Computational modeling of spectral data fitting with nonlinear distortions

Substances such as chemical compounds and biological agents are invisible to human eyes, they are usually captured by sensing equipments with their spectral fingerprints. Although spectra of pure substances can be identified by visual inspection, the spectra of their mixtures take a variety of complicated forms. Given the knowledge of spectral references of the constituent substances, the task of data fitting is to solve their weights, which usually can be solved by a least squares. Complications occur if the basis functions (reference spectra) may not be used directly to best fit the data. In fact, random spectral distortions such as shifting, compression, and expansion have been observed in some spectra. In this paper, we formulate mathematical model for such distortions and build them into data fitting algorithms. If minimal knowledge of the distortions is available, a deterministic approach termed augmented least squares is developed by fitting the spectral references together with their derivatives to the mixtures. If the distribution of the distortions is known a prior, we propose to solve the problem with maximum likelihood estimators which incorporate the distortions into the variance matrix. The proposed methods are substantiated with numerical examples including data from Raman spectroscopy, nuclear magnetic resonance, and differential optical absorption spectroscopy and show satisfactory results.     

Adaptive algorithms with nonlinear data and error functions

The tools of nonlinear system theory are used to examine several common nonlinear variants of the LMS algorithm and derive a persistence of excitation criterion for local exponential stability. The condition is tight when the inputs are periodic, and a generic counterexample is demonstrated which gives (local) instability for a large class of such nonlinear versions of LMS, specifically, those which utilize a nonlinear data function. The presence of a nonlinear error function is found to be relatively benign in that it does not affect the stability of the error system. Rather, it defines the cost function the algorithm tends to minimize. Specific examples include the dead zone modification, the cubed data nonlinearity, the cubed error nonlinearity, the signed regressor algorithm, and a single-layer version of the backpropagation algorithm     

Analyses of LED nonlinear distortions

This paper treats LED nonlinear distortions of differential gain (DG) and differential phase (DP) theoretically. Possible origins and numerical analyses are presented for AlxGa1-xAs LED's, considering various junction structures; double-heterostructure, single-heterostructure, and homostructure. It is shown that homostructure is superior to the others in linearity. The double-heterostructure is superior in optical power generation and in high-speed modulation, if the same structure parameters are adopted. The effects on linearity of doping concentrations, thermal resistance, and carrier confinement breakdown due to insufficient heterointerface barrier height are analyzed. An experimental result is shown to confirm the analyses. The results obtained will contribute to linear LED design.     

Adaptive compensation of phase distortions in synthetic-aperture radars

It is shown that the criterion of the maximum sharpness applied in incoherent adaptive optics can be used for compensation of random phase distortions in synthetic-aperture radars under the condition of effective range averaging of the sharpness function. Theoretical validation is confirmed by the results of simulation.     

SER performance of linear multiuser detectors for DS-CDMA downlink with transmitter nonlinear distortions

The aim of this paper is to examine the effects of the nonlinear distortions introduced by the high-power amplifier in the downlink of direct-sequence code-division multiple-access systems. By modeling the nonlinear distortion effects as a signal power loss and a superimposed colored Gaussian noise, we derive the symbol-error rate (SER) of the minimum mean-squared error (mmse) detector in additive white Gaussian noise channels. Successively, we make use of a semianalytical approach to obtain the SER performance of linear multiuser detectors, such as the RAKE, decorrelator, and mmse receiver, in frequency-selective fading channels. Simulation results confirm the effectiveness of the theoretical approach.     

汽轮机调速系统的非线性自适应控制

汽轮机汽门开度控制不仅对电力系统暂态稳定性的改善有着重要的作用,并且对抑制电力系统低频振荡和改善动态品质也有着极其重要的作用,汽轮机调速器的控制器设计通常是基于具有准确参数的理想汽轮机模型。考虑了汽轮发电机固有的非线性以及汽轮机参数的不确定性,基于微分几何学理论和自适应控制方法推导了汽轮机调速的非线性自适应控制律。仿真结果表明了通过该方法设计控制器的有效性和优越性。     

Adaptive nonlinear compensation for CDMA communication systems

This paper deals with nonlinear compensation in code-division multiple-access communications systems. We analyze the performance of the generalized cerebellar model articulation controller neural network in two problems: predistortion in nonlinear transmitters and adaptive interference cancellation in multiuser and multipath scenarios. Our examples demonstrate the performance of the compensators in typical mobile channels     

