on the principle the algorithm and experimental analysis, an improved maximum average correlation height algorithm fit for joint transform correlator is proposed, which has powerful capability of suppressing background noise and widening distortion tolerance. Target images with different shapes including scale or angular distortion constitute MACH filter in frequency domain, which is projected to space domain as reference template including varieties of attitude. To show the feasibility of the algorithm, an airplane with angular distortion in sky is processed by MACH filter as an example. Simulation and optical experimental results are given in this paper. The experiments show the angular distortion tolerance can reach up to 15 degrees. The actual effect of the improved MACH filter algorithm is confirmed very well.

The paper is devoted to put forward an effective algorithm for removing image impulse noise by improving the traditional median filter. The following approaches are developed in this paper. First, in a small area all the pixels are separated into signal pixels and suspicious noise pixels making use of the extremum and median filtering algorithm .Then suspicious noise pixels are further identified the signal pixels and noise pixels around pixels. If a pixel value is a noise pixel, it will be processed by median filter. A signal pixel will retain the original pixel value. The numerical experiments show that compared with traditional median filter , the details and edge information of the original image are retained when the new algorithm is used , Comparing with extremum median filter algorithm, the ratio of peak signal to noise can increase up to 2.94 at most. As a result, it is an effective algorithm for image denoising that can eliminate impulse noise in the image effectively.

In order to remove salt-and-pepper noise and Gaussian noise in image, a novel filtering algorithm is proposed in this paper. The novel algorithm can preserve image edge details as much as possible. Firstly, five-median-binary code (FMBC) is proposed and used to describe local edge type of image. Secondly, median filter algorithm is improved to remove salt-and-pepper noise by using FMBC. Then, local enhanced bilateral filter with FMBC and a new type of exponential weighting function is used to remove Gaussian noise. Simulation results show that the algorithm proposed in this paper is very effective not only in filtering mixed noise but also in preserving edge details.

On the basis of actual survey of departing passengers to reach terminal, chaotic time series prediction theory was adopted for data analysis in this paper. In order to find out the self-similarity of time series, this paper divided passenger traffic into two kinds: holiday traffic and non-holiday traffic by changing interval scale. The optimal delay time and the best embedding dimension had been calculated by using time series phase space reconstruction method. To confirm whether the time series have chaotic characteristics or not, it took the largest Lyapunov exponent as determining criterion.Then the optimal time intervals of passenger traffic time series with chaotic character were determined. The study provides a theoretical basis for the application of chaos theory in passenger traffic forecast.

When modeling background model by Gaussian mixture model, there exist the defects that parameters can not be updated adaptively. In this paper, we adopt mean-shift algorithm to overcome these defects. Firstly, this paper introduces the initialized parameters, such as variance, mean, and weights and others, when modeling and then the parameters are constantly adjusted in the subsequent calculations. Then the statistical background model based on probability density estimation is put forward and using mean-shift algorithm updates the parameters adaptively. At last, the algorithm of mixture Gaussian background modeling method based on mean-shift is implemented. The experimental results show that the algorithm can effectively update parameters adaptively and the obtained background model is better.

Fourier algorithm can be effective utilized in the Microcomputer relay protection to filter out Harmonic component in the AC signal .but the drawback is that not filter attenuation DC component. it may be affect the computation accuracy of the entire system .and it is also reduce the efficiency of the system. In this paper ,we combined with the actual project ,on the basis of analysis of the full-wave Fourier algorithm ,for the problem of it can not filter decaying a periodic component ,we proposed an improved algorithm, and combined with MATLAB simulation ,the results show that the improved algorithm has the advantages of high accuracy and the algorithm is simple. In the microcomputer relay protection of the power system need to handle all kinds of signals, provide the necessary information for the implementation of protection. however, With the rapid development of computer technology and the continuous research of computer algorithms, a lot of actual power system protection device has been widely used in our life, on the one hand, these devices reduce the complexity of building hardware circuit, on the other hand ,it can make full use of the filter function about some algorithms have itself. The most important is that it can avoid the problem of instability and low efficiency with some filter circuit. But, this characteristic need the algorithms has a high precision [2,3]. The algorithm is set up in the microcomputer protection is a mathematical model for achieve some specific function. and write the corresponding computer applications according to this model. Then do some specific mathematical operations with these digital signals will achieve the purpose of protection. At present, the widely used full wave Fourier algorithm has a function of filtering DC component and harmonic component[4],but it can not filtering the decaying DC component in the system. It will be cause a certain interference and influence to the whole system in the practical application, the result is that led to the precision of the operation drop and lose the meaning of the protecting. In this paper, combined with specific engineering example give an improved calculation method. Through example of simulation has proved that using this algorithm can accurately figure out fundamental component and harmonic component, and it has the advantage of algorithm simple and high accuracy [5].

