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Untargeted metabolomics makes it possible to identify compounds that undergo significant changes in concentration in different experimental conditions. The resulting metabolomic profile characterizes the perturbation concerned, but does not explain the underlying biochemical mechanisms. Bioinformatics methods make it possible to interpret results in light of the whole metabolism. This knowledge is modelled into a network, which can be mined using algorithms that originate in graph theory. These algorithms can extract sub-networks related to the compounds identified. Several attempts have been made to adapt them to obtain more biologically meaningful results. However, there is still no consensus on this kind of analysis of metabolic networks. This review presents the main graph approaches used to interpret metabolomic data using metabolic networks. Their advantages and drawbacks are discussed, and the impacts of their parameters are emphasized. We also provide some guidelines for relevant sub-network extraction and also suggest a range of applications for most methods.

The goal of this research is to use eyegaze tracking data to provide insights into designing radiology workstations. We designed a look-alike radiology task with artificial stimuli. The task involved a comparative visual search of two side-by-side images, using two different interaction techniques. We tracked the eyegaze of four radiologists while they performed the task and measured the duration of the fixations on the controls, the left and right images, and on the artificial targets. Response time differences between the two interaction techniques exceeded the differences of fixations on the controls. Fixations on the left-side images are longer than the right-side images, and the search for multifeatured targets occurs in two phases: first a regular scan path search phase for a likely target and then a confirmation phase of several fixations on the target in each side-by-side image. We conclude that eyegaze tracking shows that disruption of visual search leads to cognitive disruption; subjects use the left image as a reference image and multiple saccades between left and right side images are necessary, because of the limitations of the visual working memory.

The dissipated energy even of a manipulator must be decreased in order to improve the environment of the earth. This paper describes an optimal path which minimizes the dissipated energy in PTP motions of a vertically articulated manipulator. The dynamic equation of the manipulator is nonlinear due to centrifugal, Coriolis, gravity, and Coulomb friction forces. Moreover the driving system of the joints is also nonlinear in that the generating torque is expressed by a third-degree polynomial with respect to current. Therefore, an optimal path cannot be obtained by solving a two-point boundary-value problem analytically. In this paper an optimal path is searched for by a Genetic Algorithm (GA) in cases in which all kinds of nonlinear characteristics of the manipulator, including the driving system, are taken into consideration. The obtained optimal velocity functions are applied to a vertically articulated manipulator with two direct-drive motors. The dissipated energy is measured by integrating the input power to the motors. Experimental results agree with the simulation values only when all kinds of nonlinearity are taken into consideration. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 157(3): 26–34, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20437 Copyright © 2006 Wiley Periodicals, Inc.

Ant Colony System (ACS) Algorithm is new meta heuristic for hard combinatorial optimization problem. It is a population based approach that uses exploitation of positive feedback as well as greedy search. It was first proposed for tackling the well known Traveling Salesman Problem (TSP). In this paper, we introduce ACS of new method that adds reinforcement value for each edge that visit to Local/Global updating rule. and the performance results under various conditions are conducted, and the comparision between the original ACS and the proposed method is shown. It turns out that our proposed method can compete with tile original ACS in terms of solution quality and computation speed to these problem.

One of the most challenging aspects of the development, at the Institute of Agricultural and Environmental Engineering (IMAG B.V.), of an automatic harvesting machine for cucumbers was to achieve a fast and accurate eye–hand co-ordination during the picking operation. This paper presents a procedure developed for the cucumber harvesting robot to pursue this objective. The procedure contains two main components. First of all acquisition of sensory information about the working environment of the robot and, secondly, a program to generate collision-free manipulator motions to direct the end-effector to and from the cucumber. This paper elaborates on the latter. Collision-free manipulator motions were generated with a so-called path search algorithm. In this research the A*-search algorithm was used. With some numerical examples the search procedure is illustrated and analysed in view of application to cucumber harvesting. It is concluded that collision-free motions can be calculated for the seven degrees-of-freedom manipulator used in the cucumber picking device. The A*-search algorithm is easy to implement and robust. This algorithm either produces a solution or stops when a solution cannot be found. This favourable property, however, makes the algorithm prohibitively slow. The results showed that the algorithm does not include much intelligence in the search procedure. It is concluded that to meet the required 10 s for a single harvest cycle, further research is needed to find fast algorithms that produce solutions using as much information about the particular structure of the problem as possible and give a clear message if such a solution can not be found.

