Blind adaptive channel identification of communication channels is a problem of important current theoretical and practical concerns. Recently proposed solutions for this problem exploit the diversity induced by antenna array or time oversampling, leading to the so-called, second order statistics techniques. Adaptive blind channel identification techniques based on a off-line least-squares approach have been proposed but this method assumes noise-free case. The method resorts to an adaptive filter with a linear constraint. This paper proposes a new approach based on eigenvalue decomposition. Indeed, the eigenvector corresponding to the minimum eigenvalue of the covariance matrix of the received signals contains the channel impulse response. And we present a adaptive algorithm to solve this problem. The performance of the proposed technique is evaluated over real measured channel and is compared to existing algorithms.

This letter presents a low spurious frequency setting algorithm for a triple tuned type PLL synthesizer driven by a DDS. The triple tuned PLL synthesizer is based on a single PLL configuration with two variable frequency dividers. The DDS is employed for a reference source of the PLL. The proposed algorithm determines appropriate frequency tuning values of the DDS frequency and the division ratios of two frequency dividers. The division ratios are selected to achieve a desired output frequency while the low spurious condition of the DDS has been maintained. A 5 to 10 GHz synthesizer with frequency step of 500 kHz demonstrated spurious level below -46 dBc with improvement of 13 dB.

A flash analog-to-digital converter (ADC) that uses resonant-tunneling complex gates is proposed. The ternary quantizers, consisting of monostable-to-multistable transition logic (MML) circuits, convert the analog input signal into the ternary thermometer code. This code is then converted into the binary Gray-code output by a multiple-valued multiple-input monostable-bistable transition logic element (M2-MOBILE). By assuming InP-based resonant-tunneling diode (RTD) and heterojunction field-effect transistor technology, we have carried out SPICE simulation that demonstrates a 4-bit, 10-GS/s ADC operation. The input bandwidth, defined as a frequency at which the effective number of bit decreases by 0.5 LSB, was also estimated to be 500 MHz. Compact circuit configuration, which is due to the combination of MML and M2-MOBILE, reduces the device count and power dissipation by a factor of two compared with previous RTD-based ADCs.

This paper introduces a probabilistic modeling of alarm observation delay, and shows a novel method of model-based diagnosis for time series observation. First, a fault model is defined by associating an event tree rooted by each fault hypothesis with probabilistic variables representing temporal delay. The most probable hypothesis is obtained by selecting one whose Akaike information criterion (AIC) is minimal. It is proved by simulation that the AIC-based hypothesis selection achieves a high precision in diagnosis.

Independent component analysis (ICA) is a new method of extracting independent components from multivariate data. It can be applied to various fields such as vision and auditory signal analysis, communication systems, and biomedical and brain engineering. There have been proposed a number of algorithms. The present article shows that most of them use estimating functions from the statistical point of view, and give a unified theory, based on information geometry, to elucidate the efficiency and stability of the algorithms. This gives new efficient adaptive algorithms useful for various problems.

There were researches that measured effort required to understand and adapt components based on the complexity of the component, which is some general criterion related to the intrinsic quality of the component to be adapted and understood. They, however, don't consider significance of the measurement attributes and user must decide reusability of similar components for himself. Therefore, in this paper, we propose a new method that can measure the DOR (Degree Of Reusability) of the components by considering the significance of the measurement attributes. We calculates the relative significance of them by using rough set and integrate the significance with the measurement value by using Sugeno's fuzzy integral. Lastly, we apply our method to the source code components and show through statistical technique that it can be used as the ordinal and ratio scale.

In this paper, we discuss the impossible differential cryptanalysis for the block cipher Zodiac. The main design principles of Zodiac include simplicity and efficiency. However, the diffusion layer in its round function is too simple to offer enough security. The impossible differential cryptanalysis exploits such weakness in Zodiac. Our attack using a 14-round impossible characteristic derives the 128-bit master key of the full 16-round Zodiac faster than the exhaustive search. The efficiency of the attack compared with exhaustive search increases as the key size increases.

In this paper, we examine the polarimetric characteristics and the potential of the coherent decomposition in polarimetric synthetic aperture radar (SAR) interferometry. Coherent scattering decomposition based on the coherence optimization can separate effective phase center of different scattering mechanisms and can be used to generate canopy digital elevation model (DEM). This decomposition is applied to a simplified stochastic scattering model such as forest canopy. However, since the polarimetric characteristics are not well understood when the decomposition is carried out, we investigate its characteristics and potential using polarimetric entropy-alpha and three-component scattering matrix decomposition. The results show that the first and third components correspond to the lower and upper layer, respectively, in ideal case. In this investigation, SIR-C/X-SAR data of the Tien Shan flight-pass are used.

Fiber-optic MEMS which is fabricated by combining direct photo-lithography of optical fiber and silicon micro-machining is proposed. Preliminary results of micro-machining of optical fiber and variable telecommunication devices are presented.

