The article describes recent adaptive estimation algorithms over distributed networks. The algorithms rely on local collaborations and exploit the space-time structure of the data. Each node is allowed to communicate with its neighbors in order to exploit the spatial dimension, while it also evolves locally to account for the time dimension. Algorithms of the least-mean-squares and least-squares types are described. Both incremental and diffusion strategies are considered.

This paper proposes a new computational optimization method modified from the dynamic encoding algorithm for searches (DEAS). Despite the successful optimization performance of DEAS for both benchmark functions and parameter identification, the problem of exponential computation time becomes serious as problem dimension increases. The proposed optimization method named univariate DEAS (uDEAS) is especially implemented to reduce the computation time using a univariate local search scheme. To verify the algorithmic feasibility for global optimization, several test functions are optimized as benchmark. Despite the simpler structure and shorter code length, function optimization performance show that uDEAS is capable of fast and reliable global search for even high dimensional problems.

We present a novel training method for recognizing traffic sign symbols. The symbol images captured by a car-mounted camera suffer from various forms of image degradation. To cope with degradations, similarly degraded images should be used as training data. Our method artificially generates such training data from original templates of traffic sign symbols. Degradation models and a GA-based algorithm that simulates actual captured images are established. The proposed method enables us to obtain training data of all categories without exhaustively collecting them. Experimental results show the effectiveness of the proposed method for traffic sign symbol recognition.

The size-dependent array problem is a problem with systolic arrays such that the size of systolic arrays limits the size of calculations, which in a do-loop structure controls how many times it is repeated and how deep the nesting loops are. A systolic array cannot deal with larger calculations. For the size-dependent array problem, a spiral systolic array has been studied so far. It has non-adjacent connections between PEs, such as loop paths for sending data back so that data flows over the array independently of its own size. This paper takes an approach to the problem without non-adjacent connections. This paper discusses systolic messy arrays for infinite iterative algorithms so that they are independent from the size of calculations. First a systolic messy array called two-square shape is introduced then the properties of two-square shape are summarized: memory function, cyclic addition, and cyclic multiplication. Finally a way of building systolic messy arrays that calculate infinite iterative algorithms is illustrated with concrete examples such as an arithmetic progression, a geometric progression, N factorial, and Fibonacci numbers.

SAL is a toolkit for analyzing transition systems, providing several different tools. Among the tools are a BDD-based symbolic model checker (SMC) and an SMT-based infinite bounded model checker (infBMC). The unique functionality provided by SAL is k-induction, which is supported by infBMC. Given appropriate lemmas, infBMC can prove automatically by k-induction that an infinite-state transition system satisfies invariant properties. Maude is a specification language and system based on membership equational logic and rewriting logic. Maude is equipped with an on-the-fly explicit state model checker. The unique functionality provided by the Maude model checker supports inductive data types. We make a comparison of SAL (especially SMC and infBMC) and the Maude model checker by conducting case studies in which the Suzuki-Kasami distributed mutual exclusion algorithm is analyzed. The purpose of the comparison is to clarify some of the two tools' functionalities, especially the unique ones, through the case studies.

A novel high-speed and area-efficient Reed-Solomon decoder is proposed, which employs pipelining architecture of minimized modified Euclid (ME) algorithm. The logic synthesis and simulation results of its VLSI implementation show that it not only can operate at a higher clock frequency, but also consumes fewer hardware resources.

The performance of the blind multiuser detector for a DS-CDMA system with linearly constrained constant modulus (LCCM) criterion is known to highly depend on the exact knowledge of the desired user amplitude; it is usually not available at receiver end. In this paper, we propose a novel LC adaptive CM RLS (LC-ACM-RLS) algorithm to adaptively implement the optimal solution of the LCCM receiver, and to track the desired user's amplitude, simultaneously. From computer simulations, we verify the superiority of the new proposed algorithm over the conventional LCCM-RLS algorithm for multiple access interference (MAI) suppression. Also, for time-varying channel during the adaptation processes, if the amplitude of desired user is not available and varies with time, such as hand-off and Rayleigh fading environments, we show that the proposed LC-ACM-RLS algorithm has better tracking capability compared with the conventional approaches.

