Task assignment in a multiprocessor system

One of the key problems in the design of any incompletely connected multiprocessor system is to appropriately assign the set of tasks in a program to the Processing Elements (PEs) in the system. The task assignment problem has proven difficult both in theory and in practice. This paper presents a simple and efficient heuristic algorithm for assigning program tasks with precedence and communication constraints to the PEs in a Message-based Multiple-bus Multiprocessor System, M3, so that the total execution time for the program is minimized. The algorithm uses a cost function: “Minimum Distance and Parallel Transfer” to minimize the completion time. The effectiveness of the algorithm has been demonstrated by comparing the results with (i) the lower bound on the execution time of a program (task) graph and (ii) a random assignment.

Impaired conflict monitoring in Parkinson's disease patients during an oculomotor redirect task

Fallibility is inherent in human cognition and so a system that will monitor performance is indispensable. While behavioral evidence for such a system derives from the finding that subjects slow down after trials that are likely to produce errors, the neural and behavioral characterization that enables such control is incomplete. Here, we report a specific role for dopamine/basal ganglia in response conflict by accessing deficits in performance monitoring in patients with Parkinson's disease. To characterize such a deficit, we used a modification of the oculomotor countermanding task to show that slowing down of responses that generate robust response conflict, and not post-error per se, is deficient in Parkinson's disease patients. Poor performance adjustment could be either due to impaired ability to slow RT subsequent to conflicts or due to impaired response conflict recognition. If the latter hypothesis was true, then PD subjects should show evidence of impaired error detection/correction, which was found to be the case. These results make a strong case for impaired performance monitoring in Parkinson's patients.

Modelling electricity demand with representative load curves

Models for electricity planning require inclusion of demand. Depending on the type of planning, the demand is usually represented as an annual demand for electricity (GWh), a peak demand (MW) or in the form of annual load-duration curves. The demand for electricity varies with the seasons, economic activities, etc. Existing schemes do not capture the dynamics of demand variations that are important for planning. For this purpose, we introduce the concept of representative load curves (RLCs). Advantages of RLCs are demonstrated in a case study for the state of Karnataka in India. Multiple discriminant analysis is used to cluster the 365 daily load curves for 1993-94 into nine RLCs. Further analyses of these RLCs help to identify important factors, namely, seasonal, industrial, agricultural, and residential (water heating and air-cooling) demand variations besides rationing by the utility. (C) 1999 Elsevier Science Ltd. All rights reserved.

Task Dependent Boundary Mannequins in Statistical DHM

The primary objective of the paper is to make use of statistical digital human model to better understand the nature of reach probability of points in the taskspace. The concept of task-dependent boundary manikin is introduced to geometrically characterize the extreme individuals in the given population who would accomplish the task. For a given point of interest and task, the map of the acceptable variation in anthropometric parameters is superimposed with the distribution of the same parameters in the given population to identify the extreme individuals. To illustrate the concept, the task space mapping is done for the reach probability of human arms. Unlike the boundary manikins, who are completely defined by the population, the dimensions of these manikins will vary with task, say, a point to be reached, as in the present case. Hence they are referred to here as the task-dependent boundary manikins. Simulations with these manikins would help designers to visualize how differently the extreme individuals would perform the task. Reach probability at the points in a 3D grid in the operational space is computed; for objects overlaid in this grid, approximate probabilities are derived from the grid for rendering them with colors indicating the reach probability. The method may also help in providing a rational basis for selection of personnel for a given task.

Fault-tolerant Video on Demand in RSerPool Architecture

With the advent of Internet, video over IP is gaining popularity. In such an environment, scalability and fault tolerance will be the key issues. Existing video on demand (VoD) service systems are usually neither scalable nor tolerant to server faults and hence fail to comply to multi-user, failure-prone networks such as the Internet. Current research areas concerning VoD often focus on increasing the throughput and reliability of single server, but rarely addresses the smooth provision of service during server as well as network failures. Reliable Server Pooling (RSerPool), being capable of providing high availability by using multiple redundant servers as single source point, can be a solution to overcome the above failures. During a possible server failure, the continuity of service is retained by another server. In order to achieve transparent failover, efficient state sharing is an important requirement. In this paper, we present an elegant, simple, efficient and scalable approach which has been developed to facilitate the transfer of state by the client itself, using extended cookie mechanism, which ensures that there is no noticeable change in disruption or the video quality.

