We propose here a fully Spice-oriented design algorithm of op-amps for attaining the maximum gains under low power consumptions and assigned slew-rates. Our optimization algorithm is based on a well-known steepest descent method combining with nonlinear programming. The algorithm is realized by equivalent RC circuits with ABMs (analog behavior models) of Spice. The gradient direction is decided by the analysis of sensitivity circuits. The optimum parameters can be found at the equilibrium point in the transient response of the RC circuit. Although the optimization time is much faster than the other design tools, the results might be rough because of the simple transistor models. If much better parameter values are required, they can be improved with Spice simulator and/or other tools.

In this paper, we propose a method that recovers a smooth high-resolution image from several blurred and roughly quantized low-resolution images. For compensation of the quantization effect we introduce measurements of smoothness, Huber function that is originally used for suppression of block noises in a JPEG compressed image [Schultz & Stevenson '94] and a smoothed version of total variation. With a simple operator that approximates the convex projection onto constraint set defined for each quantized image [Hasegawa et al. '05], we propose a method that minimizes these cost functions, which are smooth convex functions, over the intersection of all constraint sets, i.e. the set of all images satisfying all quantization constraints simultaneously, by using hybrid steepest descent method [Yamada & Ogura '04]. Finally in the numerical example we compare images derived by the proposed method, Projections Onto Convex Sets (POCS) based conventinal method, and generalized proposed method minimizing energy of output of Laplacian.

Using a pair of matched square-root-raised-cosine (SRRC) filters in the transmitter and the receiver in a band-limited digital communication system can theoretically achieve zero inter-symbol interference (ISI). In reality, the ISI cannot be zero when both SRRC filters are approximately implemented because of some numerical precision problems in the design phase as well as in the implementation phase. In this paper, the author proposes an iterative method to design the coefficients of SRRC FIR filters. The required ISI of the system can be specified such that both ISI and frequency domain specifications are monitored in the design phase. Since the ISI can be specified beforehand, the tradeoff between performance and the filter length becomes possible in the proposed design algorithm.

In this report, we propose a tracking algorithm of speaker direction using microphones located at vertices of an equilateral triangle. The method realizes tracking by minimizing a performance index that consists of the cross spectra at three different microphone pairs in the triangular array. We adopt the steepest descent method to minimize it, and for guaranteeing global convergence to the correct direction with high accuracy, we alter the performance index during the adaptation depending on the convergence state. Through some computer simulation and experiments in a real acoustic environment, we show the effectiveness of the proposed method.

This paper describes a VLSI-oriented motion estimation algorithm using a steepest descent method (SDM) applied to MPEG-4 visual communication with a mobile terminal. The SDM algorithm is optimized for QCIF or CIF resolution video and VLSI implementation. The SDM combined with a subblock search method is developed to enhance picture quality. Simulation results show that a mean PSNR drop of the SDM algorithm processing QCIF 15 fps resolution video in comparison with a full search algorithm is -0.17 dB. Power consumption of a VLSI based on the SDM algorithm assuming 0.18 µm CMOS technology is estimated at 2 mW. The VLSI attains higher picture quality than that based on the other fast motion estimation algorithm, and is applicable to mobile video applications.

Given the importance of the traffic on modern communication networks, advanced error correction methods are needed to overcome the changes expected in channel quality. Conventional countermeasures that use high dimensionality parity codes often fail to provide sufficient error correction capability. We propose a parity code with high dimensionality that is iteratively decoded. It provides better error correcting capability than conventional decoding methods. The proposal uses the steepest descent method to increase code bit reliability and the coherency between parities and code bits gradually. Furthermore, the quantization of the decoding algorithm is discussed. It is found that decoding with quantization can keep the error correcting capability high.