In order to solve disadvantages of the ant colony algorithm in path planning of mobile robot, an enhanced ant colony optimization based on combined optimization is proposed. Improved ant colony algorithm' idea is selecting the searched full path to combined optimize with the rest of the path when all ants in a generation have completed a path search. That is choosing the shorter road segments of between two paths crossing point to combine into a better path, to produce more of the full path for the global pheromone updating and accelerate the positive feedback effect of pheromone. Experimental results show that the new algorithm is correct and effective.

An analysis of fiscal policy is needed for long-term plan in constructing infrastructures. This paper constructs a potential growth path search model with income transfer constrains. The model is based on Turnpike model maximizing the consumption utility. This model shows the optimal rate of composition at every industry, and shows that the rate of potential economic growth is 6.6%. The numerical analysis shows the change of the potential growth path and the optimal industrial structure with increase of national bonds.

In this paper, we propose a practical automatic parking system of a vehicle, which has geometric constraints for its trajectories, based on weighted graphs for path planning. The path plan consists of the ordered combination of lattice nodes virtually arranged within the system as landmarks. While, the graphs are formed more specifically with vehicle states composed by selected node I.D. number and vehicle heading angle on these node positions. The best graph is searched with Genetic Algorithm, one of the powerful methods for optimal combination problems. Besides, learning ability for impassable regions refines the path search so as to be adaptive to the layout of the path planning space surrounded by walls. As a result of computer simulations, we show the system has a potential to be emplyoed principally in real application.

We present a new, interactive tool called Intelligent Scissors which we use for image segmentation. Fully automated segmentation is an unsolved problem, while manual tracing is inaccurate and laboriously unacceptable. However, Intelligent Scissors allow objects within digital images to be extracted quickly and accurately using simple gesture motions with a mouse. When the gestured mouse position comes in proximity to an object edge, a live-wire boundary “snaps” to, and wraps around the object of interest. Live-wire boundary detection formulates boundary detection as an optimal path search in a weighted graph. Optimal graph searching provides mathematically piece-wise optimal boundaries while greatly reducing sensitivity to local noise or other intervening structures. Robustness is further enhanced with on-the-fly training which causes the boundary to adhere to the specific type of edge currently being followed, rather than simply the strongest edge in the neighborhood. Boundary cooling automatically freezes unchanging segments and automates input of additional seed points. Cooling also allows the user to be much more free with the gesture path, thereby increasing the efficiency and finesse with which boundaries can be extracted.

This paper describes a new technique for improving global path planning for mobile robots in partially known indoor environments. The method is based on the periodical measurement of the time and position reached by the robot while moving to the goal and on the modification of each branch cost. Due to non-modeled obstacles, there is generally a deviation from the original trajectory calculated using an environment map. Usually, global path planning does not consider this information for new optimal time path search. This approach takes into account statistics about the navigation time when performing different trajectories, avoiding the necessity of dealing with the sensor map information.

In order to solve the complex problems in path planning of humanoid robot, an algorithm model based on ant colony algorithm is proposed. The robot simulation modeling environment is formed by the grid. The idea of the algorithm is simulating the process of ants foraging, and many ants complete optimal path search by collaborate with each other to pass the pheromone to find food. Through simulation experiments, we achieved better results. Satisfactory results are achieved by made experiments on the humanoid robot NAO, and the algorithm are verified finally.

The application of multiple-objective route choice for in-vehicle route guidance systems is discussed. A bi-objective path search algorithm is presented and its use demonstrated. A concept of trip quality is introduced that is composed of two objectives: minimizing travel time and minimizing trip complexity. Trade-offs between the objectives are examined. The concept is illustrated through simulation modeling on a test network. The experiments serve to demonstrate the effects on the trip performance of pretrip routing and dynamic routing strategies under full market penetration (an idealized condition) and under varying levels of demand and trade-offs between time and complexity.

The advances in microscopic imaging technology have created great amount of unprecedentedly clear images of neurons. How to extract useful information from this massive data has created another challenge for neuroscientists. The connectivity relationships among these neurons provide critical clues for understanding how the information is transmitted in the brain. In this paper, we analyze the neurons in the Drosophila brain, find the possible connecting path, and furthermore, perform cluster analysis from the connectivity information of thousands of tangled neurons.

