Fuzzy inference abilities were implemented to electromagnetic problems for the first time by the authors. After very successful results of applying the developed fuzzy modeling method to input impedance of a general monopole antenna, in this paper classifying the engineering electromagnetic problems simply, we apply the abilities of the proposed fuzzy inference method to make a qualitative model for transmission lines as a general example for a certain category of problems. The proposed approach starts from observing the problem through the window of human direct understandings and uses some parameters (as calculation base) evaluated basic for modeling process. It is shown that because of using this novel view point, a very simple fuzzy system based on new parameters may model the behavior of a transmission line in general form. The knowledge of each variable can be extracted and saved as simple curves individually, through continuing to make several models considering the desired variable as parameter. Finally, it is shown that the proposed method works even in highly nonuniform transmission line cases without changing in structure and complexity.

Multilayer feedforward neural network (MFNN) trained by the backpropagation (BP) algorithm is one of the most significant models in artificial neural networks. MFNNs have been used in many areas of signal and image processing due to high applicability. Although they have been implemented as analog, mixed analog-digital and fully digital VLSI circuits, it is still difficult to realize their hardware implementation with the BP learning function efficiently. This paper describes a special BP algorithm for the logic oriented neural network (LOGO-NN) which we have proposed as a sort of MFNN with quantized weights and multilevel threshold neurons. Both weights and neuron outputs are quantized to integer values in LOGO-NNs. Furthermore, the proposed BP algorithm can reduce high precise calculations. Therefore, it is expected that LOGO-NNs with BP learning can be more effectively implemented as digital type circuits than the common MFNNs with the classical BP. Finally, it is shown by simulations that the proposed BP algorithm for LOGO-NNs has good performance in terms of the convergence rate, convergence speed and generalization capability.

This paper presents a method of statistical system optimization. The method uses a constraint generation, which is a design methodology based on a hierarchical top-down design, to give specifications to sub-circuits of the system. The specifications are generated not only to reduce the costs of sub-circuits but also to take adequate margin to achieve enough yield of the system. In order to create an appropriate amount of margin, a term which expresses a statistical figure based on Mahalanobis' distance is added to the constraint generation problem. The method is applied to a PLL, and it is confirmed that the yield of the lock-up time reaches 100% after the optimization.

Optimization procedure on higher order Delta-sigma (ΔΣ) modulator coefficients is proposed. The procedure is based on the higher order ΔΣ modulator stability judgement method. The application specification can be satisfied with the proposed method. The 4th order modulator examples are illustrated. Optimized coefficients and its behavior model simulation results demonstrated that this methodology is suitable for the design of higher order ΔΣ AD converter. The coefficients tolerance up to 2% is allowed for switched-capacitor implementation, with not more than 3.5 dB SNR (Signal to Noise Ratio) degradation. The optimized coefficients improves 2 to 3 bit of the modulator's resolution than the previous proposed algorithm, and remains the stable input limit satisfies the original design requirement.

Most scheduling applications have been classified into NP-complete problems. This fact implies that an optimal solution for a large scheduling problem is extremely time-consuming. A number of schemes are introduced to solve NP-complete scheduling applications, such as linear programming, neural network, and fuzzy logic. In this paper, we demonstrate a new approach, fuzzy Hopfield neural network, to solve the scheduling problems. This fuzzy Hopfield neural network approach integrates fuzzy c-means clustering strategies into a Hopfield neural network. In this investigation, we utilizes this new approach to demonstrate the feasibility of resolving a multiprocessor scheduling problem with no process migration, limited resources and constrained times (execution time and deadline). In the approach, the process and processor of the scheduling problem can be regarded as a data sample and a cluster, respectively. Then, an appropriate Lyapunov energy function is derived correspondingly. The scheduling results can be obtained using a fuzzy Hopfield neural network clustering technique by iteratively updating fuzzy state until the energy function gets minimized. To validate our approach, three scheduling cases for different initial neuron states as well as fuzzification parameters are taken as testbed. Simulation results reveal that imposing the fuzzy Hopfield neural network on the proposed energy function provides a sound approach in solving this class of scheduling problems.

This paper is concerned with fuzzy modeling in some reduction methods of inference rules with gradient descent. Reduction methods are presented, which have a reduction mechanism of the rule unit that is applicable in three parameters--the central value and the width of the membership function in the antecedent part, and the real number in the consequent part--which constitute the standard fuzzy system. In the present techniques, the necessary number of rules is set beforehand and the rules are sequentially deleted to the prespecified number. These methods indicate that techniques other than the reduction approach introduced previously exist. Experimental results are presented in order to show that the effectiveness differs between the proposed techniques according to the average inference error and the number of learning iterations.

