Partial transmit sequence (PTS) is a well known technique used to reduce the peak-to-average power ratio (PAPR) of an orthogonal frequency division multiplexing (OFDM) signal. However, it has relatively high complexity due to the computation of multiple inverse fast Fourier transforms (IFFTs). To reduce this complexity, we use intermediate signals within a decimation in frequency (DIF) radix IFFT and propose a new PTS subblocking technique which requires the computation of only partial IFFTs. Performance results are presented which show a PAPR reduction similar to that with other techniques such as original PTS (O-PTS). Further, we show that complexity reduction can be achieved with either low or high radix IFFT algorithms.

A new ensemble of low-density parity-check (LDPC) codes for correcting a solid burst erasure is proposed. This ensemble is an instance of a combined matrix ensemble obtained by concatenating some LDPC matrices. We derive a new bound on the critical minimum span ratio of stopping sets for the proposed code ensemble by modifying the bound for ordinary code ensemble. By calculating this bound, we show that the critical minimum span ratio of stopping sets for the proposed code ensemble is better than that of the conventional one with keeping the same critical exponent of stopping ratio for both ensemble. Furthermore from experimental results, we show that the average minimum span of stopping sets for a solid burst erasure of the proposed codes is larger than that of the conventional ones.

Tin-doped indium oxide (ITO) thin films were prepared on a polyethylene terephthalate (PET) foil by bias sputtering. In the absence of a substrate bias, films having a high resistivity of 210-2 Ωcm were formed. On the other hand, by the application of an rf substrate bias, films having a low resistivity of 2.610-4 Ωcm were formed. The energy of ions that bombarded the substrate during bias sputtering was estimated by a simulation of the ion acceleration. The optimum ion-energy required for the reduction of resistivity was found to be approximately 50 eV.

This paper investigates the problem of nonpreemptively scheduling independent parallel tasks in an environment with multiple machines, which is motivated from the recent studies in scheduling tasks in a multi-machine environment. In this scheduling environment, each machine contains a number of identical processors and each parallel task can simultaneously require a number of processors for its processing in any single machine. Whenever tasks are processed in parallel in a parallel machine, message communication among processors is often inevitable. The problem of finding a shortest schedule length on scheduling independent parallel tasks with the consideration of communication overhead in a multi-machine environment is NP-hard. The aim of this paper is to propose a heuristic algorithm for this kind of problem and to analyze the performance bound of this heuristic algorithm.

Point Pattern Matching (PPM) is an essential problem in many image analysis and computer vision tasks. This paper presents a two-stage algorithm for PPM problem using ellipse fitting and dual Hilbert scans. In the first matching stage, transformation parameters are coarsely estimated by using four node points of ellipses which are fitted by Weighted Least Square Fitting (WLSF). Then, Hilbert scans are used in two aspects of the second matching stage: it is applied to the similarity measure and it is also used for search space reduction. The similarity measure named Hilbert Scanning Distance (HSD) can be computed fast by converting the 2-D coordinates of 2-D points into 1-D space information using Hilbert scan. On the other hand, the N-D search space can be converted to a 1-D search space sequence by N-D Hilbert Scan and an efficient search strategy is proposed on the 1-D search space sequence. In the experiments, we use both simulated point set data and real fingerprint images to evaluate the performance of our algorithm, and our algorithm gives satisfying results both in accuracy and efficiency.

This paper proposes a novel chaotic multiple-bits modulation scheme that uses the parameters in the map as data carrier for chaotic digital communication. Chaotic signals modulated with the parameters corresponding to the information to be transmitted are sent to the receiver. The information sent to the receiver can be decoded by a correlation detector. This scheme can increase the number of transmittable information bit per unit carrier signals by increasing the number of mapping parameters to be used for modulation. We verify the performance of this scheme using bit error rate (BER) through computer simulation. Also, we compare the performance of the proposed method with a conventional single-bit modulation scheme.

In this paper, a new lifting-based shape-direction-adaptive discrete wavelet transform (SDA-DWT) which can be used for arbitrarily shaped segments is proposed. The SDA-DWT contains three major techniques: the lifting-based DWT, the adaptive directional technique, and the concept of object-based compression in MPEG-4. With SDA-DWT, the number of transformed coefficients is equal to the number of pixels in the arbitrarily shaped segment image, and the spatial correlation across subbands is well preserved. SDA-DWT also can locally adapt its filtering directions according to the texture orientations to improve energy compaction for images containing non-horizontal or non-vertical edge textures. SDA-DWT can be applied to any application that is wavelet based and the lifting technique provides much flexibility for hardware implementation. Experimental results show that, for still object images with rich orientation textures, SDA-DWT outperforms SA-DWT up to 5.88 dB in PSNR under 2.15-bpp (bit / object pixel) condition, and reduces the bit-budget up to 28.5% for lossless compression. SDA-DWT also outperforms DA-DWT up to 5.44 dB in PSNR under 3.28-bpp condition, and reduces the bit-budget up to 14.0%.

