We analytically investigate combinatorial effects of timer control and backoff algorithms on performance of bulk data transfer over two-state Markovian packet error channels. Numerical results for throughput, energy efficiency, and the probabilities of packet loss and loss of bulk data indicate that linear backoff algorithms outperform binary exponential ones as a whole when they are employed at the logical link sublayer with timer control.

In this paper, we present the results of a comparative analysis of Atomic Broadcast algorithms. The analysis was done by using an analytical method to compare the performance of five different classes of Atomic Broadcast algorithms. The five classes of Atomic Broadcast algorithms are determined by the mechanisms used by the algorithms to define the delivery order. To evaluate the performance of algorithms, the analysis relies on contention-aware metrics to provide a measure for both their latency and their throughput. The results thus obtained yield interesting insight into the performance tradeoffs of different Atomic Broadcast algorithms, thus providing helpful information to algorithms and systems designers.

This paper presents a unified representation of fast addition algorithms based on Counter Tree Diagrams (CTDs). By using CTDs, we can describe and analyze various adder architectures in a systematic way without using specific knowledge about underlying arithmetic algorithms. Examples of adder architectures that can be handled by CTDs include Redundant-Binary (RB) adders, Signed-Digit (SD) adders, Positive-Digit (PD) adders, carry-save adders, parallel counters (e.g., 3-2 counters and 4-2 counters) and networks of such basic adders/counters. This paper also discusses the CTD-based analysis of carry-propagation-free adders using various number representations.

The imprecise computation model is one of the flexible computation models used to construct real-time systems. It is especially useful when the worst case execution times are difficult to estimate or the execution times vary widely. Although there are several ways to implement this model, they have not attained much attentions of real-world application programmers to date due to their unrealistic assumptions and high dependency on the execution environment. In this paper, we present an integrated approach for implementing the imprecise computation model. In particular, our research covers three aspects. First, we present a new imprecise computation model which consists of a mandatory part, an optional part, and another mandatory part called wind-up part. This wind-up part allows application programmers to explicitly incorporate into their programs the exact operations needed for safe degradation of performance when there is a shortage in resources. Second, we describe a scheduling algorithm called Mandatory-First with Wind-up Part (M-FWP) which is based on the Earliest Deadline First strategy. This algorithm, unlike scheduling algorithms developed for the classical imprecise computation model, is capable of scheduling a mandatory portion after an optional portion. Third, we present a dynamic priority server method for an efficient implementation of the M-FWP algorithm. We also show that the number of the proposed server at most needed per node is one. In order to estimate the performance of the proposed approach, we have implemented a real-time operating system called RT-Frontier. The experimental analyses have proven its ability to implement tasks based on the imprecise computation model without requiring any knowledge on the execution time of the optional part. Moreover, it also showed performance gain over the traditional checkpointing technique.

The center problem of a graph is motivated by a number of facility location problems. In this paper, we propose parallel algorithms for finding the center of interval graphs and circular-arc graphs. Our algorithms run in O(log n) time algorithm using O(n/log n) processors while the intervals and arcs are given in sorted order. Our algorithms are on the EREW PRAM model.

In this paper an algorithm based on Ant Colony Optimization techniques called Ants on a Tree (AOT) is introduced. This algorithm can integrate many algorithms together to solve a single problem. The strength of AOT is demonstrated by solving a High-Level Synthesis problem. A High-Level Synthesis problem consists of many design steps and many algorithms to solve each of them. AOT can easily integrate these algorithms to limit the search space and use them as heuristic weights to guide the search. During the search, AOT generates a dynamic decision tree. A boosting technique similar to branch and bound algorithms is applied to guide the search in the decision tree. The storage explosion problem is eliminated by the evaporation of pheromone trail generated by ants, the inherent property of our search algorithm.

NK-Landscapes are stochastically generated fitness functions on bit strings, parameterized with N bits and K epistatic interactions between bits. The term epistasis describes nonlinearities in fitness functions due to changes in the values of interacting bits. Empirical studies have shown that the overall performance of random bit climbers on NK-Landscapes is superior to the performance of some simple and enhanced genetic algorithms (GAs). Analytical studies have also lead to suggest that NK-Landscapes may not be appropriate for testing the performance of GAs. In this work we study the effect of selection, drift, mutation, and recombination on NK-Landscapes for N = 96. We take a model of generational parallel varying mutation GA (GA-SRM) and switch on and off its major components to emphasize each of the four processes mentioned above. We observe that using an appropriate selection pressure and postponing drift make GAs quite robust on NK-Landscapes; different to previous studies, even simple GAs with these two features perform better than a random bit climber (RBC+) for a broad range of classes of problems (K 4). We also observe that the interaction of parallel varying mutation with crossover improves further the reliability of the GA, especially for 12 < K < 32. Contrary to intuition, we find that for small K a mutation only evolutionary algorithm (EA) is very effective and crossover may be omitted; but the relative importance of crossover interacting with varying mutation increases with K performing better than mutation alone (K > 12). This work indicates that NK-Landscapes are useful for testing each one of the major processes involved in a GA and for assessing the overall behavior of a GA on complex non-linear problems. This study also gives valuable guidance to practitioners applying GAs to real world problems of how to configure the GA to achieve better results as the non-linearity and complexity of the problem increases.