In orthogonal frequency-division multiplex access (OFDMA) uplink, the carrier-frequency offsets (CFOs) between the multiple transmitters and the receiver introduce inter-carrier interference (ICI) and severely degrade the performance. In this paper, based on the perfect estimation of each user's CFO, we propose two low-complexity iterative algorithms to cancel ICI due to CFOs, which are denoted as the basic algorithm and the improved algorithm with decision-feedback equalization (DFE), respectively. For the basic one, two theorems are proposed that yield a sufficient condition for the convergence of iterations. Moreover, the interference-power-evolution (IPE) charts are proposed to evaluate the convergence behavior of this interference cancellation algorithm. Motivated by the IPE chart, the procedure of DFE is introduced into the iterations, which is the basic idea of the improved algorithm. For this improved algorithm, the error-propagation effect are analyzed and suppressed by an efficient stopping criterion. From IPE charts and simulation results, it can be easily observed that the basic algorithm has the same capability of ICI cancellation as the linear optimal minimum mean square error (MMSE) method, but offers lower complexity, while the improved algorithm with DFE outperforms the MMSE method in terms of the bit-error rate (BER) performance.

The subspace algorithm can be utilized for the blind detection of space-time block codes (STBC) without knowledge of channel state information (CSI) both at the transmitter and receiver. However, its performance degrades when the channels are correlated. In this letter, we analyze the impact of channel correlation from the orthogonality loss between the transmit signal subspace (TSS) and the statistical noise subspace (SNS). Based on the decoding property of the subspace algorithm, we propose a revised detection in favor of the channel correlation matrix (CCM) only known to the receiver. Then, a joint transmit-receive preprocessing scheme is derived to obtain a further performance improvement when the CCM is available both at the transmitter and receiver. Analysis and simulation results indicate that the proposed methods can significantly improve the blind detection performance of STBC over the correlated channels.

We propose a new genetic fuzzy discretization method with feature selection for the pattern classification problems. Traditional discretization methods categorize a continuous attribute into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized and a discretization process increases the total amount of data being transformed. We use a genetic algorithm with feature selection not only to optimize these parameters but also to reduce the amount of transformed data by filtering the unconcerned attributes. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization with feature selection.

In recent years, network analysis has revealed that some real networks have the properties of small-world and/or scale-free networks. In this study, a simple Genetic Algorithm (GA) is regarded as a network where each node and each edge respectively represent a population and the possibility of the transition between two nodes. The characteristic path length (CPL), which is one of the most popular criteria in small-world networks, is derived analytically and shows how much the crossover operation affects the path length between two populations. As a result, the crossover operation is not so useful for shortening the CPL.

In this paper, a novel independent component analysis (ICA) approach is proposed, which is robust against the interference of impulse noise. To implement ICA in a noisy environment is a difficult problem, in which traditional ICA may lead to poor results. We propose a method that consists of noise detection and image signal recovery. The proposed approach includes two procedures. In the first procedure, we introduce a self-organizing map (SOM) network to determine if the observed image pixels are corrupted by noise. We will mark each pixel to distinguish normal and corrupted ones. In the second procedure, we use one of two traditional ICA algorithms (fixed-point algorithm and Gaussian moments-based fixed-point algorithm) to separate the images. The fixed-point algorithm is proposed for general ICA model in which there is no noise interference. The Gaussian moments-based fixed-point algorithm is robust to noise interference. Therefore, according to the mark of image pixel, we choose the fixed-point or the Gaussian moments-based fixed-point algorithm to update the separation matrix. The proposed approach has the capacity not only to recover the mixed images, but also to reduce noise from observed images. The simulation results and analysis show that the proposed approach is suitable for practical unsupervised separation problem.

Recently, the superimposed pilot channel estimation has attracted attention for wireless communications, where the pilot symbol sequence is superimposed on a data symbol sequence and transmitted together, and thus there is no drop in information rate. In the superimposed pilot channel estimation, the receiver correlates the received symbol sequence with the pilot symbol sequence, and obtains the channel estimate. However, the correlation between the pilot symbol sequence and the data symbol sequence deteriorates the channel estimation accuracy. In particular, the channel estimation accuracy of the superimposed pilot channel estimation scheme is significantly deteriorated in MIMO systems, because the pilot symbol power of each transmit antenna to the total transmit power of all transmit antennas becomes smaller as the number of transmit antennas increases. On the other hand, it has been well known that the SAGE algorithm is an effective method for channel estimation and data detection. This algorithm is particularly effective in MIMO systems, because the operation of this algorithm can cancel the interference from other transmit antennas. In this paper, we evaluate the performance of the SAGE algorithm for channel estimation and data detection using superimposed pilot channel estimation in MIMO systems. From the results of computer simulations, we show that the system using the SAGE algorithm with superimposed training can achieve the good BER performances by using the SAGE algorithm with iteration.

