In many efforts to increase the efficiency of power amplifiers of mobile terminals, compensation of nonlinear distortion based on an adaptive predistortion method has performed an important role. In the course of basic evaluation of a method using a look-up table (LUT) and a method using an approximation for compensation of nonlinear distortion, a newly developed method using approximation and a ROM type LUT with a small-sized memory has been proposed to overcome barriers to practical application and disadvantages associated with the LUT method. Experimental trials of the proposed method were applied to narrow-band digital modulation systems. As a result, the proposed method was found to provide a satisfactory capability of compensating nonlinear distortion, with next adjacent channel interference of less than -55 dBc. The proposed method has advantages such as a small memory size and excellent RF performance, and is expected to occupy an important position in many adaptive predistortion methods.

This paper describes the design, fabrication, and performance of a GaAs FET linearizer with a large source inductance, focusing mainly on (a) a mechanism of positive gain and negative phase deviations for input power, (b) stability considerations, and (c) a dependence on load impedance. In addition, in an application to the linearized amplifier, it is shown that an improvement can be achieved for adjacent channel leakage power (ACP) and third order intermodulation distortion (IM3) with the use of the linearizer.

This paper discusses the relationships of two important program classes of linearly recursive programs, that is, decomposable programs and rule commutative programs. We prove that the decomposable programs are always rule commutative. Furthermore, the rule commutative programs that satisfy certain conditions are decomposable. These results are meaningful for integrating the related specified optimization algorithms.

Conformance testing is to see if the protocol implementation conforms to its specification. A lot of test sequences have been developed for testing centers. Yet directly applying these test sequences to the simple testing system in laboratories suffers from the frequently-occurred synchronization problems. This paper proposes a new technique to disconnect a test sequence into segments based on their functions, and reconnects them into a new test sequence that simulates these functions yet suffers less from the synchronization problems.

An analysis method is proposed for nonstationary waveforms. Modelling of a nonstationary waveform is first given in this paper. A waveform is represented by multiple oscillations. The instantaneous phase angle of each oscillation is written by three terms, predictive component, residual component, and initial phase constant. By this modelling, waveform analysis results in estimations of frequency, calculation of residual pbase in instantaneous phase angle. The Instantaneous Maximum Entropy Methods (IMEN) is utilized for frequency estimation. The residual phase angle is obtained by the Vandermonde matrix and the condition of continuity of phase angle among n-neighbourhood. Another analysis method is also proposed by the normalization of waveform parameters. The evaluation of the proposed method is done using artificially composed waveform signals. Novel and useful knowledge was provided by this analysis.

In recent years, fractal processes have played important roles in various application fields. Since a 1/f process possesses the statistical self-similarity, it is considered sa a main part of fractal signal modeling. On the other hand, noise reduction is often needed in real-world signal processing. Hence, we propose an enhancement algorithm for 1/f signal disturbed by white noise. The algorithm is based on constrained minimization in a wavelet domain: the power of 1/f signal distortion in the wavelet domain is minimized under a constraint that the power of residual noise in the wavelet domain is smaller than a threshold level. We solve this constrained minimization problem using a Lagrangian equation. We also consider a setting method of the Lagrange multiplier in the proposed algorithm. In addition, we will confirm that the proposed algorithm with this Lagrange multiplier setting method obtains better enhancement results than the conventional algorithm through computer simulations.

A new method is proposed for generating synchronizable test sequences which can be applied in the distributed test architecture for protocol conformance testing. The method consists of a duplex digraph technique and a rural Chinese postman tour algorithm to generate a minimum-length synchronizable test sequence using distinguishing sequences.

A neural network approach called the "Gradual Neural Network (GNN)" for the time slot assignment problem in the TDM multicast switching system is presented in this paper. The goal of this NP-complete problem is to find an assignment of packet transmission requests into a minimum number of time slots. A packet can be transmitted from one source to several destinations simultaneously by its replication. A time slot represents a switching configuration of the system with unit time for each packet transmission through an I/O line. The GNN consists of the binary neural network and the gradual expansion scheme. The binary neural network satisfies the constraints imposed on the system by solving the motion equation, whereas the gradual expansion scheme minimizes the number of required time slots by gradually expanding activated neurons. The performance is evaluated through simulations in practical size systems, where the GNN finds far better solutions than the best existing altorithm.

A new linearization technique of a transconductor is presented. The linearization is realized by using a differential current amplifier with an emitter-coupled pair. A specific value of the linearization parameter gives a maximally flat or an equiripple characteristic. Deviations from the theoretical characteristic can be adjusted by tuning the tail current of the emitter-coupled pair. The proposed technique is demonstrated by PSPICE simulation.

ATM networks are proposed by CCITT as the solution for the future B-ISDN. In ATM networks, the cells are transmitted between user and network without flow control, therefore, a policing mechanism (PM) is needed to check that the source traffic doesn't exceed the negotiated parameters. The sources supported by ATM networks have a bursty nature. The control of the mean cell rate of the bursty sources is intended to increase the network utilization. The conventional PMs can't efficiently monitor the mean cell rate of bursty sources, therefore new PMs are needed. In this letter, we propose a fuzzy policing mechanism (FPM). The performance evaluation via simulations shows that the FPM efficiently controls the mean cell rate of the packet voice source. The selectivity characteristics of the FPM approach the ideal characteristic required for a PM.