This paper takes a parallel mechanism of a loading test device, analyzes a degree of freedom of the parallel mechanism, builds a geological model in ADAMS software, applies restriction and load, and sets a performance function; this paper solves the problem of Jacobian matrix ambiguity of the parallel mechanism through releasing three kinematic pairs in ADAMS, transforms the solved angle to Euler angle of coordinate series of two parts through local coordinate system of ADAMS, simulates and solves the included angle movement scale between following ground jack 5 and dynamic and static platforms through the performance function of ADAMS, thereby providing accordance for the design of the loading test device.

Investigations on broad null steering by only phase perturbation are presented in this paper using immune algorithm (IA) for linear array. Broad null steering in antenna pattern helps to reduce channel interference due to less energy being transmitted at unwanted directions. Phase controlling for broad null steering is relative easy realizing comparing with amplitude or elements’ position controlling. IA is an efficient approach to escape from local convergency for muti-objective optimization with the antibody colony’s diversity is enhanced by using Logistic mapping for initial colony and Gauss function for mutation operator. In this paper, excitation phases are setted as antibodies optimizing for steering broad nulls with specified width at specified directions. Well simulation results show the availability of broad nulling by phase only controlling and the superior global search capability of the improved IA.

At present, objects detection, identification and tracking according to machine vision technology, have been widely used in various economic aspects. In this paper, a real-time monitoring system was discussed. This surveillance system mainly combines two sub-systems, motion detection and objects tracking. An Adaptive Gaussian Background Model was established in order to automatically update the background and detect the outline of moving objects. By analyzing different algorithms, this paper brings out approaches to promote the performance.We proposed CamShift algorithm to complete motion detection and objects tracking, which applied for static background video sequences. And the experimental results show that our method can achieve the pre-determined targets.

An algorithm is described which simulates atmospheric phase screen (PS) distorted by von Karman atmospheric turbulence by using Zernike expansion of randomly weighted Karhunen-Loeve function. The statistics of the PS generated by using power spectrum method which is most commonly used for generating PS poorly match up with the theoretical structure function, especially at low spatial frequency, while the algorithm described in the this paper can compensate for this shortcoming. And the accuracy of the PS is verified by comparing with the theoretical results. Furthermore, comparing with the existed methods which also use the Zernike expansion to simulate PS, algorithm presented in this paper is more accurate because the effects of the finite outer scale L0.is included

The vibration signals of the running machine contain non-stationary components. Usually, these non-stationary components contain abundant information on machine faults. In this paper, the Hilbert–Huang transform (HHT) method for the machine fault diagnosis is proposed. The empirical mode decomposition (EMD) method and Hilbert transform are key parts of the Hilbert–Huang transform method. The EMD will generate a collection of intrinsic mode functions (IMF). By applying EMD method and Hilbert transform to the vibration signal, we can get the Hilbert spectrum from which the faults in a running machine can be diagnosed and fault patterns can be identified. The practical vibration signals measured from roller machine with eccentric and friction faults are analysed by the Hilbert–Huang transform and Fourier transform in this paper. Finally, HHT’s performance in rolling machine fault detection is compared with that of the Fourier transform. The comparison results have shown that the HHT is superior than the Fourier transform in machine fault diagnostics. The different failure characteristic frequencies can be distinguished in the component of different orders of IMF, and the time and frequency of failure characteristic frequency appearance can be clearly reflected in the Hilbert spectrum.

