Multi-Level Inverter Linear Predictive Phase Composition Strategy for UPQC

The power system is facing numerous issues when the distributed gen-eration is added to the existing system.The existing power system has not been planned with flawless power quality control.These restrictions in the power trans-mission generation system are compensated by the use of devices such as the Static Synchronous Compensator(STATCOM),the Unified Power Quality Con-ditioner(UPQC)series/shunt compensators,etc.In this work,UPQC’s plan with the joint activity of photovoltaic(PV)exhibits is proposed.The proposed system is made out of series and shunt regulators and PV.A boost converter connects the DC link to the PV source,allowing it to compensate for voltage sags,swells,vol-tage interferences,harmonics,and reactive power issues.In this paper,the fea-tures of a seven-level Cascaded H-Bridge Multi-Level idea are applied to shunt and series active filter changeovers to reduce Total Harmonic Distortion and com-pensate for voltage issues.Despite its power quality capacity for common cou-pling,the proposed system can inject the grid’s dynamic power.During voltage interference,it can also provide a piece of delicate burden power.The simulation is carried out with the help of MATLAB/SIMULINK programming,and the results are compared to those of other conventional methods.

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.

An adaptive strategy based on linear prediction of queue length to minimize congestion in Barabási-Albert scale-free networks

In this paper, we propose an adaptive strategy based on the linear prediction of queue length to minimize congestion in Barabaisi-Albert （BA） scale-free networks. This strategy uses local knowledge of traffic conditions and allows nodes to be able to self-coordinate their accepting probability to the incoming packets. We show that the strategy can delay remarkably the onset of congestion and systems avoiding the congestion can benefit from hierarchical organization of accepting rates of nodes. Furthermore, with the increase of prediction orders, we achieve larger values for the critical load together with a smooth transition from free-flow to congestion.

LINEAR PREDICTION APPROACH IN AIRBORNE ADAPTIVE ARRAYS

To cope with the time-varying and Dopper-broadened clutter in airborne phase array radars, it is required that the signal processing should be adaptive and two-dimensional both in time and in space. However, the optimum two-dimensional adaptive processing is hard to realize real-timely because it requires a large amount of computation. From the idea of approximating the clutter process by using an auto regressive process, a linear prediction approach is proposed to realize the adaptive space-time processing of airborne adaptive array signals. The research shows that the clutter process can be well approximated by a low-order AR process, so a low-order linear prediction receiver can get a sub-optimum performance at a very low expense. Besides, the low-order linear prediction receiver has additional degrees of freedom to cope with other colored noises and interferences. In consideration of the many advantages of the linear prediction receiver in both algorithms and realizations, it has a good prospect in its application to air borne adaptive array signal processing.

KLT-based local linear prediction of chaotic time series

In the reconstructed phase space, based on the Karhunen-Loeve transformation （KLT）, the new local linear prediction method is proposed to predict chaotic time series. ＆ noise-free chaotic time series and a noise added chaotic time series are analyzed. The simulation results show that the KLT-based local linear prediction method can effectively make one-step and multi-step prediction for chaotic time series, and the one-step and multi-step prediction accuracies of the KLT-based local linear prediction method are superior to that of the traditional local linear prediction.

Blind Adaptive MMSE Equalization of Underwater Acoustic Channels Based on the Linear Prediction Method

The problem of blind adaptive equalization of underwater single-input multiple-output (SIMO) acoustic channels was analyzed by using the linear prediction method.Minimum mean square error (MMSE) blind equalizers with arbitrary delay were described on a basis of channel identification.Two methods for calculating linear MMSE equalizers were proposed.One was based on full channel identification and realized using RLS adaptive algorithms,and the other was based on the zero-delay MMSE equalizer and realized using LMS and RLS adaptive algorithms,respectively.Performance of the three proposed algorithms and comparison with two existing zero-forcing (ZF) equalization algorithms were investigated by simulations utilizing two underwater acoustic channels.The results show that the proposed algorithms are robust enough to channel order mismatch.They have almost the same performance as the corresponding ZF algorithms under a high signal-to-noise (SNR) ratio and better performance under a low SNR.

