THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.

Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey

Nature-inspired metaheuristics comprise a compelling family of optimization techniques. These algorithms are designed with the idea of emulating some kind natural phenomena (such as the theory of evolution, the collective behavior of groups of animals, the laws of physics or the behavior and lifestyle of human beings) and applying them to solve complex problems. Nature-inspired methods have taken the area of mathematical optimization by storm. Only in the last few years, literature related to the development of this kind of techniques and their applications has experienced an unprecedented increase, with hundreds of new papers being published every single year. In this paper, we analyze some of the most popular nature-inspired optimization methods currently reported on the literature, while also discussing their applications for solving real-world problems and their impact on the current literature. Furthermore, we open discussion on several research gaps and areas of opportunity that are yet to be explored within this promising area of science.

From ants to whales: metaheuristics for all tastes

This paper proposes a novel Dynamic Adaptive Ant Lion Optimizer (DAALO) for route planning of unmanned aerial vehicle. Ant Lion Optimizer (ALO) is a new intelligent algorithm motivated by the phenomenon that antlions hunt ants in nature, showing the great potential to solve the optimization problems of engineering. In the proposed DAALO, the random walk of ants is replaced by Levy flight to make ALO escape from local optima more easily. Besides, by introducing the improvement rate of population as the feedback, the size of trap is adjusted dynamically based on the 1/5 Principle to improve the performance of ALO including convergence accuracy, convergence speed and stability. Compared to some other bio-inspired methods, the proposed algorithm are utilized to find the optimal route in two different environments such as mountain model and city model. The comparison results demonstrate the effectiveness, robustness and feasibility of DAALO.

Dynamic Adaptive Ant Lion Optimizer applied to route planning for unmanned aerial vehicle

Recent trends of aggressive technology scaling have greatly exacerbated the occurrences and impact of faults in computing systems. This has made ‘reliability’ a first-order design constraint. To address the challenges of reliability, several techniques have been proposed. This paper provides a survey of architectural techniques for improving resilience of computing systems. We especially focus on techniques proposed for microarchitectural components, such as processor registers, functional units, cache and main memory etc. In addition, we discuss techniques proposed for non-volatile memory, GPUs and 3D-stacked processors. To underscore the similarities and differences of the techniques, we classify them based on their key characteristics. We also review the metrics proposed to quantify vulnerability of processor structures. We believe that this survey will help researchers, system-architects and processor designers in gaining insights into the techniques for improving reliability of computing systems.

A Survey of Techniques for Modeling and Improving Reliability of Computing Systems

Smaller transistor sizes and reduction in voltage levels in modern microprocessors induce higher soft error rates. This trend makes reliability a primary design constraint for computer systems. Redundant multithreading (RMT) makes use of parallelism in modern systems by employing thread-level time redundancy for fault detection and recovery. RMT can detect faults by running identical copies of the program as separate threads in parallel execution units with identical inputs and comparing their outputs. In this article, we present a survey of RMT implementations at different architectural levels with several design considerations. We explain the implementations in seminal papers and their extensions and discuss the design choices employed by the techniques. We review both hardware and software approaches by presenting the main characteristics and analyze the studies with different design choices regarding their strengths and weaknesses. We also present a classification to help potential users find a suitable method for their requirement and to guide researchers planning to work on this area by providing insights into the future trend.

A Survey on Multithreading Alternatives for Soft Error Fault Tolerance

Unmanned Aerial Vehicles (UAVs) are used for many missions, including weather reconnaissance, search and rescue assisting operations over seas and mountains, aerial photographing and mapping, fire detection, and traffic control. Autonomous operation of UAVs requires the development of control systems that can work without human support for long time periods. The path planners, which generate collision-free and optimized paths, are needed to provide autonomous operation capabilities to the UAVs. The optimization of the flight trajectory is a multi-objective problem dealing with variable terrain features as well as dynamic environment conditions. This paper presents a simulation environment for offline path planning of unmanned aerial vehicles on three-dimensional terrains. Our path planner aims to identify the shortest path and/or flight envelope in a given line of sight by avoiding terrain collisions, traveling on a path that stays within the restricted minimum and maximum distances above the terrain, traveling far from the specified threat zones, and maneuvering with an angle greater than the minimum curvature radius. We present two meta-heuristics (genetic algorithms and hyper-heuristics) in order to construct the paths for UAV navigation and compare our results with a reference work given in the literature. A comparative study over a set of terrains with various characteristics validates the effectiveness of the proposed meta-heuristics, where the quality of a solution is measured with the total cost of a constructed path, including the penalties of all constraints.

