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In this paper, we derive an algorithm for enabling a single robotic unmanned aerial vehicle to herd a flock of birds away from a designated volume of space, such as the air space around an airport. The herding algorithm, referred to as the $m$ -waypoint algorithm, is designed using a dynamic model of bird flocking based on Reynolds’ rules. We derive bounds on its performance using a combination of reduced-order modeling of the flock's motion, heuristics, and rigorous analysis. A unique contribution of the paper is the experimental demonstration of several facets of the herding algorithm on flocks of live birds reacting to a robotic pursuer. The experiments allow us to estimate several parameters of the flocking model, and especially the interaction between the pursuer and the flock. The herding algorithm is also demonstrated using numerical simulations. [ABSTRACT FROM AUTHOR]

Simulated ultrasound data is a valuable tool for development and validation of quantitative image analysis methods in echocardiography. Unfortunately, simulation time can become prohibitive for phantoms consisting of a large number of point scatterers. The COLE algorithm by Gao et al. is a fast convolution-based simulator that trades simulation accuracy for improved speed. We present highly efficient parallelized CPU and GPU implementations of the COLE algorithm with an emphasis on dynamic simulations involving moving point scatterers. We argue that it is crucial to minimize the amount of data transfers from the CPU to achieve good performance on the GPU. We achieve this by storing the complete trajectories of the dynamic point scatterers as spline curves in the GPU memory. This leads to good efficiency when simulating sequences consisting of a large number of frames, such as B-mode and tissue Doppler data for a full cardiac cycle. In addition, we propose a phase-based subsample delay technique that efficiently eliminates flickering artifacts seen in B-mode sequences when COLE is used without enough temporal oversampling. To assess the performance, we used a laptop computer and a desktop computer, each equipped with a multicore Intel CPU and an NVIDIA GPU. Running the simulator on a high-end TITAN X GPU, we observed two orders of magnitude speedup compared to the parallel CPU version, three orders of magnitude speedup compared to simulation times reported by Gao et al. in their paper on COLE, and a speedup of 27000 times compared to the multithreaded version of Field II, using numbers reported in a paper by Jensen. We hope that by releasing the simulator as an open-source project we will encourage its use and further development. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm to evaluate the performance of the mobile edge computing system. Specifically, a new metric called computation efficiency is defined as the number of calculated data bits divided by the corresponding energy consumption. In order to compute the required data timely, we combine two schemes, local computing and data offloading, into a joint computation algorithm. An optimization problem is formulated with the objective to maximize the sum of computation efficiency among users with weighting factors. With iterative and gradient descent methods, the problem can be solved efficiently. Simulation results show that the proposed scheme outperforms traditional approaches. In addition, the tradeoff study between local computing and data offloading reveals that when data size is small, local computing plays a more important role, but when the size grows, data offloading becomes preferable. [ABSTRACT FROM AUTHOR]

A new formulation of the auxiliary difference equation (ADE) finite-difference time-domain (FDTD) algorithm for the simulation of dispersive materials has been presented in the literature. Although flexible and efficient, this algorithm suffers from instability when modeling lossy high contrast dielectrics. In this paper, we adapt this ADE-FDTD formulation and present alternative algorithms for modeling static conductivity and Debye dispersion. The stability of these algorithms is assessed by numerical simulation in a wide variety of dielectric media, and their performance is compared to the existing algorithm by means of a simulation of the reflection of a plane wave from a dielectric boundary. Results and comparison with theory demonstrate the stability and accuracy of the new methods. The flexibility, computational efficiency, and ability to model a wide range of materials make these new methods highly attractive compared to other dispersive FDTD algorithms, particularly for modeling materials with multiple dispersion models. [ABSTRACT FROM PUBLISHER]

