This paper deals with CE-SS (Constant-Envelope Spread Spectrum) signals, focusing on a novel generation technique based upon using digital processing blocks to drive a frequency modulator with a random sequence. The system described herein allows for flexibility in achieving a variety of user defined goal spectra. The foundation upon which this work is built was laid by Callegari et al. who introduced a novel synthesis procedure for 'non-stationary' modulations. This novel synthesis technique uses an iterative algorithm to arrive at an output spectrum which is a good approximation to a user-defined goal spectrum. The architecture which this paper details uses programmable logic to tune the system parameters in striving towards user defined goal spectra. The architecture can generate CE-SS waveforms whose spectra match those which the aforementioned algorithm deems achievable.

In this paper we consider an approximation method of a formal linearization which transform time-varying nonlinear systems into time-varying linear ones and its applications. This linearization is a kind of a coordinate transformation by introducing a linearizing function which consists of the Chebyshev polynomials. The nonlinear time-varying systems are approximately transformed into linear time-varying systems with respect to this linearizing functions using Chebyshev expansion to the state variable and Laguerre expansion to the time variable. As applications, nonlinear observer and filter are synthesized for time-varying nonlinear systems. Numerical experiments are included to demonstrate the validity of the linearization. The results show that the accuracy of the approximation by the linearization improves as the order of the Chebyshev and Laguerre polynomials increases.

This paper describes a fast-lock delay-lock loop (DLL) with a power-on reset (POR) circuit. A novel POR circuit and coarse tune (CT) circuit are used to overcome the false locking problems associated with conventional DLL's and offer a faster locking time. The CT circuit is used to control the DLL loop bandwidth to reduce the locking time while maintaining stability and better jitter performance. Moreover, a new voltage-controlled delay line is proposed to reduce dynamic switching power dissipation and noise. An experimental chip is designed and fabricated based on the TSMC 0.35 µm single-poly four-metal CMOS process. From the measurement results, this DLL can operate correctly when the input clock frequency is changed from 100 to 190 MHz and generate equally spaced eight-phase clocks. When the input clock frequencies are 100 MHz and 190 MHz, the measured output clock rms jitter are 12.44 ps and 8.463 ps, respectively. Furthermore, the locking time is less than 43 clock cycles based on the HSPICE simulation results.

Single electron devices are ultra low power and extremely small devices, and suitable for implementation of large scale integrated circuits. Artificial neural networks (ANNs), which require a large number of transistors for being to be applied to practical use, is one of the possible applications of single electron devices. In order to simplify a single electron circuit configuration, we apply stochastic logic in which various complex operations can be done with basic logic gates. We design basic subcircuits of a single electron stochastic neural network, and confirm that backgate bias control and a redundant configuration are necessary for a feedback loop configuration by computer simulation based on Monte Carlo method. The proposed single electron circuit is well-suited for hardware implementation of a stochastic neural network because we can save circuit area and power consumption by using a single electron random number generator (RNG) instead of a conventional complementary metal oxide semiconductor (CMOS) RNG.

The hardware implementation of a neural network model using stochastic logic has been able to integrate numerous neuron units on a chip. However, the limitation of applications occurred since the stochastic neurosystem could execute only discrete-time dynamics. We have contrived a neuron model with continuous-time dynamics by using stochastic calculations. In this paper, we propose the circuit design of a new neuron circuit, and show the fabricated neurochip comprising 64 neurons with experimental results. Furthermore, a new asynchronous updating method and a new activation function circuit are proposed. These improvements enhance the performance of the neurochip greatly.

In this study, nonlinear wave phenomena related to transmissions and reflections of the phase-inversion waves around a discontinuity of a coupled system consisting of two kinds of arrays of van der Pol oscillators are investigated. By computer simulations, behavior of the phase-inversion waves around the discontinuity in the coupled system is classified into eight types. Further, the mechanisms of the transmission and the reflection of a phase-inversion wave at the discontinuity are explained. Circuit experiments confirm the simulated results.

