This paper addresses a maximum likelihood method for source separation in the case of overdetermined mixtures corrupted by additive white Gaussian noise. We consider an approximate likelihood which is based on the Laplace approximation and develop a natural gradient adaptation algorithm to find a local maximum of the corresponding approximate likelihood. We present a detailed mathematical derivation of the algorithm using the Lie group invariance. Useful behavior of the algorithm is verified by numerical experiments.

When we use an adaptive array antenna (AAA) with the minimum mean square error (MMSE) criterion under the multipath environment, where the receiving signal level varies, it is difficult for the AAA to converge because of the distortion of the desired wave. Then, we need the equalization both in space and time domains. A tapped-delay-line adaptive array antenna (TDL-AAA) and the AAA with linear equalizer (AAA-LE) have been proposed as simple space-temporal equalization. The AAA-LE has not utilized the recursive least square (RLS) algorithm. In this paper, we propose a space-temporal simultaneous processing equalizer (ST-SPE) that is an AAA-LE with the RLS algorithm. We proposed that the first tap weight of the LE should be fixed and the necessity of that is derived from a normal equation in the MMSE criterion. We achieved the space-temporal simultaneous equalization with the RLS algorithm by this configuration. The ST-SPE can reduce the computational complexity of the space-temporal joint equalization in comparison to the TDL-AAA, when the ST-SPE has almost the same performance as the TDL-AAA in multipath environment with minimum phase condition such as appeared at line-of-sight (LOS).

The optimization of path bandwidth allocation in large-scale telecommunication networks is studied. By introducing a decomposition-coordination scheme to global optimization of the path bandwidth allocation which aims at minimizing the worst case call blocking probabilities in the network, the spatial and time complexities are both reduced, while the accuracy is almost the same as that given by direct optimization.

OPKFDDs (Ordered Pseudo-Kronecker Functional Decision Diagrams) are a data structure that provides compact representation of Boolean functions. The size of OPKFDDs depends on a variable ordering and on decomposition type choices. Finding an optimal representation is very hard and the size of the search space is n! 32n-1, where n is the number of input variables. To overcome the huge search space of the problem, a genetic algorithm is proposed for the generation of OPKFDDs with minimal number of nodes.

In this paper, a new method is proposed for supervised classification of ground cover types by using polarimetric synthetic aperture radar (SAR) data. The concept of similarity parameter between two scattering matrices is introduced for characterizing target scattering mechanism. Four similarity parameters of each pixel in image are used for classification. They are the similarity parameters between a pixel and a plane, a dihedral, a helix and a wire. The total received power of each pixel is also used since the similarity parameter is independent of the spans of target scattering matrices. The supervised classification is carried out based on the principal component analysis. This analysis is applied to each data set in image in the feature space for getting the corresponding feature transform vector. The inner product of two vectors is used as a distance measure in classification. The classification result of the new scheme is shown and it is compared to the results of principal component analysis with other decomposition coefficients, to demonstrate the effectiveness of the similarity parameters.

A shared binary decision diagram (SBDD) represents a multiple-output function, where nodes are shared among BDDs representing the various outputs. A partitioned SBDD consists of two or more SBDDs that share nodes. The separate SBDDs are optimized independently, often resulting in a reduction in the number of nodes over a single SBDD. We show a method for partitioning a single SBDD into two parts that reduces the node count. Among the benchmark functions tested, a node reduction of up to 23% is realized.

Functional decomposition is an essential technique of logic synthesis and is important especially for FPGA design. Bertacco and Damiani proposed an efficient algorithm finding simple disjoint decomposition using Binary Decision Diagrams (BDDs). However, their algorithm is not complete and does not find all the decompositions. This paper presents a complete theory of simple disjoint decomposition and describes an efficient algorithm using BDDs.

In this paper, we consider a flexible method for designing n-entities communication protocols and services. The proposed technique considers alternative and parallel composition of n service specifications and n protocol specifications, where n 2. The specifications are specified in Basic LOTOS which is a Formal Description Technique (FDT). We use the weak bisimulation equivalence () to represent the correctness properties between the service specification and the protocol specification.