Pyramid Linear Prediction Coding for Images

Motivated by wavelet transform, this paper presents a pyramid linear prediction coding (PLPC) algorithmfor digitial images.The algorithm otltpots the rough colltour of an image and a prediction ermr sequence. In contrastto the conventional linear prediction method, PLPC exhibits very little sensitivity to channel ermrs and provides amore efficient compression performance. The results of simulations with Lena 512 X 512 and bitrates ranging from0.17 to 3.2 (lossless)bits/pixel are given to show that the PLPC method is very suitable for the human visualperception.

AN IMPROVED 2.4kb/s MIXED EXCITATION LINEAR PREDICTION VOCODER

This letter presents two improvements on 2.4 kb/s Mixed-Excitation Linear Prediction (MELP) vocoder. The one is a new parameter Redzc named energy to differential zerocrossing rate which is used in adaptation of V/UV decision of transitional segments and low energy level speech segments. The other is a multi-path searching method for Multi-Stage Vector Quantization (MSVQ) of line spectral frequency. Subjective tests show that the intelligiblity and naturallity of improved MELP vocoder are preferable to those of the original one.

Development of a Quantitative Prediction Support System Using the Linear Regression Method

The development of prediction supports is a critical step in information systems engineering in this era defined by the knowledge economy, the hub of which is big data. Currently, the lack of a predictive model, whether qualitative or quantitative, depending on a company’s areas of intervention can handicap or weaken its competitive capacities, endangering its survival. In terms of quantitative prediction, depending on the efficacy criteria, a variety of methods and/or tools are available. The multiple linear regression method is one of the methods used for this purpose. A linear regression model is a regression model of an explained variable on one or more explanatory variables in which the function that links the explanatory variables to the explained variable has linear parameters. The purpose of this work is to demonstrate how to use multiple linear regressions, which is one aspect of decisional mathematics. The use of multiple linear regressions on random data, which can be replaced by real data collected by or from organizations, provides decision makers with reliable data knowledge. As a result, machine learning methods can provide decision makers with relevant and trustworthy data. The main goal of this article is therefore to define the objective function on which the influencing factors for its optimization will be defined using the linear regression method.

Dynamic Wavelength and Bandwidth Allocation Using Adaptive Linear Prediction in WDM/TDM Ethernet Passive Optical Networks

Hybrid wavelength-division-multiplexing(WDM)/time-division-multiplexing(TDM) ethernet passive optical networks(EPONs) can achieve low per-subscriber cost and scalability to increase the number of subscribers. This paper discusses dynamic wavelength and bandwidth allocation(DWBA) algorithm in hybrid WDM/TDM EPONs.Based on the correlation structure of the variable bit rate(VBR) video traffic,we propose a quality-ofservice (QoS) supported DWBA using adaptive linear traffic prediction.Wavelength and timeslot are allocated dynamically by optical line terminal(OLT) to all optical network units(ONUs) based on the bandwidth requests and the guaranteed service level agreements(SLA) of all ONUs.Mean square error of the predicted average arriving rate of compound video traffic during waiting period is minimized through Wiener-Hopf equation.Simulation results show that the DWBA-adaptive-linear-prediction(DWBA-ALP) algorithm can significantly improve the QoS performances in terms of low delay and high bandwidth utilization.

An improved algorithm for noise-robust sparse linear prediction of speech

The performance of linear prediction analysis of speech deteriorates rapidly under noisy environments. To tackle this issue, an improved noise-robust sparse linear prediction algorithm is proposed. First, the linear prediction residual of speech is modeled as Student-t distribution, and the additive noise is incorporated explicitly to increase the robustness, thus a probabilistic model for sparse linear prediction of speech is built, Furthermore, variational Bayesian inference is utilized to approximate the intractable posterior distributions of the model parameters, and then the optimal linear prediction parameters are estimated robustly. The experimental results demonstrate the advantage of the developed algorithm in terms of several different metrics compared with the traditional algorithm and the l1 norm minimization based sparse linear prediction algorithm proposed in recent years. Finally it draws to a conclusion that the proposed algorithm is more robust to noise and is able to increase the speech quality in applications.

Improved Populating Code-Excited Linear Predictive Coder

Although CELP coding has provided good quality synthetic speech at medium and low bit rates,the computation of an exhaustive search for stochastic codebook is extremely complex. This paper studies the exhaustive search procedure for determining the optimum excitation,and develops an effective search method by using improved populating codebook as excitation source. The computational cost of CELP coder was reduced to 1/26 that of a conventional full-gaussian codebook search.