This paper presents a novel design procedure for class E oscillator. It is the characteristic of the proposed design procedure that a free-running oscillator is considered as a forced oscillator and the feedback waveform is tuned to the timing of the switching. By using the proposed design procedure, it is possible to design class E oscillator that cannot be designed by the conventional one. By carrying out two circuit experiments, we find that the experimental results agree with the calculated ones quantitatively, and show the validity of the proposed design procedure. One experimental measured power conversion efficiency is 90.7% under 6.8 W output power at an operating frequency 2.02 MHz, the other is 89.7% under 2.8 W output power at an operating frequency 1.97 MHz.

This paper presents a technique for the variation of hysteresis band in delta-sigma modulation. A sinusoidal, and a random hystersis band are combined to achieve an optimal performance in terms of constant switching frequency and the harmonic spikes. The sinusoidal hysteresis band technique produces a constant switching frequency while the random hysteresis band suppresses the harmonic spikes. The effects of various variations of hysteresis band on the harmonic spectrum characteristic were described. The technique is experimented in a single-phase inverter and the harmonic peaks and the distortion of output voltage were used to measure the performance of the proposed technique.

From the bifurcation viewpoint, this study examines a boost PFC converter with average-current-mode control. The boost PFC converter is considered to be a nonlinear circuit because of its use of a multiplier and its large duty cycle variation for input current control. However, most previous studies have implemented linear analysis, which ignores the effects of nonlinearity. Therefore, those studies were unable to detect instability phenomena. Nonlinearity produces bifurcations and chaos when circuit parameters change. The classical PFC design is based on a stable periodic orbit that has desired characteristics. This paper describes the main bifurcations that this orbit may undergo when the parameters of the circuit change. In addition, the instability regions in the PFC converter are delimited. That fact is of practical interest for the design process. Moreover, a prototype PFC circuit is introduced to examine these instability phenomena experimentally. Then, a special numerical program is developed. Bifurcation maps are provided based on this numerical study. They give a comprehensive outstanding for stability conditions and identify stable regions in the parameter space. Moreover, these maps indicate PFC converter dynamics, power factors, and regulation. Finally, numerical analyses and experimentation show good agreement.

Modulo-2 addition (or exclusive-OR) is one of fundamental operations for binary variables. In this paper, we discuss statistical properties of sequences obtained by modulo-2 addition of two binary sequences. Firstly, we theoretically evaluate statistics of sequences obtained by modulo-2 addition of two general binary random variables. Secondly, we consider statistics of modulo-2 added chaotic binary sequences generated by a class of one-dimensional chaotic maps. Furthermore, we consider synthesis of an aperiodic binary sequence and a periodic one by modulo-2 addition with the purpose of generating aperiodic sequences with good statistical properties. We also perform computer experiments about such sequences.

In this paper, we propose a new method which makes transient simulation faster for the circuit including both nonlinear and linear elements. First, the method for generating the projection matrix with Krylov-subspace technique is described. The order of the circuit equation is reduced by congruence transformation with the projection matrix. Next, we suggest a method which can calculate the reduced Jacobian matrix directly in each Newton-Raphson iteration. Since this technique does not need to calculate the original size of Jacobian matrix, the calculation cost is reduced drastically. Therefore, efficient circuit simulation can be achieved. Finally, our method is applied to some example circuits and the validity of the nonlinear circuit reduction technique is verified.

Analytical derivation of bit error rates for multi-user coherent chaos-shift-keying (CSK) communication systems are presented in this paper. Nearly exact results are obtained by applying the central limit theorem of statistics to sums of independent variables. Based on χ² distribution approximations, more viable but still very accurate results decrease complexity of the calculations. The χ² approach is compared with the widely used Gaussian approximation approach to show its superiority in most cases. Bit error performance bounds for the multi-user CSK system from the approach are deduced as further contributions of this paper. The theoretical results obtained are entirely consistent with a range of simulations.

