One of unique features of CDMA slotted ALOHA (CDMA S-ALOHA) is that user must synchronize his transmission to given slot. Thus orthogonal sequence as spreading sequence would achieve ideal throughput if each of packets accomplish perfect synchronization. In the presence of any ambiguity in synchronizations, however, quasi-synchronous (QS) sequences suit well with CDMA S-ALOHA system. In this paper, we introduce new QS-sequences obtained from the orthogonal Gold sequences and discuss their performance when applying to CDMA S-ALOHA systems. As a result, withstanding to access timing error, good performance is ensured with this sequence under the environment of AWGN, MAI (multiple access interference) and frequency non-selective fading, that is, micro or pico cellular systems and indoor wireless LANs.

This paper is to propose a Markov reliability model which includes the effects of permanent fault, intermittent fault, and transient fault for reliability evaluations. We also provide a new neural network and an improved training algorithm to evaluate the reliability of the fault-tolerant systems. The simulation results show that the neuro-based reliability model can converge faster than that of the other methods. The system state equations for the Markov model are a set of first-order linear differential equations. Usually, the system reliability can be evaluated from the combined state solutions. This technique is very complicated and very difficult in the complex fault-tolerant systems. In this paper, we present a Grey Models (GM(1,1), DF-GM(1,1) and ERC-GM(1,1)) to evaluate the reliability of computer system. It can obtain the system reliability more directly and simply than the Markov model. But the data number for grey model that gets minimal error is different in each time step. Therefore, a feedforward neural network is designed on the basis of more accurate prediction for the grey modeling to evaluate the reliability. Finally, the simulation results show that this technique can lead to better accuracy than the Grey Model.

Array redistribution is required very often in programs on distributed memory parallel computers. It is essential to use efficient algorithms for redistribution, otherwise the performance of programs may degrade considerably. In this paper, we focus on automatic generation of communication routines for multi-dimensional redistribution. The principal advantage of this work is to gain the ability to handle redistribution between arbitrary source and destination processor sets and between arbitrary source and destination distribution schemes. We have implemented these algorithms using Parallelware communication library. Some experimental results show the efficiency and flexibility of our techniques compared to the other redistribution works.

We apply evolutionary algorithms to neural network model of associative memory. In the model, some of the appropriate configurations of the synaptic weights allow the network to store a number of patterns as an associative memory. For example, the so-called Hebbian rule prescribes one such configuration. However, if the number of patterns to be stored exceeds a critical amount (over-loaded), the ability to store patterns collapses more or less. Or, synaptic weights chosen at random do not have such an ability. In this paper, we describe a genetic algorithm which successfully evolves both the random synapses and over-loaded Hebbian synapses to function as associative memory by adaptively pruning some of the synaptic connections. Although many authors have shown that the model is robust against pruning a fraction of synaptic connections, improvement of performance by pruning has not been explored, as far as we know.

In this paper, we discuss the initial acquisition of the code division multiplexed DS/SS down-link signals at a user terminal of multiple LEO mobile satellite communication systems. In LEO systems, a receiver generally receives signals from plural satellites for soft hand-off and for satellite diversity as a countermeasure to shadowing. In this situation, the signal from each satellite becomes the interference to the signals from other satellites. In addition to this inter-satellite interference, we have to consider the intra-satellite interference from user channels to a pilot channel because of the loss of orthogonality of channels at initial acquisition stage especially under frequency offsets due to Doppler effect. Thus in this paper, we analytically evaluate the performance of an initial acquisition scheme, taking the intra/inter-satellite interference under Doppler shift into account.

A generalized radial basis function network consisting of (1 + cosh x)-1 as the basis function of the same class as Gaussian functions is investigated in terms of the feasibility of analog-hardware implementation. A simple way of hardware-implementing (1 + cosh x)-1 is proposed to generate the exact input-output response curve on an analog circuit constructed with bipolar transistors. To demonstrate that networks consisting of the basis function proposed actually work, the networks are applied to numerical experiments of forecasting chaotic time series contaminated with observational random noise. Stochastic gradient descent is used as learning rule. The networks are capable of learning and making short-term forecasts about the dynamic behavior of the time series with comparable performance to Gaussian radial basis function networks.

