In this paper, a new overload control strategy is proposed for a call control processor (CCP) in the base station controller (BSC) and processor utilization is measured. The proposed overload control strategy can regulate the call attempts by adopting measured processor utilization. A function, specifically a linear interpolation function based on Inverse Transform theory is used to convert controlled predictive average load rate in a call rejection rate. Then a call admission rate is obtained from the call rejection rate. Simulation shows that the proposed algorithm yields better performance than the conventional algorithm does under the heavy transient overload status in terms of call admission rate.

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.

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.

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.

We propose a novel soft-decision decoding algorithm for cyclic codes based on energy minimization principle. The well-known soft-decision decoding algorithms for block codes perform algebraic (hard-decision) decoding several times in order to generate candidate codewords using the reliability of received symbols. In contrast, the proposed method defines energy as the Euclidean distance between the received signal and a codeword and alters the values of information symbols so as to decrease the energy in order to seek the codeword of minimum energy, which is the most likely codeword. We let initial positions be the information parts of signals obtained by cyclically shifting a received signal and look for the point, which represents a codeword, of minimum energy by moving each point from several initial positions. This paper presents and investigates reducing complexity of the soft-decision decoding algorithm. We rank initial positions in order of reliability and reduce the number of initial positions in decoding. Computer simulation results show that this method reduces decoding complexity.

We discuss the security of the improved knapsack cryptosystem that Kobayashi and Kimura have proposed. Two attacking methods for their cryptosystem are proposed; one is the method for obtaining secret keys from public keys by using the continued fraction, and the other is for decrypting the ciphertext without knowing secret keys. We show that their cryptosystem is not secure against these attacks.

In this paper, we give lower bounds for the generalize Hamming weights of linear codes constructed on affine algebraic varieties. By introducing a number g*, which is determined by a given affine variety, we show that when the order t of generalized Hamming weights is greater than g*, the proposed lower bound agrees with their true generalize Hamming weights. Moreover, if we use the notion of well-behaving, we can obtain a more precise bound. Finally, we compare the proposed bound and the conventional one for algebraic geometric code on the curve Cab.

A method is presented for determining the minimum weight of cyclic codes. It is a probabilistic algorithm. This algorithm is used to find, the minimum weight of codes far too large to be treated by any known algorithm. It is based on a probabilistic algorithm for determining the minimum weight of linear code by Jeffrey S. Leon. By using this method, the minimum weight of cyclic codes is computed efficiently.

In this paper the performance of the Block Sorting algorithm proposed by Burrows and Wheeler is evaluated theoretically. It is proved that the Block Sorting algorithm is asymptotically optimal for stationary ergodic finite order Markov sources. Our proof is based on the facts that symbols with the same Markov state (or context) in an original data sequence are grouped together in the output sequence obtained by Burrows-Wheeler transform, and the codeword length of each group can be bounded by a function described with the frequencies of symbols included in the group.

In the design of nonlinear reliable controllers, one major issue is to solve for the solutions of the Hamilton-Jacobi inequality. In general, it is hard to obtain a closed form solutions due to the nonlinear nature of the inequality. In this paper, we seek for the existence conditions of quadratic type positive semidefinite solutions of Hamilton-Jacobi inequality. This is achieved by taking Taylor's series expansion of system dynamics and investigating the negative definiteness of the associated Hamilton up to fourth order. An algorithm is proposed to seek for possible solutions. The candidate of solution is firstly determined from the associated algebraic Riccati inequality. The solution is then obtained from the candidate which makes the truncated fourth order polynomial of the inequality to be locally negative definite. Existence conditions of the solution are explicitly attained for the cases of which system linearization possesses one uncontrollable zero eigenvalue and a pair of pure imaginary uncontrollable eigenvalues. An example is given to demonstrate the application to reliable control design problem.

A reconfiguration method for processor array is proposed in this paper. In the method, genetic algorithm (GA) is used for searching effective spare arrangement, which leads to successful reconfiguration. The effectiveness of the method is demonstrated by computer simulations.

We present a cascade neural network for blind source extraction. We propose a family of unconstrained optimization criteria, from which we derive a learning rule that can extract a single source signal from a linear mixture of source signals. To prevent the newly extracted source signal from being extracted again in the next processing unit, we propose another unconstrained optimization criterion that uses knowledge of this signal. From this criterion, we then derive a learning rule that deflates from the mixture the newly extracted signal. By virtue of blind extraction and deflation processing, the presented cascade neural network can cope with a practical case where the number of mixed signals is equal to or larger than the number of sources, with the number of sources not known in advance. We prove analytically that the proposed criteria both for blind extraction and deflation processing have no spurious equilibria. In addition, the proposed criteria do not require whitening of mixed signals. We also demonstrate the validity and performance of the presented neural network by computer simulation experiments.

