The Navigational Programming (NavP) methodology is based on the principle of self-migrating computations. It is a truly incremental methodology for developing parallel programs: each step represents a functioning program, and each intermediate program is an improvement over its predecessor. The transformations are mechanical and straightforward to apply. We illustrate our methodology in the context of matrix multiplication, showing how the transformations lead from a sequential program to a fully parallel program. The NavP methodology is conducive to new ways of thinking that lead to ease of programming and high performance. Even though our parallel algorithm was derived using a sequence of mechanical transformations, it displays certain performance advantages over the classical handcrafted Gentleman's Algorithm.

This work proposes a new method for RLCG interconnect model-order reductions in consideration with the adjoint network. Relationships between an original MNA network and its corresponding adjoint MNA network will be explored first. It will be shown that the congruence transformation matrix used in the one-sided projection can be constructed by using the bi-orthogonal bases developed from the Lanczos-type algorithms. In particular, if the multi-port driving-point impedance of RLCG interconnect circuits is the main concern, the transfer functions and system moments of the adjoint network can be directly calculated from those of the original RLCG interconnect network by exploring symmetric properties of the MNA formulation. Therefore, the cost of constructing the congruence transformation matrix can be simplified by up to 50% of the previous methods. Comparative studies among various standard methods and the proposed methods are also investigated. Experimental results on large-scale RLCG interconnect circuits will demonstrate the accuracy and the efficiency of the proposed method.

The computational grid provides a promising platform for the deployment of various high-performance computing applications. Problem in implementing computational grid environments is how to effectively use various resources in the system, such as CPU cycle, memory, communication network, and data storage. There are many effective heuristic algorithms for scheduling in the computational grid, however most scheduling strategies have no theoretical guarantee at all. In this paper, a cost-based online scheduling algorithm is presented for job assignment in the grid environment with theoretical guarantee. Firstly, a scheduling framework is described, where the grid environment is characterized, and the online job model is defined. Secondly, the cost-based online scheduling algorithm is presented where costs of resources are exponential functions of their loads, and the performance of this algorithm is theoretically analyzed against the performance of the optimal offline algorithm. Finally, we implement the algorithm in the grid simulation environment, and compare the performance of the presented algorithm with the other three algorithms, and experimental results indicate that the cost-based online scheduling algorithm can outperform the other three online algorithms.

The 2-terminal shortest path problem is to find a shortest path between two specified vertices in a given graph G. In this paper, we consider this problem in the following situation: G is given before the two vertices are specified so that some preprocessing is allowed to reduce the response time. We present a method for calculating lower bounds of the length of the shortest path for any pair of vertices. Experimental results show that the A* algorithm with our method performs much better than previous methods.

Montgomery algorithm has demonstrated its effectiveness in applications like cryptosystems. Most of the existing works on finding the Montgomery inverse of an element over the Galois field are based on the software implementation, which is then extended to derive the scalable hardware architecture. In this work, we consider a fundamental change at the algorithmic level and eliminate the potential problems in hardware implementation which makes the resulting modified Montgomery inverse algorithm over GF(2m) very suitable for hardware realization. Due to its structural simplicity, the modified algorithm can be easily mapped onto a high-speed and possibly low-complexity circuit. Experimental results show that our development can achieve both the area and speed advantages over the previous work when the inversion operation over GF(2m) is under consideration and the improvement becomes more significant when we increase the value of m as in the applications of cryptosystems. The salient property of our development sustains the high-speed operation as well as low hardware complexity over a wide range of m for commercial cryptographic applications and makes it suitable for both the scalable architecture and direct hardware implementation.

By applying the differential space-time block code (DSTBC) to wireless multicarrier transmission, Diggavi et al. were the first to propose the two-input-multiple-output (2IMO) differentially space-time-time block coded OFDM (TT-OFDM) system. In this paper, we propose three novel differentially transmit-diversity block coded OFDM (DTDBC-OFDM) systems, namely, the FT-, FF-, and TF-OFDM systems. For instance, the TF-OFDM stands for the differentially space-time-frequency block coded OFDM. Moreover, the noncoherent maximum-likelihood sequence detector (NSD), and its three special cases, namely, the noncoherent one-shot detector, the linearly predictive decision-feedback (DF) detector, and the linearly predictive Viterbi receiver are incorporated to the 2IMO DTDBC-OFDM systems. Furthermore, a simple closed-form BER expression for the systems utilizing the noncoherent one-shot detector in the time-varying multipath Rayleigh fading channels is given. Numerical results have revealed that 2IMO DTDBC-OFDM systems employing the noncoherent one-shot detector can obtain significant performance improvement. However, when few antennas are available, the implementation of the linearly predictive DF detector or the linearly predictive Viterbi receiver is necessary for achieving better performance.