This letter studies the tracking error in Multi-input Multi-output Feedback Error Learning (MIMO-FEL) system having insufficient excitation. It is shown that the error converges to zero exponentially even if the reference signal lacks the persistently excitation (PE) condition. Furthermore, by making full use of this fast convergence, we estimate the plant parameter while in operation based on frequency response. Simulation results show the effectiveness of the proposed method compared to a conventional approach.

In this paper, we propose a method for enhancing performance of a sequential version of the belief-propagation (BP) decoding algorithm, the group shuffled BP decoding algorithm for low-density parity-check (LDPC) codes. An improved BP decoding algorithm, called the shuffled BP decoding algorithm, decodes each symbol node in serial at each iteration. To reduce the decoding delay of the shuffled BP decoding algorithm, the group shuffled BP decoding algorithm divides all symbol nodes into several groups. In contrast to the original group shuffled BP, which automatically generates groups according to symbol positions, in this paper we propose a method for grouping symbol nodes which generates groups according to the structure of a Tanner graph of the codes. The proposed method can accelerate the convergence of the group shuffled BP algorithm and obtain a lower error rate in a small number of iterations. We show by simulation results that the decoding performance of the proposed method is improved compared with those of the shuffled BP decoding algorithm and the group shuffled BP decoding algorithm.

Given a simple graph G with n vertices, m edges and k connected components. The spanning forest problem is to find a spanning tree for each connected component of G. This problem has applications to the electrical power demand problem, computer network design, circuit analysis, etc. An optimal parallel algorithm for finding a spanning tree on the trapezoid graph is given by Bera et al., it takes O(log n) time with O(n/log n) processors on the EREW (Exclusive-Read Exclusive-Write) PRAM. Bera et al.'s algorithm is very simple and elegant. Moreover, it can correctly construct a spanning tree when the graph is connected. However, their algorithm can not accept a disconnected graph as an input. Applying their algorithm to a disconnected graph, Concurrent-Write occurs once for each connected component, thus this can not be achieved on EREW PRAM. In this paper we present an O(log n) time parallel algorithm with O(n/log n) processors for constructing a spanning forest on trapezoid graph G on EREW PRAM even if G is a disconnected graph.

We propose a multiparty protocol for comparator networks which are used to compute various functions in statistical analysis, such as the maximum, minimum, median, and quartiles, for example, through sorting and searching. In the protocol, all values which are inputted to a comparator network and all intermediate outputs are kept secret assuming the presence of an honest majority. We also introduce an application of the protocol for a secure (M+1)-st price auction.

Programming the multiprocessor system-on-chip (MPSoC) requires partitioning the sequential reference programs onto multiple processors running in parallel. However, designers still need to partition the code manually due to the lack of automated partition techniques. To settle this issue, this paper proposes a partition exploration algorithm based on the search space smoothing techniques, and implements the proposed method using a commercial extensible processor (Xtensa LX2 processor from Tensilica Inc.). We have verified the feasibility of the algorithm by implementing the MPEG2 benchmark on the Xtensa-based two-processor system. The final experimental results indicate a performance improvement of at least 1.6 compared to the single-processor system.

In this paper, we consider a collision detection problem of spheres which asks to detect all pairs of colliding spheres in a set of n spheres located in d-dimensional space. We propose a collision detection algorithm for spheres based on slab partitioning technique and a plane sweep method. We derive a theoretical upper bound on the time complexity of the algorithm. Our bound tells that if both the dimension and the maximum ratio of radii of two spheres are bounded, then our algorithm runs in O(n log n + K) time with O(n + K) space, where K denotes the number of pairs of colliding spheres.

This paper presents an integration architecture of content addressable memory (CAM) and a massive-parallel memory-embedded SIMD matrix for constructing a versatile multimedia processor. The massive-parallel memory-embedded SIMD matrix has 2,048 2-bit processing elements, which are connected by a flexible switching network, and supports 2-bit 2,048-way bit-serial and word-parallel operations with a single command. The SIMD matrix architecture is verified to be a better way for processing the repeated arithmetic operation types in multimedia applications. The proposed architecture, reported in this paper, exploits in addition CAM technology and enables therefore fast pipelined table-lookup coding operations. Since both arithmetic and table-lookup operations execute extremely fast, the proposed novel architecture can realize consequently efficient and versatile multimedia data processing. Evaluation results of the proposed CAM-enhanced massive-parallel SIMD matrix processor for the example of the frequently used JPEG image-compression application show that the necessary clock cycle number can be reduced by 86% in comparison to a conventional mobile DSP architecture. The determined performances in Mpixel/mm2 are factors 3.3 and 4.4 better than with a CAM-less massive-parallel memory-embedded SIMD matrix processor and a conventional mobile DSP, respectively.