In this paper, we give an algorithm for the node-to-set disjoint paths problem in pancake graphs with its evaluation results. The algorithm is of polynomial order of n for an n-pancake graph. It is based on recursion and divided into two cases according to the distribution of destination nodes in classes into which all the nodes in a pancake graph are categorized. The sum of lengths of paths obtained and the time complexity of the algorithm are estimated and the average performance is evaluated based on computer simulation.

A new approach to correcting the array amplitude failure by a combination of B-spline techniques and genetic algorithms is proposed. Some array elements indicate the control knots for a B-spline curve by their nominal positions and amplitudes; others distribute the excitation amplitudes according to the sampling points on the curve. The inherent smoothness of the B-spline curves reduce the effect of excessive coupling between adjacent elements. Genetic algorithms are used to search for a quasi-optimized B-spline curve to produce the ultimate amplitude distribution for correcting the array failure. To demonstrate the method's effectiveness, simulation results for correcting failures with three- and four-element failures are presented.

Multiple access interference and near-far effect cause the performance of the conventional single user detector in DS/CDMA systems to degrade. Due to high complexity of the optimum multiuser detector, suboptimal multiuser detectors with less complexity and reasonable performance have received considerable attention. In this paper we apply the classic and a new modified genetic algorithm for multiuser detection of DS/CDMA signals. It is shown that the classic genetic algorithm (GA) reaches an error floor at high signal to noise ratios (SNR) while the performance of proposed modified GA is much better than the classic one and is comparable to the optimum detector with much less complexity. The results hold true for AWGN and fading channels. We also describe another GA called as meta GA to find the optimum parameters of the modified GA. We compare the performance of proposed method with the other detectors used in CDMA.

A Radio Network (RN, for short) is a distributed system with no central arbiter, consisting of p radio stations each of which is endowed with a radio transceiver. In this work we consider single-hop, single channel RNs, where each station S(i), (1ip), initially stores si items which are tagged with the unique destination they must be routed. Since each item must be transmitted at least once, every routing protocol must take at least n = s1 + s2 + + sp time slots to route each item to its final destination. Similarly, each station S(i), (1ip), must be awake for at least si + di time slots to broadcast si items and to receive di items, where di denotes the number of items destined for S(i). The main contribution of this work is to present a randomized time- and energy-optimal routing protocol on the RN. Let qi, (1ip), be the number of stations that have items destined for S(i), q=q1 +q2 ++ qp, and ri be the number of stations for which S(i) has items. When qi is known to station S(i), our routing protocol runs, with probability exceeding 1 - , (f > 1), in n + O(q + log f) time slots with each station S(i) being awake for at most si + di + O(qi + ri + log f) time slots. Since qidi, risi, and qn always hold, our randomized routing protocol is optimal. We also show that, when the value of di is known to S(i), our routing protocol runs, with probability exceeding 1 - , (f > 1), in O(n + log f) time slots with no station being awake for more than O(si + di + log f) time slots.

We discuss applications of quantum computation to geometric data processing. Especially, we give efficient algorithms for intersection problems and proximity problems. Our algorithms are based on Brassard et al. 's amplitude amplification method, and analogous to Buhrman et al. 's algorithm for element distinctness. Revealing these applications is useful for classifying geometric problems, and also emphasizing potential usefulness of quantum computation in geometric data processing. Thus, the results will promote research and development of quantum computers and algorithms.

Karger, Motwani and Sudan presented a graph coloring algorithm based on semidefinite programming, which colors any k-colorable graph with maximum degree Δ using (Δ1-2/k) colors. This algorithm leads to an algorithm for k-colorable graph using (n 1-3/(k+1)) colors. This improved Wigderson's algorithm, which uses O(n1-1/(k-1)) colors, containing as a subroutine an algorithm using (Δ+1) colors for graphs with maximum degree Δ. It is easy to imagine that an algorithm which uses less colors in terms of Δ leads to an algorithm which uses less colors in terms of n. In this paper, we consider this influence assuming that we have an algorithm which uses (Δ 1-x/k) colors for 2

Counting the number of points of Jacobian varieties of hyperelliptic curves over finite fields is necessary for construction of hyperelliptic curve cryptosystems. Recently Gaudry and Harley proposed a practical scheme for point counting of hyperelliptic curves. Their scheme consists of two parts: firstly to compute the residue modulo a positive integer m of the order of a given Jacobian variety, and then search for the order by a square-root algorithm. In particular, the parallelized Pollard's lambda-method was used as the square-root algorithm, which took 50CPU days to compute an order of 127 bits. This paper shows a new variation of the baby step giant step algorithm to improve the square-root algorithm part in the Gaudry-Harley scheme. With knowledge of the residue modulo m of the characteristic polynomial of the Frobenius endomorphism of a Jacobian variety, the proposed algorithm provides a speed up by a factor m, instead of in square-root algorithms. Moreover, implementation results of the proposed algorithm is presented including a 135-bit prime order computed about 15 hours on Alpha 21264/667 MHz and a 160-bit order.