This paper proposes a method of improving demodulation performance for chaotic synchronization based multiplex communications systems. In a conventional system, the number of data demodulated correctly is limited because transmitted chaotic signals interfere with each other. The proposed system uses a generalized inverse of a matrix formed from chaotic signals at the transmitter. Since this completely cancels the interference between chaotic signals, demodulation performance is greatly improved. The proposed system has the following features: A simple correlation receiver suitable for small terminals can be used; The magnitude of the correlator output is constant for binary data transmission; Analog information data can also be transmitted. Two methods to reduce the peak-to-average power ratio of the transmitted signal are presented.

We propose a post-filter (digital filter applied after the correlator) to reduce multiple-access interference (MAI) in the correlator output in asynchronous communications. Optimum filter coefficients are derived for Markov and i.i.d. codes. It is shown that post-filter is not needed for Markov case. Variance of MAI is reduced in i.i.d. codes and it becomes equal to that of Markov codes; thus, both will have the same bit error rate (BER) performance. This post-filter reduces level of MAI in the correlator output for Gold codes as well.

Let us introduce n ( 2) nonlinear mappings f_i (i = 1,2,,n) defined on complete linear metric spaces (X_i-1,ρ) (i = 1,2,,n), respectively, and let f_i:X_i-1 X_i be completely continuous on bounded convex closed subsets X_i-1, (i = 1,2,,n 0), such that f_i() . Moreover, let us introduce n fuzzy-set-valued nonlinear mappings F_i:X_i-1X_i {a family of all non-empty closed compact fuzzy subsets of X_i}. Here, by introducing arbitrary constant β_i (0,1], for every integer i (i = 1,2,,n 0), separately, we have a fixed point theorem on the recurrent system of β_i -level fuzzy-set-valued mapping equations: x_i F_iβi(x_i-1, f_i(x_i-1)), (i = 1,2,,n 0), where the fuzzy set F_i is characterized by a membership function µ_Fi(x_i):X_i [0,1], and the β_i -level set F_iβi of the fuzzy set F_i is defined as F_iβi {ξ_i Xi |µ_Fi (ξ_i) β_i}, for any constant β_i (0,1]. This theorem can be applied immediately to discussion for characteristics of ring nonlinear network systems disturbed by undesirable uncertain fluctuations and to extremely fine estimation of available behaviors of those disturbed systems. In this paper, its mathematical situation and proof are discussed, in detail.

This study investigates nonlinear reliable output tracking control issues. Both passive and active reliable control laws are proposed using Variable Structure Control technique. These reliable laws need not the solution of Hamilton-Jacobi (HJ) equation or inequality, which are essential for optimal approaches such as LQR and H reliable designs. As a matter of fact, this approach is able to relax the computational burden for solving the HJ equation. The proposed reliable designs are also applied to a bank-to-turn missile system to illustrate their benefits.

Since most process systems have nonlinearities, it is necessary to consider the design of schemes to deal with such systems. In this paper, a new design scheme of PID controllers is proposed. This scheme is designed based on the internal model control (IMC) which is a kind of the model driven controllers. The internal model consists of the design-oriented model and the full model. The full model is designed by using the neural network. The primary PID control system is firstly constructed for the augmented system which is composed of the controlled object and the internal model, and this control system is designed by the pole-assignment method. Furthermore, the secondary PID controller is designed in order to remove the steady state error. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

Moment matrix analysis (MMA) that can derive statistical properties of non-linear equations is presented in this paper. First, non-linear stochastic differential, or difference, equations are approximately expressed by simultaneous linear equations of moments defined at discrete events. Next, by eliminating higher order moments, the simultaneous linear equations are reduced to a linear vector equation of their coefficient matrix and a moment vector comprised of the moments of the system state. By computing the eigenvectors and eigenvalues of the coefficient matrix, we can analyze the moments, transient response, and spectrum of the system state. The behavior of Internet traffic was evaluated by using MMA and computer simulation, and it is shown that MMA is effective for evaluating simultaneous non-linear stochastic differential equations.