Multi-UAV Task Allocation using Team Theory

A multiple UAV search and attack mission in a battlefield involves allocating UAVs to different target tasks efficiently. This task allocation becomes difficult when there is no communication among the UAVs and the UAVs sensors have limited range to detect the targets and neighbouring UAVs, and assess target status. In this paper, we propose a team theoretic approach to efficiently allocate UAVs to the targets with the constraint that UAVs do not communicate among themselves and have limited sensor range. We study the performance of team theoretic approach for task allocation on a battle field scenario. The performance obtained through team theory is compared with two other methods, namely, limited sensor range but with communication among all the UAVs, and greedy strategy with limited sensor range and no communication. It is found that the team theoretic strategy performs the best even though it assumes limited sensor range and no communication.

Understanding How the Brain Changes Its Mind: Microstimulation in the Macaque Frontal Eye Field Reveals How Saccade Plans Are Changed

Accumulator models that integrate incoming sensory information into motor plans provide a robust framework to understand decision making. However, their applicability to situations that demand a change of plan raises an interesting problem for the brain. This is because interruption of the current motor plan must occur by a competing motor plan, which is necessarily weaker in strength. To understand how changes of mind get expressed in behavior, we used a version of the double-step task called the redirect task, in which monkeys were trained to modify a saccade plan. We microstimulated the frontal eye fields during redirect behavior and systematically measured the deviation of the evoked saccade from the response field to causally track the changing saccade plan. Further, to identify the underlying mechanisms, eight different computational models of redirect behavior were assessed. It was observed that the model that included an independent, spatially specific inhibitory process, in addition to the two accumulators representing the preparatory processes of initial and final motor plans, best predicted the performance and the pattern of saccade deviation profile in the task. Such an inhibitory process suppressed the preparation of the initial motor plan, allowing the final motor plan to proceed unhindered. Thus, changes of mind are consistent with the notion of a spatially specific, inhibitory process that inhibits the current inappropriate plan, allowing expression of the new plan.

A relational contract for water demand management

For necessary goods like water, under supply constraints, fairness considerations lead to negative externalities. The objective of this paper is to design an infinite horizon contract or relational contract (a type of long-term contract) that ensures self-enforcing (instead of court-enforced) behaviour by the agents to mitigate the externality due to fairness issues. In this contract, the consumer is induced to consume at firm-supply level using the threat of higher fair price for future time periods. The pricing mechanism, computed in this paper, internalizes the externality and is shown to be economically efficient and provides revenue sufficiency.

Mechanism design for time critical and cost critical task execution via crowdsourcing

An exciting application of crowdsourcing is to use social networks in complex task execution. In this paper, we address the problem of a planner who needs to incentivize agents within a network in order to seek their help in executing an atomic task as well as in recruiting other agents to execute the task. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We identify a set of desirable properties that should ideally be satisfied by a crowdsourcing mechanism. In particular, sybil-proofness and collapse-proofness are two complementary properties in our desiderata. We prove that no mechanism can satisfy all the desirable properties simultaneously. This leads us naturally to explore approximate versions of the critical properties. We focus our attention on approximate sybil-proofness and our exploration leads to a parametrized family of payment mechanisms which satisfy collapse-proofness. We characterize the approximate versions of the desirable properties in cost critical and time critical domain.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Certificate-less on-demand public key management (CLPKM) for self-organized MANETs

In this paper, we have proposed a novel certificate-less on-demand public key management (CLPKM) protocol for self-organized MANETs. The protocol works on flat network architecture, and distinguishes between authentication layer and routing layer of the network. We put an upper limit on the length of verification route and use the end-to-end trust value of a route to evaluate its strength. The end-to-end trust value is used by the protocol to select the most trusted verification route for accomplishing public key verification. Also, the protocol uses MAC function instead of RSA certificates to perform public key verification. By doing this, the protocol saves considerable computation power, bandwidth and storage space. The saved storage space is utilized by the protocol to keep a number of pre-established routes in the network nodes, which helps in reducing the average verification delay of the protocol. Analysis and simulation results confirm the effectiveness of the proposed protocol.