Among ITS applications, it is very important to acquire detailed statistics of traffic flows. For that purpose, vision sensors have an advantage because of their rich information compared to such spot sensors such as loop detectors or supersonic wave sensors. However, for many years, vehicle tracking in traffic images has suffered from the problems of occlusion effect and illumination effect. In order to resolve occlusion problems, we have been proposing the Spatio-Temporal Markov Random Field model(S-T MRF) for segmentation of Spatio-Temporal images. This S-T MRF model optimizes the segmentation boundaries of occluded vehicles and their motion vectors simultaneously by referring to textures and segment labeling correlations along the temporal axis as well as the spatial axis. Consequently, S-T MRF has been proven to be successful for vehicle tracking even against severe occlusions found in low-angle traffic images with complicated motions, such at highway junctions. In addition, in this paper, we define a method for obtaining illumination-invariant images by estimating MRF energy among neighbor pixel intensities. These illumination-invariant images are very stable even when sudden variations in illumination or shading effect are occurred in the original images. We then succeeded in seamlessly integrating the method for MRF energy images into our S-T MRF model. Thus, vehicle tracking was performed successfully by S-T MRF, even against sudden variations in illumination and against shading effects . Finally, in order to verify the effectiveness of our tracking algorithm based on the S-T MRF for practical uses, we developed an automated system for acquiring traffic statistics out of a flow of traffic images. This system has been operating continuously for ten months, and thus effectiveness of the tracking algorithm based on S-T MRF model was proven.

This paper presents a method of extracting car license plates captured from the arbitrary directions by using symmetry features. The generalized symmetry transform (GST) produces continuous features of symmetry between two points by combining locality constraint and reflectional symmetry, but its time complexity of GST is increased by the second power of the radius of a searching window. To achieve considerable reduction of this time complexity, we propose a scan line based GST (SLGST) which calculates the symmetry between a pair of edge pixels along the scan lines. Instead of calculating the global symmetry of a license plate, we assign the symmetry contribution acquired from SLGST to the corner point estimated from two edge points and their gradient orientations. The right angle tuned SLGST (RATSLGST) is newly proposed to detect the right angle corners more effectively. Image normalization by image warping is adopted to make such segmentation of license plate and later identification much easier. We also adopt the verifier that evaluates a candidate license plate to enhance extraction rate. Our experiments show that the proposed method for extracting perspectively distorted license plates is fairly reliable.

The one-sample locally optimum rank detector test statistics for composite signals in multiplicative and signal-dependent noise are obtained. Since the one-sample locally optimum rank detector makes use of the sign statistics of observations as well as the rank statistics, both 'even' and 'odd' score functions have to be considered. Although the one-sample locally optimum rank detector requires two score functions while the two-sample detector requires only one score function, the one-sample detector requires fewer calculations since it has to rank fewer observations.

DS/CDMA systems employing long-period PN sequences are becoming a widespread standard of wireless communication systems. However, fast acquisition of long-period PN sequences at a low hardware cost is conventionally a difficult problem. This paper proposes a new fast acquisition algorithm for a class of PN sequences, which includes m- and GMW sequences as special cases, and shows that the mean (correct) acquisition time can be considerably reduced under input SNR values well below those used in modern DS/CDMA systems. Its fast acquisition capability is based on a decomposition of long PN sequences into a number of short ones and achieves a significantly reduced code phase uncertainty of acquisition at relatively small hardware cost. It can be applied as a (part of) acquisition system of a DS/CDMA system instead of a slow sliding correlator or a costly matched filter.

The two-sample locally optimum rank detector test statistics for composite signals in additive, multiplicative, and signal-dependent noise are obtained in this letter. Compared with the structure of the one-sample locally optimum rank detector, that of the two-sample locally optimum rank detector is shown to be simpler, although it needs more computations. It is known that there is a trade-off of computational complexity and structural simplicity between the one- and two-sample detectors.