Localization has been a major challenge in Wireless Sensor Networks (WSNs), especially for the applications requiring the accurate position of the sensed information. In this paper, we propose a new localization algorithm based on the Centroid algorithm and the DV-Hop algorithm to improve the positioning accuracy without increasing any extra hardware for sensor nodes. This paper firstly analyzed the advantages and disadvantages of the centroid algorithm and the DV-Hop algorithm. Then we put forward an iterated hybrid algorithm, which is comprised of three steps. Firstly, obtaining the initial location of each unknown node by using the centroid algorithm; secondly, computing the distances among each unknown node to the anchor nodes based on the DV-Hop algorithm; finally, Taylor Series Expansion (TSE) algorithm is utilized to estimate coordinate of each unknown node. Simulation results show that our iterated hybrid algorithm has better positioning accuracy.

This paper put forward a mine location algorithm based on multiple linear regression, which using only simple RSSI value to get a higher location accuracy under long narrow and sensitive mine environment. General RSSI measurement method and its drawbacks are discussed in the paper. In order to acquire smaller location error, we filtered some abnormal RSSI data through Gaussian filter method. And we deduced regression equation according to multiple linear regression principle. Combined with training sample, we got their regression parameter. We did relevant location experiment again in the same environment---40m long and narrow bomb shelter which may imitate mine tunnel to a great extent, which shows that the total errors are limited in 3m and 75% errors are less than 2m. What’s more, it can be extended to infinite measuring range with the same set regression coefficient in similar environment.

Efficient surrogate modelling of computer models (herein defined as simulators) becomes of increasing importance as more complex simulators and non-deterministic methods, such as Monte Carlo simulations, are utilised. This is especially true in large multidimensional design spaces. In order for these technologies to be feasible in an early design stage context, the surrogate model (oremulator) must create an accurate prediction of the simulator in the proposed design space. Gaussian Processes (GPs) are a powerful non-parametric Bayesian approach that can be used as emulators. The probabilistic framework means that predictive distributions are inferred, providing an understanding of the uncertainty introduced by replacing the simulator with an emulator, known as code uncertainty. An issue with GPs is that they have a computational complexity of O(N3) (where N is the number of data points), which can be reduced to O(NM2) by using various sparse approximations, calculated from a subset of inducing points (where M is the number of inducing points). This paper explores the use of sparse Gaussian process emulators as a computationally efficient method for creating surrogate models of structural dynamics simulators. Discussions on the performance of these methods are presented along with comments regarding key applications to the early design stage.

WSN’s practical application has high level of spontaneity in positioning information and is susceptible to the outside interference, which may lead to the reduction of positioning accuracy. This paper studies Gauss distribution function, the single sensor batch estimate fusion theory and RSSI positioning technique based on the design of the CC2430/1 wireless sensor network positioning system hardware and puts forward a kind of positioning algorithm of WSN based on the Gauss distribution function and batch estimation fusion theory. The algorithm first uses Gauss model to analyse RSSI, reject RSSI value of a small probability event through the Gauss model, and then applies single sensor batch estimation fusion theory to estimate in batches the fushion theory’s calculation of the RSSI value of high probability events as the RSSI value of the final positioning operation. Experiments show that using this algorithm, the positioning precision of the system can be improved to be within 0.5 meters.

The target tracking technology in image sequence is of great meanings in the military and civilian areas, by using Monte Carlo method to complete the Bayesian recursive, particle filter is widely used in the systems of non-linear and non - Gaussian and good results are gained. However, particle filter there are also disadvantages in terms of sample impoverishment, the choosing of proper proposal distribution, real time and so on. In this paper, the particle filter is utilized to in the feature fusion of the moving target, and the experimental results show that the proposed algorithm has certain effect both on the remission of the sample impoverishment and the enhancement of the tracking robustness.

