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Summary: MANET refers to wireless networks in which mobile nodes can be easily adapted to change the location dynamically due to their mobility and offers flexibility in the network configuration. An efficient mechanism for reliable exchanging of the multimedia voice messages through MANET nodes is achieved by route discovery of broadcasting operation. A major problem in such systems is the broadcast overhead and retransmission associated with high dense network and less reachability in sparse network. In systems of vastly dynamic nature, to reduce this broadcast overhead in nodes and to increase the reachability in static or semi static nodes in an environment, an enhanced mechanism shore up called On Demand Knowledge‐Based Memory‐Aided Rebroadcast Algorithm for Multimedia voice Data Transmission in MANET (OMRA) is herewith proposed, which operates on both dense and sparse network. In this scheme, hello multimedia voice messages are broadcast to collect one‐hop neighbor information such as mobility factor, node velocity, node density ratio and uncovered neighbors. Additionally, it determines and preserves inquiry data of mobile nodes when the mobility factor is low ensue in sparse network. By considering these varying nature the rebroadcast delay and rebroadcast probability are adjusted adaptively to decide whether to rebroadcast the inward multimedia voice messages. This paper concentrates only on broadcasting the multimedia voice message through MANET nodes in a reliable manner. The performance study of this work is analyzed by various existing algorithms in the literature such as flooding, Gossip1, DBA, and NCPR. The experimental results proves that the proposed OMRA has got an edge over the existing broadcast schemes in terms of broadcast overhead and retransmission of multimedia voice messages, especially to increase reachability in sparse networks. [ABSTRACT FROM AUTHOR]

Summary: This paper presents implementations of in‐place algorithms for transposing rectangular matrices. One implementation is a swap‐based algorithm described by Tretyakov and Tyrtyshnikov,1 to which we have introduced a number of variations. In particular, we show how the original algorithm can be modified to require constant additional memory. A proof of correctness is also sketched. This algorithm is compared with cycle‐following approaches and with the swap‐based GCD Transpose algorithm that partitions the matrix into a hierarchy of square submatrices. The performance of parallel implementations on a multicore system is also investigated. [ABSTRACT FROM AUTHOR]

Summary: Mobile agents are examples of distributed systems which may dispute for the same resources on their hosts. Treating such concurrency adequately is essential, particularly in real‐time applications. Due to intrinsic time restrictions, mobile agents in real‐time environments are only considered successful if they fulfill their mission by respecting their deadlines. Scheduling algorithms with different policies can be applied in these scenarios. However, the efficiency of these algorithms may deviate according to the missions and deadlines of the mobile agents. Also, these algorithms can be preemptive, or calculate the order of executions without interrupting an ongoing task. In this paper, we propose a middleware extension to the JADE platform that brings real‐time scheduling support with preemption to mobile agents. The proposed solution uses best effort scheduling policy in the context of soft real‐time applications. We evaluate the performance of the scheduling algorithms, with and without preemption, and the impact of the selected algorithms on mission fulfillment. The results of the proposed middleware showed a great improvement on mission accomplishment when compared to the FIFO algorithm provided by the JADE platform. [ABSTRACT FROM AUTHOR]

Summary: In multiprocessor environments, the scheduling algorithms play a significant role in maximizing system performance. In this paper, we propose a clustering‐based task scheduling algorithm called Edge Priority Scheduling (EPS) for multiprocessor environments. The proposed algorithm extends the idea of edge zeroing heuristic and uses the concept of edge priority to minimize the makespan of the task graph. The complexity of the EPS algorithm is O(|V||E|(|V| + |E|)), where |E| represents the number of edges and |V| denotes the number of nodes in the task graph. The experiments are performed for random task graphs and the task graphs generated from some representative real‐world applications such as Gaussian Elimination and Fast Fourier Transform. The performance of the EPS algorithm is compared with six well‐known algorithms such as EZ (Edge Zeroing), LC (Linear Clustering), CPPS (Cluster Pair Priority Scheduling), DCCL (Dynamic Computation Communication Load), RDCC (Randomized Dynamic Computation Communication), and LOCAL. The results show that the EPS algorithm outperforms the compared algorithms in terms of the normalized schedule length and speedup. [ABSTRACT FROM AUTHOR]

