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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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**.

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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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