Fire is one of the disasters that often occur in everyday life. There has been much research done to build a fire Simulator. In this paper, we propose a fire Simulator that is applied optimal path search algorithm to arrange the quick evacuation. This simulator considers the structure of a building and the location where the fire has initially occurred, applying it to the simulator. Especially when implementing the training simulation for firefighters, optimized evacuation path for the required virtual rescuer awaiting rescue was informed to implement more realistic firefighting training simulator. For the simulation for the test, fixed data was set to apply the shortest path search algorithm and flame smoke spread algorithm. As a result of the test, the optimized path was derived through avoiding the flame occurred from flame smoke spread algorithm and following the shortest path search algorithm.

This paper provides a detailed description of work in progress on the algorithmic challenges of generating textual route guidance. More specifically, we show how to transform the generation of meaningful turn instructions into a path search problem on a graph. We develop a thorough algorithmic framework with well-defined data structures. Subsequently, we show how to use this algorithmic framework in a way that gives flexible route guidance reflecting either user preferences of personal local knowledge. The structural properties of the graph allow us to generate guidance along the route in time linear in the number of junctions on the computed routes in theory and within milliseconds in practice. As the results are preliminary some of the explanations are given by example.

Efficient and robust retrieval of commercial videos is an important topic for many applications such as commercial monitoring, market investigation. In this paper, we propose a two-step scheme to optimally incorporate the information of both visual and audio modalities into commercial retrieval. Firstly an efficient search method based on the extracted audio fingerprint feature is proposed to yield candidate results, and then visual signatures are extracted fused with audio features to validate the candidate results. The computational efficiency of audio modality and the robustness of visual modality are utilized simultaneously. The proposed optimal path search further guarantees the effectiveness in real applications. Comparison experiments were carried out over 123 video commercials and real TV programs from four channels. Results demonstrate both high efficiency and high robustness of the proposed method.

In this paper the comparisons of some solution approaches for vehicle routing are reported. The scenarios of vehicle routing with static travel costs, pseudodynamic travel costs, dynamic travel costs are considered. Some characteristics of link costs are discussed. For each scenario the changes in vehicle routing (i.e. path search) are highlighted; a test application for freight distribution in city logistic is done. The goal is to verify how the cost variation in the time influences the route optimization.

We propose a SPAM (simultaneous planning and mapping) technique for a manipulator-type robot working in an uncertain environment via a novel Best Next Move algorithm. Demands for a smart decision to move a manipulator such as humanoid arms in uncertain or crowded environments call for a simultaneous planning and mapping technique. In the present work, we focus more on rapid map generation rather than global path search to reduce ignorance level of a given environment. The motivation is that the global path quality will be improved as the ignorance level of the unknown environment decreases. The 3D sensor introduced in the previous work has been improved for better mapping capability and the real-time rehearsal idea is used for c-space cloud point generation. Captured cloud points by 3D sensors, then, create an instantaneous c-space map whereby the Best Next Move algorithm directs the local motion of the manipulator. The direction of the Best Next Move utilizes the gradient of the density distribution of the k-nearest-neighborhood sets in c-space. It has a tendency of traveling along the direction by which the point clouds spread in space, thus rendering faster mapping of c-space obstacles possible. The proposed algorithm is compared with a sensor-based algorithm such as sensor-based RRT for performance comparison. Performance measures, such as mapping efficiency, search time, and total number of c-space point clouds, are reported as well. Possible applications include semiautonomous telerobotics planning and humanoid arm path planning.

In this article, we propose an improved cryptanalysis of the double-branch hash function standard RIPEMD-160. Using a carefully designed non-linear path search tool, we study the potential differential paths that can be constructed from a difference in a single message word and show that some of these message words can lead to very good differential path candidates. Leveraging the recent freedom degree utilization technique from Landelle and Peyrin to merge two branch instances, we eventually manage to obtain a semi-free-start collision attack for 42 steps of the RIPEMD-160 compression function, while the previously best know result reached 36 steps. In addition, we also describe a 36-step semi-free-start collision attack which starts from the first step.

In this paper we deal with the heuristic solution of the classical job shop problem. Both the constructive and the iterative phase of our algorithm apply insertion techniques combined with beam search. In the first phase we successively insert the operations into feasible partial schedules. In the iterative phase we generate paths in a particular neighbourhood graph instead of investigating the neighbourhood completely. To select “interesting” neighbours, we use the combinatorial path structure of feasible solutions of the job shop problem. The results of our algorithm are compared with those from other well-known methods on benchmark problems.