This paper presents a compendious summary of power hardware-in-the-loop (PHIL) simulations that are used for designing, analyzing, and testing of electrical power system components. PHIL simulations are an advanced application of real-time simulations that represent novel methods, which conjoin software and hardware testing. This contribution outlines necessary requirements for the implementation of PHIL simulations, which are defined by the nature of the digital real-time simulator, the power amplifier, and the power interface (PI). Fundamental characteristics, such as the input/output systems, PI, interface algorithm, and system stability considerations, are discussed for PHIL setups, in order to illustrate both flexibility and complexity of this compound simulation method. The objective of this work is to elaborate an understandable overview of PHIL simulation for electrical power systems and to constitute a contemporary state-of-the-art status of this research area. [ABSTRACT FROM AUTHOR]

This paper focuses on an important query in scientific simulation data analysis: the Spatial Distance Histogram (SDH). The computation time of an SDH query using brute force method is quadratic. Often, such queries are executed continuously over certain time periods, increasing the computation time. We propose highly efficient approximate algorithm to compute SDH over consecutive time periods with provable error bounds. The key idea of our algorithm is to derive statistical distribution of distances from the spatial and temporal characteristics of particles. Upon organizing the data into a Quad-tree based structure, the spatiotemporal characteristics of particles in each node of the tree are acquired to determine the particles’ spatial distribution as well as their temporal locality in consecutive time periods. We report our efforts in implementing and optimizing the above algorithm in graphics processing units (GPUs) as means to further improve the efficiency. The accuracy and efficiency of the proposed algorithm is backed by mathematical analysis and results of extensive experiments using data generated from real simulation studies. [ABSTRACT FROM AUTHOR]

Influence Maximization aims to find the top-$(K)$ influential individuals to maximize the influence spread within a social network, which remains an important yet challenging problem. Proven to be NP-hard, the influence maximization problem attracts tremendous studies. Though there exist basic greedy algorithms which may provide good approximation to optimal result, they mainly suffer from low computational efficiency and excessively long execution time, limiting the application to large-scale social networks. In this paper, we present IMGPU, a novel framework to accelerate the influence maximization by leveraging the parallel processing capability of graphics processing unit (GPU). We first improve the existing greedy algorithms and design a bottom-up traversal algorithm with GPU implementation, which contains inherent parallelism. To best fit the proposed influence maximization algorithm with the GPU architecture, we further develop an adaptive $(K)$-level combination method to maximize the parallelism and reorganize the influence graph to minimize the potential divergence. We carry out comprehensive experiments with both real-world and sythetic social network traces and demonstrate that with IMGPU framework, we are able to outperform the state-of-the-art influence maximization algorithm up to a factor of 60, and show potential to scale up to extraordinarily large-scale networks. [ABSTRACT FROM AUTHOR]

A computational model is proposed in this paper to capture learning capacity of a human subject adapting his or her movements in novel dynamics. The model uses an iterative learning control algorithm to represent human learning through repetitive processes. The control law performs adaptation using a model designed using experimental data captured from the natural behavior of the individual of interest. The control signals are used by a model of the body to produced motion without the need of inverse kinematics. The resulting motion behavior is validated against experimental data. This new technique yields the capability of subject-specific modeling of the motor function, with the potential to explain individual behavior in physical rehabilitation. [ABSTRACT FROM PUBLISHER]

We present in this paper an integrated solution to rapidly recognizing dynamic objects in surveillance videos by exploring various contextual information. This solution consists of three components. The first one is a multi-view object representation. It contains a set of deformable object templates, each of which comprises an ensemble of active features for an object category in a specific view/pose. The template can be efficiently learned via a small set of roughly aligned positive samples without negative samples. The second component is a unified spatio-temporal context model, which integrates two types of contextual information in a Bayesian way. One is the spatial context, including main surface property (constraints on object type and density) and camera geometric parameters (constraints on object size at a specific location). The other is the temporal context, containing the pixel-level and instance-level consistency models, used to generate the foreground probability map and local object trajectory prediction. We also combine the above spatial and temporal contextual information to estimate the object pose in scene and use it as a strong prior for inference. The third component is a robust sampling-based inference procedure. Taking the spatio-temporal contextual knowledge as the prior model and deformable template matching as the likelihood model, we formulate the problem of object category recognition as a maximum-a-posteriori problem. The probabilistic inference can be achieved by a simple Markov chain Mento Carlo sampler, owing to the informative spatio-temporal context model which is able to greatly reduce the computation complexity and the category ambiguities. The system performance and benefit gain from the spatio-temporal contextual information are quantitatively evaluated on several challenging datasets and the comparison results clearly demonstrate that our proposed algorithm outperforms other state-of-the-art algorithms. [ABSTRACT FROM PUBLISHER]

