The generalized sorting problem is to find the first k largest elements among n input elements and to report them in a sorted order. In this paper, we propose a fast generalized sorting algorithm under the single hop wireless networks model with collision detection (WNCD). The algorithm is based on the maximum finding algorithm and the sorting algorithm. The key point of our algorithm is to use successful broadcasts to build broadcasting layers logically and then to distribute the data elements into those logic layers properly. Thus, the number of broadcast conflicts is reduced. We prove that the average time complexity required for our generalized sorting algorithm is Θ(k + log(n - k)). When k = 1, our generalized sorting algorithm does the work of finding maximum, and when k = n, it does the work of sorting. Thus, the analysis of our algorithm builds a connection between the two extremely special cases which are maximum finding and sorting.

Temperature-tracking is becoming of paramount importance in modern electronic design automation tools. In this paper, we present a deterministic thermal placement algorithm for standard cell based layout which can lead to a smooth temperature distribution over the die. It is mainly based on Fiduccia-Mattheyses partition scheme and a former substrate thermal model that can convert the known temperature constraints into the corresponding power distribution constraints. Moreover, a kind of force-directed heuristic based on cells' power consumption is introduced in the above process. Experimental results demonstrate a comparatively uniform temperature distribution and show a reduction of the maximal temperature on the die.

Genetic Algorithm (GA) and other Evolutionary Algorithms (EAs) have been successfully applied to solve constrained minimum spanning tree (MST) problems of the communication network design and also have been used extensively in a wide variety of communication network design problems. Choosing an appropriate representation of candidate solutions to the problem is the essential issue for applying GAs to solve real world network design problems, since the encoding and the interaction of the encoding with the crossover and mutation operators have strongly influence on the success of GAs. In this paper, we investigate a new encoding crossover and mutation operators on the performance of GAs to design of minimum spanning tree problem. Based on the performance analysis of these encoding methods in GAs, we improve predecessor-based encoding, in which initialization depends on an underlying random spanning-tree algorithm. The proposed crossover and mutation operators offer locality, heritability, and computational efficiency. We compare with the approach to others that encode candidate spanning trees via the Pr?fer number-based encoding, edge set-based encoding, and demonstrate better results on larger instances for the communication spanning tree design problems.

This paper presents a personal identification method based on BMME and LDA for images acquired at anterior and posterior occlusion expression of teeth. The method consists of teeth region extraction, BMME, and pattern recognition for the images acquired at the anterior and posterior occlusion state of teeth. Two occlusions can provide consistent teeth appearance in images and BMME can reduce matching error in pattern recognition. Using teeth images can be beneficial in recognition because teeth, rigid objects, cannot be deformed at the moment of image acquisition. In the experiments, the algorithm was successful in teeth recognition for personal identification for 20 people, which encouraged our method to be able to contribute to multi-modal authentication systems.

In order to improve the efficiency and speed of match seeking in fractal compression, this paper presents an Average-Variance function which can make the optimal choice more efficiently. Based on it, we also present a fast optimal choice fractal image compression algorithm and an optimal method of constructing data tree which greatly improve the performances of the algorithm. Analysis and experimental results proved that it can improve PSNR over 1 dB and improve the coding speed over 30-40% than ordinary optimal choice algorithms such as algorithm based on center of gravity and algorithm based on variance. It can offer much higher optimal choice efficiency, higher reconstructive quality and rapid speed. It's a fast fractal encoding algorithm with high performances.

This work addresses the critical problem of authentication in mobile ad hoc networks. It includes a new approach based on the Zero-Knowledge cryptographic paradigm where two different security levels are defined. The first level is characterized by the use of an NP-complete graph problem to describe an Access Control Protocol, while the highest level corresponds to a Group Authentication Protocol based on a hard-on-average graph problem. The main goal of the proposal is to balance security strength and network performance. Therefore, both protocols are scalable and decentralized, and their requirements of communication, storage and computation are limited.

A discriminative modeling is applied to optimize the structure of a Partly-Hidden Markov Model (PHMM). PHMM was proposed in our previous work to deal with the complicated temporal changes of acoustic features. It can represent observation dependent behaviors in both observations and state transitions. In the formulation of the previous PHMM, we used a common structure for all models. However, it is expected that the optimal structure which gives the best performance differs from category to category. In this paper, we designed a new structure optimization method in which the dependence of the states and the observations of PHMM are optimally defined according to each model using the weighted likelihood-ratio maximization (WLRM) criterion. The WLRM criterion gives high discriminability between the correct category and the incorrect categories. Therefore it gives model structures with good discriminative performance. We define the model structure combination which satisfy the WLRM criterion for any possible structure combinations as the optimal structures. A genetic algorithm is also applied to the adequate approximation of a full search. With results of continuous lecture talk speech recognition, the effectiveness of the proposed structure optimization is shown: it reduced the word errors compared to HMM and PHMM with a common structure for all models.