Image restoration using estimated parameters of image model and noise statistics is presented. The image is modeled as the output of a 2-D noncausal autoregressive (NCAR) model. The parameter estimation process is done by using the autocorrelation function and a biased term to a conventional least-squares (LS) method for the noncausal modeling. It is shown that the proposed method gives better results than the other parameter estimation methods which ignore the presence of the noise in the observation data. An appropriate image model selection process is also presented. A genetic algorithm (GA) for solving a multiobjective function with single constraint is discussed.

In this paper, we present a hierarchical technique for simultaneous pin assignment and global routing during floorplanning based on the minimum cost maximum integer flow algorithm with several heuristic cost functions. Furthermore, our algorithm handles feedthrough pins and equi-potential pins taking into account global routes. Our algorithm allows various user specified constraints such as pre-specified pin positions, wiring paths, wiring widths and critical nets. Experimental results including Xerox floorplanning benchmark have shown the effectiveness of the heuristics.

Adaptive estimation of nonstationary sinusoidal signals or quasi-periodic signals in additive noise is of essential importance in many diverse engineering fields, such as communications, biomedical engineering, power systems, pitch detection in transcription and so forth. So far, Kalman filtering based techniques, recursive least square (RLS), simplified RLS (SRLS) and LMS algorithms, for examples, have been developed for this purpose. This work presents in detail a performance analysis for the SRLS algorithm proposed recently in the literature, which is used to estimate an enhanced sinusoid. Its dynamic and tracking properties, noise and lag misadjustments are developed and discussed. It is found that the SRLS estimator is biased, and its misadjustments are functions of not only the noise variance but also, unpleasantly, of the signal parameters. Simulations demonstrate the validity of the analysis. Application of the SRLS to a real-life piano sound is also given to peek at its effectiveness.

In this paper, we propose two new pipelined adaptive digital filter architectures. The architectures are based on an equivalent expression of the least mean square (LMS) algorithm. It is shown that one of the proposed architectures achieves the minimum output latency, or zero without affecting the convergence characteristics. We also show that, by increasing the output latency be one, the other architecture can be obtained which has a shorter critical path.

Recently, a great deal of effort has been devoted to the design problem of "constrained least squares M-D FIR filter" because a significant improvement of the squared error is expected by a slight relaxation of the minimax error condition. Unfortunately, no design method has been reported, which has some theoretical guarantee of the convergence to the optimal solution. In this paper, we propose a class of novel design methods of "constrained least squares M-D FIR filter. " The most remarkable feature is that all of the proposed methods have theoretical guarantees of convergences to the unique optimal solution under any consistent set of prescribed maximal error conditions. The proposed methods are based on "convex projection techniques" that computes the metric projection onto the intersection of multiple closed convex sets in real Hilbert space. Moreover, some of the proposed methods can still be applied even for the problem with any inconsistent set of maximal error conditions. These lead to the unique optimal solution over the set of all filters that attain the least sum of squared distances to all constraint sets.

This paper presents a neural network-based control system for Adaptive Noise Control (ANC). The control system derives a secondary signal to destructively interfere with the original noise to cut down the noise power. This paper begins with an introduction to feedback ANC systems and then describes our adaptive algorithm in detail. Three types of noise signals, recorded in destroyer, F16 airplane and MR imaging room respectively, were then applied to our noise control system which was implemented by software. We obtained an average noise power attenuation of about 20 dB. It was shown that our system performed as well as traditional DSP controllers for narrow-band noise and achieved better results for nonlinear broadband noise problems. In this paper we also present a hardware implementation method for the proposed algorithm. This hardware architecture allows fast and efficient field training in new environments and makes real-time real-life applications possible.

In this paper, the performances of texture classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for texture classification is the determination of the suitable features that yields the best classification results. A Max-Max algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that without feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted from the pyramidal decomposition are more representative than those from the uniform decomposition. Experimental results have verified the selectivity of the proposed approach and its texture capturing characteristics.

In this paper, we propose a novel power distribution method which can be adopted in case of the nonuniform distribution for mobiles in DS/CDMA distributed power cellular system. DS/SS distributed power cellular system has been proposed for achieving RAKE reception in micro-cellular environment. In forward link of this system, optimum power distribution method which can minimize the required total transmitting power has been discussed. The performance of this system has been shown in case of the uniform distribution for mobiles. In this paper, first, we propose a novel method in case of the nonuniform distribution. In the proposed method, replacing the path and its combinations of signals from base stations successively, we can find a new condition of less power distribution which is passed over in a conventional distribution method. We adopt simple distribution models for mobiles and compare the proposed method with the other methods by computing the total transmitting power and the quantity of calculations. As a result, we show that it is possible to almost obtain optimum power distribution by using the proposed method. Next, we adopt a nonuniform distribution model, in which the difference of the number of users exists only in the center cell. Using this model, we compare the proposed method with the other methods by computing the total transmitting power, the quantity of calculations, and a probability of impossible power distribution. Finally, in order to simplify and decrease the quantity of calculations of the proposed method, we propose a modified calculation algorithm which is applicable in case of that a new mobile station has increased. And we show the performance of this algorithm.