Adaptive nonlinear cancellation for high-speed fiber-optic systems

The authors propose techniques for adaptive nonlinear cancellation of intersymbol interference (ISI) in the electrical signal at the receiver in Gb/s lightwave systems and describe several demonstrations of these techniques. Techniques for adjustable nonlinear cancellations are discussed and demonstrations of these techniques using commercially available integrated circuits (ICs) at data rates as high as 1.7 Gb/s are described. Techniques for automatic adjustment are discussed, and a demonstration of adaptive nonlinear cancellation at 450 Mb/s is described. The authors discuss how these techniques can be integrated onto the detector IC for operations at 2.5 Gb/s and higher data rates. These techniques allow a single IC detector with adaptive nonlinear cancellation to be used in long-haul and undersea lightwave systems to optimize the detector threshold and compensate for the ISI     

放大器非线性失真对电缆电视系统的影响

本文详强地分析了放大器非线性失真的主要参数－交扰调制比，二阶、三阶载波互调经的物理意义和计算公式，并由此推导出电缆电视系统的设计方法。另外，本文还讨论了西德ＤＩＮ４５００４测试法。     

Adaptive learning architecture-based predistorter for nonlinear VLC system

Photonic Network Communications - A light-emitting diode (LED) acts as a transmitter in a visible-light communication (VLC) system. However, the nonlinear characteristics of LED limit the...     

Adaptive nonlinear congestion controller for a differentiated-services framework

The growing demand of computer usage requires efficient ways of managing network traffic in order to avoid or at least limit the level of congestion in cases where increases in bandwidth are not desirable or possible. In this paper we developed and analyzed a generic Integrated Dynamic Congestion Control (IDCC) scheme for controlling traffic using information on the status of each queue in the network. The IDCC scheme is designed using nonlinear control theory based on a nonlinear model of the network that is generated using fluid flow considerations. The methodology used is general and independent of technology, as for example TCP/IP or ATM. We assume a differentiated-services network framework and formulate our control strategy in the same spirit as IP DiffServ for three types of services: Premium Service, Ordinary Service, and Best Effort Service. The three differentiated classes of traffic operate at each output port of a router/switch. An IDCC scheme is designed for each output port, and a simple to implement nonlinear controller, with proven performance, is designed and analyzed. Using analysis performance bounds are derived for provable controlled network behavior, as dictated by reference values of the desired or acceptable length of the associated queues. By tightly controlling each output port, the overall network performance is also expected to be tightly controlled. The IDCC methodology has been applied to an ATM network. We use OPNET simulations to demonstrate that the proposed control methodology achieves the desired behavior of the network, and possesses important attributes, as e.g., stable and robust behavior, high utilization with bounded delay and loss, together with good steady-state and transient behavior.     

Adaptive Volterra filters for active control of nonlinear noiseprocesses

This paper presents a Volterra filtered-X least mean square (LMS) algorithm for feedforward active noise control. The research has demonstrated that linear active noise control (ANC) systems can be successfully applied to reduce the broadband noise and narrowband noise, specifically, such linear ANC systems are very efficient in reduction of low-frequency noise. However, in some situations, the noise that comes from a dynamic system may he a nonlinear and deterministic noise process rather than a stochastic, white, or tonal noise process, and the primary noise at the canceling point may exhibit nonlinear distortion. Furthermore, the secondary path estimate in the ANC system, which denotes the transfer function between the secondary source (secondary speaker) and the error microphone, may have nonminimum phase, and hence, the causality constraint is violated. If such situations exist, the linear ANC system will suffer performance degradation. An implementation of a Volterra filtered-X LMS (VFXLMS) algorithm based on a multichannel structure is described for feedforward active noise control. Numerical simulation results show that the developed algorithm achieves performance improvement over the standard filtered-X LMS algorithm for the following two situations: (1) the reference noise is a nonlinear noise process, and at the same time, the secondary path estimate is of nonminimum phase; (2) the primary path exhibits the nonlinear behavior. In addition, the developed VFXLMS algorithm can also be employed as an alternative in the case where the standard filtered-X LMS algorithm does not perform well     

Method of contour integrals for the analysis of complex nonlinear distortions of narrowband Gaussian random processes

The method of contour integrals can be used to analyze interference in radio equipment of modern broadband radio-access systems resulting from the presence of nonlinear devices (NDs) with amplitude-to-phase conversion. Several nonlinear models of power amplifiers are considered as such devices. The analysis of interference is reduced to the determination of the interference power density at the ND output and the signal-to-interference ratio in the fundamental band of the signal.     

