control algorithm

Top PDF control algorithm:

Probability Model Based Energy Efficient and Reliable Topology Control Algorithm

Probability Model Based Energy Efficient and Reliable Topology Control Algorithm

Abstract: Topology control is an effective method for improving the performance of wireless sensor networks (WSNs). Many topology control algorithms can achieve high energy efficiency by dynamically changing the transmission range of nodes. However, these algorithms prefer to choose short multihop communication links rather than the long directly communication links which also energy efficient probabilistic. Note that these fact, in this paper, we propose a mathematic model to explore the probability that the long directly communication links are more energy efficient than the short links. We investigate the properties of this probability and find out the optimal transmission range which has highest probability of energy efficient. Based on this conclusion, we propose the energy efficient and reliable topology control algorithm (ERTC) to maintain the r-range for the nodes instead of the k-connection; moreover, ERTC can achieve energy efficient and network connection at the same time.

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Surface electomyography signal processing algorithm and movement control algorithm fo mechatronic-assisted rehabilition of anterior cruciate ligament injuries

Surface electomyography signal processing algorithm and movement control algorithm fo mechatronic-assisted rehabilition of anterior cruciate ligament injuries

Several programs have been developed to prevent, and to decrease the incidence of non-contact ACL injuries. The focus of these preventions is to obtain a proper nerve/muscle control of the knee. Plyometric exercises consist in a rapid, powerful movement with an eccentric and concentric phase where the subject would jump from a small box and immediately jump back into the air after the contact. Balance training consists of the use of wobble or balance boards. Other balance exercises are throwing a ball with a partner while balancing on one leg. To improve strength and stability, athletes jump, and land on one leg with the knee flexed, followed by a momentary position maintenance [1]. For example, the Hospital for Special Surgery presented complementary tips and exercises to prevent ACL injuries [52]:

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Towards a Shared Control Navigation Function:Efficiency Based Command Modulation

Towards a Shared Control Navigation Function:Efficiency Based Command Modulation

We propose a new shared control algorithm -collaborative control- based on Navigation Functions (NF). NF is a cost function for the optimization paradigm, first described in [7]. The NF’s properties guarantee smooth convergence to an unique minimum in the space where they are defined. This paper describes how Collaborative Control approaches human-robot collaboration to a NF based autonomous control as much as possible. Human intervention introduces unpredictable variations, making impossible to obtain a NF from them in all situations.

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Contributions to Communication and Networking for the Internet of Things Environments

Contributions to Communication and Networking for the Internet of Things Environments

that the ADPV is superior in preserving super node connectivity. The authors in [66] consider that topology control has never achieved breakthroughs in real world deployment; moreover, the authors identify five practical obstacles of topology control algorithms at present. To address these obstacles, the authors propose a re-usable framework for implementation and evaluation of topology control. In [67] the authors propose the concept of a disjoint path vector (DPV) algorithm for a heterogeneous network in which the large number of sensor nodes has limited energy and computing capability and there are several supernodes with limited energy and unlimited computing capability. The DPV algorithm addresses the k-degree any-cast topology control problem where the main objective is to assign each sensor’s transmission range such that each node has at least k-vertex-disjoint paths to super nodes and the total power consumption is minimized. The resulting topologies are tolerant up to k-1 node failures in the worst case. In [68], to enhance the energy efficiency and reduce the radio interference in WSNs, the authors propose a new distributed topology control algorithm. In this algorithm, each node makes local decisions about its transmission power and the culmination of these local decisions produces a network topology that preserves global connectivity. The main idea of this topology control algorithm is the novel Smart Boundary Yao Gabriel Graph (SBYaoGG) and the appropriate optimizations to ensure that all links in network are symmetric and energy efficient. The more recent researches on topology control can be found in [69–73]. Moreover, detailed introductions and comparisons between different topology control algorithms can be found in reviews, such as [74–76].