Speech processing in hearing aid often operates in noisy environments. So noise reduction algorithm is very important for hearing aid. This paper reviews conventional wiener filter algorithm and points out its remained problems----musical residual noise and low speech intelligibility for low input signal-to-noise ratios (SNR). To solve the problems, the proposed algorithm divides the frequency (0-8000 Hz) into 16 bands in Mel-frequency scale and processes the signal separately to remove musical residual noise. Meanwhile, a new Voice Activity Detector (VAD) applied for digital hearing aid is also presented. Based on the accurate judgment of speech frame, the filter processes the speech frame and non-speech frame in different ways. The method is realized in matlab to test output SNR and PESQ (Perceptual Evaluation of Speech Quality). It is also realized in android app for real-time test. Both of the experimental results demonstrate the advances of the proposed algorithm.

This paper presents the kinematics for 3-PTT parallel machine tools, the kinematic is analytically performed, the forward and inverse position kinematic equations are explicit functions and workspace is derived and there is no singularity configuration and kinematic coupling in the whole workspace. The manipulability analytical of the parallel machine tool is investigated by the Jacobian matrix.

In the process of imaging, digitalization and transmission, images are generally contaminated by Gaussian noise and salt & pepper noise, which cannot be eliminated completely at the same time only by Mean filter or Median filter. Aiming at solving this problem, an improved hybrid median-mean filter algorithm based on the Improved Median Filtering (IMF) algorithm is proposed in this paper. The experimental results show that the new algorithm shows better performance than either Median filtering algorithm or Mean filtering algorithm, which can not only get rid of Gaussian noise and salt & pepper noise simultaneously, but also minimize the contradictions between noise erasing and image details protecting effectively.

Unified encrypted technology has led to many key advances, including e-commerce and public-private key pairs. Despite the fact that it is never a key purpose, it fell in line with our expectations. In this position paper, we validate the development of Boolean logic, which embodies the significant principles of electrical engineering. CoolCoiner, our new algorithm for the construction of sensor networks, is the solution to all of these grand challenges. CoolCoiner verifies that e-commerce and fiber-optic cables can collaborate to realize this objective. To fix this challenge for the robust unification of SMPs and the location-identity split, we explored an analysis of hierarchical databases. As a result, the characteristics of our system, in relation to those of more acclaimed systems, are dubiously more confirmed.

This paper used the Empirical Mode Decomposition (EMD) ,solving the problem of the modulation type recognition of Orthogonal Frequency Division Multiplexing (OFDM) on blind signal Processing of communication .This method uses the characteristics of the OFDM' envelope nearly Gaussian .Using the EMD decomposition algorithm of signal decomposition, extract the intrinsic mode function (IMF) after signal decomposition and computes the correlation co-efficient of IMF and the original signal as the recognition feature .So as to achieve that identification the OFDM signals and single carrier linearly digitally modulated (SCLD) signals (MFSK(2FSK, 4FSK), MPSK(BPSK, QPSK)) without prior knowledge. Experiments show that this method has a good performance with less computation, and this method also has well Real-time and robustness compare with the methods of the wavelet transform and the higher order cumulants.

This paper proposes an improved amplitude spectrum based Fourier-Mellin algorithm Fourier by studying the nature of the Fourier transform image of the amplitude spectrum and the application of phase spectrum in estimation for image motion vector according to the shortcomings of traditional Fourier-Mellin algorithm when the video image translation, rotation and scaling of the situation exist. Experimental results show that the algorithm can estimate the rotation, translation and other vector parameters of a complicated motion model .The complexity of the algorithm has greatly improved comparing to the traditional Fourier-Mellin algorithm.

Due to that the Doppler parameters vary according to slant and the resolution is lower using imaging algorithm of traditional pulse compression in processing raw echo data of the missile-borne synthetic aperture radar (SAR). Moreover, an algorithm is proposed to solve these problems, which is based on the fractional Fourier transform (FrFT) for missile-borne SAR imaging. Firstly, an echo signal model is built for the terminal guidance stage of the missile-borne SAR. Secondly, measure the chirp rate of the echo signal through the local optimum processing and get the optimum angles for the FrFT, and then the entire SAR image can be obtained by using FrFT with the optimum angles of the azimuth and range. Finally, the performances of the algorithms are assessed using simulated and real Radarsat-1 data sets. The results confirm that the FrFT-based missile-borne SAR processing methods provide enhanced resolution yielding both lower side lobes effects and improved target detection. The method introduced in this paper has important theoretical significance in detection and recognition for military targets and precision guidance.

