In this paper, a two-dimensional (2-D) binary-valued (BV) lapped transform (LT) is proposed. The proposed LT has basis images which take only BV elements and satisfies the axial-symmetric (AS) property. In one dimension, there is no 2-point LT with the symmetric basis vectors, and the property is achieved only with the non-overlapping basis which the Hadamard transform (HT) has. Hence, in two dimension, there is no 22-point separable ASLT, and only 2-D HT can be the 22-point separable AS orthogonal transform. By taking non-separable BV basis images, this paper shows that a 22-point ASLT can be obtained. Since the proposed LT is similar to HT, it is referred to as the lapped Hadamard transform (LHT). LHT of larger size is shown to be provided with a tree structure. In addition, LHT is shown to be efficiently implemented by a lattice structure.

An efficient algorithm is proposed to identify the active users and extracting their respective timing information in asynchronous direct sequence CDMA (DS-CDMA) communication system over Rayleigh fading channel. The joint identification and timing estimation algorithm is derived by performing discrete Fourier transform (DFT) on the observation vector and exploiting the uniqueness and nullity characteristics of the root-MUSIC test polynomial. The root-MUSIC based algorithm is shown to be asymptotically near-far resistant. Compared to the maximum a posteriori (MAP) or maximum likelihood (ML) based multiuser timing estimator, the complexity is greatly reduced by separating the multi-dimensional optimization problem into several polynomial rooting problems. Moreover, we characterize the dependence of system performance with respect to signature sequence length, number of active users, window size, desired user's signal-to-noise ratio (SNR) and crosscorrelation property of the code structure. The analytical results reveal that under the uncorrelated Rayleigh fading model, the root-MUSIC timing estimator tends to achieve the Cramer-Rao lower bound (CRLB) at interesting signature sequence length and desired user's SNR.

In a variety of communication systems, the multipath propagation due to various reflections is often encountered. In this paper, the directions-of-arrival (DOA) estimation of the cyclostationary coherent signals is investigated. A new approach is proposed for estimating the DOA of the coherent signals impinging on a uniform linear array (ULA) by utilizing the spatial smoothing (SS) technique. In order to improve the robustness of the DOA estimation by exploiting the cyclic statistical information sufficiently and handling the coherence effectively, we give a cyclic algorithm with multiple lag parameters and the optimal subarray size. The performance of the presented method is verified and compared with the conventional methods through numerical examples.

A constant modulus adaptive array algorithm is derived using analysis and synthesis filter banks to permit adaptive digital beamforming for wideband signals. The properties of the CMA adaptive array using the filter banks are investigated. This array would be used to realize adaptive digital beamforming when this is difficult by means of ordinary (that is, non-subband) processing due to the limited speed of signal processor operations. As an actual application, we present a beamspace adaptive array structure that combines the analysis and synthesis filter banks with RF-domain multibeam array antennas, such as those utilizing optical signal processing.

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.

In this paper, a novel method is proposed for designing two channel biorthogonal filter banks with general IIR filters, which satisfy both the perfect reconstruction and causal stable conditions. Since the proposed filter banks are structurally perfect reconstruction implementation, the perfect reconstruction property is still preserved even when all filter coefficients are quantized. The proposed design method is based on the formulation of a generalized eigenvalue problem by using Remez multiple exchange algorithm. Then, the filter coefficients can be computed by solving the eigenvalue problem, and the optimal solution is easily obtained through a few iterations. One design example is presented to demonstrate the effectiveness of the proposed method.

This paper discusses a design technique for multidimensional (M-D) multirate filters which cause no checkerboard distortion. In the first part of this paper, a necessary and sufficient condition for M-D multirate filters to be checkerboard-distortion-free is derived in the frequency domain. Then, in the second part, this result is applied to a scanning line conversion system for television signals. To confirm the effectiveness of the derived condition, band-limiting filters with and without considering the condition are designed, and the results by these filters are compared. A reducibility of the number of delay elements in such a system is also considered to derive efficient implementation.

The checkerboard effect is caused by the periodic time-variant property of multirate filters which consist of up-samplers and digital filters. Although the conditions for some one-dimensional (1D) multirate systems to avoid the checkerboard effect have been shown, the conditions for Multidimensional (MD) multirate systems have not been considered. In this paper, some theorems about the conditions for MD multirate filters without checkerboard effect are derived. In addition, we also consider MD multirate filter banks without checkerboard effect. Simulation examples show that the checkerboard effect can be avoided by using the proposed conditions.