An adaptive digital predistortion for compensating nonlinear distortions in RF power amplifier with memory effects

In this paper, an adaptive digital predistortion based on a memory polynomial model is proposed in order to linearize the power amplifier with memory effect. The coefficients of the power amplifier model have been extracted using a least square method and those of predistortion have been identified by applying an indirect learning structure. Finally, the performance of digital predistortion has been demonstrated using the simulation of the power amplifier and the digital predistortion excited by a modulated 16 QAM signal in Matlab software. According to the simulation results, the criterion of adjacent channel power ratio (ACPR) declined by around 15 dB and the input/output power spectrum density of the power amplifier has quite similar curves. The linearized power amplifier output spectrum demonstrates the superiority of the proposed predistorter in eliminating the spectral regrowth which is caused by the memory effect in comparison to the other linearization methods.     

On compensating nonlinear distortions of an OFDM system using anefficient adaptive predistorter

This paper presents an efficient adaptive predistortion technique compensating for nonlinear distortions caused by a high-power amplifier (HPA) cascaded with a linear filter in an OFDM system. In the proposed approach, the memoryless HPA, preceded by a linear filter with memory in OFDM systems, is modeled by the Wiener system, which is then precompensated by the proposed adaptive predistorter with a minimum number of filter taps. It is confirmed by computer simulation that the proposed approach produces a faster convergence speed than the previous adaptive predistortion technique, and provides a small output backoff as low as 5.5 dB for an OFDM system employing an HPA with a linear filter     

Analysis of the suppression of nonlinear distortions in amplifiers using the envelope signal

The suppression of the nonlinear distortions in amplifier using the effect of the envelope signal of the amplified HF oscillations on the amplifier parameters is analyzed. A slow (on the time scale of the HF oscillations) variation in the parameters gives rise to additional frequency components of oscillations that compensate for the nonlinear distortions of the original signal. Several variants to employ the compensating signal using the feedback circuits in the transistor amplifiers and variations in the electron-beam current in TWT in the absence of such circuits are considered. The suppression of the nonlinear intermodulation distortions (IMDs) of the test two-frequency signal is studied for the above variants and the suppression of the third-order IMD by 6–19 dB corresponds to the known experimental data on the microwave transistor amplifier. The generalization of the quasi-stationary method for the analysis of the nonlinear transformation of signals allows the analysis of the amplification and suppression of IMD for more complicated multifrequency signals that are used in radio systems.     

Neural net nonlinear prediction for speech data

A new, nonlinear, neural network based predictor has been devised fro the encoding of speech data. It may be used in the design of a differential pulse code modulation (DPCM) coder for speech. A hybrid neural network architecture has been employed which combines the perceptron and backpropagation paradigms, thus called the PB-hybrid (PBH). Only two neurons are needed in the backpropagation section, keeping the required overhead modest. This predictor is designed by supervised training, based on a typical sequence of digitised values of samples in a speech frame. Simulation experiments have been carried out using 15 ms frames of 16 kHz speech data. The results obtained for the prediction gain show a 3 dB advantage of the PBH network over the linear predictor.<>     

Decoding and Design of LDPC Codes for High-Order Modulations

A concatenated code model is proposed for high-order low-density parity-check (LDPC) coded modulations. A corresponding concatenated-code belief propagation (CCBP) decoding algorithm is derived for our proposed concatenated code. Moreover, the design of LDPC codes under the CCBP decoding is developed using extrinsic information transfer (EXIT) charts. Compared with other algorithms, the CCBP method provides an excellent parallel decoding process, and the EXIT-based design method offers highly accurate LDPC code ensembles. Simulation results show that the performance of the proposed CCBP algorithm is superior to that of the conventional belief propagation decoding within a wide range of modulation orders, and the EXIT-based method can design capacity-approaching LDPC codes for high-order modulations.     

Adaptive control of uncertain nonlinear teleoperation systems

Kinematic parameters of a robotic manipulator are hard to measure precisely and the varying size and shape of tools held by the robot end-effector introduce further kinematic uncertainties. Moreover, the exact knowledge of the robot nonlinear dynamics may be unavailable due to model uncertainties. While adaptive master–slave teleoperation control strategies in the literature consider the dynamic uncertainties in the master and the slave robots, they stop short of accounting for the robots’ kinematic uncertainties, which can undermine the transparency of the teleoperation system. In this paper, for a teleoperation system that is both dynamically and kinematically uncertain, we propose novel nonlinear adaptive controllers that require neither the exact knowledge of the kinematics of the master and the slave nor the dynamics of the master, the slave, the human operator, and the environment. Therefore, the proposed controllers can provide the master and slave robots with a high degree of flexibility in dealing with unforeseen changes and uncertainties in their kinematics and dynamics. A Lyapunov function analysis is conducted to mathematically prove the stability and master–slave asymptotic position tracking. The validity of the theoretical results is verified through simulations as well as experiments on a bilateral teleoperation test-bed of rehabilitation robots.     

Adaptive nonlinear PCA algorithms for blind source separation without prewhitening

Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals.