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.

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.

A siphon-trap of a Petri net N is defined as a place set S with S = S, where S = { u| N has an edge from u to a vertex of S} and S = { v| N has an edge from a vertex of S to v}. A minimal siphon-trap is a siphon-trap such that any proper subset is not a siphon-trap. The following polynomial-time algorithms are proposed: (1) FDST for finding, if any, a minimal siphon-trap or even a maximal class of mutually disjoint minimal siphon-traps of a given Petri net; (2) FDSTi that repeats FDST i times in order to extract more minimal siphon-traps than FDST. (3) STFM_T (STFM_Ti, respectively) which is a combination of the Fourier-Motzkin method and FDST (FDSTi) and which has high possibility of finding, if any, at least one minimal-support nonnegative integer invariant.

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.

The fields in the junctions between straight and curved rectangular waveguides are analyzed by using the method of separating variables. This method was succeeded because the authors developed the method of numerical calculation of the cylindrical functions of complex order. As a result, we numerically calculate the reflection and transmission coefficients in the junctions in various situations, and we compare these results with the results by the perturbation method and with the results by Jui-Pang et al.

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.

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 UCHT (Unified Complex Hadamard Transform) has been proposed as a new family of spreading sequences for DS-SSMA systems recently. In this Letter, the periodic autocorrelation (PAC) properties of the Unified Complex Hadamard Transform (UCHT) sequences are analyzed. Upper bounds for the out-of-phase PAC are derived for two groups of the UCHT sequences, namely the HSP-UCHT and the NHSP-UCHT sequences (the later is a more general representation of the well-known Walsh-Hadamard (WH) sequences). A comparison of the two bounds is performed. It turns out that the HSP-UCHT sequences have a lower upper bound for the out-of-phase PAC. This makes the HSP-UCHT sequences more effective than the WH sequences in combating multipath effect for DS-SSMA systems.

This paper proposes a test generation method using a compacted test table and a test generation method using a compacted test plan table for RTL data path circuits with DFT where hierarchical test generations are applicable. Moreover, a heuristic algorithm for a compacted test plan table generation is proposed. The proposed methods could shorten test lengths for some RTL data path circuits compared with the conventional hierarchical test generation method.

Test compression/decompression is an efficient method for reducing the test application cost. In this paper we propose a test generation method for obtaining test-patterns suitable to test compression by statistical coding. In general, an ATPG generates a test-pattern that includes don't-care values. In our method, such don't-care values are specified based on an estimation of the final probability of 0/1 occurrence in the resultant test set. Experimental results show that our method can generate test patterns that are able to be highly compressed by statistical coding, in small computational time.

This letter proposes a method which can embed index data such as memos into JPEG images. The method embeds digital watermarks using the quantitative relation between quantized DCT coefficients in JPEG images. In the method, we can embed extra data to represent index data and can extract the index data without parameters used in embedding process. Furthermore, the method is tolerant of JPEG recompression and prevents the degradation of image quality by rewriting index data.

When solving combinatorial optimization problems with a binary Hopfield-type neural network, the updating process in neural network is an important step in achieving a solution. In this letter, we propose a new updating procedure in binary Hopfield-type neural network for efficiently solving combinatorial optimization problems. In the new updating procedure, once the neuron is in excitatory state, then its input potential is in positive saturation where the input potential can only be reduced but cannot be increased, and once the neuron is in inhibitory state, then its input potential is in negative saturation where the input potential can only be increased but cannot be reduced. The new updating procedure is evaluated and compared with the original procedure and other improved methods through simulations based on N-Queens problem. The results show that the new updating procedure improves the searching capability of neural networks with shorter computation time. Particularly, the simulation results show that the performance of proposed method surpasses the exiting methods for N-queens problem in synchronous parallel computation model.

In this work we study the performance of a distributed GA that incorporates in its core parallel cooperative-competitive genetic operators. A series of controlled experiments are conducted using various large and difficult 0/1 multiple knapsack problems to test the robustness of the distributed GA. Simulation results verify that the proposed distributed GA compared with a canonical distributed GA significantly gains in search speed and convergence reliability with less communication cost for migration.

A simple system configuration was used to generate transform-limited optical pulses at 160 Gbit/s in the sub-picosecond range (625 fs). Pulse compression was achieved by broadening the spectrum using supercontinuum generation followed by a linear frequency chirping compensation.

This paper gives a new sufficient condition for cellular neural networks with delay (DCNNs) to be completely stable. The result is a generalization of two existing stability conditions for DCNNs, and also contains a complete stability condition for standard CNNs as a special case. Our new sufficient condition does not require the uniqueness of equilibrium point of DCNNs and is independent of the length of delay.

Vector quantization (VQ) is an attractive image compression technique. VQ utilizes the high correlation between neighboring pixels in a block, but disregards the high correlation between the adjacent blocks. Unlike VQ, side-match VQ (SMVQ) exploits codeword information of two encoded adjacent blocks, the upper and left blocks, to encode the current input vector. However, SMVQ is a fixed bit rate compression technique and doesn't make full use of the edge characteristics to predict the input vector. Classified side-match vector quantization (CSMVQ) is an effective image compression technique with low bit rate and relatively high reconstruction quality. It exploits a block classifier to decide which class the input vector belongs to using the variances of neighboring blocks' codewords. As an alternative, this paper proposes three algorithms using gradient values of neighboring blocks' codewords to predict the input block. The first one employs a basic gradient-based classifier that is similar to CSMVQ. To achieve lower bit rates, the second one exploits a refined two-level classifier structure. To reduce the encoding time further, the last one employs a more efficient classifier, in which adaptive class codebooks are defined within a gradient-ordered master codebook according to various prediction results. Experimental results prove the effectiveness of the proposed algorithms.

The cooperative and competitive network suitable for circuit realization is presented, based on the network proposed by Amari and Arbib. To ensure WTA process, the output function of the original network is replaced with the piecewise linear function and supplying the inputs as pulse waveforms is obtained. In the SPICE simulations, it is confirmed that the network constructed by operational amplifiers attains WTA process, even if the scale of the network becomes large.

Algorithms are presented for the four elementary arithmetic operations, to perform reliable floating-point arithmetic operations. These arithmetic operations can be achieved by applying residue techniques to the weighted number systems and performed with no accuracy lost in the process of the computing. The arithmetic operations presented can be used as elementary tools (on many existing architectures) to ensure the reliability of numerical computations. Simulation results especially for the solutions of ill-conditioned problems are given with emphasis on the practical usability of the tools.

This paper presents an efficient graph-based evolutionary optimization technique called Evolutionary Graph Generation (EGG) and its extension to a parallel version. A new version of parallel EGG system is based on a coarse-grained model of parallel processing and can synthesize heterogeneous networks of various different components efficiently. The potential capability of parallel EGG system is demonstrated through the design of current-mode logic circuits.

The starting point of this paper is the definition of a nonlinear model of the read out process in high density optical discs. Under high density condition, the signal read out is not a linear process, and suffers also from cross talk. To cope with these problems, the identification of a suitable nonlinear model is required. A physical model based on the optical scalar theory is used to identify the kernels of a nonlinear model based on the Volterra series. Both analysis and simulations show that a second order bidimensional model accurately describes the read out process. Once equipped with the Volterra channel model, we evaluate the performance of various nonlinear receivers. First we consider Nonlinear Adaptive Volterra Equalization (NAVE). Simulations show that the performance of classical structures for linear channels is significantly affected by the nonlinear response. The nonlinear NAVE receiver can achieve better performance than Maximum Likelihood Sequence Estimator (MLSE), with lower complexity. An innovative Nonlinear Maximum Likelihood Sequence Estimator (NMLSE), based on the combination of MLSE and nonlinear Inter-Symbol Interference (ISI) cancellation, is presented. NMLSE offers significant advantages with respect to traditional MLSE, and performs better than traditional equalization for nonlinear channels (like NAVE). Finally, the paper deals with cancellation of cross talk from adjacent tracks. We propose and analyze an adaptive nonlinear cross talk canceller based on a three spot detection system. For the sake of simplicity, all the performance comparisons presented in this paper are based on the assumption that noise is Additive, White, and Gaussian (AWGN model).