A VLSI implementation of a low-power DSP core is described, which is dedicated to the G. 723. 1 low bitrate speech codec. A number of sophisticated DSP microarchitectures are devised mainly on dual multiply accumulators, rounding and saturation mechanisms, and two-banked on-chip memory. The main attempt is focused on lowering the clock frequency, and therefore on reducing the total power consumption, at the cost of a fairly small increase of chip area. The proposed DSP architecture has been integrated in the total area of 7. 75 mm² by using a 0. 35 µm CMOS technology, which can operate at 10 MHz with the dissipation of 44. 9 mW from a single 3 V supply.

An optimum filter for extracting a time-varying harmonic signal from the noise-corrupted measurement is proposed. It is derived as a solution of the least mean square estimation with consideration of the pitch estimation error even without any assumption on the filter model. We obtain a comb-like impulse response which consists of homologous and dilated distribution of weights just located periodically with a pitch interval. This remarkable structure is well suited to the proportionally expanding error of pitch repetition times. Examples of the filter design are presented, and the performance of noise suppression is examined by comparison with conventional comb filters.

To improve speech coding quality, in particular, the long-term dependency prediction characteristics, we propose a new nonlinear predictor, i. e. , a fully connected recurrent neural network (FCRNN) where the hidden units have feedbacks not only from themselves but also from the output unit. The comparison of the capabilities of the FCRNN with conventional predictors shows that the former has less prediction error than the latter. We apply this FCRNN instead of the previously proposed recurrent neural networks in the code-excited predictive speech coding system (i. e. , CELP) and shows that our system (FCRNN) requires less bit rate/frame and improves the performance for speech coding.

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, the application of an unsupervised parallel approach called the Fuzzy Hopfield Neural Network (FHNN) for vector qunatization in image compression is proposed. The main purpose is to embed fuzzy reasoning strategy into neural networks so that on-line learning and parallel implementation for codebook design are feasible. The object is to cast a clustering problem as a minimization process where the criterion for the optimum vector qunatization is chosen as the minimization of the average distortion between training vectors. In order to generate feasible results, a fuzzy reasoning strategy is included in the Hopfield neural network to eliminate the need of finding weighting factors in the energy function that is formulated and based on a basic concept commonly used in pattern classification, called the "within-class scatter matrix" principle. The suggested fuzzy reasoning strategy has been proven to allow the network to learn more effectively than the conventional Hopfield neural network. The FHNN based on the within-class scatter matrix shows the promising results in comparison with the c-means and fuzzy c-means algorithms.

We introduce an image contour clustering method based on a multiscale image representation and its application to image compression. Multiscale gradient planes are obtained from the mean squared sum of 2D wavelet transform of an image. The decay on the multiscale gradient planes across scales depends on the Lipshitz exponent. Since the Lipshitz exponent indicates the spatial differentiability of an image, the multiscale gradient planes represent smoothness or sharpness around edges on image contours. We apply vector quatization to the multiscale gradient planes at contours, and cluster the contours in terms of represntative vectors in VQ. Since the multiscale gradient planes indicate the Lipshitz exponents, the image contours are clustered according to its gradients and Lipshitz exponents. Moreover, we present an image recovery algorithm to the multiscale gradient planes, and we achieve the skech-based image compression by the vector quantization on the multiscale gradient planes.

This paper describes a classification method for rotated and scaled textured images using invariant parameters based on spectral-moments. Although it is well known that rotation invariants can be derived from moments of grey-level images, the use is limited to binary images because of its computational unstableness. In order to overcome this drawback, we use power spectrum instead of the grey levels to compute moments and adjust the integral region of moment evaluation to the change of scale. Rotation and scale invariants are obtained as the ratios of the different rotation invariants on the basis of a spectral-moment property with respect to scale. The effectiveness of the approach is illustrated through experiments on natural textures from the Brodatz album. In addition, the stability of the invariants with respect to the change of scale is discussed theoretically and confirmed experimentally.

In this paper, a dedicated hardware design for motion estimation LSI of MPEG2 is presented. Combining our bits truncation adaptive pyramid (BTAP) algorithm with Window-MSPA architecture, the hardware cost is tremendously reduced without PSNR performance degradation for mean pyramid algorithm. The core of the test chip working at 83 MHz, performs a search range of 67 for image size of 1920 1152 and achieves video rate of 60 field/s. It can be used for HDTV purpose. The chip size is 4. 8 mm 4. 8 mm with 0. 5u 2-level metal CMOS technology. The result in this paper shows our promising future to realize one chip HDTV MPEG2 encoder.