International audience; Factored planning consists in driving a modularor distributed system to a target state, in an optimal manner,assuming all actions are controllable. Such problems take theform of path search in a product of graphs. The state space ofeach component is a graph, in which one must find a path tothe local goal of this component. But when all components areconsidered jointly, the problem amounts to finding a path ineach of these state graphs, while ensuring their compatibility onthe actions that must be performed jointly by some componentsof the system. This paper proposes a solution under the form ofa multi-agent version of A*, assembling several A*, each oneperforming a biased depth-first search in the graph of eachcomponent.

In this paper we extend the Predication-based Semantic Indexing (PSI) approach to search efficiently across triple-predicate pathways in a database of predications extracted from the biomedical literature by the SemRep system. PSI circumvents the combinatorial explosion of possible pathways by converting the task of traversing individual predications into the task of measuring the similarity between composite concept vectors. Consequently, search time for single, double or triple predicate paths is identical once the relevant concept vectors have been constructed. This paper describes the application of PSI to infer double and triple predicate pathways connecting example pairs of therapeutically related drugs and diseases; and to use these inferred pathways to guide search for treatments for other diseases. In an evaluation of the utility of vector-based dual- and triple-predicate path search in a simulated discovery experiment, these approaches are found to be complementary, with best performance obtained through their application in combination.

An approach of 3D path planning for AUV on the basis of virtual terrain and thread map is proposed. The virtual terrain is constructed to predigest the 3D search space, transforming the 3D path planning to 2D path planning. Then, the thread map is made for unification of hostile threat and terrain. To obtain standout expansibility, an improved A∗ algorithm based on circle searching is applied for path search. Simulation results illustrate the proposed approach is simple, and fast, and the path posed is safe for trail.

Many attempts have been made in recent years to automate fully the task of generating railway timetables, mainly by using one of the numerical optimisation techniques. Virtually all of these attempts depend on some kind of technique to evaluate the timetable. Upon evaluation of a railway timetable, it is important to take into account the passengers’ points of view, including the disbenefit (inconvenience) for any single passenger during his/her entire journey on the railway. As one of such evaluators, Sujic was first developed in 1988, and rewritten in 1996, at the research group at the University of Tokyo, and has since been used by many researchers in a number of universities in Tokyo. However, this program is slow and memory-eating because of its poor implementation of path search algorithms. Also, the program has no capability to deal with rescheduling. The standard Dijkstra’s algorithm could not be used by the old Sujic program because of the path search criteria. In the new Sujic, the author applied the slightly modified Dijkstra’s algorithm and drastically improved the efficiency of the search without changing the criteria, the efficiency of the search has drastically improved. The evaluation of train re-scheduling is slightly different from that of scheduling because the arrival and departure times of trains that passengers expect (which is the scheduled times) are different from the actual arrival and departure times under re-scheduling. Although evaluation can be performed by simply providing old Sujic with the actual timetables, it yields a result in which passengers act as if they “expect” that an accident will happen in the future. In the new version, this problem is addressed by adding the functionality to use multiple schedules in one evaluation.

Researching on the power supply path is one aspect of analyzing gird physics topology structure, the paper aims to discuss the power supply paths which would describe the power supply process from a certain source, based on power components theory. In general, path search method is divided into depth-first search and breadth-first search. Improving on the depth-first search and analyzing the complexity of the both methods. To choice the more preponderant depth-first search method in the end.

This paper presents an online multi-query path planner for exploration tasks planned onboard an unmanned helicopter. While the desirable properties of roadmaps can be exploited in offline path planning, the dynamic nature of exploration scenarios hinders to utilize conventional roadmap planners. Hence, the presented path planning approach utilizes a deterministically sampled roadmap which is dynamically indexed in real time. To address situations of partial terrain knowledge, the roadmap can be extended from its a priori dimensions towards locations of unknown terrain that are outside its original, a priori boundaries. The multi-query property of the planning system allows for combinatorial optimization such that a rapidly acting decisional autonomy is achievable during exploration flights. D*-Lite is used as dynamic heuristic path searcher in order to re-plan efficiently. Inspired by the original work on this path search algorithm, the roadmap graph is augmented with an exploration vertex which steers the exploration behavior of the vehicle. As a result, the presented roadmap guides an unmanned rotorcraft through a priori unknown urban terrain in real time.