In this paper we describe a method, which allows the likelihood normalization technique, widely used for speaker verification, to be implemented in a text-independent speaker identification system. The essence of this method is to apply likelihood normalization at frame level instead of, as it is usually done, at utterance level. Every frame of the test utterance is inputed to all the reference models in parallel. In this procedure, for each frame, likelihoods from all the models are available, hence they can be normalized at every frame. A special kind of likelihood normalization, called Weighting Models Rank, is also experimented. We have implemented these techniques in speaker identification system based on VQ-distortion codebooks or Gaussian Mixture Models. Evaluation results showed that the frame level likelihood normalization technique gives higher speaker identification rates than the standard accumulated likelihood approach.

The supervisory control theory of discrete event dynamic systems was proposed in the framework of automata and formal languages. The concept of decentralized supervisory control was developed for the local supervisor Si whose concurrent operation results in the closed-loop language L (Si/G) equal to that of global supervisor, L (S/G). In this letter we extend this concept by considering the problem of optinal combination of decentralized with centralized control in case pure decentralized control happens to be inadequate. We introduce the concept of locally controllable complementary tuple and present an analytical framework for nonhomogeneous decentralized supervisory control systems.

The filtered-x algorithm, which is widely applied to active noise control system, requires setting a small step gain. Such a small step gain reduces the noise reduction effect when the alogrithm is implemented by fixed point processing. This paper presents an experimental result that the 'polarized-g' individually normalized least mean square (INLMS) algorithm can provide almost the same noise reduction effect even in the fixed point processing of 16 bits as that in floating point processing.

In the measurement of actual random phenomenon, the observed data often contain the fuzziness due to the existence of confidence limitation in measuring instruments, permissible error in experimental data, some practical simplification of evaluation procedure and a quantized error in digitized observation. In this study, by introducing the well-known fuzzy theory, a state estimation method based on the above fuzzy observations is theoretically proposed through an establishment of wide sense digital filter under the actual situation of existence of the background noise in close connection of the inverse problem. The validity and effectiveness of the proposed method are experimentally confirmed by applying it to the actual fuzzy data observed in an acoustic environment.

An effective learning method for the fuzzy ARTMAP in the recognition of noisy input patterns is presented. the weight vectors of the system are updated using the weighted average of the noisy input vector and the weight vector itself. This method leads to stable learning and prevents the excessive update of the weight vectors which may cause performance degradation. Simulation results show that the proposed method not only reduces the generation of spurious categories, but aloso increases the recognition ratio in the noisy environment.

In this paper, we investigate bifurcation phenomena ovserved from two autonomous three-dimensional chaotic circuits coupled by an inductor. Two types of synchronization modes are ovserved in this coupled system, i.e., in-phase synchronization and anti-phase synchronization. For the purpose of detailed analysis, we consider the case that the diodes in the subcircuits are assumed to operate as ideal switches. In this case Poincare map is derived as a three-dimensional map, and Lyapunov exponents can be calculated by using exact solutions. Various bifurcation phenomena related with chaos synchronization are clarified. We confirm that various bifurcation phenomena are observed from circuit experiments.

We present a very fast method for calculating an inverse filter for audio reproduction system. The proposed method of FFT-based inverse filter design, which combines the well-known principles of least squares optimization and regularization, can be used for inverting systems comprising any number of inputs and outputs. The method was developed for the purpose of designing digital filters for multi-channel sound reproduction. It is typically several hundred times faster than a conventional steepest descent algorithm implemented in the time domain. A matrix of causal inverse FIR (finite impulse response) filters is calculated by optimizing the performance of the filters at a large number of discrete frequencies. Consequently, this deconvolution method is useful only when it is feasible in practice to use relatively long inverse filters. The circular convolution effect in the time domain is controlled by zeroth-order regularization of the inversion problem. It is necessary to set the regularization parameter β to an appropriate value, but the exact value of β is usually not critical. For single-channel systems, a reliable numerical method for determining β without the need for subjective assessment is given. The deconvolution method is based on the analysis of a matrix of exact least squares inverse filters. The positions of the poles of those filters are shown to be particularly important.

This paper proposes a data-localization scheduling scheme inside a processor-cluster for multigrain parallel processing, which hierarchically exploits parallelism among coarsegrain tasks like loops, medium-grain tasks like loop iterations and near-fine-grain tasks like statements. The proposed scheme assigns near-fine-grain or medium-grain tasks inside coarse-grain tasks onto processors inside a processor-cluster so that maximum parallelism can be exploited and inter-processor data transfer can be minimum after data-localization for coarse-grain tasks across processor-clusters. Performance evaluation on a multiprocessor system OSCAR shows that multigrain parallel processing with the proposed data-localization scheduling can reduce execution time for application programs by 10% compared with multigrain parallel processing without data-localization.

New network survivability measures are developed and compared with conventional ones. The advantages of using multiple survivability measures, including the new ones, are discussed. The measures are illustrated and interpreted through several numerical examples. We also show how survivability can be included as a constraint in network optimization models.

In this paper,we propose general fast one dimensional (1-D) and two dimensional (2-D) slant transform algorithms. By introducing simple and structural permutations, the heavily computational operations are centralized to become standardized and localized processing units. The total numbers of multiplications for the proposed fast 1-D and 2-D slant transforms are less than those of the existed methods. With advantages of convenient description in formulation and efficient computation for realization, the proposed fast slant transforms are suitable for applications in signal compression and pattern recognition.