Recent physiological studies on synaptic plasticity have shown that synaptic weights change depending on fine timing of presynaptic and postsynaptic spikes. Here, we show that a phenomenon similar to stochastic resonance with respect to background noise is observed on spike-timing dependent synaptic plasticity (STDP) that can contribute to stable propagation of precisely timed spikes in a multi-layered feedforward neural network.

We propose bit error rate (BER) evaluation methods for a trellis coded modulation (TCM) scheme over a Rayleigh fading channel by using importance sampling (IS). The simulation probability density function for AWGN and Rayleigh fading is separately designed. For efficient simulation of a system model with finite interleaver, frequency of the generation of fading sequences is reduced. The proposed method gives a good BER estimates over a Rayleigh fading channel.

In this paper, a high performance 3232-bit multiplier for a DSP core is proposed. The multiplier is composed of a block of Booth Encoder, a block of data compression, and a block of a 64-bit adder. In the block of Booth encoder, a conditional sign decision Booth encoder that reduces the gate delay and power consumption is proposed. In the block of data compression, 4-2 and 9-2 data compressors based on a novel compound logic are used for the efficient compressing of extra sign bit. In the block of 64-bit adder, an adaptive MUX-based conditional select adder with a separated carry generation block is proposed. The proposed 3232-bit multiplier is designed by a full-custom method and there are about 28,000 transistors in an active area of 900 µm 500 µm with 0.25 µm CMOS technology. From the experimental results, the multiplication time of the multiplier is about 3.2 ns at 2.5 V power supply, and it consumes about 50 mW at 100 MHz.

The lossy magnetic composite material made from soft magnetic metal powder and rubber is widely used as an EMI countermeasure material, due to its higher magnetic loss than those of spinel ferrites in microwave frequencies. In this paper, we clarify the material characteristics by measuring the relative complex permeability r and relative complex permittivity r of two kinds of composite materials in microwave frequencies. Since the composite materials are anisotropic, both r and r are measured as diagonal tensors by utilizing extended S-parameter method. The results show that the imaginary part of r of flaky-powder composite exceeded the Snoek's limit for the spinel ferrites which has been reported so far. The measured r and r are partially compared with those measured by cavity resonator method, and good agreement is obtained.

In this paper, a novel continuous complementary sliding control was proposed to improve the tracking performance given the available control bandwidth and the extend of parameter uncertainty. With this control law, the ultimate bound of tracking error was shown to be reduced at least by half, as compared with the conventional continuous sliding control. More strikingly, the proposed control can effectively improve the error transient response during the reaching phase. We presented a composite complementary sliding control scheme for a class of uncertain nonlinear systems including the nonlinear electrohydraulic position servo control system, which will be used as an illustrated example. Simulation results indicated exceptional good tracking performance to step and sine wave reference inputs can be obtained. In addition, the disturbance rejection property of the controller to single-frequency sinusoidal disturbances is also outstanding.

We have recently developed a programmable composite noise generator (P-CNG) which can easily control noise parameters such as average power, time-based amplitude probability distribution (APD), crossing rate distribution, occurrence frequency distribution and burst duration. Two applications of the P-CNG are demonstrated to show its usefulness. For the first application, Middleton's Class A noise is simulated. A method of setting parameters for Class A noise is demonstrated. The APD of P-CNG output is in good agreement with that of true Class A noise. In the second application, the P-CNG is used for subjective evaluation test (opinion test) of TV picture degradation. Five simple composite noise models with two kinds of APD are used. Other parameters such as average power are kept constant. Experimental results show that the envelope and APD of composite noises do not greatly influence the subjective evaluation. Finally the capabilities of the P-CNG are shown.

This paper proposes a fast method for the calculation of exponential B-splines sampled at regular intervals. This algorithm is based on a combination of FIR and IIR filters which enables a fast decomposition and reconstruction of a signal. When complex values are selected for the parameters of the exponentials, complex trigonometric functions are obtained. Only the real part of these functions are used for the interpolation of real signals, leading less bandlimited signals when they are compared with the polynomial B-spline counterparts. These characteristics were verified with 1-D and 2-D examples. This paper also discusses the effectiveness of exponential B-splines, when they are applied to image processing.