Driving simulators are increasingly being used for driver evaluation and/or education. In this paper, we describe the design and the modeling aspects of a 2-degree-of-freedom (2-DOF) low-cost motion platform allowing the rendering of the longitudinal and yaw movements. This prototype will be used to study various configurations of motion rendering and the impact of these variants on controllability and simulator sickness. The whole motion platform is considered as two coupled systems that are linked mechanically. The first system consists of a motorized rail for the longitudinal movement, which is mounted on top of the second system, which is a motorized turret allowing rotation of the platform. We present the platform mechanics and a number of experimental studies that have been carried out to obtain a characterization of the platform capabilities and frequency responses, as well as to assess platform performance in a classical drive operation. First conclusions and directions of future work are presented. [ABSTRACT FROM PUBLISHER]

The simulation of remote-sensing hyperspectral images is a useful tool for a variety of tasks such as the design of systems, the understanding of the image formation process, and the development and validation of data processing algorithms. The lack of ground truth and the incomplete knowledge of the Martian environment make simulation studies of Mars hyperspectral images a useful tool for automated analysis of Mars data. Hyperspectral near-infrared scenes of mineral mixtures have been simulated to analyze the contributions of surface minerals, atmosphere, and sensor noise on images of Mars. Modeling the remote-sensing process creates a means for the independent analysis of the influence of the environment and instruments on the detection accuracy of the surface composition (e.g., the scene endmembers). The end-to-end model builds surface reflectance scenes based on laboratory sample spectra, creates atmospheric effects using radiative transfer routines, simulates the instrument response function using CRISM data files, and adds instrument noise from thermal and other sources. The purpose of this paper is to understand the hyperspectral remote-sensing process to eventually enable the elevated detection accuracy of minerals on the surface of Mars. The viability of a linear approximation of the complete model is also investigated. The approximation is compared to the complete model in an image classification task. [ABSTRACT FROM AUTHOR]

In the age of cloud, Grid, P2P, and volunteer distributed computing, large-scale systems with tens of thousands of unreliable hosts are increasingly common. Invariably, these systems are composed of heterogeneous hosts whose individual availability often exhibit different statistical properties (for example stationary versus nonstationary behavior) and fit different models (for example exponential, Weibull, or Pareto probability distributions). In this paper, we describe an effective method for discovering subsets of hosts whose availability have similar statistical properties and can be modeled with similar probability distributions. We apply this method with about 230,000 host availability traces obtained from a real Internet-distributed system, namely SETI@home. We find that about 21 percent of hosts exhibit availability, that is, a truly random process, and that these hosts can often be modeled accurately with a few distinct distributions from different families. We show that our models are useful and accurate in the context of a scheduling problem that deals with resource brokering. We believe that these methods and models are critical for the design of stochastic scheduling algorithms across large systems where host availability is uncertain. [ABSTRACT FROM AUTHOR]

This paper presents a novel parametric representation for bidirectional texture functions. Our method mainly relies on two original techniques, namely, multivariate spherical radial basis functions (SRBFs) and optimized parameterization. First, since the surface appearance of a real-world object is frequently a mixed effect of different physical factors, the proposed sum-of-products model based on multivariate SRBFs especially provides an intrinsic and efficient representation for heterogenous materials. Second, optimized parameterization particularly aims at overcoming the major disadvantage of traditional fixed parameterization. By using a parametric model to account for variable transformations, the parameterization process can be tightly integrated with multivariate SRBFs into a unified framework. Finally, a hierarchical fitting algorithm for bidirectional texture functions is developed to exploit spatial coherence and reduce computational cost. Our experimental results further reveal that the proposed representation can easily achieve high-quality approximation and real-time rendering performance. [ABSTRACT FROM AUTHOR]