Blind Multipath Identification: A Weighted Linear Prediction Approach

Blind channel identification exploits the measurable channel output signaland some prior knowledge of the statistics of the channel input signal. However, in many scenarios,more side information is available, In digital communication systems, the pulse-shaping filter inthe transmitter and the anti-aliasing filter in the receiver are often known to the receiver.Exploitation of this prior knowledge can simplify the channel identification problem. In this paper,we pose the multipath identification problem as solving a group of linear equations. While we solvethe linear equations in the least-square meaning, a weight matrix can be introduced to improve theperformance of the estimator. The optimal weight matrix is derived. Compared with the existingLinear Prediction (UP) based multipath identification approach, the proposed approach offers asubstantial performance gain.

On Optimal Dyadic Linear Prediction and Filtering in the Sequency Domain

In this paper dyadic linear prediction and dyadic linear filtering on a dyadic generalized stationary random process are dealt with via the Walsh transform. Taking the minimum of the mean-square errorof the dyadic system as the index of the working precision of the system, we consider and analyze theoptimal dyadic linear system, dyadic linear prediction and dyadic linear filtering. Finally, we study the precision of the optimal dyadic linear system.

Levinson-durbin's recursive solution |k_i| =1 for voice linear prediction coefficients

This paper includes three parts: (1) As introduction to briefly summarize L-D recursion procedure and the principle for computing voicc Lp coefficient; (2) Qucstions or problems at the reflection coefficients |ki|; (3) Discussion and solutions of different cases.

A NEW ALGORITHM FOR ADAPTIVE LATTICE FILTERAND ITS APPLICATION IN THE SPEECH LINEARPREDICTIVE SYNTHESIS

In this paper, an adaptive line spectral pair filter is derived from an adaptive lattice filter. A least-mean-square(LMS) type adaptive algorithm used to calculate directly the line spectral pair(LSP) coefficients on a stage-by-stage basis is presented. Experimental results show that the algorithm has higher convergence rate and lower misadjustment as compared with the other algorithms. The LSP coefficients calculated by the algorithm have been used to carry out speech linear predictive synthesis, resulting in better results than PARCOR coefficients.

A Novel Low-bit-rate Speech Coding Based on Decomposition of the Pitch-cycle Waveform of the Linear Predictive Residual

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An Improved Robust Model Predictive Control Approach to Systems with Linear Fractional Transformation Perturbations

In this paper, a robust model predictive control approach is proposed for a class of uncertain systems with time-varying, linear fractional transformation perturbations. By adopting a sequence of feedback control laws instead of a single one, the control performance can be improved and the region of attraction can be enlarged compared with the existing model predictive control （MPC） approaches. Moreover, a synthesis approach of MPC is developed to achieve high performance with lower on-line computational burden. The effectiveness of the proposed approach is verified by simulation examples.

ASP-Based Programs of Best Linear Unbiased Prediction-Estimated Breeding Values in Breeding Stock

In order to improve the breeding effect of livestock, the data were read from an Excel file with Active Server Page （ASP） programs, and the breeding values of breeding stock were calculated by best linear unbiased prediction （BLUP） method.

Adaptive nonlinear model predictive control design of a flexible-link manipulator with uncertain parameters

This paper presents a novel adaptive nonlinear model predictive control design for trajectory tracking of flexible-link manipulators consisting of feedback linearization, linear model predictive control, and unscented Kalman filtering. Reducing the nonlinear system to a linear system by feedback linearization simplifies the optimization problem of the model predictive controller significantly, which, however, is no longer linear in the presence of parameter uncertainties and can potentially lead to an undesired dynamical behaviour. An unscented Kalman filter is used to approximate the dynamics of the prediction model by an online parameter estimation, which leads to an adaptation of the optimization problem in each time step and thus to a better prediction and an improved input action. Finally, a detailed fuzzy-arithmetic analysis is performed in order to quantify the effect of the uncertainties on the control structure and to derive robustness assessments. The control structure is applied to a serial manipulator with two flexible links containing uncertain model parameters and acting in three-dimensional space.