An efficient clustering strategy for estimation of distribution algorithms (EDAs) is presented. It is used for properly fitting probabilistic models that play an important role in guiding search direction. To this end, a fitness-aided ordering scheme is devised for deciding the input sequence of samples (i.e., individuals) for clustering. It can effectively categorise the individuals by using the (available) information about fitness landscape. Moreover, a virtual leader is introduced for providing a reliable reference for measuring the distance from samples to its own cluster. The proposed algorithm incorporates them within the framework of random the leader algorithm (RLA). Experimental results demonstrate that the proposed approach is more effective than the existing ones with regard to probabilistic model fitting.

An efficient adaptation technique of the delay is introduced for accomplishing more accurate adaptive linear equalization of nonminimum phase channels. It is focused that the filter structure and adaptation procedure of the adaptive Butler-Cantoni (ABC) equalizer is very suitable to deal with a variable delay for each iteration, compared with a classical adaptive linear transversal equalizer (LTE). We derive a cost function by comparing the system mismatch of an optimum equalizer coefficient vector with an equalizer coefficient vector with several delay settings. The cost function is square of difference of absolute values of the first element and the last element for the equalizer coefficient vector. The delay adaptation method based on the cost function is developed, which is involved with the ABC equalizer. The delay is adapted by checking the first and last elements of the equalizer coefficient vector and this results in an LTE providing a lower mean square error level than the other LTEs with the same order. We confirm the performance of the ABC equalizer with the delay adaptation method through computer simulations.

This paper presents an approach that uses the Viterbi algorithm in a stereo correspondence problem. We propose a matching process which is visualized as a trellis diagram to find the maximum a posterior result. The matching process is divided into two parts: matching the left scene to the right scene and matching the right scene to the left scene. The last result of stereo problem is selected based on the minimum error for uniqueness by a comparison between the results of the two parts of matching process. This makes the stereo matching possible without explicitly detecting occlusions. Moreover, this stereo matching algorithm can improve the accuracy of the disparity image, and it has an acceptable running time for practical applications since it uses a trellis diagram iteratively and bi-directionally. The complexity of our proposed method is shown approximately as O(N2P), in which N is the number of disparity, and P is the length of the epipolar line in both the left and right images. Our proposed method has been proved to be robust when applied to well-known samples of stereo images such as random dot, Pentagon, Tsukuba image, etc. It provides a 95.7 percent of accuracy in radius 1 (differing by 1) for the Tsukuba images.

We clarify a relation between the XL algorithm and known Grobner basis algorithms. The XL algorithm was proposed to be a more efficient algorithm to solve a system of algebraic equations under a special condition, without calculating a whole Grobner basis. But in our result, it is shown that to solve a system of algebraic equations with a special condition under which the XL algorithm works is equivalent to calculate the reduced Grobner basis of the ideal associated with the system. Moreover we show that the XL algorithm is a Grobner basis algorithm which can be represented as a redundant variant of a known Grobner basis algorithm F4.

The least squares (LS) and the weighted least squares (WLS) algorithms are well known procedures that are used in the design of quadrature mirror filters (QMFs). It is known that these design techniques suffer from pass-band anomaly under certain conditions. A recent method, that overcomes pass-band anomaly for LS QMFs using a frequency sampling design for the initial filter, is extended to WLS design in this letter. A comparison between the modified LS and WLS designs based on experimental results is presented. Although WLS designs have been reported to have superior near-equiripple stop-band performance as compared to LS designs, we find that this is not always true. Specifically, LS designs, with inherent computational and robustness advantages, also have better peak stop-band ripple and transition bandwidth at higher cut-off frequencies than WLS.

In this paper we propose a novel and efficient method for the optimization of the power/ground (P/G) network in VLSI circuit layouts with reliability constraints. Previous algorithms in the P/G network sizing used the sequence-of-linear-programming (SLP) algorithm to solve the nonlinear optimization problems. However the transformation from nonlinear network to linear subnetwork is not optimal enough. Our new method is inspired by the biological evolution and use the grid-genetic-algorithm (GGA) to solve the optimization problem. Experimental results show that new P/G network sizes are smaller than previous algorithms, as the fittest survival in the nature. Another significant advance is that GGA method can be applied for all P/G network problems because it can get the results directly no matter whether these problems are linear or not. Thus GGA can be adopted in the transient behavior of the P/G network sizing in the future, which recently faces on the obstacles in the solution of the complex nonlinear problems.

A hardware algorithm for modular multiplication/division which performs modular division, Montgomery multiplication, and ordinary modular multiplication is proposed. The modular division in our algorithm is based on the extended Euclidean algorithm. We employ our newly proposed computation method that consists of processing the multiplier from the most significant digit first to calculate Montgomery multiplication. Finally, the ordinary modular multiplication is based on shift-and-add multiplication. Each of these three operations is carried out through the iteration of simple operations such as shifts and additions/subtractions. To avoid carry propagation in all additions and subtractions, the radix-2 signed-digit representation is employed. A modular multiplier/divider based on the algorithm has a linear array structure with a bit-slice feature and carries out n-bit modular multiplication/division in O(n) clock cycles, where the length of the clock cycle is constant and independent of n. This multiplier/divider can be implemented using a hardware amount only slightly larger than that of the modular divider.