This paper proposes a new method to recover the sign of local Gaussian curvature from multiple (more than three) shading images. The information required to recover the sign of Gaussian curvature is obtained by applying Principal Components Analysis (PCA) to the normalized irradiance measurements. The sign of the Gaussian curvature is recovered based on the relative orientation of measurements obtained on a local five point test pattern to those in the 2-D subspace called the eigen plane. Using multiple shading images gives a more accurate and robust result and minimizes the effect of shadows by allowing a larger area of the visible surface to be analyzed compared to methods using only three shading images. Furthermore, it allows the method to be applied to specular surfaces. Since PCA removes linear correlation among images, the method can produce results of high quality even when the light source directions are not widely dispersed.

This paper proposes a linear algorithm for metric reconstruction from projective reconstruction. Metric reconstruction problem is equivalent to estimating the projective transformation matrix that converts projective reconstruction to Euclidean reconstruction. We build a quadratic form from dual absolute conic projection equation with respect to the elements of the transformation matrix. The matrix of quadratic form of rank 2 is then eigen-decomposed to produce a linear estimate. The algorithm is applied to three different sets of real data and the results show a feasibility of the algorithm. Additionally, our comparison of results of the linear algorithm to results of bundle adjustment, applied to sets of synthetic image data having Gaussian image noise, shows reasonable error ranges.

Let t and n be positive integers. We show that the necessary and sufficient condition for the existence of a balanced t-foil decomposition of the complete graph Kn is n 1 (mod 6t). Decomposition algorithms are also given.

Spatiotemporal chaos in a multidomain regime in a Gunn-effect device is numerically investigated as an example of collective domain oscillations under global constraints. The dynamics of carrier densities are computed using a set of model partial differential equations. Numerical results reveal some distinctive and chaotic clustering features caused by the global coupling and boundary effects. The chaotic regime is then characterized in terms of a Lyapunov spectrum and Lyapunov dimension, the latter increasing with the size of the system.

This paper presents a new framework for synthesizing look-up table (LUT) networks. Some of the existing LUT network synthesis methods are based on one or two functional (Boolean) decompositions. Our method also uses functional decompositions, but we try to use various decomposition methods, which include algebraic decompositions. Therefore, this method can be thought of as a general framework for synthesizing LUT networks by integrating various decomposition methods. We use a cost database file which is a unique characteristic in our method. We also present comparisons between our method and some well-known LUT network synthesis methods, and evaluate the final results after placement and routing. Although our method is rather heuristic in nature, the experimental results are encouraging.

This paper presents a new adaptive blind separation of sources (BSS) method for linear and non-linear mixtures. The sources are assumed to be statistically independent with non-uniform and symmetrical PDF. The algorithm is based on both simulated annealing and density estimation methods using a neural network. Considering the properties of the vectorial spaces of sources and mixtures, and using some linearization in the mixture space, the new method is derived. Finally, the main characteristics of the method are simplicity and the fast convergence experimentally validated by the separation of many kinds of signals, such as speech or biomedical data.

We often see reflection phenomenon in our life. For example, through window glass, we can see real objects, but reflection causes virtual objects to appear in front of the glass. Thus, it is sometimes difficult to recognize the real objects. Some works have been proposed to separate these real and virtual objects using an optical property called polarization. However, they have a restriction on one assumption: the angle of incidence. In this paper, we overcome this difficulty using independent component analysis (ICA). We show the efficiency of the proposed method, by experimental results.

In this paper, we deal with the problem of compatibility class encoding, and propose a novel algorithm for finding a good functional decomposition with application to LUT-based FPGA synthesis. Based on exploration of the design space, we concentrate on extracting a set of components, which can be merged into the minimum number of multiple-output CLBs or LUTs, such that the decomposition constructed from these components is also minimal. In particular, to explore more degrees of freedom, we introduce pliable encoding to take over the conventional rigid encoding when it fails to find a satisfactory decomposition by rigid encoding. Experimental results on a large set of MCNC91 logic synthesis benchmarks show that our method is quite promising.

This paper proposes a novel fast algorithm for the decomposition and reconstruction of two-dimensional (2-D) signals by box splines. The authors have already proposed an algorithm to calculate the discrete box splines which enables the fast reconstruction of 2-D signals (images) from box spline coefficients. The problem still remains in the decomposition process to derive the box spline coefficients from an input image. This paper first investigates the decomposition algorithm which consists of the truncated geometric series of the inverse filter and the steepest descent method with momentum (SDM). The reconstruction process is also developed to correspond to the enlargement of images. The proposed algorithm is tested for the expansion of several natural images. As a result, the peak signal-to-noise ratio (PSNR) of the reconstructed images became more than 50 dB, which can be considered as enough high level. Moreover, the property of box splines are discussed in comparison with 2-D (the tensor product of) B-splines.

This paper presents an accurate method for finding the 3D control points of the B-Spline curves. This method can automatically fit a set of data points with piecewise geometrically continuous cubic B-Spline curves. Iterating algorithm has been used for finding the 2D control points. And a new approach for shape reconstruction based on the control points of the curves on the object's surface is proposed. B-Spline patch, the extension of the B-Spline curves to surface, provides recovering the shape of the object in 2D approach. The 3D control points of the cubic B-Spline curves are computed from the factor decomposition of the measurement matrix of 2D control points. The multiple object approach is also proposed to reconstruct the 3D shape of each curves of an object. Some experiments are demonstrated to confirm the effectiveness of our proposed method.

In two- or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space of the adaptive neuro-fuzzy inference system (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for two frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS under the same conditions.