This paper discusses stability boundaries in an electric power system with dc transmission based on a differential-algebraic equation (DAE) system. The DAE system is derived to analyze transient stability of the ac/dc power system: the differential equation represents the dynamics of the generator and the dc transmission, and the algebraic equation the active and reactive power relationship between the ac system and the dc transmission. In this paper complete characterization of stability boundaries of stable equilibrium points in the DAE system is derived based on an energy function for the associated singularly perturbed (SP) system. The obtained result completely describes global structures of the stability boundaries in solution space of the DAE system. In addition the characterization is confirmed via several numerical results with a stability boundary.

This paper presents novel algorithms for image restoration by state sharing methods with the stochastic model. For inferring the original image, in the first approach, a degraded image with gray scale transforms into binary images. Each binary image is independently inferred according to the statistical fluctuation of stochastic model. The inferred images are returned to a gray-scale image. Furthermore the restored image is constructed from the average of the plural inferred images. In the second approach, the binary state is extended to a multi-state, that is, the degraded image with Q state is transformed into n images with τ state and image restoration is performed. The restoration procedure is described as follows. The degraded image with Q state is prepared and is transformed into n images with τ state. The n images with τ state are independently inferred by the stochastic model and are returned to one image. Moreover the restored image is constructed from the average of the plural inferred images. Finally, the properties of the present approaches are described and the validity of them is confirmed through numerical experiments.

The transformation of a data set using a second-order polynomial mapping to find statistically independent components is considered (quadratic independent component analysis or ICA). Based on overdetermined linear ICA, an algorithm together with separability conditions are given via linearization reduction. The linearization is achieved using a higher dimensional embedding defined by the linear parametrization of the monomials, which can also be applied for higher-order polynomials. The paper finishes with simulations for artificial data and natural images.

Learning algorithms for Vector Quantization (VQ) are categorized into two types: batch learning and incremental learning. Incremental learning is more useful than batch learning, because, unlike batch learning, incremental learning can be performed either on-line or off-line. In this paper, we develop effective incremental learning methods by using Stochastic Relaxation (SR) techniques, which have been developed for batch learning. It has been shown that, for batch learning, the SR techniques can provide good global optimization without greatly increasing the computational cost. We empirically investigates the effective implementation of SR for incremental learning. Specifically, we consider five types of SR methods: ISR1, ISR2, ISR3, WSR1 and WSR2. ISRs and WSRs add noise input and weight vectors, respectively. The difference among them is when the perturbed input or weight vectors are used in learning. These SR methods are applied to three types of incremental learning: K-means, Neural-Gas (NG) and Kohonen's Self-Organizing Mapping (SOM). We evaluate comprehensively these combinations in terms of accuracy and computation time. Our simulation results show that K-means with ISR3 is the most comprehensively effective among these combinations and is superior to the conventional NG method known as an excellent method.

This paper deals with an on-line identification method based on a radial basis function (RBF) network model for continuous-time nonlinear systems. The nonlinear term of the objective system is represented by the RBF network. In order to track the time-varying system parameters and nonlinear term, the recursive least-squares (RLS) method is combined in a bootstrap manner with the genetic algorithm (GA). The centers of the RBF are coded into binary bit strings and searched by the GA, while the system parameters of the linear terms and the weighting parameters of the RBF are updated by the RLS method. Numerical experiments are carried out to demonstrate the effectiveness of the proposed method.

Using daily infection data for Hong Kong we explore the validity of a variety of models of disease propagation when applied to the SARS epidemic. Surrogate data methods show that simple random models are insufficient and that the standard epidemic susceptible-infected-removed model does not fully account for the underlying variability in the observed data. As an alternative, we consider a more complex small world network model and show that such a structure can be applied to reliably produce simulations quantitative similar to the true data. The small world network model not only captures the apparently random fluctuation in the reported data, but can also reproduce mini-outbreaks such as those caused by so-called "super-spreaders" and in the Hong Kong housing estate of Amoy Gardens.