A new identification algorithm based on output over-sampling scheme is proposed for a IIR model whose input signal can not be available directly. By using only an output signal sampled at higher rate than unknown input, parameters of the IIR model can be identified. It is clarified that the consistency of the obtained parameter estimates is assured under some specified conditions. Further an efficient recursive algorithm for blind parameter estimation is also given for practical applications. Simulation results demonstrate its effectiveness in both system and channel identification.

Since IIR filters have lower computational complexity than FIR filters, some design methods for IIR filter banks have been presented in the recent literatures. Smith et al. have proposed a class of linear phase IIR filter banks. However this method restricts the order of the numerator to be odd and has some drawbacks. In this paper, we present two design methods for linear phase IIR filter banks. One is based on Lagrange-Multiplier method, and optimal IIR filter banks in least squares sense are obtained. In an other approach, IIR filter banks with the maximum number of zeros are derived analytically.

This paper presents a variant of the "Third-Order Recursion (TOR)" method for bispectral estimation of transfer-function parameters of a non-minimum-phase all-poles system. The modification is based on the segmentation of system-output data into coupled records, instead of independent records. It consists of considering the available data at the left and the right of each record as not null and taking them as the data corresponding to the preceding and succeeding record respectively. The proposed variant can also be interpreted as a "Constrained Third-Order Mean (CTOM)" method with a new segmentation in overlap records. Simulation results show that this new segmentation procedure gives more precise system parameters than the TOR and CTOM methods, to be obtained. Finally, in order to justify the use of bispectral techniques, the influence of added white and colored Gaussian noise on the parameter estimation is also considered.

In mobile communications, power is a very important factor and nonlinear amplification of power amplifiers cannot be avoided due to their high power efficiency. This article presents the performance of π/2-shift BPSK modulation scheme used in DS/SS/CDMA wireless communications over multipath Rayleigh fading channel and compares the performance with the performance of conventional BPSK and offset QPSK CDMA systems. The performance parameters: Out-of-Band power, average Bit Error Rate (BER) and Spectral Efficiency have been evaluated. In order to obtain improved performance on fading channels, a RAKE receiver has been employed. Finally it is shown that π/2-shift BPSK outperforms conventional BPSK and offset QPSK in the presence of nonlinear amplification.

This paper proposes a novel asynchronous direct sequence/code division multiple access (DS/CDMA) communication system using analog pseudo noise (PN) sequences that have an orthogonal relation for all active users. Analog PN sequences are produced by an adaptive filter called a "code-orthogonalizing filter" (COF). In a base station receiver, the tap coefficients of the COF can be adaptively controlled "to orthogonalize" or "to approach to orthogonalize" various received PN sequences. The elements of the analog PN sequences consist of the tap coefficients of the COF. The analog PN sequence produced is assigned to the transmitter of each user in order. As a result, multiple access interference (MAI) caused by other users can be reduced considerably, and multiple access capacity increased by the proposed system compared with matched filter (MF) reception and COF reception.

This paper formulates problems of fitting two corresponding sets of points by translation, rotation and scaling, and proposes efficient algorithms for the fitting. The algorithms are based on the theory of lower envelopes, or Davenport-Schinzel sequences, and linearization techniques in computational geometry, and are related to dynamic furthest Voronoi diagrams.

We have studied the electro-optical properties in Polymer Network Liquid Crystal Displays which are composed of a three-dimensional polymer network formed in continuous liquid crystal layers prepared by photo-polymerization induced phase separation processes. In view of the behavior in the orientation of the liquid crystal molecule at the polymers surface, it can be clarified that the electro-optical properties are strongly governed by the polymer. By a combination of two different characteristics of the polymers with respect to a temperature dependence, a variation in the driving voltage for a temperature change can be improved.