This paper presents the development of multi-objective population-based optimization method, called Non-dominated Sorting Genetic Algorithm II (NSGA-II), to optimize the aerodynamic characteristic of a low Reynolds number airfoil. The optimization is performed by changing the shape of the airfoil to obtain geometry with the best aerodynamic characteristics. The results of the study show that the developed optimization tool, coupled with modified PARSEC parameterization, has yielded optimum airfoils with better aerodynamic characteristics compared to original airfoil. Additionally, it is found that the developed method has better performance compared to similar methods found in literature.

In this paper, we present a modified discrete Fourier transform (DFT)-based channel estimator for orthogonal frequency-division multiplexing (OFDM) systems with null carriers. Due to the channel leakage, the conventional DFT-based estimator has the problem of error floor. In order to suppress the leakage, we adopt the optimal leakage suppression method. Moreover, a novel denoising method based on dual-tree complex wavelet is proposed to further improve the performance. Simulation results show that the proposed channel estimator can effectively overcome the performance degradation caused by the leakage and additive-white gaussian noise (AWGN).

Independent component analysis with reference is a general framework to incorporate a priori information of interesting source signal into the cost function as constrained terms to form an augmented Lagrange function, and utilizes Newton method to optimize the cost function. It can extract any interesting source signal without extracting all source signals comparing with the traditional Independent component analysis method. In this paper, to accelerate the convergence speed of the Independent component analysis with reference, two improved algorithms are presented. The new algorithms, firstly whiten the observed signals to avoid matrix inverse operation to reduce algorithm complexity, secondly use improved Newton method with fast convergence speed to optimize cost function,in the end deduce the improved Independent component analysis with reference algorithms. Simulation result demonstrates the new algorithms have faster convergence speed with smaller error compared with the original method.

Modern industrial manufacturing contains different kinds of surface modeling, which is processed by planar materials. For the principle of the most efficient materials using, how to optimize subdivision undevelopable surface before development becomes an important topic in CAM design and manufacturing industries. The paper transforms the basic parameters of surface before subdivision, and gives the standards of subdivision surfaces. We proved the feasibility and the existence of triangle subdivision algorithm. By introducing double parameters MÖbius transformation , the subdivision surface is more uniform. At last, numerical examples prove that the algorithm about the approximate development of the undevelopable surfaces is effective and practical.

Obtaining the flight attitude of high accuracy, high reliability is a prerequisite for achieving the autonomous flight of the multi-rotor UAV. MEMS gyroscope can solve attitude angle individually. The paper adopts Euler angle and quaternion algorithms respectively to calculate attitude angle of the multi-rotor UAV. The flight experiment shows that the quaternion algorithm can guarantee the accuracy and the stability of the attitude angle calculation. The root-mean-square errors of the pinch roll and heading angles which are calculated by the quaternion algorithm are 2.947o, 3.606o and 9.769o, which can meet the demand for the autonomous flight of the multi-rotor UAV.

In order to predict the failure probability of a complicated structure, the structural responses usually need to be estimated by a numerical procedure, such as finite element method (FEM). To reduce the computational effort required for reliability analysis, response surface method could be used. However the conventional response surface method is still time consuming especially when the number of random variables is large. In this paper, a Fourier orthogonal neural network (FONN)-based response surface method is adopted to solve the reliability analysis of the automobile engine. The working process of the connecting rod is simulated with UG software, the dynamics analysis on crank-connecting rod-piston mechanism is performed with ANSYS and ADAMS software, with FEM analysis results, the stress information of the critical point of the structure can be obtained, so the performance function of the structure can be established. The FONN method is used to fit the performance function as well as its derivatives, so as to calculate the reliability of the structure.