This paper deals with the chaoticity and fractality analysis of price time series for artificial stock market generated by the multi-agent systems based on the co-evolutionary Genetic Programming (GP). By simulation studies, if the system parameters and the system construction are appropriately chosen, the system shows very monotonic behaviors or sometime chaotic time series. Therefore, it is necessary to show the relationship between the realizability (reproducibility) of the system and the system parameters. This paper describe the relation between the chaoticity of an artificial stock price and system parameters. We also show the condition for the fractality of a stock price. Although the Chaos and the Fractal are the signal which can be obtained from the system which is generally different, we show that those can be obtained from a single system. Cognitive behaviors of agents are modeled by using the GP to introduce social learning as well as individual learning. Assuming five types of agents, in which rational agents prefer forecast models (equations) or production rules to support their decision making, and irrational agents select decisions at random like a speculator. Rational agents usually use their own knowledge base, but some of them utilize their public (common) knowledge base to improve trading decisions. By assuming that agents with random behavior are excluded and each agent uses the forecast model or production rule with most highest fitness, those assumptions are derived a kind of chaoticity from stock price. It is also seen that the stock price becomes fractal time series if we utilize original framework for the multi-agent system and relax the restriction of systems for chaoticity.

A directional audible sound can be generated by amplitude-modulated (AM) into ultrasound wave from a parametric array. To synthesize audio signals produced by the self-demodulation effect of the AM sound wave, a quasi-linear analytical solution, which describes the nonlinear wave propagation, is developed for fast numerical evaluation. The radiated sound field is expressed as the superposition of Gaussian Beams. Numerical results are presented for a rectangular parametric loudspeaker, which are in good agreement with the experimental data published previously.

In this study, a CG animation tool was designed that allows interpolation and extrapolation of two types of repeated motions including finger actions, for quantitative analyses of the relationship between features of human motions and subjective impressions. Three-dimensional human motions are measured with a magnetic motion capture and a pair of data gloves, and then relatively accurate time-series joint data are generated utilizing statistical characteristics. Based on the data thus obtained, time-series angular data of each joint for two dancing motions is transformed into frequency domain by Fourier transform, and spectral shape of each dancing action is interpolated. The interpolation and extrapolation of two motions can be synthesized with simple manner by changing an weight parameter while keeping good harmony of actions. Using this CG animation tool as a motion synthesizer, repeated human motions such as a dancing action that gives particular impressions on the observers can be quantitatively measured and analyzed by the synthesis of actions.

The chaotic property of a new open addressing hash function, called exponential hashing, is presented. Our analysis indicates the connection between ergodic theory and hashing. Based on that, concepts from ergodic theory are applied to predict the performance of exponential hashing. Experimental results are presented to verify our theoretic analysis and the prediction.

A digital/analog hybrid system is presented which implements the cardinal polynomial spline interpolation of arbitrary degree. Based on the fact that the (m-1)st derivative of a spline of degree m-1 is a staircase function, this system generates a cardinal spline of degree m-1 by m-1 cascaded integrators with a staircase function input. A given sequence of sampled values are transformed by a digital filter into coefficients for the B-spline representation of the spline interpolating the sampled values. The values of its (m-1)st derivative with respect to time are computed by the recurrence formula interpreting differentiation of the spline as difference of the coefficients. Then a digital-to-analog converter generates a staircase function representing the (m-1)st derivative, which is integrated by a cascade of m-1 analog integrators to make the expected spline. In order to cope with the offset errors involved in the integrators, a dynamical sampled-data control is attached. An analog-to-digital converter is employed to sample the output of the cascaded integrators. Target state of the cascaded integrators at each sampling instance is computed from the coefficients for the B-spline representation. The state error between the target and the estimated is compensated by feeding back a weighted sum of the state error to the staircase input.

A novel method is presented to the symbolic computation of Noise Figure (NF) of transistor circuits. Several computationally efficient macromodels of BJTs and MOSFETs by using nullors, are introduced. To demonstrate the suitability of the proposed method, the fully-symbolic expression of NF of a CMOS current-mirror is computed, and the total output noise-voltage is compared with HSPICE simulations. The calculated NF and the simulated noise are in good agreement. Finally, the method is extended to compute NF of a CMOS translinear circuit biased in weak inversion.