Backfire quadrifilar helical antennas combined with parasitic loops are investigated in detail, focusing on clarifying the function of parasitic loops. First, the basic property is examined for the case of one parasitic loop, and it is found that the loop behaves as a director when the circumferential length of the loop is nearly 0. 9λ, and a reflector when the circumferential length of the loop is nearly 1. 2λ provided the distance between the parasitic loop and the top plane of helical antennas is approximately 0. 1λ, where λ is the wavelength. Next, the function of the parasitic loop is investigated by comparing the current distributions on the helices and the loop with those on a monofilar helix with a ground plane. It is found that the function of the parasitic loop is quite different from that of the ground plane. Then, the case of two parasitic loops is examined, and it is shown that the use of two parasitic loops is very effective and simple measures to control the radiation pattern and gain of the backfire quadrifilar helical antennas. Finally, for this type of antennas with two parasitic loops, an example of structural parameters suited to the use in satellite communications is presented.

This paper describes a factorization-based algorithm that reconstructs 3D object structure as well as motion from a set of multiple uncalibrated perspective images. The factorization method introduced by Tomasi-Kanade is believed to be applicable under the assumption of linear approximations of imaging system. In this paper we describe that the method can be extended to the case of truly perspective images if projective depths are recovered. We established this fact by interpreting their purely mathematical theory in terms of the projective geometry of the imaging system and thereby, giving physical meanings to the parameters involved. We also provide a method to recover them using the fundamental matrices and epipoles estimated from pairs of images in the image set. Our method is applicable for general cases where the images are not taken by a single moving camera but by different cameras having individual camera parameters. The experimental results clearly demonstrates the feasibility of the proposed method.

We discuss optimal rotation estimation from two sets of 3-D points in the presence of anisotropic and inhomogeneous noise. We first present a theoretical accuracy bound and then give a method that attains that bound, which can be viewed as describing the reliability of the solution. We also show that an efficient computational scheme can be obtained by using quaternions and applying renormalization. Using real stereo images for 3-D reconstruction, we demonstrate that our method is superior to the least-squares method and confirm the theoretical predictions of our theory by applying bootstrap procedure.

Because of non-negligible ISI due to the Gaussian filter and delay spread in the GSM system, an equalizer is required. In this letter, a joint sliding window channel estimation and timing adjustment method is proposed for maximum likelihood sequence equalizer. And also a smoothing algorithm is presented in order to improve the equalizer performance. This smoothing scheme utilizes a variant of LMS algorithm to tune the channel coefficient estimates. Simulation results show that the proposed scheme is adequate for channel estimation of the adaptive equalizer.

This letter proposes a new retransmission control scheme for slow-frequency-hopped communication systems, in which the number of (re)transmitted packets is adaptively decreased in a certain period. Performance of the proposed scheme is analyzed and compared with that of the conventional scheme in terms of the normalized throughput and the 98% packet transmission delay. The numerical results show the superiority of the proposed scheme.

Initial leveling of strapdown inertial navigation system is a prerequisite work for distinguishing between gravity and acceleration effects in the accelerometer sensing's. This study presents an on-line methodology to resolve the initial leveling problem of a vehicle, which is subject to a large, long duration, and abrupt disturbance input with a deterministic nature under noisy circumstances. The developed method herein is the Kalman filter based scheme with a robust input estimator, generalized M estimator, and a testing criterion. The generalized M estimator identifies the unexpected disturbance inputs in real time. In addition, hypothetical testing based on the least-squares estimator is devised to detect the input's onset and presence. A required regression equation between the observed value of the residual sequence with an unknown input and theoretical residual sequence of the Kalman filter with no input is formulated. Input estimation and detection are then provided on the basis of the derived regression equation. Moreover, Monte Carlo simulations are performed to assess the superior capabilities of the proposed method in term of rapid responses, accuracy, and robustness. The efficient initial leveling can facilitate the entire alignment of the inertial system.

Image classification is an important task in document image analysis and understanding, page segmentation-based document image compression, and image retrieval. In this paper, we present a new approach for distinguishing textual images from pictorial images using the Kolmogorov Complexity (KC) measure with randomly extracted blocks. In this approach, a number of blocks are extracted randomly from a binarized image and each block image is converted into a one-dimensional binary sequence using either horizontal or vertical scanning. The complexities of these blocks are then computed and the mean value and standard deviation of the block complexities are used to classify the image into textual or pictorial image based on two simple fuzzy rules. Experimental results on different textual and pictorial images show that the KC measure with randomly extracted blocks can efficiently classified 29 out 30 images. The performance of our approach, where an explicit training process is not needed, is comparable favorably to that of a neural network-based approach.