One of the most important types of applications currently being used to share knowledge across the Inter-net are social networks. In addition to their use in social, professional and organizational spheres, social networks are also frequently utilized by researchers in the social sciences, particularly in anthropology and social psychology. In or-der to obtain information related to a particular social net-work, analytical techniques are employed to represent the network as a graph, where each node is a distinct member of the network and each edge is a particular type of relation-ship between members including, for example, kinship or friendship. This article presents a proposal for the efficient solution to one of the most frequently requested services on social networks; namely, taking different types of relation-ships into account in order to locate a particular member of the network. The solution is based on a biologically-inspired modification of the ant colony optimization algorithm. The authors would like to thank two anonymous reviewers and the editors for their helpful suggestions during the preparation of this article. This study was funded through a competitive grant awarded by the Spanish Ministry of Education and Science for the THUBAN Project (TIN2008-02711) and through MA2VICMR consortium (S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government. Publicado

The traditional path search algorithms for single-model transportation network have no application to the integrated transportation network, since there is vast difference between the two kinds of network. In this study, we present novel algorithms for the shortest and k-shortest path search problems under the integrated transportation network. Firstly, the service network structure is processed by considering the coefficients of transit cost weight and time weight respectively, after this work, the list of transit scenes is added to each node information and some artificial nodes are constructed. then the shortest and K-shortest path search algorithms are presented in detail. Further, by analyzing the special character of service network, a new search strategy is proposed which can deal with the transit scenario efficiently and reduce the time cost of the path search algorithms. Examples with different scales of network and OD sets are then given to verify the proposed algorithms. The results prove the efficiency of our proposed approaches.

This paper considers a method for the definition of pedestrian escape routes by means of the simulation of pedestrian outflow. Feasible solutions are generated and compared in terms of evacuation time. In the proposed method an aggregate model for the estimation of evacuation time of a building is adopted. Such a method can be easily implemented and can be used to give a first evaluation of evacuation procedures without performing evacuation drills and can be used to give a fast response in identifying critical points on the network. To check the capabilities of the models used within the proposed approach, results obtained from simulations are compared with data recorded from an experiment on a test site conducted in a primary school located in an Italian town; an application consisting of designing escape routes for a school is also presented.

Proceedings of: Third International Conference on Computational Aspects of Social Networks (CASoN).Took place 2011, October,19-21 , in Salamanca (Sapin).The event Web site is http://www.mirlabs.net/cason11/ Nowadays the Social Networks are experiencing a growing importance. The reason of this is that they enable the information exchange among people, meeting people in the same field of work or establishing collaborations with other research groups. In order to manage social networks and to find people inside them, they are usually represented as graphs with persons as nodes and relationships between them as edges. Once this is done, establishing contact with anyone involves searching the chain of people to reach him/her, that is, the search of the path inside the graph which joins two nodes. In this paper, a new algorithm based on nature is proposed to realize this search: SoS-ACO (Sense of Smell - Ant Colony Optimization). This algorithm improves the classical ACO algorithm when it is applied in huge graphs. This study was funded through a competitive grant awarded by the Spanish Ministry of Education and Science for the THUBAN Project (TIN2008-02711) and through MA2VICMR consortium (S2009/TIC-1542, http://www.mavir.net), a network of excellence funded by the Madrid Regional Government. Publicado

According to the great significance of 3D game technology, this paper will do detailed research on main technologies of 3D. Firstly, this paper puts forward to design concept, designs the main frame of 3D game. Secondly, through researching collision detection technologies, the improved collision detection method is proposed based on Aligned Axis Bounding Box according to some disadvantages of collision detection methods. The improved collision detection method effectively reduces operation time, advanced game speed. Thirdly, through researching artificial intelligence for 3D game, path search method is improved according to the needs of system. The improved idea uses restricting search range of A* search arithmetic with the shortest path. It can get intelligent path search efficiently and factually. Finally, the 3D game system is realized based on 3D engine technologies. The system is rendered smoothly, authentically, manipulated easily.