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An adaptive admission control and load balancing algorithm for a QoS-aware Web system

An adaptive admission control and load balancing algorithm for a QoS-aware Web system

Table 3.1 sums up some of the characteristics of these previous works, as it is difficult to classify most of them in less than one category. The columns are the references of the previous works in chronological order. Rows (1)-(5) have already been commented on above. The row (6) indicates the performance metric that is monitored by the algorithm and the row (7) considers the invocation frequency of the admission control algorithm, that normally is the same as the monitoring frequency of the performance metric that is used as the input of the algorithm. This performance metric is needed to take admission control decisions and can be demanded in different ways depending on the proposal. Some authors define a fixed time interval to obtain monitored performance values and invoke the admission control algorithm [6, 15, 39, 139, 123, 84, 111], while others check the performance metric selected and then execute the admission control algorithm when a determined event has occurred, i.e. each time a new request or session arrives to the Web system [7, 108, 22, 51, 82, 85, 136, 35, 34, 36, 52, 8, 119, 152]. Two recent works introduce a dynamic variation of the invocation times of the admission control algorithm that depend on the workload [133, 19] with the goal of overhead reduction. In both cases, there is a switch in the admission control policy depending on the overload of the Web system.

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A novel topology control approach to maintain the node degree in dynamic wireless sensor networks

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks

Abstract: Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.

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Probabilistic algorithm for computed tomography

Probabilistic algorithm for computed tomography

Essentially, a computed tomography is a tridimensional image of an object constructed from a certain number of photographs of the attenuated radiation passing through the object at different angles. In order to construct a perfect tomography, infinite projections are required. However, certain images can be reconstructed from a finite number of projections (although with some distortion). The present work is oriented to develop an image processing system, which takes advantage of radiation flashes, optimizing the emission-detection-reconstruction procedure. An optimization technique based in a probabilistic algorithm for the assessment of the best projection-angles is presented. The proposed algorithm is applied to design a strategy for an interactive on-line scanning of elliptical objects from limited projection data.

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Active Vibration Control Design Using the Coral Reefs Optimization with Substrate Layer Algorithm

Active Vibration Control Design Using the Coral Reefs Optimization with Substrate Layer Algorithm

Table 2 shows the results obtained by the proposed CRO-SL, com- pared to different alternative algorithms. Specifically, all the algorithms which form the substrate layers in the CRO-SL approach have been tried on their own: the CRO with a single substrate has been run, with the same number of function evaluations than in the case of five substrates. This will show how the competitive co-evolution process promoted by the CRO-SL is able to obtain accurate solutions for the AVC design and location problem. In addition, a comparison with a high-performance recently proposed meta-heuristics for structures optimization, the En- hanced Colliding Bodies Optimization (ECBO) [49] is included. This approach is an improved version of the CBO [39], which includes memory and a specific mechanism to scape from local optima. The computer code for the ECBO [50] has been released by the authors in order to implementation it with small adaptations to the problem at hand. In Table 2 it can be seen how the CRO-SL (five substrates) obtain the best performance, both in the SISO and MIMO cases, with two ac- tuators/sensors ( p = q = 2). In the SISO case, the differences among different methods are small, since it is the simplest case. In fact, the CRO-SL and CRO with DE substrate and the ECBO algorithm obtain a similar value of the PI. In this case, the HS substrate is the next algo- rithm in terms of performance, whereas the Gaussian mutation and the two crossover operators (2-points and multi-point) are the poorest in terms of the PI. In the case of the MIMO, the di ff erences are much more significant. The CRO-SL with five substrates in co-evolution clearly obtains the best performance. The ECBO algorithm also performs well in this problem, as it obtains the second best result overall. In this case, the DE, is the third best approach among the tested algorithms. The HS is also the next better substrate in this version of the problems, and again the Gaussian and crossover operators do not obtain competitive results on their own in this case.