Many parallel Fast Fourier Transform (FFT) algorithms adopt multiple stages architecture to increase performance. However, data permutation between stages consumes volume memory and processing time. One FFT array processing mapping algorithm is proposed in this paper to overcome this demerit. In this algorithm, arbitrary 2k butterfly units (BUs) could be scheduled to work in parallel on n=2s data (k=0,1,..., s-1). Because no inter stage data transfer is required, memory consumption and system latency are both greatly reduced. Moreover, with the increasing of BUs, not only does throughput increase linearly, system latency also decreases linearly. This array processing orientated architecture provides flexible tradeoff between hardware cost and system performance. In theory, the system latency is (s2s-k)tclk and the throughput is n/(s2s-ktclk), where tclk is the system clock period. Based on this mapping algorithm, several 18-bit word-length 1024-point FFT processors implemented with TSMC0.18 µm CMOS technology are given to demonstrate its scalability and high performance. The core area of 4-BU design is 2.9911.121 mm2 and clock frequency is 326 MHz in typical condition (1.8 V,25). This processor completes 1024 FFT calculation in 7.839 µs.

An order selection scheme for two-sided oblique projection-based interconnect reduction will be investigated. It will provide a guideline for terminating the conventional nonsymmetric Pade via Lanczos (PVL) iteration process. By exploring the relationship of the system Grammians of the original network and those of the reduced network, it can be shown that the system matrix of the reduced-order system generated by the two-sided oblique projection can also be expressed as those of the original interconnect model with some additive perturbations. The perturbation matrix only involves bi-orthogonal vectors at the previous step of the nonsymmetric Lanczos algorithm. This perturbation matrix will provide the stopping criteria in the order selection scheme and achieve the desired accuracy of the approximate transfer function.

In pure peer-to-peer (P2P) file sharing applications and protocols using a flooding-based query algorithm, a large number of control packets (query packets) are transmitted on the network to search for target files. This clearly leads to a degradation of communication quality on the network and terminals as the number of users of the application increases. To solve such problems, this paper proposes: (1) a unified framework to describe a wide variety of query algorithms for pure P2P and (2) a new query algorithm based on this framework. Our framework determines the number of destinations for query packets based on the hop value recorded in received query packets. Simulation results revealed that the proposed query algorithm can reduce the overhead in the flooding-based query algorithm and k-random walks without decreasing the success rate of retrieval regardless of the density of target files in the network.

This paper proposes a genetically inspired adaptive clustering algorithm for numerical and categorical data sets. To this end, unique encoding method and fitness functions are developed. The algorithm automatically discovers the actual number of clusters and efficiently performs clustering without unduly compromising cluster-purity. Moreover, it outperforms existing clustering algorithms.

The IEEE 802.11 distributed coordination function (DCF) provides a contention-based distribution channel access mechanism for stations to share the wireless medium. However, performance of the DCF drops dramatically in terms of throughput, delay and jitter as the number of active stations becomes large. In this paper, we propose a simple and effective scheme, called distributed coordination function with virtual group (DCF/VG), for improving the performance of the IEEE 802.11 DCF mechanism. In this scheme, each station independently decides a virtual group cycle taking into account the current contention level. The virtual group cycle consists of one or more virtual groups and a virtual group includes an idle and a busy period. Each station chooses one virtual group and operates only in the chosen group of the cycle. In other words, each station decreases its backoff counter and tries to transmit its packet during the period of the chosen virtual group like in the IEEE 802.11 DCF. Performance of the proposed scheme is investigated by numerical analysis and simulation. Our results show that the proposed scheme is very effective and has high throughput and low delay and jitter behaviors under a wide range of contention levels.

This paper addresses the parameter estimation problem of an interval-based hybrid dynamical system (interval system). The interval system has a two-layer architecture that comprises a finite state automaton and multiple linear dynamical systems. The automaton controls the activation timing of the dynamical systems based on a stochastic transition model between intervals. Thus, the interval system can generate and analyze complex multivariate sequences that consist of temporal regimes of dynamic primitives. Although the interval system is a powerful model to represent human behaviors such as gestures and facial expressions, the learning process has a paradoxical nature: temporal segmentation of primitives and identification of constituent dynamical systems need to be solved simultaneously. To overcome this problem, we propose a multiphase parameter estimation method that consists of a bottom-up clustering phase of linear dynamical systems and a refinement phase of all the system parameters. Experimental results show the method can organize hidden dynamical systems behind the training data and refine the system parameters successfully.

This letter develops convergence analysis of normalized sign-sign algorithm (NSSA) for FIR-type adaptive filters, based on an assumption that filter tap weights are Gaussian distributed. We derive a set of difference equations for theoretically calculating transient behavior of filter convergence, when the filter input is a White & Gaussian process. For a colored Gaussian input and a large number of tap weights, approximate difference equations are also proposed. Experiment with simulations and theoretical calculations of filter convergence demonstrates good agreement between simulations and theory, proving the validity of the analysis.