In this paper, we propose a novel higher order time-frequency distribution (GDH) for a discrete time signal. This distribution is defined over the original discrete time-frequency grids through a delicate discretization of an equivalent expression of a higher order distribution, for a continuous time signal, in [4]. We also present a constructive design method, for the kernel of the GDH, by which the distribution satisfies (i) the alias free condition as well as (ii) the marginal conditions. Numerical examples show that the proposed distributions reasonably suppress the artifacts which are observed severely in the Wigner distribution and its simple higher order generalization.

In this letter, we remark a well-known nonlinear filtering technique realize immediate effect to suppress the influence of the additive measurement noise in the input to a set theoretic linear blind deconvolution scheme. Numerical examples show ε-separating nonlinear pre-filtering techniques work suitably to this noisy blind deconvolution problem.

In this paper, we propose a projection based design of near perfect reconstruction QMF banks. An advantage of this method is that additional design specifications are easily implemented by defining new convex sets. To apply convex projection technique, the main difficulty is how to approximate the design specifications by some closed convex sets. In this paper, introducing a notion of Magnitude Product Space where a pair of magnitude responses of analysis filters is expressed as a point, we approximate design requirements of QMF banks by multiple closed convex sets in this space. The proposed method iteratively applies a convex projection technique, Hybrid Steepest Descent Method, to find a point corresponding to the optimal analysis filters at each stage, where the closed convex sets are dynamically improved. Design examples show that the proposed design method leads to significant improvement over conventional design methods.

Recently, Kundur and Hatzinakos showed that a linear restoration filter designed by using the almost obvious a priori knowledge on the original image, such as (i) nonnegativity of the true image and (ii) the smallest rectangle encompassing the original object, can realize a remarkable performance for a blind image deconvolution problem. In this paper, we propose a new set-theoretic blind image deconvolution scheme based on a recently developed convex projection technique called Hybrid Steepest Descent Method (HSDM), where some partial information can be utilized set-theoretically by parallel projections onto convex sets while the others are incorporated in a cost function to be minimized by a steepest descent method. Numerical comparisons with the standard set-theoretic scheme based on POCS illustrate the effectiveness of the proposed scheme.

We propose a set of new algorithms for linear programming. These algorithms are derived by accelerating the method of averaged convex projections for linear inequalities. We provide strict proofs for the convergence of our algorithms. The algorithms are so simple that they can be calculated by super-parallel processing. To this effect, we propose networks for implementing the algorithms. Furthermore, we provide illustrative examples to demonstrate the capability of our algorithms.

In this paper we discuss the limiting behavior of the search direction of the steepest descent method in minimizing the Rayleigh quotient. This minimization problem is equivalent to finding the smallest eigenvalue of a matrix. It is shown that the search direction asymptotically alternates between two directions represented by linear combinations of two eigenvectors of the matrix. This is similar to the phenomenon in minimizing the quadratic form. We also show that these eigenvectors correspond to the largest and second-smallest eigenvalues, unlike in the case of the quadratic form.

For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.

This paper addresses approaches to enhancement of performance of the CMA (Constant Modulus Algorithm) adaptive array antenna in multipath environments that characterize the mobile radio communications. The cost function of the CMA reveals that it has an AGC (Automatic Gain Control) procedure of holding the array output voltage at a constant value. Therefore, if the output voltage by the initial weights is different from the object value, then the CMA may suffer from slow convergence because suppression of the multipath waves is delayed by the AGC behavior. Our objective is to improve the convergence characteristics by adopting the differential CMA for the adaptive array algorithm. First, the basic performance of the differential CMA is clarified via computer simulation. Next, the differential CMA is incorporated into the eigen-beamspace system in which the eigenvectors of the correlation matrix of array inputs are used in the BFN (Beam Forming Network). This BFN creates the optimum orthogonal multibeams for radio environments and works helpfully as a preprocessor of the differential CMA. The computer simulation results have demonstrated that the differential CMA with the eigen-beamspace system has much better convergence characteristics than the conventional CMA with the element space system. Furthermore, a modified algorithm is introduced which gives the stable array output voltages after convergence, and it is confirmed that the algorithm can carry out more successful adaptation even if the radio environments are changed abruptly.