In this paper, we propose a hybrid fine-tuned multi-objective memetic algorithm hybridizing different solution fitness evaluation methods for global exploitation and exploration. To search across all regions in objective space, the algorithm uses a widely diversified set of weights at each generation, and employs a simulated annealing to optimize each utility function. For broader exploration, a grid-based technique is adopted to discover the missing nondominated regions on existing tradeoff surface, and a Pareto-based local perturbation is performed to reproduce incrementing solutions trying to fill up the discontinuous areas. Additional advanced feature is that the procedure is made dynamic and adaptive to the online optimization conditions based on a function of improvement ratio to obtain better stability and convergence of the algorithm. Effectiveness of our approach is shown by applying it to multi-objective 0/1 knapsack problem (MOKP).

As turbo decoding is a highly memory-intensive algorithm consuming large power, a major issue to be solved in practical implementation is to reduce power consumption. This paper presents an efficient reverse calculation method to lower the power consumption by reducing the number of memory accesses required in turbo decoding. The reverse calculation method is proposed for the Max-log-MAP algorithm, and it is combined with a scaling technique to achieve a new decoding algorithm, called hybrid log-MAP, that results in a similar BER performance to the log-MAP algorithm. For the W-CDMA standard, experimental results show that 80% of memory accesses are reduced through the proposed reverse calculation method. A hybrid log-MAP turbo decoder based on the proposed reverse calculation reduces power consumption and memory size by 34.4% and 39.2%, respectively.

A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.

In parallelizing compilers on distributed memory systems, distributions of irregular sized array blocks are provided for load balancing and irregular problems. The irregular data redistribution is different from the regular block-cyclic redistribution. This paper is devoted to scheduling message for irregular data redistribution that attempt to obtain suboptimal solutions while satisfying the minimal communication costs condition and the minimal step condition. Based on the list scheduling, an efficient algorithm is developed and its experimental results are compared with previous algorithms. The improved list algorithm provides more chance for conflict messages in its relocation phase, since it allocates conflict messages through methods used in a divide-and-conquer algorithm and a relocation algorithm proposed previously. The method of selecting the smallest relocation cost guarantees that the improved list algorithm is more efficient than the other two in average.

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.

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.

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.

We consider the problem of computing the optimal swap edges of a shortest-path tree. This problem arises in designing systems that offer point-of-failure shortest-path rerouting service in presence of a single link failure: if the shortest path is not affected by the failed link, then the message will be delivered through that path; otherwise, the system will guarantee that, when the message reaches the node where the failure has occurred, the message will then be re-routed through the shortest detour to its destination. There exist highly efficient serial solutions for the problem, but unfortunately because of the structures they use, there is no known (nor foreseeable) efficient distributed implementation for them. A distributed protocol exists only for finding swap edges, not necessarily optimal ones. We present two simple and efficient distributed algorithms for computing the optimal swap edges of a shortest-path tree. One algorithm uses messages containing a constant amount of information, while the other is tailored for systems that allow long messages. The amount of data transferred by the protocols is the same and depends on the structure of the shortest-path spanning-tree; it is no more, and sometimes significantly less, than the cost of constructing the shortest-path tree.

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.

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.

Let G = (V,E) be an edge weighted graph with n vertices and m edges. For a given integer p with 1 < p < n, we call a set X V of p vertices a p-maximizer if X has a property that the edge-connectivity of the graph obtained by contracting X into a single vertex is no less than that of the graph obtained by contracting any other subset of p vertices. In this paper, we first show that there always exists an ordering v1,v2,...,vn of vertices in V such that, for each i = 2,3,...,n - 1, set {v1,v2,...,vi} is an i-maximizer. We give an O(mn + n2log n) time algorithm for finding such an ordering and then show an application to the source location problem.

The belief propagation (BP) algorithm is a tool with which one can calculate beliefs, marginal probabilities, of probabilistic networks without loops (e.g., Bayesian networks) in a time proportional to the number of nodes. For networks with loops, it may not converge and, even if it converges, beliefs may not be equal to exact marginal probabilities although its application is known to give remarkably good results such as in the coding theory. Tatikonda and Jordan show a theoretical result on the convergence of the algorithm for probabilistic networks with loops in terms of the theory of Markov random fields on trees and give a sufficient condition of the convergence of the algorithm. In this paper, we discuss the "impatient" update rule as well as the "lazy" update rule discussed in Tatikonda and Jordan. In the viewpoint of the theory of Markov random fields, it is shown that the rule for updating both gives essentially the same results and the impatient update rule is expected to converge faster than the lazy one. Numerical experiments are also given.