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 propose a new approach for describing image patterns. We integrate the concepts of multiscale image analysis, aura matrix (Gibbs random fields and cooccurrences related statistical model of texture analysis) to define image features, and to obtain the features having robustness with illumination variations and shading effects, we analyse images based on the Topographic Structure described by the Surface-Shape Operator, which describe gray-level image patterns in terms of 3D shapes instead of intensity values. Then, we illustrate usefulness of the proposed features with texture classifications. Results show that the proposed features extracted from multiscale images work much better than those from a single scale image, and confirm that the proposed features have robustness with illumination and shading variations. By comparisons with the MRSAR (Multiresolution Simultaneous Autoregressive) features using Mahalanobis distance and Euclidean distance, the proposed multiscale features give better performances for classifying the entire Brodatz textures: 112 categories, 2016 samples having various brightness in each category.

We show that characteristic functions of elements of self-similar tilings can be used as scaling functions of multiresolution analysis of L²(Rⁿ). This multiresolution analysis is a generalization of a self-affine tiling multiresolution analysis using a characteristic function of element of self-affine tiling as a scaling function. We give a method of constructing a wavelet basis which realizes such an MRA.

The authors have proposed a design method for two-dimensional (2-D) separable-denominator (SD) periodically time-variant digital filters (PTV DFs) and confirmed their superiority over 2-D time-invariant DFs. In that result, the periodicity matrix representing the periodicity of the varying filter coefficients is, however, restricted to two cases. This paper extends that idea so that the input-output relation of 2-D SD PTV DFs with an arbitrary periodicity matrix can be determined. This enables us to design wide range of 2-D PTV DFs.

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 some new results for analog hardware realization of secure communication system using chaos synchronization have been presented. In particular the effect of the use of transmission line as channel has been considered assuming practical implementation. The influence of the loss of transmission line and mismatching on synchronization has been investigated in chaotic systems based on the Pecora-Carroll concept. It has been shown that desynchronization due to loss can be checked by using an amplifier with appropriate gain. Moreover the bit error rate (BER) has been evaluated in a digital communication system based on the principle of chaotic masking.

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.

Secret sharing schemes are good for protecting the important secrets. They are, however, inefficient if the secret shadow held by the shadowholder cannot be reused after recovering the shared secret. Traditionally, the (t, n) secret sharing scheme can be used only once, where t is the threshold value and n is the number of participants. To improve the efficiency, we propose an efficient dynamic secret sharing scheme. In the new scheme, each shadowholder holds a secret key and the corresponding public key. The secret shadow is constructed from the secret key in our scheme, while in previously proposed secret sharing schemes the secret key is the shadow. In addition, the shadow is not constructed by the shadowholder unless it is necessary, and no secure delivery channel is needed. Morever, this paper will further discuss how to change the shared secret, the threshold policy and cheater detection. Therefore, this scheme provides an efficient way to maintain important secrets.

The VF (Variable-to-Fixed length) arithmetic coding method combines the advantage of an ordinary stream arithmetic code with the simplicity of a block code. One of the advantages of VF codes is that the transmission errors or channel errors do not propagate infinitely and are restricted to the block in question. In this paper, we propose a modified type of non-proper VF arithmetic coding method that defines an input alphabet subset according to both the number of codewords in the current codeword set and input symbol probability and that splits the codeword set completely for a newly defined alphabet subset when the codeword set becomes smaller by each splitting. The proposed coding method carrys out independence of each codeword and guarantees that there is no collision while there is a waste of codeword(s) in conventional AB-coding due to collision. We examine the performance of the proposed method and compare it with that of other VF codes in terms of compression ratio and algorithmic complexity.

The phase and frequency commands of a rotating radar system, that utilizes the frequency scanning and phase shifters to steer the beam in the azimuth and elevation directions, respectively, are derived in terms of the angles of the ground based coordinate system. The frequency equation derived is approximated to a simple form to reduce the calculation time for real time multi-function radar systems. It is shown that the approximate frequency commands are in good agreement with the exact ones if the range of the azimuth scanning is not too wide.

The paper obtains an algorithm to estimate the irregular sampling in wavelet subspaces. Compared to our former work on the problem, the new estimate is relaxed for some wavelet subspaces.

An efficient active noise control algorithm based on the lattice-transversal joint (LTJ) filter structure is presented, and applied to the active control of broadband noise in a 3-dimensional enclosure. The presented algorithm implements the filtered-x LMS within the LTJ structure obtained by cascading the lattice and transversal structures. Simulation results show that the LTJ-based noise control algorithm has fast convergence speed that is comparable to the lattice-based algorithm while its computational complexity is less demanding.