Processes in image recognition include target detection and shape extraction. Active Net has been proposed as one of the methods for such processing. It treats the target detection in an image as an energy optimization problem. In this paper, a problem of the conventional Active Net is presented and the new Active Net is proposed. The new net is improved the ability for detecting a target. Finally, the validity of the proposed net is confirmed by experimental results.

This paper provides a normalized Iterative Feedback Tuning (IFT) method that assures the boundedness of the gradient vector estimate (ρ) and the Hessian matrix estimate without the assumption that the internal signals are bounded. The proposed method uses the unbiased Gauss-Newton direction by the addition of the 4-th experiment. We also present blended control criteria and a PID-like controller as new design choices. In examples, the normalized IFT method results in a good convergence although the internal signal or the measurement noise variance is large.

Generally, a siren of ambulance is used to let drivers know that an ambulance is approaching. Although the siren system is effective to alert the drivers and pedestrians, the sound of siren is noisy in a cabin of ambulance. In this paper, an active noise control (ANC) system to control the sound of siren using headset is proposed. The proposed ANC system selectively controls only the sound of siren, and other sound such as speech is not affected at all. The achieved attenuation level by the proposed ANC system is more than 20 dB.

2.4-GHz-band mid-speed (1- to 2-Mbit/sec) wireless LAN systems are being widely used in offices and factories. Electromagnetic interference can occur between these systems because they use the same frequency range. In this paper, we investigate the characteristics of the interference between wireless LAN systems that use direct-sequence (DS) systems and frequency-hopping (FH) systems. The interference characteristics were measured for three DS systems and one FH system that meet the IEEE 802.11 and RCR standards and that use different modulation methods. Our results indicate that throughput depends on the system and the modulation method. We have also developed a model that can be used to calculate the interference characteristics between DS and FH systems by considering the bandwidth of their transmission signals, the dwell time of the FH system, and the time that the DS system needs to transmit a data frame. We used this model to calculate the bit error rate (BER) characteristics of the systems used in our experiment, and the results indicate that BER characteristics depend on the modulation method. The throughput characteristics of the systems used in our experiment were also calculated, and agreed with the experiment results within +/- 5 dB. The throughput characteristics of wireless LAN systems based on IEEE 802.11 were also calculated when the signal level was higher than the receiver noise level. The results show that FH systems require a D/U ratio about 7 or 8 dB higher than the ratio required in DS systems because the parameters in the standard differ between FH and DS systems.

This paper proposes two novel Multi-bit Delta-Sigma Modulator (Δ Σ M) architectures based on a Dual-Quantization architecture. By using multi-bit quantization with single-bit feedback, Both eliminate the need for a multi-bit digital-to-analog converter (DAC) in the feedback loop. The first is a Digital quantization-Error Canceling Multi-bit (DECM)-Δ Σ M architecture that is able to achieve high resolution at a low oversampling ratio (OSR) because, by adjusting the coefficients of both analog and digital circuits, it is able to cancel completely the quantization error injected into the single-bit quantizer. Simulation results show that a signal-to-quantization-noise ratio of 90 dB is obtained with 3rd order 5-bit quantization DECM-Δ Σ M at an OSR of 32. The second architecture, an analog-to-digital mixed (ADM)-Δ Σ M architecture, uses digital integrators in place of the analog integrator circuits used in the Δ Σ M. This architecture reduces both die area and power dissipation. We estimate that a (2+2)-th order ADM-Δ Σ M with two analog-integrators and two digital-integrators will reduce the area of a 4-th order Δ Σ M by 15%.

This paper presents a new implementation of fuzzy filters for edge-preserving smoothing of an image corrupted by impulsive and white Gaussian noise. This filter structure is expressed as an adaptive weighted mean filter that uses fuzzy control. The parameters of this filter can be adjusted by learning. Finally, simulation results demonstrate the effectiveness of the proposed technique.

A new adaptive multichannel filtering approach is introduced and analyzed in this paper. The technique is simpler and more appropriate than traditional approaches that have been addressed by means of groupwise vector ordering information. These filters are a two-stage filters based on rational functions (RF) using fuzzy transformations of the Euclidean and angular distances among the different vectors to adapt to local data in the color image. The output is the result of vector rational operation taking into account three fuzzy sub-function outputs. Simulation studies indicate that the filters are computationally attractive and have excellent performance such as edge and details preservation and accurate chromaticity estimation.