Product quantization (PQ) is an efficient and effective vector quantization approach to fast approximate nearest neighbor (ANN) search especially for high-dimensional data. The basic idea of PQ is to decompose the original data space into the Cartesian product of some low-dimensional subspaces and then every subspace is quantized separately with the same number of codewords. However, the performance of PQ depends largely on the distribution of the original data. If the distributions of every subspace have larger difference, PQ will achieve bad results as shown in our experiments. In this paper, we propose a uniform variance product quantization (UVPQ) scheme to project the data by a uniform variance projection before decompose it, which can minimize the subspace distribution difference of the whole space. UVPQ can guarantee good results however the data rotate. Extensive experiments have verified the superiority of UVPQ over PQ for ANN search.

In sky-wave over-the-horizon radar system, it is difficult to detect ship because the Doppler shift of the ship is very close to the strong ocean clutter. Traditional clutter cancellation algorithms suppress ocean clutter by estimating clutter parameters and removing the clutter signal in time domain. However, they estimate the clutter parameters by the maximum value of the post DFT spectrum and thus lead to the low accuracy. In this paper, a modified clutter cancellation algorithm is proposed, which corrects estimated frequency by utilizing the three maximum spectrum values and overcomes the poor cancellation performance caused by inaccurate estimation of clutter parameters. Compared with traditional clutter cancellation algorithm, the proposed algorithm can get more accurate clutter parameters and reduce the energy and expansion of the remaining clutter. Simulation demonstrated that the proposed algorithm had a better clutter cancellation performance compared to the traditional ones.

Nonlocal differential operators have been extensively applied to variational models for image restoration due to its texture-preserving capability. In this paper, we propose a nonlocal TV (total variation)-L1 model for texture image inpainting, which, technically, combines nonlocal operators for regularization term and L1 norm for data term. The former is used to regularize texture and the latter to preserve contrast of images. In addition, we develop augmented Lagrangian algorithm for proposed model by introducing nonlocal auxiliary variable and Lagrangian multiplier. Finally, extensive experiments on synthetic and real texture images are presented to validate the effectiveness and efficiency of our proposed model and algorithm.

In this paper, we present a scheme to improve immune cloning selection algorithm. The improved algorithm, which is referred to as adaptive cloning selection algorithm (ACSA), is proposed and then applied to function optimization. At first, we present adaptive gene mutation which decides mutation probability of each code point (locus) based on the quality of antibodies and the number of evolution iterations. Secondly, we present an iteratively increasing method from one locus to the all ones, which can be used in function optimization. Then, the cloning selection process of evolution is divided into two-stages. The first step is to increase locus of antibodies. In the other one, the Baldwin effect learning operator is employed. And finally, an experiment is carried out to verify the theoretical analyses on several testing functions.

This paper deals with the problem of adaptive signal detection in the presence of Gaussian noise with unknown covariance matrix. An improved adaptive sidelobe blanker (ASB) is proposed, which consists of an adaptive matched filter (AMF) followed by a whitened adaptive beamformer orthogonal rejection test (W-ABORT). A statistical characterization for the proposed two-stage test statistic is provided,under both noise-only and signal-plus-noise hypotheses. The associated probability of false alarm (Pfa) and probability of detection (Pd) are derived in closed form. The performance assessment confirms the effectiveness of the newly-proposed detection algorithms also in comparison to previously-proposed ones.

The echo signals of sky-wave over-the-horizon radar involve ionospheric phase contamination with spectrum expansion. The bragg peaks expand and cover the frequency spectrum of low speed target. So ionospheric phase decontamination is necessary before coherent integration. The traditional Hankel Rank Reduction (HRR) phase decontamination method constructs the Hankel matrix by folding the echo signal, estimating instantaneous frequency through singular value decomposition. But HRR method requires the prior information of signal components. The estimation is invalid without priori information. The algorithm presented in this paper does not require the priori information. The algorithm based on matched fourier transform can accurately estimate the phase contamination function for the clutter noise ratio is high. Simulation shows that the proposed algorithm has better performance in phase decontamination.

A UCA-Root-MUSIC algorithm for direction-of-arrival (DOA) estimation is proposed in this paper which is based on UCA-RB-MUSIC [1]. The method utilizes not only a unitary transformation matrix different from UCA-RB-MUSIC but also the multi-stage Wiener filter (MSWF) to estimate the signal subspace and the number of sources, so that the new method has lower computational complexity and is more conducive to the real-time implementation. The computer simulation results demonstrate the improvement with the proposed method.

