Automated Systems in the Aviation and Aerospace Industries

A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.National Aviation Universit

XML-based genetic rules for scene boundary detection in a parallel processing environment

Genetic programming is based on Darwinian evolutionary theory that suggests that the best solution for a problem can be evolved by methods of natural selection of the fittest organisms in a population. These principles are translated into genetic programming by populating the solution space with an initial number of computer programs that can possibly solve the problem and then evolving the programs by means of mutation, reproduction and crossover until a candidate solution can be found that is close to or is the optimal solution for the problem. The computer programs are not fully formed source code but rather a derivative that is represented as a parse tree. The initial solutions are randomly generated and set to a certain population size that the system can compute efficiently. Research has shown that better solutions can be obtained if 1) the population size is increased and 2) if multiple runs are performed of each experiment. If multiple runs are initiated on many machines the probability of finding an optimal solution are increased exponentially and computed more efficiently. With the proliferation of the web and high speed bandwidth connections genetic programming can take advantage of grid computing to both increase population size and increasing the number of runs by utilising machines connected to the web. Using XML-Schema as a global referencing mechanism for defining the parameters and syntax of the evolvable computer programs all machines can synchronise ad-hoc to the ever changing environment of the solution space. Another advantage of using XML is that rules are constructed that can be transformed by XSLT or DOM tree viewers so they can be understood by the GP programmer. This allows the programmer to experiment by manipulating rules to increase the fitness of a rule and evaluate the selection of parameters used to define a solution

Automated Systems in the Aviation and Aerospace Industries

A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.A solution is proposed for the task of controlling a group of UAVs moving along a given route. The group is considered as a limited-size formation consisting of n-agents moving relative to the leader, which allows us to treat the group as some aggregate with the center of motion. The quantitative composition of a group can change while maintaining the integrity of the group. The chapter proposes the use of smooth laws governing the motion of a group. The safety of motion is ensured by introducing into the law of control the components equivalent to the creation of attractive and repulsive fields.National Aviation Universit

Running parallel applications on a heterogeneous environment with accessible development practices and automatic scalability

Grid computing makes it possible to gather large quantities of resources to work on a problem. In order to exploit this potential, a framework that presents the resources to the user programmer in a form that maintains productivity is necessary. The framework must not only provide accessible development, but it must make efficient use of the resources. The Seeds framework is proposed. It uses the current Grid and distributed computing middleware to provide a parallel programming environment to a wider community of programmers. The framework was used to investigate the feasibility of scaling skeleton/pattern parallel programming into Grid computing. The research accomplished two goals: it made parallel programming on the Grid more accessible to domain­specific programmers, and it made parallel programs scale on a heterogeneous resource environ­ ment. Programming is made easier to the programmer by using skeleton and pat­ tern­based programming approaches that effectively isolate the program from the envi­ ronment. To extend the pattern approach, the pattern adder operator is proposed, imple­ mented and tested. The results show the pattern operator can reduce the number of lines of code when compared with an MPJ­Express implementation for a stencil algorithm while having an overhead of at most ten microseconds per iteration. The research in scal­ ability involved adapting existing load­balancing techniques to skeletons and patterns re­ quiring little additional configuration on the part of the programmer. The hierarchical de­ pendency concept is proposed as well, which uses a streamed data flow programming model. The concept introduces data flow computation hibernation and dependencies that can split to accommodate additional processors. The results from implementing skeleton/patterns on hierarchical dependencies show an 18.23% increase in code is neces­ sary to enable automatic scalability. The concept can increase speedup depending on the algorithm and grain size

A Praxical Solution of the Symbol Grounding Problem

Peer reviewe

On the acceleration of wavefront applications using distributed many-core architectures

In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P). Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures

PYDAC: A DISTRIBUTED RUNTIME SYSTEM AND PROGRAMMING MODEL FOR A HETEROGENEOUS MANY-CORE ARCHITECTURE

Heterogeneous many-core architectures that consist of big, fast cores and small, energy-efficient cores are very promising for future high-performance computing (HPC) systems. These architectures offer a good balance between single-threaded perfor- mance and multithreaded throughput. Such systems impose challenges on the design of programming model and runtime system. Specifically, these challenges include (a) how to fully utilize the chip’s performance, (b) how to manage heterogeneous, un- reliable hardware resources, and (c) how to generate and manage a large amount of parallel tasks. This dissertation proposes and evaluates a Python-based programming framework called PyDac. PyDac supports a two-level programming model. At the high level, a programmer creates a very large number of tasks, using the divide-and-conquer strategy. At the low level, tasks are written in imperative programming style. The runtime system seamlessly manages the parallel tasks, system resilience, and inter- task communication with architecture support. PyDac has been implemented on both an field-programmable gate array (FPGA) emulation of an unconventional het- erogeneous architecture and a conventional multicore microprocessor. To evaluate the performance, resilience, and programmability of the proposed system, several micro-benchmarks were developed. We found that (a) the PyDac abstracts away task communication and achieves programmability, (b) the micro-benchmarks are scalable on the hardware prototype, but (predictably) serial operation limits some micro-benchmarks, and (c) the degree of protection versus speed could be varied in redundant threading that is transparent to programmers

Food away from home: predicting frequency and changing selections

Since the 1970s, the rates of overweight and obesity have increased among all age groups in the US. The greatest increase has been in young adults, including college aged students, placing them at risk for early onset chronic diseases and shortened lifespans. One potential cause of the increased rates of obesity is the rise in consumption of away from home foods, which are often high in calories, saturated fat, and added sugar. The Dietary Guidelines for Americans encourage people to eat more meals at home and to choose lower calorie meals and snacks while dining out. Two sources of away from home meals that often sell high calorie meals and snacks are fast food restaurants and vending machines. College students frequently consume foods from both. Research suggests that the affordability or financial access of fast food meals and the availability of fast food restaurants are two factors that promote the consumption of fast food meals. However, it is not known what predicts fast food consumption among college students who can access fast food meals with their meal plans. Research also suggests that providing nutrition information at fast food restaurants can lead to a reduction in the average number of calories purchased there, but it is not known if providing nutrition information at vending machines will lead to a reduction in calories purchased by college students. The purpose of this dissertation research was to identify factors associated with fast food consumption among college students and to test whether a particular strategy (i.e., providing nutrition information at the vending site) could change purchasing behavior among college students. The first study tested whether days on campus, financial access, and health consciousness were associated with the number of meals that college students obtained from fast food restaurants. In April 2013, a sample of 1246 students who were currently enrolled in a UNCG meal plan completed an online survey in which they accounted for where they obtained their past week's meals. There was a positive association between financial access as measured by the amount of flex dollars on a student's purchased meal plan and the number of meals they obtained from fast food meals restaurants in the past week. There was a negative association between a student's level of health consciousness (i.e., monitoring calorie and fat intake and using nutrition labels) and the number of meals obtained from fast food restaurants in the past week. Specifically, a one-unit increase in level of health consciousness was associated with a 23% decrease in number of fast food meals. Exposure to fast food restaurants, as measured by the number of days spent on campus in the last week, was not associated with the number of meals obtained from fast food restaurants. The second study tested the effect of a multi-component nutrition information labeling intervention at the vending site. In the fall of 2012, 18 UNCG residence halls (1 machine per hall) were randomly assigned to either a treatment or control condition. In the treatment condition, nutrition information was provided next to the vending machines, five snacks were identified on the sign as "Better Choice" items (i.e., relatively lower in saturated fat, sugar and calories compared to the other items in the machine) and a promotional email was sent to students living in those residence halls (n = 9 vending machines). In the control condition information was not provided at the vending machine and no email was sent to students living in those residence halls (n = 9 vending machines). Sales data were collected for 4 weeks before and 4 weeks during the intervention for each of the machines. At the end of the 8 weeks, the average number of calories and the proportion of Better Choice snacks sold per and post intervention was compared. No difference in either outcome was found. The dissertation concludes with a discussion of strengths and limitations of both studies, and suggestions for next steps for programming and research

Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training

We consider the convex quadratic linearly constrained problem with bounded variables and with huge and dense Hessian matrix that arises in many applications such as the training problem of bias support vector machines. We propose a decomposition algorithmic scheme suitable to parallel implementations and we prove global convergence under suitable conditions. Focusing on support vector machines training, we outline how these assumptions can be satisfied in practice and we suggest various specific implementations. Extensions of the theoretical results to general linearly constrained problem are provided. We included numerical results on support vector machines with the aim of showing the viability and the effectiveness of the proposed scheme