Summary: FDK algorithm is a popular analytical reconstruction method for practical cone‐beam CT scanners. Compared with iterative methods, the FDK algorithm is computationally efficient. However, the reconstruction speed remains a limitation for its application when dealing with high resolution images. In this paper, we propose a fast method for parallel implementation of the FDK algorithm by the use of multi‐GPU. First, we optimize the backprojection operation of FDK according to the property of geometric symmetry and the correlation between adjacent slices. Then, we utilize the multi‐thread technology to realize the parallel implementation of the optimized FDK algorithm on multi‐GPU. Finally, we implement the proposed method on a multi‐GPU platform. Numerical experiment shows that the proposed multi‐GPU‐based approach can reconstruct a 512 cubed volume in 1.9 seconds from 360 projections of resolution 512 ×512, which is 511 times faster than a traditional CPU–based approach and 5 times faster than a single GPU–based approach. In addition, the reconstruction results also indicate that the proposed method can maintain the same precision with traditional method. [ABSTRACT FROM AUTHOR]

Summary: Israeli‐Jalfon's self‐stabilization algorithm provides a solution to the problem of fault tolerance in distributed systems. To quantitatively evaluate the algorithm and discover the factors contributing to its performance, we used a probabilistic model checking technique to study the algorithm across different configurations. The mainstream probabilistic model checker PRISM assisted with our final assessment. We focus here on three aspects of the algorithm's performance: convergence, time complexity, and maximum execution time. Our experimental results show that time complexity is O (N^2) when N is less than 23, and we examine the factors contributing to this performance. These prove to be: the number of tokens, the number of processes, the probability of token transmission, and how tokens are spaced. Performance degrades as the number of tokens grows. For a certain number of processes, better performance can be obtained when the probability of token transmission is 0.5. Three tokens spaced evenly, meanwhile, yields the worst performance. The main contribution of this paper is its exhaustive quantitative performance analysis of Israeli‐Jalfon's algorithm and the presentation of accurate rather than approximate numerical results. Moreover, our assessment was undertaken in a reducible fashion, with stable states being set against a proper subset of the set of possible configurations rather than allowing two sets to coincide. [ABSTRACT FROM AUTHOR]

Summary: SVM (Support Vector Machine) is a popular machine‐based learning method, with wide applicability and excellent generalization performance in a large number of real‐time classification problems. However, SVM is sensitive to the noise and outliers. As an improved algorithm based on SVM, FSVM (Fuzzy Support Vector Machine) gives the training samples different fuzzy membership values in order to reduce the interference of the noise and outliers. However, like the normal SVM algorithm, the FSVM algorithm still needs to solve the problems such as how to improve the accuracy of classification and how to accurately recognize the noise and outliers. In this paper, an improved FSVM of data classification algorithm (IFSVM) has been proposed. Firstly, this algorithm deletes the outliers or noise based on the average density algorithm and removes them from the samples, thus avoiding the influence of the noise to the classification accuracy. Secondly, the centers and radiuses of the two minimum hyperspheres are extracted based on the SVDD algorithm. Finally, this algorithm sets the membership function values by comparing the distance between each sample and the center of the sample's hypersphere, the distance between the sample and the opposite hypersphere center, and the distance between the two hypersphere centers. In this way, this algorithm highlights the importance of the boundary vectors, which could be support vectors for classification, and improves the classification accuracy. The experimental results show that this algorithm has improved the anti‐noise ability and the accuracy of classification when compared with the normal SVM algorithm and three other FSVM algorithms. [ABSTRACT FROM AUTHOR]

Summary: Whale Optimization Algorithm (WOA) is a recent swarm intelligence based meta‐heuristic optimization algorithm, which simulates the natural behavior of bubble‐net hunting strategy of humpback whales and has been successfully applied to solve complex optimization problems in a wide range of disciplines. However, when applied to large‐size problems, its performance degrades due to the need of massive computational work load. Distributed computing is one of the effective ways to improve the scalability of WOA for solving large‐scale problems. In this paper, we propose a simple and robust distributed implementation of WOA using Hadoop MapReduce named MR‐WOA. MapReduce paradigm is adopted as the distribution model since it is one of the most mature technologies to develop parallel algorithms which automatically handles communication, load balancing, data locality, and fault tolerance. The design of MR‐WOA is discussed in details using the MapReduce paradigm. Experiments are conducted for a set of well‐known benchmarks for evaluating the quality, speedup, and scalability of MR‐WOA. The conducted experiments reveal that our approach achieves a promising speedup. For some benchmarks, speedup scales linearly with increasing the number of computational nodes. [ABSTRACT FROM AUTHOR]

Summary: Finding link/node‐disjoint paths between a pair of nodes is capable of providing Quality of Service and reliable routing which is very critical for mesh‐connected Network‐on‐Chip. State‐of‐art works usually aim at random topologies and multiple constraints. Therefore, it is difficult to optimize their time complexity. In this paper, MDPPIM (Manhattan‐distance‐constrained Disjoint Path Pair Problem in Incomplete Mesh) problem is presented based on Network‐on‐Chip application scenarios. Then, PCDP (Path‐Counting Disjoint Path) algorithm is proposed to solve the MDPPIM problem with low time complexity. Compared with previous disjoint path algorithms, PCDP algorithm does not have to use Dijkstra's algorithm to find a shortest path. It is optimized according to the feature of incomplete mesh such as the regularity of mesh and Manhattan‐distance constraint. Therefore, it is with low time complexity. Numerical results demonstrate the proposed PCDP algorithm's effectiveness and low time complexity. [ABSTRACT FROM AUTHOR]