This paper investigated the radiate source target localization techniques using airborne ESM sensor. Two algorithms are represented for different application environments. Pseudo linear estimation algorithm is used for estimating static target states with bearings-only measurements. The measurement data are bearings which are affected by random Gaussian observation noises. Iterated least squares estimation algorithm used iterative techniques to enhance the estimation accuracy. It is a kind of estimation algorithm based on the least squares estimation. Finally two scenarios are represented in Matlab simulation environments to validate the effectiveness of the algorithms.

During the near-field acoustic holographic reconstruction with sampling GREEN’s function in k space, there will be aliasing which results in the wrapping error for the infinite extension of GREEN’s function in real space. The magnitude of wrapping error lies on the decrement of GREEN’s function in real space. The paper applies a 2-D low-pass filter to remove the hyper space element in order to reduce the wrapping error. The simulation indicates that the method can reduce the wrapping error in the holographic reconstruction efficiently.

This paper proposes a novel finite-time consensus tracking protocol for guaranteeing first-order multi-agent systems with a virtual leader to achieve the fast finite-time consensus tracking. The Lyapunov function method, algebra graph theory, homogeneity with dilation and some other techniques are employed to prove that first-order multi-agent systems with a virtual leader applying the proposed protocol can reach the finite-time consensus tracking. Furthermore, theoretical analysis and numerical simulations show that compared with the traditional finite-time consensus tracking protocols, the proposed protocol can accelerate the convergence speed of achieving the finite-time consensus tracking.

This paper proposes a kind of the side face recognition algorithm base on Fractional Brownian motion and Fourier descriptors. This method is mainly innovating for feature extraction and making up for a shortage of the side face recognition algorithm. Firstly, by Fractional Brownian motion of Hurst index will get silhouette extracted. Then we through Fourier descriptors to obtain the desired feature points. Further study of this algorithm can solve the side of the face recognition rotation, scaling, and translation transform the impact. Comparative characteristics will make distance classifier and BP neural network classifier. This article also processes the analysis of the algorithm and the future development.

In this paper, a novel design of a multi-sectioned, remotely-actuated, continuum type manipulator is presented. Spatially Hyper-Redundant Robot (SHRR) is based on a continuous backbone model which is divided into four sections. In the area of hyper redundant robotics, kinematic redundant systems result in non square Jacobian matrix which requires a pseudo inverse method to inverse the matrix. A methodology has been devised to solve the Inverse Kinematics (IK) problem of SHRR by predicting the curvature values of each of the section. Redundant IK techniques like Pseudo-Inverse Method (PIM), Jacobian Transpose Method (JTM), Damped Least Squares Method (DLS) and Selectively Damped Least Squares Method (SDLS) are tested on the formulated kinematic model of SHRR using MATLAB and a comparative study has been made.

This paper deals with quantification of the noise located in the digital signal by an innovative process. This process is associated with the filter that represents the novel information conveyed by the desired signal, residual interference and residual noise which are used to reduce the noise. A typical uniform quantization operation of a sampled signal is identified and interpreted with the framework of stochastic difference equation. A new theorem is proposed with all possible assumptions to support our result to signal noise ratio. AMS [200] Subject Classification: 39A10, 39A30, 39A60, 39B82, 39B99.

Gravity has huge impact on the accuracy of heavy-duty machine tools. To investigate errors caused by gravity, it is essential to figure out the most influential factor. This paper presents a geometric error modeling for heavy-duty CNC machine tools. Regarding a machine tool as a rigid multi-body system (MBS), the geometric error model has been established by utilizing kinematics chain and homogeneous transfer matrix (HTM). By analyzing the Jacobi matrix, the influence of all the geometric error parameters has been calculated to find out the key geometric error that affect the accuracy most. It is revealed that gravity of beam and tool affect the accuracy of the machine tool most through the ANSYS simulation. It supports a theoretical basis for the further research on error compensation of the key component of a machine tool.