Results 281 to 290 of about 662,614 (319)

An algorithm for control memory minimization

Proceedings of the 8th annual workshop on Microprogramming - MICRO 8, 1975
This paper presents a procedure for determining the minimum number of microinstructions of a given bit length B required to code a microprogram. The procedure is applied to different values of B to determine the optimal method of implementing the microprogram in the control memory of a digital computer.
Masahiro Tsuchiya, T. Jacobson
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A Symbiosis Algorithm for Robotic Control

2005 IEEE Congress on Evolutionary Computation, 2005
An algorithm inspired by the biological phenomenon of symbiosis is presented in this paper. The genetic diversity obtained from the creation of symbiotic relationships is investigated and the symbiosis algorithm is applied to a robotic forward kinematics control problem.
K. M. Ward, Mia Nazmul Haque Siddique, Liam P. Maguire, T. Martin McGinnity   +3 more
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A Congestion Control Algorithm for Datacenters

2013 IEEE Eighth International Conference on Networking, Architecture and Storage, 2013
Recently, there has been renewed interest in latency as a primary metric for mainstream data center applications. In this paper, we propose a coordinated mechanism for congestion control and multi-path transmission to reduce flow completion time in data centers.
Zhengwei Zhao, Zhixiong Jiang, Chunyang Lu, Yushan Cai, Jingping Bi   +4 more
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Topology control algorithms in WISELIB

Proceedings of the 2010 ICSE Workshop on Software Engineering for Sensor Network Applications, 2010
In wireless sensor networks, topology control algorithms are distributed algorithms whose goal is to infer a sub-network from the real network topologyin order to reduce the number of active nodes and active communication links, but preserving some desired property of the network (e.g., connectivity or low degree).
Jaume Anguera, Maria J. Blesa, J. Farré, V. López, Jordi Petit   +4 more
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Acquisition of Vehicle Control Algorithms

2003
In this paper, we present a multi-input multi-output (MIMO) dataacquired controller using system of hierarchical decision tables for a simulated vehicle driving control problem. The simulator incorporates dynamic mathematical model of a vehicle driving on a track.
Shang Fulian, Wojciech Ziarko
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Synchronization and control of parallel algorithms

Parallel Computing, 1985
Abstract We propose a modest collection of primitives for synchronization and control in parallel numerical algorithms. These are phrased in a syntax that is compatible with FORTRAN, creating a publication language for parallel software. A preprocessor may be used to map code written in this extended FORTRAN into standard FORTRAN with calls to the ...
Paul O. Frederickson, Rondall E. Jones, Brian T. Smith   +2 more
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Adaptive model algorithmic control

Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), 2001
Considers the design and analysis of a robust adaptive model algorithmic controller based on the internal model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a model algorithmic internal model controller to obtain an adaptive model algorithmic internal model ...
Lei Xing, Aniruddha Datta
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A connectionless congestion control algorithm

ACM SIGCOMM Computer Communication Review, 1989
This paper describes a congestion control algorithm that operates within a connectionless network environment. The algorithm controls a source's transmission rate, with that rate determined by feedback from the point of congestion. The algorithm is well suited to operate from within the IP layer of the TCP/IP protocol suite.
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Algorithm Control: Efficient Learning to Control Algorithm Parameters

Es ist weithin bekannt, dass die Performanz von vielen Algorithmen von ihren Parametern abhängt. Um mühsames und fehleranfälliges Parameteroptimieren zu vermeiden, wurden automatisierte Ansätze entwickelt, um effizient Spitzenleistungen von Algorithmen zu erreichen.
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Endocrine Control Evolutionary Algorithm

2010 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, 2010
The paper presents a new technique for the control of the population's dynamic in evolutionary optimization. The technique is inspired by the endocrine system, respectively, by the control mechanism of the hormones concentration. The approach is suitable for multimodal optimization in static or dynamic environment.
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