Summary: Compared with traditional geometric modeling techniques, image‐based modeling technology has the advantages of simple operation, quick reconstruction, and strong sense of reality. Compared with the three‐dimensional scanning technology, it has the characteristics of low cost and simple algorithm. In a broad sense, the image‐based 3D modeling technique not only includes the geometric modeling of the entity but also includes visual restoration, ie, reconstruction of the geometric model of the entity using the image, and restoration of its corresponding surface texture, reflection properties, lighting conditions, and the like. This paper focuses on the application of geometric reconstruction methods to study the application of VR computer images in the three‐dimensional virtualization of sports human science. By combining the background difference method and the interframe difference method, the target region where the moving human body is located can be accurately extracted from the video, and then the idea of the human body can be divided along the virtual plane. Finally, the end‐point feature points of the human body are determined by the geodesic distance, and a three‐dimensional model skeleton is buried using a discrete penalty function, and an image‐based three‐dimensional reconstruction system is realized. [ABSTRACT FROM AUTHOR]

Summary: In order to realize green society, it is critical to reduce electric energy consumed by servers in clusters. In our previous studies, these types of algorithms are proposed to select an energy‐efficient server to perform an application process issued by a client. In this paper, we newly discuss a migration approach to reducing the electric energy consumption of servers where virtual machines with application processes migrate to more energy‐efficient servers. We propose a new algorithm called ISEAM2 to reduce the electric energy consumption of servers. Here, a pair of a host server and a virtual machine on the host server are first selected to perform a process issued by an application. In addition, a target virtual machine on a host server and a guest server to which the target virtual machine migrates are selected so that the electric energy consumption of the host and guest servers can be minimized. Thus, virtual machines with application processes migrate to more energy‐efficient servers. We also propose a simple way to estimate the termination time of every process on each server. In the evaluation, we show the total electric energy consumption and total active time of servers, and the average execution time of processes can be reduced in the ISEAM2 algorithm compared with other algorithms. [ABSTRACT FROM AUTHOR]

Summary: Nonbinary low‐density parity‐check (NB‐LDPC) codes are excellent error correcting codes and outperform their binary counterparts under the same code length. NB‐LDPC decoders are based on Belief Propagation Algorithm, which demands intensive message‐passing computation. Recently, to achieve both flexibility and good throughput performance, NB‐LDPC decoders have been ported from dedicated hardware solutions to multi/many‐core systems. In this paper, we propose an FFT‐based q‐ary Sum‐Product Algorithm (QSPA) decoding architecture for NB‐LDPC codes with layered and flooding schedules on a graphics processing unit (GPU). To improve the throughput performance of the proposed decoder, four optimization methods are presented to not only accelerate the decoding kernel execution but also improve the data transfer efficiency. The experiments are mainly accomplished on NVIDIA GTX580 and GTX Titan X. Throughputs up to 63 Mbps over GF(16) and 7.65 Mbps over GF(256) are achieved on GTX580 when executing 5 layered decoding iterations. Throughputs can reach up to 139 Mbps over GF(16) and17 Mbps over GF(256) on GTX Titan X. Experimental results show that the speedups of the decoding throughputs range from ×1.7 to ×16.8 by comparison with the existing FFT‐based QSPA decoders on GPU. [ABSTRACT FROM AUTHOR]

Deep packet inspection (DPI) is important for network security. In this paper, we consider a software-defined network where several DPI proxy nodes are available for serving flows from ingress switches. These DPI proxy nodes can be implemented in either software or hardware. We study an integrated proxy allocation and routing determining problem with the objective of minimizing the total delay of flows from ingress switches to DPI proxies. This problem is formulated as an integer linear programming problem that is NP-hard in general. To solve this problem, we design a 2-phase algorithm that can quickly select proxy and find routing paths for incoming flows. Finally, extensive simulations are conducted to evaluate the performance of our proposed algorithm. Some useful parameter setting insights are obtained. [ABSTRACT FROM AUTHOR]

The article presents a collection of papers discussed at the 1st Workshop on Economic Models and Algorithms for Grid Systems, which was held as part of the eighth Institute of Electrical and Electronics Engineers Inc. (IEEE) International Conference Grid 2007 in Austin, Texas on September 19-21, 2007.