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CONTROL DE EJECUCIÓN. POTENCIA SIN CONTROL

CONTROL DE EJECUCIÓN. POTENCIA SIN CONTROL

contratos?, pues de ningún modo, y esto tiene consecuencias trágicas, pues no sólo afecta al contrato en ejecución y, en definitiva, a la eficiente utilización de los fondos destinados a las obras y servicios públicos, sino lo que es más importante, a la potencial mejora continua de la prestación, sobre la base de los datos obtenidos en una ejecución del contrato anterior. De ahí que, habitualmente, para la prestación de los servicios municipales unos contratos sucedan a otros sin apenas cambios, sin mejoras en su contenido regulador y de control, con resistencias de las Unidades proponentes a los cortos plazos de ejecución y, por ende, a la licitación continua, y a la inclusión de cláusulas sociales y ambientales que, normalmente, según sea la fase del procedimiento en la que se establezcan, acarrearán un importante esfuerzo de control directo, al menos por aquéllas.

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Accelerating multi channel filtering of audio signal on ARM processors

Accelerating multi channel filtering of audio signal on ARM processors

Prior to multiplication, the bits that configure the coefficient values must be shifted to the left (coefficient value is multiplied by a scale factor [17] to cast the real number into an integer). After the multiplication, the same shift is undone. This operation aims to improve the accuracy of the multiplication. Algorithm 1, Algorithm 2, and Algorithm 3 detail our hand-tuned vectorized implementations using NEON intrinsics of the three different filter structures: FIR, IIRI, and PIIR, respectively. Table 1 briefly summarizes and describes the set of NEON instructions that were utilized for the implementations.

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Evolutionary algorithm for state encoding

Evolutionary algorithm for state encoding

Abstract. This paper presents an encoding technique that is common for many different logic synthesis problems. It enables us to construct a system of Boolean functions, and then to decompose this system into sub-systems in such a way that a dependency of functions, included into each sub-system, on the respective arguments is reduced. For complex applications such type of encoding has a high computational complexity and the paper proposes a novel evolutionary algorithm for the solution of this problem.

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Robot arm fuzzy control by a neuro-genetic algorithm

Robot arm fuzzy control by a neuro-genetic algorithm

The SANE (Symbiotic Adaptive Neuro Evolution) algorithm was pro- posed by Miikkulainen and Moriarty [1] [2]. The novel approach of SANE, is that it encodes one unit of a neural network as one string (chromosome). The tness of a unit is determined by its degree of cooperation with the other units used to form the network. SANE keeps a population of units that represents the hidden units in a standard feed-forward neural network. The input and output units are determined by the problem itself.

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BASC_slides_edit.pdf

BASC_slides_edit.pdf

minimize communications between processors and the memory hierarchy, by reformulating the communication patterns specified within the algorithm. This method has been implemented in the TRILINOS framework, a highly-regarded suite of software, which provides functionality for researchers around the

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A job allocation algorithm for parallel processors

A job allocation algorithm for parallel processors

The minimization problem P 2 can be solved by the repeated application of an algorithm for the corresponding feasibility problem PF : We iteratively increase the due dates of all jobs by some constant until we nd a feasible schedule of the feasibility problem with the modied data. Since the maximum lateness will depend on the number of jobs, we may need to apply such an algorithm O ( n ) times. Thus, algorithms [8,9] if applied to problem P 1 have the time complexity O ( n 4 log log n )

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Partitioning algorithm for mixed criticality systems

Partitioning algorithm for mixed criticality systems

Three use cases in the MultiPARTES projects (wind power, aerospace, and video surveillance) have relied on this partitioning algorithm for generating their sys- tem partitioning. In this subsection, a more complex case is used for illustrat- ing the algorithm behaviour. The system is composed of a set of applications {a, b, c, d, e, f, g, h, i}. Application models include information, such as their criticality level and operating system, as shown in table 1.

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Evolutionary computation with simulated annealing: conditions for optimal equilibrium distribution

Evolutionary computation with simulated annealing: conditions for optimal equilibrium distribution

converge to a globally optimal population with a certain predetermined probability (i.e. to know a priori the asymptotic probability distribution) is not a purely theoretical one, since it may substantially affect their efficiency both in the accuracy of the solution obtained for a problem and in the time spent in getting that solution. The possibility to know a priori the probability of global convergence and, moreover, the ability to control it via a proper parameter setting is a quite attractive perspective, especially for those applications in which an optimal or near-optimal solution is needed.