The task of joint channel and data estimation based on the maximum likelihood principle is addressed using a continuous and discrete particle swarm optimization (PSO) algorithm over additive white Gaussian noise channels. The PSO algorithm works at two levels. At the upper level continuous PSO estimates the channel while at the lower level, discrete PSO detects the data. Simulation results indicate that under the same conditions, PSO outperforms the best of the published alternatives.

This letter proposes a novel scheme of joint antenna combination and symbol detection in multi-input multi-output (MIMO) systems, which simultaneously determines the antenna combination coefficients to lower the RF chains and designs the minimum bit error rate (MBER) detector to mitigate the interference. The joint decision statistic, however, is highly nonlinear and the particle swarm optimization (PSO) algorithm is employed to reduce the computational overhead. Simulations show that the new approach yields satisfactory performance with reduced computational overhead compared with pervious works.

The problem of designing a robust adaptive control for nonlinear systems with uncertain time-varying parameters is addressed. The upper bound of uncertain parameters, considered even in control coefficients, are not required to be known. An adaptive tracking controller is presented and, using the Lyapunov theory, the closed-loop stability and tracking error convergence is shown. In order to improve the performance of the method, a robust mechanism is incorporated into the adaptive controller yielding a robust adaptive algorithm. The proposed controller guarantees the boundedness of all closed-loop signals and robust convergence of tracking error in spite of time-varying parameter uncertainties with unknown bounds. The parametric uncertain systems under consideration describes a wide class of nonlinear circuits and systems. As an application, a novel parametric model is derived for nonlinear Chua's circuit and then, the proposed method is used for its control. The effectiveness of the method is demonstrated by some simulation results.

In this paper we compare various parallel preconditioners such as Point-SSOR (Symmetric Successive OverRelaxation), ILU(0) (Incomplete LU) in the Wavefront ordering, ILU(0) in the Multi-color ordering, Multi-Color Block SOR (Successive OverRelaxation), SPAI (SParse Approximate Inverse) and pARMS (Parallel Algebraic Recursive Multilevel Solver) for solving large sparse linear systems arising from two-dimensional PDE (Partial Differential Equation)s on structured grids. Point-SSOR is well-known, and ILU(0) is one of the most popular preconditioner, but it is inherently serial. ILU(0) in the Wavefront ordering maximizes the parallelism in the natural order, but the lengths of the wavefronts are often nonuniform. ILU(0) in the Multi-color ordering is a simple way of achieving a parallelism of the order N, where N is the order of the matrix, but its convergence rate often deteriorates as compared to that of natural ordering. We have chosen the Multi-Color Block SOR preconditioner combined with direct sparse matrix solver, since for the Laplacian matrix the SOR method is known to have a nondeteriorating rate of convergence when used with the Multi-Color ordering. By using block version we expect to minimize the interprocessor communications. SPAI computes the sparse approximate inverse directly by least squares method. Finally, ARMS is a preconditioner recursively exploiting the concept of independent sets and pARMS is the parallel version of ARMS. Experiments were conducted for the Finite Difference and Finite Element discretizations of five two-dimensional PDEs with large meshsizes up to a million on an IBM p595 machine with distributed memory. Our matrices are real positive, i.e., their real parts of the eigenvalues are positive. We have used GMRES(m) as our outer iterative method, so that the convergence of GMRES(m) for our test matrices are mathematically guaranteed. Interprocessor communications were done using MPI (Message Passing Interface) primitives. The results show that in general ILU(0) in the Multi-Color ordering and ILU(0) in the Wavefront ordering outperform the other methods but for symmetric and nearly symmetric 5-point matrices Multi-Color Block SOR gives the best performance, except for a few cases with a small number of processors.

In this letter, a genetic programming method is used to synthesize filters. In order to improve the group delay characteristics, we propose a novel two-stage fitness function reflecting not only the frequency response but also the group delay characteristics of the evolved filters. We also deal with two practical design considerations, i.e., the filters include parasitic effects and are composed of elements with discrete values. The proposed method is applied to low-pass filter design cases. The experimental results show the method can effectively generate filters satisfying the design considerations and possessing improved group delay characteristics when compared with traditional filters.

A (k,2)-track layout of a graph G consists of a 2-track assignment of G and an edge k-coloring of G with no monochromatic X-crossing. This paper studies the problem of (k,2)-track layout of bipartite graph subdivisions. Recently V. Dujmovi and D.R. Wood showed that for every integer d ≥ 2, every graph G with n vertices has a (d+1,2)-track layout of a subdivision of G with 4 log d qn(G) +3 division vertices per edge, where qn(G) is the queue number of G. This paper improves their result for the case of bipartite graphs, and shows that for every integer d ≥ 2, every bipartite graph Gm,n has a (d+1,2)-track layout of a subdivision of Gm,n with 2 log d n -1 division vertices per edge, where m and n are numbers of vertices of the partite sets of Gm,n with m ≥ n.