Branch migration of the Holliday junction takes place at the center of the RuvA tetramer. To elucidate how branch migration occurs, umbrella sampling simulations were performed for complexes of the RuvA tetramer and Holliday junction DNA. Although conventional umbrella sampling simulations set sampling points a priori, the umbrella sampling simulation in this study set the sampling points one by one in order to search for a realistic path of the branch migration during the simulations. Starting from the X-ray structure of the complex, in which the hydrogen bonds between two base-pairs were unformed, the hydrogen bonds between the next base-pairs of the shrinking stems were observed to start to disconnect. At the intermediate stage, three or four of the eight unpaired bases interacted closely with the acidic pins from RuvA. During the final stage, these bases moved away from the pins and formed the hydrogen bonds of the new base-pairs of the growing stems. The free-energy profile along this reaction path showed that the intermediate stage was a meta-stable state between two free-energy barriers of about 10 to 15 kcal/mol. These results imply that the pins play an important role in stabilizing the interactions between the pins and the unpaired base-pairs. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010

This paper presents a rapid path planning approach enabling an unmanned aircraft to quickly react to newly located or changed obstacles. All three spatial dimensions are considered during planning, as it is likely to be necessary inside or under urban obstacles. A sampling based motion planning approach is combined with an anytime path search such that an online trade-off between path optimality and rapid path computation is possible. This substantial increase in system autonomy is achieved for holonomic manoeuvring through scenes with unforeseen obstacles changes. The proposed online planning approach does not require a highly reliable, real time remote control link for collision avoidance.

This paper presents a research study on skyline path queries. Given a source s and a destination d on a road network and multiple path search criteria (e.g., short distance and short travel time), a skyline query returns a set of non-dominated paths from s to d. These non-dominated paths are called skyline paths. Efficient computation of skyline path queries is very challenging due to expensive network traversals and extensive path comparisons in dominance tests. In this paper, we explore the characteristics of skyline paths, based on which a novel skyline path search algorithm called SkyPath is proposed. To narrow down the search scope for result skyline paths, partial dominance test and full path dominance test are devised as two components of SkyPath. Evaluation results show the superiority of the SkyPath algorithm over the state-of-the-art approaches.

In this paper, we address the problem of finding a minimal set of execution paths that achieve a user-specified level of observability coverage. Under this metric, a program statement is only considered covered if its execution has influence on some output. We use Pseudo-Boolean Optimization (PBO) to model the problem of finding the paths that are most likely to increase code coverage. Generated paths are then validated to check for feasibility. This methodology was implemented into a fully functional tool that is capable of handling real programs specified in the C language.

"The temporal demand matrix is an essential input to both on-line and off-line applications. of dynamic traffic assignment (DTA). This paper presents a new method to solve the simultaneous. adjustment of a dynamic traffic demand matrix, searching for a reliable solution. with acceptable computational times for off-line applications and using as an input traffic. counts and speeds, prior O–D matrices and other aggregate demand data (traffic demand. productions by zone). The proposed solving procedure is a modification of the basic Simultaneous. Perturbation Stochastic Approximation (SPSA) path search optimization method;. it can find a good solution when the starting point (the seed matrix) is assumed to be. ‘‘near” the optimal one, working with a gradient approximation based on a simultaneous. perturbation of each demand variable."

Publisher Summary This chapter aims to establish systematic approaches to address the incremental stimulation (IS) alternation issues, and create effective and efficient MUNE automation for clinical applications. The frameworks of two proposed approaches to deal with the alternation issues are described. The first is through global optimization. The second involves direct identification of possible alternation patterns. Data analysis is presented to illustrate the results of each approach. The chapter reviews that motor unit number estimation (MUNE) is useful in many clinical applications. Several methods have been developed to provide estimates of the motor unit number (MUN) to allow diagnosis and assessment of neuromuscular disorders. To estimate the MUN, the total response from a muscle group of interest is measured as well as the responses from a sample of individual motor units from the same muscle group. The total muscle response is usually represented by the compound muscle action potential (CMAP) acquired under supramaximal stimulation. The average single motor unit potential (SMUP) is derived from the responses of individual motor units. MUNE is the ratio between the CMAP and the average SMUP. It reviews that an accurate estimate of SMUP depends upon the samples of motor units. In the case of the incremental stimulation IS method, the only parameter that varies is stimulus intensity (SI).