The 2-vertex-connectivity augmentation problem for specified vertices (2VCA-SV) is defined as follows: Given an undirected graph G=(V,E), a subgraph G0=(V,E') of G, a specified set of vertices S V and a weight function w:E R^+ (nonnegative real numbers), find a set E" E-E' with the minimum total weight, such that G0+E"=(V,E' E") has at least two internally disjoint paths between any pair of vertices in S. In this paper, we propose an O(|V||E|+ |V|2 log |V|) time algorithm 2-ABIS, whose performance ratio is 2 (3, respectively), for 2VCA-SV if G0 has a connected component containing S (otherwise).

In this letter, base station placement is automatically determined from pre-defined candidate sites using a genetic approach, and the transmit power is obtained taking the interference situation into account in cases of interference-dominant systems. In order to apply a genetic algorithm to the base station placement problem, a real-valued representation scheme is proposed. Corresponding operators such as crossover and mutation are also introduced. The proposed algorithm is applied to an inhomogeneous traffic density environment, where a base station's coverage may be limited by offered traffic loads. An objective function is designed for performing the cell planning in a coverage- and cost-effective manner.

The wavelet transform (WT) has recently emerged as a powerful tool for image compression. In this paper, a new image compression technique combining the genetic algorithm (GA) and grey-based competitive learning network (GCLN) in the wavelet transform domain is proposed. In the GCLN, the grey theory is applied to a two-layer modified competitive learning network in order to generate optimal solution for VQ. In accordance with the degree of similarity measure between training vectors and codevectors, the grey relational analysis is used to measure the relationship degree among them. The GA is used in an attempt to optimize a specified objective function related to vector quantizer design. The physical processes of competition, selection and reproduction operating in populations are adopted in combination with GCLN to produce a superior genetic grey-based competitive learning network (GGCLN) for codebook design in image compression. The experimental results show that a promising codebook can be obtained using the proposed GGCLN and GGCLN with wavelet decomposition.

In this paper, we consider the problem of multicasting a message in k-ary n-cubes under the store-and-forward model. The objective of the problem is to minimize the size of the resultant multicast tree by keeping the distance to each destination over the tree the same as the distance in the original graph. In the following, we first propose an algorithm that grows a multicast tree in a greedy manner, in the sense that for each intermediate vertex of the tree, the outgoing edges of the vertex are selected in a non-increasing order of the number of destinations that can use the edge in a shortest path to the destination. We then evaluate the goodness of the algorithm in terms of the worst case ratio of the size of the generated tree to the size of an optimal tree. It is proved that for any k 5 and n 6, the performnance ratio of the greedy algorithm is c kn - o(n) for some constant 1/12 c 1/2.

We propose two simple algorithms based on bounded tickets for the mutual exclusion problem on the asynchronous single-writer/multi-reader shared memory model. These algorithms are modifications of the Bakery algorithm. An unattractive property of the Bakery algorithm is that the size of its shared memory is unbounded. Initially we design a provisional algorithm based on bounded tickets. It guarantees mutual exclusion in the case where a certain condition is satisfied. To remove the condition, we use an additional process that does not correspond to any user. The algorithm with the additional process is a lockout-free mutual exclusion algorithm on the asynchronous single-writer/multi-reader shared memory model. We then modify this algorithm to reduce the shared memory size with the cost of using another additional process. The maximum waiting time using each of the algorithms proposed in this paper is bounded by (n-1)c+O(nl), where n is the number of users, l is an upper bound on the time between two successive atomic steps, and c is an upper bound on the time that any user spends using the resource. The shared memory size needed by the first algorithm and the second algorithm are (n+1)(1+log (4n)) bits and n(1+log (4n-4))+2 bits, respectively.

The radiation patterns are synthesized by properly disposing surface variations on dielectric rod waveguides. The genetic algorithm (GA) is applied for searching the optimum disposition of variation sections. A very fast calculation method used in the optimization is presented. The guided waves are related in the form of a 2-port circuit and the radiation field is expressed by superposition of the waves from variation sections. Various conical beams can be synthesized. Short variation sections and combination of several variation sections with different height are used to improve the synthesis performance. The ripple of the mainlobe and the sidelobe levels become small. Spherical sector patterns with a steep fall are synthesized and the agreement with the experimental values is confirmed.