Locality Aware Optimal Task Scheduling Algorithm for TriBA——A Novel Scalable Architecture

An optimal algorithmic approach to task scheduling for, triplet based architecture（TriBA）, is proposed in this paper. TriBA is considered to be a high performance, distributed parallel computing architecture. TriBA consists of a 2D grid of small, programmable processing units, each physically connected to its three neighbors. In parallel or distributed environment an efficient assignment of tasks to the processing elements is imperative to achieve fast job turnaround time. Moreover, the sojourn time experienced by each individual job should be minimized. The arriving jobs are comprised of parallel applications, each consisting of multiple-independent tasks that must be instantaneously assigned to processor queues, as they arrive. The processors independently and concurrently service these tasks. The key scheduling issues is, when some queue backlogs are small, an incoming job should first spread its tasks to those lightly loaded queues in order to take advantage of the parallel processing gain. Our algorithmic approach achieves optimality in task scheduling by assigning consecutive tasks to a triplet of processors exploiting locality in tasks. The experimental results show that tasks allocation to triplets of processing elements is efficient and optimal. Comparison to well accepted interconnection strategy, 2D mesh, is shown to prove the effectiveness of our algorithmic approach for TriBA. Finally we conclude that TriBA can be an efficient interconnection strategy for computations intensive applications, if tasks assignment is carried out optimally using algorithmic approach.     

Scalable Compression Algorithm for Video Monitoring System

A layered compression algorithm is presented which delivers spatial scalable encoded bit streams forremote video monitoring system. The complexity of the algorithm is modest and is well suited to real timeimplementation. Based on the layered compression algorithm a codec system model is established. High-speedvideo compression can be realized with parallel data compression in this codec system. For image reconstruction, a     

A Hybrid Artificial Neural Network-based Scheduling Knowledge Acquisition Algorithm

It is a key issue that constructing successful knowledge base to satisfy an efficient adaptive scheduling for the com- plex manufacturing system.Therefore,a hybrid artificial neural network (ANN)-based scheduling knowledge acquisition algo- rithm is presented in this paper.We combined genetic algorithm (GA) with simulated annealing (SA) to develop a hybrid opti- mization method,in which GA was introduced to present parallel search architecture and SA was introduced to increase escaping probability from local optima and ability to neighbor search.The hybrid method was utilized to resolve the optimal attributes subset of manufacturing system and determine the optimal topology and parameters of ANN under different scheduling objectives;ANN was used to evaluate the fitness of chromosome in the method and generate the scheduling knowledge after obtaining the optimal at- tributes subset,optimal ANN's topology and parameters.The experimental results demonstrate that the proposed algorithm pro- duces significant performance improvements over other machine learning-based algorithms.     

A Quadratic Programming Model for Blast Scheduling

A quadratic programming model is established to choose the blocks to be blasted in a given period. The length of this period depends on the production planning requirements. During the given period, the blocks' parameters are available from the geological database of the mine. The objective is to minimize the deviation of the average ore grade of blasted blocks from the standard ore grade required by the mill. Transportation ability constraint. production quantity demand constraint. minimum safety bench constraint. block size constraint and block, bench precedence constraints are considered in forming the programming model. This model has more practical objective function and reasonable constraints compared with the existing model for this kind of problems.     

Optimal Design of Materials for DJMP Based on Genetic Algorithm

The genetic algorithm was used in optimal design of deep jet method pile. The cost of deep jet method pile in one unit area of foundation was taken as the objective function . All tire restrains were listed following the corresponding specification. Suggestions were proposed and the modified. The real-coded Genetic Algorithm was given to deal with the problems of excessive computational cost and premature convergence. Software system of optimal design of deep jet method pile was developed.     

