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.

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.

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.

Automated reasoning of inductive theorems is considered important in program verification. To verify inductive theorems automatically, several implicit induction methods like the inductionless induction and the rewriting induction methods have been proposed. In studying inductive theorems on higher-order rewritings, we found that the class of the theorems shown by known implicit induction methods does not coincide with that of inductive theorems, and the gap between them is a barrier in developing mechanized methods for disproving inductive theorems. This paper fills this gap by introducing the notion of primitive inductive theorems, and clarifying the relation between inductive theorems and primitive inductive theorems. Based on this relation, we achieve mechanized methods for proving and disproving inductive theorems.

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.

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.

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.

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.

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.

In order to simultaneously combat both of the inter-carrier interferences (ICIs) and multiple access interferences (MAIs) to achieve reliable performance in multi-carrier code division multiple access (MC-CDMA) systems, this letter proposes a maximum likelihood based scheme for joint frequency offset estimation and multiuser symbol detection. To reduce the computational complexity called for by the joint decision statistic without extra mechanisms, the genetic algorithm (GA) is employed to solve the nonlinear optimization involved. Due to the robustness of the GA, the joint decision statistic can be efficiently solved, and, as shown by furnished simulation results, the proposed approach can offer satisfactory performance in various scenarios.

In the letter, the fast one-dimensional (1-D) and two-dimensional (2-D) algorithms for realizing low-complexity 44 discrete cosine transform (DCT) for H.264 applications are developed. Through applying matrix utilizations with Kronecker product and direct sum, the efficient fast 2-D 44 DCT algorithm can be developed from the proposed fast 1-D 44 DCT algorithm by matrix decompositions. The fast 1-D and 2-D low-complexity 44 DCT algorithms requires fewer multiplications and additions than other fast DCT algorithms. Owing to regular modularity, the proposed fast algorithms can achieve real-time H.264 video signal processing with VLSI implementation.

To cut down the sidelobe level of radiation pattern, a novel adaptive algorithm is proposed for electronic steering parasitic antenna. The composite objective function in this algorithm takes both directivity and sidelobe level of pattern into account, and the steepest gradient algorithm is selected to search the optimum value of reactive load. Simulations are carried out to validate the algorithm, simulated results show that the levels of sidelobe are both below -4 dB in different beamforming cases, and the front to back ratios are better than 10 dB.

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.

The minimum initial marking problem MIM of Petri nets is described as follows: "Given a Petri net and a firing count vector X, find an initial marking M0, with the minimum total token number, for which there is a sequence δ of transitions such that each transition t appears exactly X(t) times in δ, the first transition is enabled at M0 and the rest can be fired one by one subsequently." This paper proposes two heuristic algorithms AAD and AMIM + and shows the following (1) and (2) through experimental results: (1) AAD is more capable than any other known algorithm; (2) AMIM + can produce M0, with a small number of tokens, even if other algorithms are too slow to compute M0 as the size of an input instance gets very large.

Given a set of n stations, the initialization problem is to assign each station a unique identification number, from 1 to n. In the single-hop wireless Networks with collision detection, Nakano and Olariu proposed an algorithm to build a partition tree and solve the problem. In this paper, we shall classify the partition tree into four parts. By reviewing the classification, we find that three ideas can improve the algorithm. We show that it needs 2.88n time slots for solving the problem containing n stations. After applying our three ideas, the number of time slots will be improved to 2.46n.

In our previous work, the hyper H∞ filter is developed for tracking of unknown time-varying systems. Additionally, a fast algorithm, called the fast H∞ filter, of the hyper H∞ filter is derived on condition that the observation matrix has a shifting property. This algorithm has a computational complexity of O(N) where N is the dimension of the state vector. However, there still remains a possibility of deriving alternative forms of the hyper H∞ filter. In this work, a fast J-unitary form of the hyper H∞ filter is derived, providing a new H∞ fast algorithm, called the J-fast H∞ filter. The J-fast H∞ filter possesses a computational complexity of O(N), and the resulting algorithm is very amenable to parallel processing. The validity and performance of the derived algorithm are confirmed by computer simulations.

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.

A fast decoding algorithm for low-density parity-check codes is presented based on graph decomposition and two-way message passing schedule. Simulations show that the convergence speed of the proposed algorithm is about twice that of the conventional belief propagation algorithm.