A meta-heuristic based three-dimensional path planning environment for unmanned aerial vehicles

Thread vulnerability in parallel applications

In systems with complex many-core cache hierarchy, exploiting data locality can significantly reduce execution time and energy consumption of parallel applications. Locality can be exploited at various hardware and software layers. For instance, by implementing private and shared caches in a multi-level fashion, recent hardware designs are already optimised for locality. However, this would all be useless if the software scheduling does not cast the execution in a manner that promotes locality available in the programs themselves. Since programs for parallel systems consist of tasks executed simultaneously, task scheduling becomes crucial for the performance in multi-level cache architectures. This paper presents a heuristic algorithm for homogeneous multi-core systems called locality-aware task scheduling (LeTS). The LeTS heuristic is a work-conserving algorithm that takes into account both locality and load balancing in order to reduce the execution time of target applications. The working principle of LeTS is based on two distinctive phases, namely; working task group formation phase (WTG-FP) and working task group ordering phase (WTG-OP). The WTG-FP forms groups of tasks in order to capture data reuse across tasks while the WTG-OP determines an optimal order of execution for task groups that minimizes the reuse distance of shared data between tasks. We have performed experiments using randomly generated task graphs by varying three major performance parameters, namely: (1) communication to computation ratio (CCR) between 0.1 and 1.0, (2) application size, i.e., task graphs comprising of 50-, 100-, and 300-tasks per graph, and (3) number of cores with 2-, 4-, 8-, and 16-cores execution scenarios. We have also performed experiments using selected real-world applications. The LeTS heuristic reduces overall execution time of applications by exploiting inter-task data locality. Results show that LeTS outperforms state-of-the-art algorithms in amortizing inter-task communication cost.

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Locality-aware task scheduling for homogeneous parallel computing systems

As the distributed computing systems have been widely used in many research and industrial areas, the problem of allocating tasks to available processors in the system efficiently has been an important concern Since the problem is proven to be NP-hard, heuristic-based optimization techniques have been proposed to solve the task allocation problem Particularly, the current cloud-based systems have been grown massively requiring multiple features like lower cost, higher reliability, and higher throughput; therefore, the problem has become more challenging and approximate methods have gained more importance Migrating birds optimization (MBO) algorithm offers successful solutions, especially for quadratic assignment problems Inspired by the movement of the birds, it exhibits good results by its population-based approach  Since the algorithm needs to deal with many individuals in the population, and the neighbor solution generation phase takes substantial time for large problem instances, we need parallelism to have execution time improvements and make the algorithm practical for large-scale problems In this work, we propose a scalable parallel implementation of the MBO algorithm, PMBO, for the multi-objective task allocation problem We redesigned the implementation of the MBO algorithm so that its computationally heavy independent tasks are executed concurrently in separate threads We compare our implementation with three parallel island-based approaches The experimental results demonstrate that our implementation exhibits substantial solution quality improvements for difficult problem instances as the computing resources, namely parallelism, increase Our scalability analysis also presents that higher parallelism levels offer larger solution improvement for the PMBO over the island-based parallel implementations on very hard problem instances

Isil Oz

Papers

A Survey on Multithreading Alternatives for Soft Error Fault Tolerance

A meta-heuristic based three-dimensional path planning environment for unmanned aerial vehicles

Thread vulnerability in parallel applications

Locality-aware task scheduling for homogeneous parallel computing systems

Scalable parallel implementation of migrating birds optimization for the multi-objective task allocation problem