This paper proposes a suboptimal algorithm for the maximum likelihood detection (MLD) in multiple-input multiple-output (MIMO) communications. The proposed algorithm regards transmitted signals as continuous variables in the same way as a common method for the discrete optimization problem, and then searches for candidates of the transmitted signals in the direction of a modified gradient vector of the metric. The vector is almost proportional to the direction of the noise enhancement, from which zero-forcing (ZF) or minimum mean square error (MMSE) algorithms suffer. This method sets the initial guess to the solution by ZF or MMSE algorithms, which can be recursively calculated. Also, the proposed algorithm has the same complexity order as that of conventional suboptimal algorithms. Computer simulations demonstrate that it is much superior in BER performance to the conventional ones.

Data detection based on self organizing maps is presented for hematopoietic tumor patients. Learning data for the maps are generated from the screening data of examinees. The incomplete screening data without some item values is then supplemented by substituting averaged non-missing item values. In addition, redundant items, which are common to all the data and tend to have an unfavorable influence on data detection, are eliminated by a genetic algorithm and/or an immune algorithm. It is basically judged, by observing the label of a winner neuron in the map, whether the data presented to the map belongs to the class of hematopoietic tumors. Some experimental results are provided to show that the proposed methods achieve the high probability of correctly identifying examinees as hematopoietic tumor patients.

This paper presents an automated design of linear and non-linear differential analog circuits accelerated by reuse of genetic operations. The system first synthesizes circuits using pairs of simplified MOSFET model. During the evolutionary process, genetic operations that improve circuit characteristics are stored in a database and reused to effectively obtain a better circuit. Simplified elements in a generated circuit are replaced by MOSFETs and optimization of the transistor size is performed using an optimizer available in market if necessary. The capability of this method is demonstrated through experiments of synthesis of a differential voltage amplifier, a circuit having cube-law characteristic in differential mode and square-law characteristic in common-mode, and a dB-linear VGA (Variable Gain Amplifier). The results show the reuse of genetic operations accelerates the synthesis and success rate becomes 100%.

We present a computationally efficient sequential detection scheme using a modified Fano algorithm (MFA) for V-BLAST systems. The proposed algorithm consists of the following three steps: initialization, tree searching, and optimal selection. In the first step, the proposed detection scheme chooses several candidate symbols at the tree level of one. Based on these symbols, the MFA then finds the remaining transmitted symbols from the second tree level in the original tree structure. Finally, an optimal symbol sequence is decided among the most likely candidate sequences searched in the previous step. Computer simulation shows that the proposed scheme yields significant saving in complexity with very small performance degradation compared with that of sphere detection (SD).

We consider the capacitated multi-source multicast tree routing problem (CMMTR) in an undirected graph G=(V,E) with a vertex set V, an edge set E and an edge weight w(e) ≥ 0, e ∈ E. We are given a source set S ⊆ V with a weight g(e) ≥ 0, e ∈ S, a terminal set M ⊆ V-S with a demand function q : M → R+, and a real number κ > 0, where g(s) means the cost for opening a vertex s ∈ S as a source in a multicast tree. Then the CMMTR asks to find a subset S′⊆ S, a partition {Z1,Z2,...,Zl} of M, and a set of subtrees T1,T2,...,Tl of G such that, for each i, ∑t∈Ziq(t) ≤ κ and Ti spans Zi∪{s} for some s ∈ S′. The objective is to minimize the sum of the opening cost of S′and the constructing cost of {Ti}, i.e., ∑s∈S′g(s)+w(Ti), where w(Ti) denotes the sum of weights of all edges in Ti. In this paper, we propose a (2ρUFL+ρST)-approximation algorithm to the CMMTR, where ρUFL and ρST are any approximation ratios achievable for the uncapacitated facility location and the Steiner tree problems, respectively. When all terminals have unit demands, we give a ((3/2)ρUFL+(4/3)ρST)-approximation algorithm.

It is well known that a basic version (i.e., maximizing the number of base-pairs) of the RNA secondary structure prediction problem can be solved in O(n3) time by using simple dynamic programming procedures. For this problem, an O(n3(log log n)1/2/(log n)1/2) time exact algorithm and an O(n2.776+(1/ε)O(1)) time approximation algorithm which has guaranteed approximation ratio 1-ε for any positive constant ε are also known. Moreover, when two RNA sequences are given, there is an O(n6) time exact algorithm which can optimize structure and alignments. In this paper, we show an O(n5) time approximation algorithm for optimizing structure and alignments of two RNA sequences with assuming that the optimal number of base-pairs is more than O(n0.75). We also show that the problem to optimize structure and alignments for given N sequences is NP-hard and introduce a constant-factor approximation algorithm.

In this paper we give a simple algorithm to generate all partitions of a positive integer n. The problem is one of the basic problems in combinatorics, and has been extensively studied for a long time. Our algorithm generates each partition of a given integer in constant time for each without repetition, while best known algorithm generates each partition in constant time on "average." Also, we propose some algorithms to generate all partitions of an integer with some additional property in constant time.