A 3 to 5-GHz Si-bipolar quadrature modulator and demodulator are described. Both feature a wideband frequency-doubling 90-degree phase shifter that has a mechanism for self-correction of phase errors caused by an original 90-degree phase-shift network at the half frequency of the carrier. Therefore, the phase shifter produces accurate quadrature carrier signals with doubled frequency. The quadrature modulator and demodulator in 30-GHz Si bipolar technology dissipate 80 mA at a 3-V supply. Image rejection of the modulator is more than 40 dB between 3.2 to 5.2 GHz. The phase and amplitude errors of the demodulator are less than 1.5 degrees and less than 0.15 dB, respectively, between 3.5 to 5.2 GHz. Therefore, both are suitable for either direct conversion or image-rejection transceivers for 5-GHz applications.

In this paper, we propose a lower bound for the minimum distance of [n,k] linear codes which are specified by generator matrices whose rows are k vectors of a given sequence of n linearly independent vectors over a finite field. The Feng-Rao bound and the order bound give the lower bounds for the minimum distance of the dual codes of the codes considered in this paper. We show that the proposed bound gives the true minimum distance for Reed-Solomon and Reed-Muller codes and exceeds the Goppa bound for some L-type algebraic geometry codes.

This paper deals with the problem of autoregressive (AR) spectral estimation from a finite set of noisy observations without a priori knowledge of additive noise power. A joint technique is proposed based on the high-order and true-order AR model fitting to the observed noisy process. The first approach utilizes the uncompensated lattice filter algorithm to estimate the parameters of the over-fitted AR model and is one-pass. The latter uses the noise compensated low-order Yule-Walker (LOYW) equations to estimate the true-order AR model parameters and is iterative. The desired AR parameters, equivalently the roots, are extracted from the over-fitted model roots using a root matching technique that utilizes the results obtained from the second approach. This method is highly accurate and is particularly suitable for cases where the system of unknown equations are strongly nonlinear at low SNR and uniqueness of solution from the LOYW equations cannot be guaranteed. In addition, fuzzy logic is adopted for calculating the step size adaptively with the cost function to reduce the computational time of the iterative total search technique. Several numerical examples are presented to evaluate the performance of the proposed scheme in this paper.

This paper presents a multi-thread evolutionary programming (MEP) technique that is composed of global, local, and minimal search units. An appropriate search routine is called depending on the current situation and the individuals are updated by using the selected routine. In each search routine, the individuals are updated with a normalized relative fitness function to improve the robustness of the algorithm. The proposed method is applied to the problem of backing up a truck-and-trailer system to a loading dock. A fuzzy logic controller is designed for a truck-and-trailer backer-upper system and the MEP algorithm is used to optimize the representative parameters of the fuzzy logic controller. The simulation results show that the proposed controller performs well even under a large variety of initial positions.

In most access control systems, authorization is specified using binary decisions, "yes" or "no," to the access requests resulting in access being permitted or denied respectively. We argue that emerging Internet applications require that this binary decision be extended to "allow access provided some actions are taken. " We propose the notion of provisional actions that specifies the necessary actions to be performed in addition to the binary decision and introduce an access control model for it. We also provide an administrative model for policy management purpose.

Phase locked loops (PLL's) are well known as a threshold extension demodulator for analogue FM signals. This capability may lead to the low bit error rate demodulation for digital FM signals. A PLL has also its native frequency tracking ability and is suited to the demodulation of the signals having large Doppler shifts, for example signals from Low Earth Orbit (LEO) satellites. In this paper, we study the demodulation scheme of Continuous Phase FSK (CPFSK) and Gaussian filtered MSK (GMSK) signals using a Digital Signal Processing type Digital PLL (DSP DPLL). First we propose a DSP DPLL completely equivalent to an Analog PLL (APLL). Next we adopt the sequence estimation scheme to compensate the Inter-Symbol Interference (ISI) associated with the finite loop bandwidth of the DSP DPLL. Through computer simulations it is clarified that the proposed DSP DPLL with sequence estimator can achieve better BER performance compared with the conventional Limiter Discriminator (LD) detection on the AWGN channel. We have also shown that the DSP DPLL with sequence estimator has excellent BER characteristics on Rician fading channels having actual large Doppler shifts.

Recently, antenna selection diversity has been widely used for hand-held phones to overcome a fading problem. A monopole antenna (MPA) and an inverted-F antenna (IFA) are the typical antennas used for this purpose. However, strong mutual coupling generally appears between these two antennas and often makes the diversity antenna design difficult. In particular, in the case that the MPA is unselected antenna the mutual coupling can be minimized using the open terminating impedance. On the other hand, in the case that the IFA is unselected antenna the terminating impedance, which can minimize the mutual coupling, has not been clarified. This paper presents a novel analytical method for optimizing the terminating impedance of the IFA. The method exploits the Z-matrix, and the final expression of the terminating impedance is expressed by self- and mutual-impedance. The numerical and experimental results confirm that the proposed optimization method is effective for minimizing the mutual coupling.