We present mutually pulse-coupled two relaxation oscillators having refractoriness. The system can be implemented by a simple electrical circuit, and various periodic synchronization phenomena can be observed experimentally. The phenomena are characterized by a ratio of phase locking. Using a return map having a trapping window, the ratio can be analyzed in a parameter subspace rigorously. We then clarify effects of the refractoriness on the pulse coding ability of the system.

In this paper, we propose a method for constructing quasi-cyclic low-density parity-check codes randomly using cyclic shift submatrices on the basis of the girth of the Tanner graphs of these codes. We consider (3, K)-regular codes and first derive the necessary and sufficient conditions for weight-4 and weight-6 codewords to exist. On the basis of these conditions, it is possible to estimate the probability that a random method will generate a (3, K)-regular code with a minimum distance less than or equal to 6, and the proposed method is shown to offer a lower probability than does conventional random construction. Simulation results also show that it is capable of generating good codes both regular and irregular.

We analyze the performance of multiple input single output (MISO) synchronous downlink CDMA system over Rayleigh fading channels. We propose an upper bound on its bit error rate (BER) assuming maximum likelihood (ML) multiuser detection by extending notion of removable error vector to fading channels. From the upper bound, we discuss the optimality of space-time spreading and its extension to non-orthogonal base sequences. We also give some numerical results.

In this paper, we propose a fixed monocular camera, which changes the focus cyclically to recognize completely the three-dimensional translational motion of a rigid object. The images captured in a half cycle of the focus change form a multi-focus image sequence. The motion in depth or the focus change of the camera causes defocused blur. We develop an in-focus frame tracking operator in order to automatically detect the in-focus frame in a multi-focus image sequence of a moving object. The in-focus frame gives a 3D position in the motion of the object at the time that the frame was captured. The reconstruction of the motion of an object is performed by utilizing non-uniform sampling theory for the 3D position samples, of which information were inferred from the in-focus frames in the multi-focus image sequences.

Whereas the autocorrelation is frequently used for pitch estimation, the resultant estimates usually suffer from inaccuracy. Instead of upsampling, we can improve the accuracy of the estimates by applying polynomial interpolation to the autocorrelation directly. For that purpose, four kernels, which are interpolating quadratic, quadratic-B spline, cubic-B spline, and cubic convolution kernels respectively, have been compared. Experiments show that the cubic B spline kernel shows the best performance, a little inferior to the computationally intensive upsampling procedure. The quadratic B spline kernel shows also reasonable performance with the merit of the further reduced computational complexities compared with the cubic B spline kernel.

In this paper, we propose a robust adaptive algorithm for impulsive noise suppression. The perturbation of the input signal as well as the perturbation of the estimation error are restricted by M-estimation. The threshold used in M-estimation is obtained from the proposed adaptive variance estimation. Simulations show that the proposed algorithm is less vulnerable to the impulsive noise than the conventional algorithm.

Noise generation systems are used to generate noise signals with specified characteristics. In recent study, noise generation system using DCT outperforms the conventional noise generation system when a noise model requires complicated PSD(Power Spectral Density) specifications. In this paper, low area and low power structures of non-DCT block in DCT-based noise generation system are proposed. Simulation results show that the low area structure results in area reduction by 61-64% and the low power structure achieves power reduction by 88-89% except DCT blocks.

In this letter, we present an efficient stream authentication scheme that is an improvement of SAIDA. It is shown that under the same communication overhead its verification probability is higher than that of SAIDA. Moreover, its computation cost is lower than that of SAIDA.

In this letter, we show the effects of the chip waveform selection on the detection performance of the energy detector in DS/SS communications. Three chip waveforms such as rectangular, half-sine and raised-cosine are examined as the DS/SS chip waveform. It is demonstrated that the partial-band detection can enhance the detection performance of the energy detector approximately 50-70% compared with the full-band detection. When the chip rate is identical, the raised-cosine waveform shows lower detection probability due to its wider spreading bandwidth. However, when the spreading bandwidth is identical, the rectangular waveform shows lower detection probability due to its lower partial-band energy factor.