A Construction Algorithm for Tree Structure

1 Introduction Tree structure is one of the best display mode in some application system,especially,ininformation man-agement system.Using tree structure ,the user inteface is more visual and more friendly,because it can displaythe hierarchical relationship among the data[1 ,2].Usually,however ,before the construction of tree structure ,thespecial storage organization must be establishedfirstlyfor the data which will be displayedinthe tree structure .For example ,sequential storage and chain s…     

An Improved Ant Colony Algorithm for a Single-machine Scheduling Problem with Setup Times

Motivated by industrial applications we study a single-machine scheduling problem in which all the jobs are mutu- ally independent and available at time zero.The machine processes the jobs sequentially and it is not idle if there is any job to be pro- cessed.The operation of each job cannot be interrupted.The machine cannot process more than one job at a time.A setup time is needed if the machine switches from one type of job to another.The objective is to find an optimal schedule with the minimal total jobs'completion time.While the sum of jobs'processing time is always a constant,the objective is to minimize the sum of setup times.Ant colony optimization(ACO)is a meta-heuristic that has recently been applied to scheduling problem.In this paper we propose an improved ACO-Branching Ant Colony with Dynamic Perturbation(DPBAC)algorithm for the single-machine schedul- ing problem.DPBAC improves traditional ACO in following aspects:introducing Branching Method to choose starting points;im- proving state transition rules;introducing Mutation Method to shorten tours;improving pheromone updating rules and introduc- ing Conditional Dynamic Perturbation Strategy.Computational results show that DPBAC algorithm is superior to the traditional ACO algorithm.     

A Modified Algorithm for Feedforward Neural Networks

As a most popular learning algorithm for the feedforward neural networks, the classic BP algorithm has its many shortages. To overcome some of the shortages, a medified learning algorithm is proposed in the article. And the simulation result illustrate the modified algorithm is more effective and practicable.     

A Practical Algorithm for Analysis of AM-FM Signals

The demodulation algorithm for AM-FM sinusoids proposed in reference[1]canonly deal with complex exponential sequences.In real applications,one needs to con-struct an analytic signal from real samples.Influenced by the Hilbert Transformation,thecomplex noise in the analytic signal is colored.This paper analyzes the colored noise andthe disadvantage caused by the noise in the estimations of the instantaneous frequencyand amplitude factor using the algorithm in[1]and formulates a practical algorithm.     

A Simulated Annealing-Based Algorithm for Traveling Salesman Problem

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A New Polynomial Algorithm for Computing Minimal Hitting Sets

ＡＮｅｗＰｏｌｙｎｏｍｉａｌＡｌｇｏｒｉｔｈｍｆｏｒＣｏｍｐｕｔｉｎｇＭｉｎｉｍａｌＨｉｔｔｉｎｇＳｅｔｓＹＵＢａｉｓｈｅｎｇ,ＨＯＵＺｈｏｎｇｓｈｅｎｇ,ＨＵＡＮＧＷｅｎｈｕ（于百胜）（侯忠生）（黄文虎）（Ｄｅｐｔ．ｏｆＡｓｔｒｏｎａｕｔｉｃｓａｎ...     

A Modified Genetic Algorithm for Maximum Independent Set Problems

0umConsideranundirectedgraphG=(V,E)whereVisthesetofverticesandEisthesetofedgesinG.AnindePendentsetisasetofverticesofGsothatnotw0venicesofthesetareadjacent;thatis,notwoverticesarejoinedbyanedgofG.AnindePendentsetissaidtobe~ifIhesetisnotaproPersubsetofanyotherindePendentset-Furthermore,anindePendentsetissaldtobe~ifthereisnoindePendentsetwithagreat-ernumberofvertices.Theopposideconcept0faninde-PendentsetisacliqUe-Thatis,acliqUeofGisasub-gIaPhinducedbyasetSofvenicesthatiscomplete.The..mm…     

A Novel Drug Delivery System for Osteosarcoma Chemotherapy

1Introduction Osteosarcoma,35%ofhumanprimarybonecan cers,ishighlymalignantandtendstotransferandgrow permeantly.Chemotherapyisaneffectiveandbasicthera pyforosteosarcoma,buttheside effectofantineoplasticdrugslimitstheconcentrationofthemwhichinfluences thecu…     

A New Image Processing Algorithm for Log Cross Section Image

ＡＮｅｗＩｍａｇｅＰｒｏｃｅｓｉｎｇＡｌｇｏｒｉｔｈｍｆｏｒＬｏｇＣｒｏｓＳｅｃｔｉｏｎＩｍａｇｅＬＵＡＮＸｉｎＷＡＮＧＹａｎ（栾新）（王炎）（ＲｏｂｏｔＲｅｓｅａｒｃｈＩｎｓｔｉｔｕｔｅ，ＨａｒｂｉｎＩｎｓｔｉｔｕｔｅｏｆＴｅｃｈｎｏｌｏｇｙ，Ｈａ...     