Computational analysis of pathways in metabolic networks has numerous applications in systems biology. While graph theory–based approaches have been presented that find biotransformation routes from one metabolite to another in these networks, most of these approaches suffer from finding too many routes, most of which are biologically infeasible or meaningless. We present a novel approach for finding relevant routes based on atom mapping rules (describing which educt atoms are mapped onto which product atoms in a chemical reaction). This leads to a reformulation of the problem as a lightest path search in a degree-weighted metabolic network. The key component of the approach is a new method of computing optimal atom mapping rules.

A new approach to the false path problem in timing verifiers is presented. This approach is based on the modeling of both the logic and timing behavior of a circuit. Using the logic propagation conditions associated with each delay, efficient algorithms have been developed to find statically sensitizable paths. These algorithms simultaneously perform a longest path search and a partial verification of the sensitization of the paths. The resulting paths undergo a final and complete sensitization. The algorithms find the longest statically sensitizable path, whose length is a lower bound to the critical path length, and its associated sensitizing input vector. The algorithms can be easily modified to provide an ordered list of all the statically sensitizable paths above a given threshold. An initial analysis of the circuit by the PERT algorithm guides the critical path search and allows pruning of subgraphs that cannot lead to the solution. Results show that these techniques succeed in curbing the combinatorial explosion associated with the longest statically sensitizable path search. >

Automatic speech recognitionmainly relies on hiddenMarkov models (HMM) which make little use of phonetic knowledge. As an alternative, landmark based recognizers rely mainly on precise phonetic knowledge and exploit distinctive features. We propose a theoretical framework to combine both approaches by introducing phonetic knowledge in a non stationary HMM decoder. To demonstrate the potential of the method, we investigate how broad phonetic landmarks can be used to improve a HMM decoder by focusing the best path search. We show that, assuming error free landmark detection, every broad phonetic class brings a small improvement. The use of all the classes reduces the error rate from 22%to 14% on a broadcast news transcription task. We also experimentally validate that landmarks boundaries does not need to be detected precisely and that the algorithm is robust to non detection errors.

We give a framework for the globalization of a nonsmooth Newton method for solving Lipschitz equations introduced by B. Kummer. We start with recalling Kummers approach to convergence analysis of this method and state his results for local convergence. In a second part we give a globalized version of this method. In our approach we use first a monotone path search idea to control the descent. After elaborating the single steps, we analyze and discuss the proof of global convergence resp. of local superlinear or quadratic convergence of the algorithm. We sketch also a nonmonotone version of the algorithm. In the last part we discuss and illustrate the details of the general algorithm (e.g the computation of a path) for some interesting examples and present results from numerical tests.

We give a framework for the globalization of a nonsmooth Newton method introduced by B. Kummer. We start with recalling Kummer's approach to convergence analysis of this method and state his results for local convergence. In a second part we give a globalized version of this method. In our approach we use a path-search idea to control the descent. After elaborating the single steps, we analyze and discuss the proof of global convergence resp. of local superlinear or quadratic convergence of the algorithm. We end with illustrating our ideas at some interesting examples.

The negotiated congestion mechanism forms the basis of most published FPGA routers today, with many routers projecting congestion and any other requirements onto a scalar search space to evaluate candidate paths. In this paper, we study the numerical stability of these scalar projections as the number of iterations increase. We show that in these scalar search spaces the norm of path costs increase exponentially with the number of iterations, leading to floating-point absorption and representation problems in computer arithmetic. We propose a novel two-component totally-ordered monoid space for path candidate evaluation, that guarantees the A* search finds a path with minimum congestion cost, and has linear norm growth with respect to the number of iterations. We demonstrate the efficacy of our new algorithm by testing on hard open routing problems in the FPGA Place and Route Challenge. The router successfully found 18 new routing solutions to instances that were previously unroutable, reducing the lowest track count to route 20 standard FPGA routing benchmarks by 10.2%.

Wireless sensor networks for irrigation applications normally consist of low-cost and low-power nodes deployed in a harsh environment. It turns out that the radio links between the coordinating node and actuating nodes are more critical and are expected to be more robust to radio interference and node failure than links between the coordinating node and sensing nodes. We present an efficient method to create robust links for irrigation sensor networks built on the ZigBee specification. In particular, multipaths between the coordinating node and actuating nodes are created and used to enhance the critical links. It is shown that, by utilizing the properties of addresses of ZigBee networks, those multipaths can be dynamically created and released adapting to topology changes without any path search activities. Simulation results confirm the robustness of the proposed multipath links.