Locating the attacker of wormhole attack by using the honeypot

Mobile Ad hoc Networks (MANETs) having strikingly superior features also come with notable disadvantage and troubles and the most exigent amongst all being security related issues. Such an ringent network dexterously pave approach for the malicious nodes. Hence providing security is a tedious task. For such a dynamic environment, a security system which dynamically observes the attacker's plans and protect the highly sophisticated resources is in high demand. In this paper we present a method of providing security against wormhole attacks to a MANET by learning about the environment dynamically and adapting itself to avoid malicious nodes. We accomplish this with the assistance of Honeypot. Our method predicts the wormhole attack that may take place and protect the resources well-in advance. Also it cleverly deal with the attacker by using previous history and different type of messages to locate the attacker. Several experiments suggest that the system is accurate in handling wormhole attack.

Reach task-associated excitatory overdrive of motor cortical neurons following infusion with ALS-CSF

Converging evidence from transgenic animal models of amyotrophic lateral sclerosis (ALS) and human studies suggest alterations in excitability of the motor neurons in ALS. Specifically, in studies on human subjects with ALS the motor cortex was reported to be hyperexcitable. The present study was designed to test the hypothesis that infusion of cerebrospinal fluid from patients with sporadic ALS (ALS-CSF) into the rat brain ventricle can induce hyperexcitability and structural changes in the motor cortex leading to motor dysfunction. A robust model of sporadic ALS was developed experimentally by infusing ALS-CSF into the rat ventricle. The effects of ALS-CSF at the single neuron level were examined by recording extracellular single unit activity from the motor cortex while rats were performing a reach to grasp task. We observed an increase in the firing rate of the neurons of the motor cortex in rats infused with ALS-CSF compared to control groups. This was associated with impairment in a specific component of reach with alterations in the morphological characteristics of the motor cortex. It is likely that the increased cortical excitability observed in the present study could be the result of changes in the intrinsic properties of motor cortical neurons, a dysfunctional inhibitory mechanism and/or an underlying structural change culminating in a behavioral deficit.

CFD Analysis of two phase flow with a prediction of torque demand of a Bi-lobe rotor of a mixing chamber

An attempt to study the fluid dynamic behavior of two phase flow comprising of solid and liquid with nearly equal density in a geometrical case that has an industrial significance in theareas like processing of polymers, food, pharma ceutical, paints. In this work,crystalline silica is considered as the dispersed medium in glycerin. In the CFD analysis carried out,the two phase components are considered to be premixed homogeneously at the initial state. The flow in a cylinder that has an axially driven bi-lobe rotor, a typical blender used in polymer industry for mixing or kneading to render the multi-component mixture to homogeneous condition is considered. A viscous, incompressible, isothermal flow is considered with an assumption that the components do not undergo any physical change and the solids are rigid and mix in fully wetting conditions. Silica with a particle diameter of 0.4 mm is considered and flow is analyzed for different mixing fractions. An industry standard CFD code is used for solving 3D-RANS equations. As the outcome of the study the torque demand by the bi-lobe rotor for different mixture fractions which are estimated show a behavioral consistency to the expected physical phenomena occurring in the domain considered.

Cooperative Communication of UAV to Perform Multi-task Using Nature Inspired Techniques

In this paper, the approach for assigning cooperative communication of Uninhabited Aerial Vehicles (UAV) to perform multiple tasks on multiple targets is posed as a combinatorial optimization problem. The multiple task such as classification, attack and verification of target using UAV is employed using nature inspired techniques such as Artificial Immune System (AIS), Particle Swarm Optimization (PSO) and Virtual Bee Algorithm (VBA). The nature inspired techniques have an advantage over classical combinatorial optimization methods like prohibitive computational complexity to solve this NP-hard problem. Using the algorithms we find the best sequence in which to attack and destroy the targets while minimizing the total distance traveled or the maximum distance traveled by an UAV. The performance analysis of the UAV to classify, attack and verify the target is evaluated using AIS, PSO and VBA.