28489429
Transmission Scheduling in Data-Driven Peer-to-Peer Streaming towards Optimal Throughput
Peer-to-peer media streaming has been an important service on the internet in recent years. The Data-driven (or mesh-based) structure is adopted by most working systems,in which data scheduling is one of the important problems.However, those frequently used scheduling algorithms are often faced with such a case: A neighbor peer takes up its bandwidth to deliver the packets that other neighbors can also supply, but some packets only held by it are not delivered.These packets can not be delivered in the current scheduling cycle, even though that the other neighbors have surplus bandwidth. This is a kind of waste of bandwidth and decreases the throughput of transmission. In this paper we propose anew scheduling algorithm aiming at the optimal throughput:Bipartite-matching based Block Scheduling algorithm(BBS).We convert the original data scheduling problem to a problem of finding a maximum match on the correspond bipartite graph, then assign data packets to neighbors according to the maximum match. We evaluate the performance of BBS with extensive experiments and the results show that BBS throughput and provides better streaming quality than those frequently used scheduling algorithms.
6983597
A Comparative Study of Parallel Prefix Adders in FPGA Implementation of EAC
Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and in- terconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.
12282317
A note on “a mapping algorithm for computer-assisted exploration in the design of embedded systems”
The scaling of autoclave and leaching-equipment surfaces during the high pressure leaching of nickeliferous oxide and silicate ores is controlled during leaching to favor the formation of scale containing substantial amounts of magnesium sulfate that is more easily removed by chemical dissolution using water or dilute sulfuric acid at temperatures ranging from about 50 DEG C. to 250 DEG C. than scale containing substantial amounts of alunite.
7197821
A case study on system-level modeling by aspect-oriented programming
The paper presents a novel and synergistic system-level modeling approach based on aspect-oriented programming (AOP) and SystemC. A case study on a 128-bit binary floating-point adder is performed to demonstrate the modeling strength of the approach. The validation experiment shows the effectiveness of the proposed approach. This work constitutes a first attempt to employ AOP in modeling digital systems.
13252008
Anode-ALM: An Aggregation-Based Application Level Multicast for Media Streaming
Building application level multicast (ALM) for media streaming, we had better keep the invariability of source to-end delay (S2EDelay) of any node on the tree, so as to avoid delay jitter problem. In this paper, we proposed an aggregation-based ALM architecture, which aggregates the unstable nodes within a local area into a relative stable aggregation node Anode, and then builds tree by Anodes. Anode shields node’s dynamicity inside and behaves as a stable one outside. To build the architecture, we first proposed an Anode structure by n-dimension anti-cube, and proposed a distributed algorithm to bind the logic view and physical view of an Anode dynamically by the differentiation and assimilation operations, which maintain the integrality of the logic view while the physical view changes dynamically. Experiment results indicate that our algorithm can keep theS2EDelay invariability of the built ALM tree effectively.
33185687
Supremum of Agent Number Needed in Analyzing Security Protocols Based on Horn Logic
20485137
Multi-objective scheduling problems subjected to special process constraint
The problem of parallel machine multi-objective scheduling subjected to special process constraint in the textile industries, as one of the most important combinational optimization problems, is different from other parallel machine scheduling problems in the following characteristics. On one hand, processing machines are non-identical; on the other hand, the sort of job processed on every machine can be restricted Considering one of the multi-objective problems, either minimizing the maximum completion time among all the machines(makespan) or minimizing the total earliness/tardiness penalty of all the jobs has been cornerstone of most studies done so far. However, under special process constraint, taking them into account as a multi-objective problem has not been well studied Therefore, in this paper, a multi-objective model based on them is presented and a new parallel genetic algorithm based on a vector group coding method is also proposed in order to effectively solve this model. The algorithm shows the following advantages: the coding method is simple and can effectively reflect the virtual scheduling policy, which can vividly reflect the numbers and sequences of these processed jobs on every machine, and then enables the individuals generated by crossover and mutation to satisfy process constraint. Numerical experiments show that it is efficient, and is better than the common genetic algorithm, and has the better parallel efficiency. A much better prospect of application can be optimistically expected.
35120658
Max-Relevance and Min-Redundancy Greedy Bayesian Network Learning on High Dimensional Data
Existing algorithms for learning Bayesian network require a lot of computation on high dimensional itemsets which affects accuracy especially on limited datasets and takes up a large amount of time. To address the above problem, we propose a novel Bayesian network learning algorithm MRMRG, Max Relevance-Min Redundancy Greedy. MRMRG algorithm is a variant of K2 which is a well- known BN learning algorithm. We also analyze the time complexity of MRMRG. The experimental results show that MRMRG algorithm has much better efficiency and accuracy than most of existing algorithms on limited datasets.
62166223
Study on the Si-Ge Nanowire MOSFETs with the Core-Shell Structure
This paper investigates the transport properties of the silicon-germanium nanowire MOSFETs with core-shell structure by using a finite element numerical method for electronic structure, energy level, and channel current computation. Coupled Poisson's equation to Schrodinger's equation for electrostatics calculation and electron structure to current transport equation for channel current computation, the electronic structure, quantized energy levels, relevant wave functions and charge distribution are solved selfconsistently for the core-shell structure MOSFETs. Furthermore, based on these findings, the transistor performances, including the capacitance characteristics and drain current, are also predicted.
11689877
A Projection and Search Algorithm for the Bicluster Problem
The advent of microarray technologies has made the experimental study of gene expression faster and more efficient. It is an important task to analyze the gene expression data acquired from the microarray experiments. Biclustering is a popular method to search a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. In this paper, we propose a projection and search algorithm (PSA) for finding biclusters in the gene expression matrix. The algorithm adopts projection method to find a raw submatrix, then refines and extends the raw submatrix into a bicluster using greedy algorithms. We test the algorithm on simulation data and real data. The test results show that the algorithm has good performance on both simulation data and real data. Additionally, compared with other methods, PSA isnpsilat sensitive to noise level.
18928608
An Improved Particle Swarm Optimizer for Truss Structure Optimization
This paper presents an improved particle swarm optimizer (IPSO) for solving truss structure optimization problems. The algorithm is based on the particle swarm optimizer with passive congregation (PSOPC) and a harmony search (HS) scheme. It handles the problem-specified constraints using a 'fly-back mechanism' method and the variables' constraints using the harmony search scheme. The IPSO is tested on a planar truss structure optimization problem and is compared with the PSO and the PSOPC algorithm respectively. The result shows that the IPSO method presented in this paper is able to accelerate the convergence rate effectively and has the fastest convergence rate among these three other algorithms