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TÉCNICAS EVOLUTIVAS EN PROBLEMAS MULTI-OBJETIVOS EN EL PROCESO DE PLANIFICACIÓN DE LA PRODUCCIÓN / EVOLUTIONARY TECHNIQUES FOR MULTI-OBJECTIVE PROBLEMS IN PRODUCTION PLANNING

TÉCNICAS EVOLUTIVAS EN PROBLEMAS MULTI-OBJETIVOS EN EL PROCESO DE PLANIFICACIÓN DE LA PRODUCCIÓN / EVOLUTIONARY TECHNIQUES FOR MULTI-OBJECTIVE PROBLEMS IN PRODUCTION PLANNING

La planificación, en el ámbito productivo, se encarga de diseñar, coordinar, administrar y controlar todas las operaciones que se hallan presentes en la explotación de los sistemas productivos. En este marco de trabajo, aparecen numerosos Problemas de Optimización Multi-objetivo (MOPs). Éstos constan de varias funciones que suelen ser complejas y evaluarlas puede ser muy costoso. La optimización multi-objetivo es la disciplina que trata de encontrar las soluciones, denominadas Pareto óptimas, a este tipo de problemas. La compleja resolución de los MOPs es debida a las dimensiones propias del problema, al carácter combinatorio de los algoritmos y a la naturaleza de los objetivos, los cuales están vinculados a la eficiencia del sistema. En las últimas décadas muchos MOPs vinculados a la producción han sido tratados con éxito con técnicas de resolución basadas en Algoritmos Genéticos. En este trabajo se evalúa a NSGAII (Non-dominated Sorting Genetic Algorithm II), SPEAII (Strength Pareto Evolutionary Algorithm II) y a sus antecesores, NSGA y SPEA, en el proceso de planificación de la producción no estandarizada. Luego de la experiencia realizada, el algoritmo NSGAII mostró mayor eficiencia.

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The multivariate bisection algorithm

The multivariate bisection algorithm

Figure 5 illustrates the algorithm behaviour for the testing maps with the re- finement procedure. The systems of equations and their successive possible precon- ditionings are represented by a zero contour level on an mesh on the initial guess K 0 and the refinement procedure was illustrated using the rectangle Matlab ’s functions.

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The colorimetric randomized algorithm

The colorimetric randomized algorithm

Since Monte Carlo algorithms can give inccorect solutions and these false solutions need not be specially marked, it is more difficult to transform Monte Carlo algorithms into Las Vegas algorithms. This is possible if you also have a validator for the solution, an algorithm that can test that for a suggested solution, the proposal is correct. Obtain a Las Vegas algorithm by running the given Monte Carlo algorithm, then verify with the verifier that the calculated solution is correct, and iterate this process until a correct solution is calculated. Although the worst-case computation time of this approach is not limited to the top, one can estimate the expected value of the number of iterations upward. If you do not have a verifier at your disposal, it is generally not clear how to construct a Las Vegas algorithm from a Monte Carlo algorithm.

10 Lee mas

SLBN: A Scalable Max-min Fair Algorithm for Rate-Based Explicit Congestion Control

SLBN: A Scalable Max-min Fair Algorithm for Rate-Based Explicit Congestion Control

In this paper we achieve congestión control by providing each session source with an explicit rate that it can use to limit the traffic it injeets in the network. This requires max- min fair rates to be explicitly computed. The cornerstone of our approach is the algorithm that we cali SLBN, which computes a rate for each session in real-time. These rates converge very quickly to their max-min fair valúes, even in the presence of sessions joining and leaving the network. Since current core routers must cope with packets from hundreds of thousands of different session flows, in order to achieve scalability, it is desirable to minimize the processing time and the storage required at the routers. SLBN does not require processing any data packet, and the RTT valúes of the sessions do not affect its convergence (unlike above mentioned control-loop-based protocols). Additionally, it is scalable because routers only maintain a small amount of state information (only three integer variables per link) and only incur a constant amount of computation per protocol packet, independently of the number of sessions that cross the router. This is mainly achieved by moving the per-session state (w.r.t. the above mentioned max-min fair algorithms, and in particular to the one proposed in [20]) from the routers to the session sources and protocol packets.

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