A Novel and Efficient Method for Iris Automatic Location

An efficient and robust iris location algorithm plays a very important role in a real iris recognition system. A novel and efficient iris automatic location method is presented in this study. It includes following two steps mainly： pu- pil location and iris outer boundary location. A digital eye image was divided into many small rectangular blocks with fixed size in the pupil location, and the block with the smallest average intensity was selected as a reference area. Then image binarization was implemented taking the average intensity of the reference area as a threshold. At last the center coordinates and radius of pupil were estimated by extending the reference area to the pupil＇s boundaries in the binary iris image. In the iris outer location, two local parts of the eye image were selected and transformed into polar coordinates from Cartesian reference. In order to detect the fainter outer boundary of the iris quickly, a novel edge detector was used to locate boundaries of the two parts. The center coordinates and radius of the iris outer boundary can be estimated using the fusion of the locating results of the two local parts and the location information of the pupil. The algorithm was tested on CASIA vl.0 and MMU vl.0 digital eye image databases and experimental results show that the proposed method has satisfying performance and good robustness.     

A Novel Process for Synthesis of Ultrafine BaTiO3 Powders

A novel process termed low-temperature combustion-synthesis(LCS)of Ba(ON3)2-TiO2-C6H8O7.H2O system was investigated at the initial temperature of 600℃ and ultrafine BiTiO3 powders with a particle size of 200-350nm were prepared.It was found that the molar ratio of NO3^-/citric acid and the homogeneity of combustion have remarkable effect on the characterstice of the powder,The reaction mechanism of LCS BaTiO3 powders was proposed on the basis of thermodynamic analysis.     

A Novel Method for Measuring Electro-optic Coefficient of Poled Polymer Waveguide

ＡＮｏｖｅｌＭｅｔｈｏｄｆｏｒＭｅａｓｕｒｉｎｇＥｌｅｃｔｒｏ－ｏｐｔｉｃＣｏｅｆｆｉｃｉｅｎｔｏｆＰｏｌｅｄＰｏｌｙｍｅｒＷａｖｅｇｕｉｄｅＹＡＮＧＬｉｊｕｎ,ＧＥＮＧＷａｎｚｈｅｎ,ＸＵＪｉａｎｄｏｎｇ,ＨＯＮＧＪｉｎｇ（杨黎俊）（耿完桢）（徐建...     

A Novel Modification Approach for Natural Graphite Anode of Li-ion Batteries

To improve the rate caqability and cyclability of natural graphite anode for Li-ion batteries, a novel modifwatio, approach was developed. The modification approach included two steps:(a) high-energy ball milling in a rotary aatoelave containing alumina balls, H3PO4 and ethanol;( b ) coating with pyrolytic carbon from phenlic resin. The treated graphite sluws obrious improvement compared with the original natural graphite in electroehemical properties such as cyclahility and rate capability, especially at high current density. The primaryreasons leading to the improvement in rate capability and cyclability are that the diffusion impedance of Li^ in graphite is reduced due to the fact that P filtered into graphite layers can mildly increase interlayer distances, and the fact that the structural stability of graphite surface is enhanced since the coated pyrolytic carbon can depress the co-intercalation of solvated lithium ion.     

Self—Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases

The trpical characteristic of the topology of Baycsian nctworks(BNs)is the interdependence among difffcrent nodes(variables),which makes it impossible to optimize one variablc indcpcndcntly of others,and the learning of BNs structures by general genetic algorithms is liable to converge to local extrcmum.To resolve effi-ciently this problem,a self-organizing gcnctic algorithm(SGA)based method for constructing BNs from databas-es in presented.This method makes use of a self-otganizing mechanism to dcvclop a genetic algorithm that extend-ed the crossover operalor from one to two,providing mutual competition between them,evcn adiusting the num-bers of patents in recombination(crossover/recomposition)schemes.With the K2 algorithm,this metod also optimizes the genetic operators,and utilizes adcquatcly the domain knowledge.As a result,with this method it is able to find a glohal optimum of the topology of BNs,avoiding ptcmaturc convergence to local extremum.The experimental results ptoved to be and the convergence of the SGA was discussed.