LLF schedulability analysis on multiprocessor platforms

LLF (Least Laxity First) scheduling, which assigns a higher priority to a task with smaller laxity, has been known as an optimal preemptive scheduling algorithm on a single processor platform. However, its characteristics upon multiprocessor platforms have been little studied until now. Orthogonally, it has remained open how to efficiently schedule general task systems, including constrained deadline task systems, upon multiprocessors. Recent studies have introduced zero laxity (ZL) policy, which assigns a higher priority to a task with zero laxity, as a promising scheduling approach for such systems (e.g., EDZL). Towards understanding the importance of laxity in multiprocessor scheduling, this paper investigates the characteristics of ZL policy and presents the first ZL schedulability test for any work-conserving scheduling algorithm that employs this policy. It then investigates the characteristics of LLF scheduling, which also employs the ZL policy, and derives the first LLF-specific schedulability test on multiprocessors. It is shown that the proposed LLF test dominates the ZL test as well as the state-of-art EDZL test.

F-LQE: a Fuzzy Link Quality Estimator for wireless sensor networks

Radio Link Quality Estimation (LQE) is a fundamental building block for Wireless Sensor Networks, namely for a reliable deployment, resource management and routing. Existing LQEs (e.g. PRR, ETX, Fourbit, and LQI ) are based on a single link property, thus leading to inaccurate estimation. In this paper, we propose F-LQE, that estimates link quality on the basis of four link quality properties: packet delivery, asymmetry, stability, and channel quality. Each of these properties is defined in linguistic terms, the natural language of Fuzzy Logic. The overall quality of the link is specified as a fuzzy rule whose evaluation returns the membership of the link in the fuzzy subset of good links. Values of the membership function are smoothed using EWMA filter to improve stability. An extensive experimental analysis shows that F-LQE outperforms existing estimators.

Exploiting a prioritized MAC protocol to efficiently compute interpolations

Consider a network where all nodes share a single broadcast domain such as a wired broadcast network. Nodes take sensor readings but individual sensor readings are not the most important pieces of data in the system. Instead, we are interested in aggregated quantities of the sensor readings such as minimum and maximum values, the number of nodes and the median among a set of sensor readings on different nodes. In this paper we show that a prioritized medium access control (MAC) protocol may advantageously be exploited to efficiently compute aggregated quantities of sensor readings. In this context, we propose a distributed algorithm that has a very low time and message-complexity for computing certain aggregated quantities. Importantly, we show that if every sensor node knows its geographical location, then sensor data can be interpolated with our novel distributed algorithm, and the message-complexity of the algorithm is independent of the number of nodes. Such an interpolation of sensor data can be used to compute any desired function; for example the temperature gradient in a room (e.g., industrial plant) densely populated with sensor nodes, or the gas concentration gradient within a pipeline or traffic tunnel.

Preliminary discussion on globally prioritized medium access for multi-channel wireless systems

We discuss the development of a simple globally prioritized multi-channel medium access control (MAC) protocol for wireless networks. This protocol provides “hard” pre-run-time real-time guarantees to sporadic message streams, exploits a very large fraction of the capacity of all channels for “hard” real-time traffic and also makes it possible to fully utilize the channels with non real-time traffic when hard real-time messages do not request to be transmitted. The potential of such protocols for real-time applications is discussed and a schedulability analysis is also presented.

Coding mode decision algorithm for binary descriptor coding

In visual sensor networks, local feature descriptors can be computed at the sensing nodes, which work collaboratively on the data obtained to make an efficient visual analysis. In fact, with a minimal amount of computational effort, the detection and extraction of local features, such as binary descriptors, can provide a reliable and compact image representation. In this paper, it is proposed to extract and code binary descriptors to meet the energy and bandwidth constraints at each sensing node. The major contribution is a binary descriptor coding technique that exploits the correlation using two different coding modes: Intra, which exploits the correlation between the elements that compose a descriptor; and Inter, which exploits the correlation between descriptors of the same image. The experimental results show bitrate savings up to 35% without any impact in the performance efficiency of the image retrieval task. © 2014 EURASIP.

Efficient Implementation Of A Single-Precision Floating-Point Arithmetic Unit on FPGA

This paper presents a single precision floating point arithmetic unit with support for multiplication, addition, fused multiply-add, reciprocal, square-root and inverse squareroot with high-performance and low resource usage. The design uses a piecewise 2nd order polynomial approximation to implement reciprocal, square-root and inverse square-root. The unit can be configured with any number of operations and is capable to calculate any function with a throughput of one operation per cycle. The floatingpoint multiplier of the unit is also used to implement the polynomial approximation and the fused multiply-add operation. We have compared our implementation with other state-of-the-art proposals, including the Xilinx Core-Gen operators, and conclude that the approach has a high relative performance/area efficiency. © 2014 Technical University of Munich (TUM).

Optimization study of hybrid spot-welded/bonded single-lap joints

Joining of components with structural adhesives is currently one of the most widespread techniques for advanced structures (e.g., aerospace or aeronautical). Adhesive bonding does not involve drilling operations and it distributes the load over a larger area than mechanical joints. However, peak stresses tend to develop near the overlap edges because of differential straining of the adherends and load asymmetry. As a result, premature failures can be expected, especially for brittle adhesives. Moreover, bonded joints are very sensitive to the surface treatment of the material, service temperature, humidity and ageing. To surpass these limitations, the combination of adhesive bonding with spot-welding is a choice to be considered, adding a few advantages like superior static strength and stiffness, higher peeling and fatigue strength and easier fabrication, as fixtures during the adhesive curing are not needed. The experimental and numerical study presented here evaluates hybrid spot-welded/bonded single-lap joints in comparison with the purely spot-welded and bonded equivalents. A parametric study on the overlap length (LO) allowed achieving different strength advantages, up to 58% compared to spot-welded joints and 24% over bonded joints. The Finite Element Method (FEM) and Cohesive Zone Models (CZM) for damage growth were also tested in Abaqus® to evaluate this technique for strength prediction, showing accurate estimations for all kinds of joints.

Characterization of aluminium single-lap joints for high temperature applications

In this study, an experimental investigation into the shear strength behaviour of aluminium alloy single-lap adhesive joints was carried out in order to understand the effect of temperature on the strength of adhesively bonding joints. Single lap joints (SLJs) were fabricated and tested at RT and high temperatures (100ºC, 125ºC, 150ºC, 175ºC and 200ºC). Results showed that the failure loads of the single-lap joint test specimens vary with temperature and this needs to be considered in any design procedure. It is shown that, although the tensile stress decreased with temperature, the lap-shear strength of the adhesive increased with increasing of temperature up to the glass transition of the adhesive (Tg) and decreased for tests above the Tg.

Assessment of climate change statistical downscaling methods: Application and comparison of two statistical methods to a single site in Lisbon

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia do Ambiente

Numerical and experimental study of tensile strength of single and double-strap repairs

Adhesive bonding as a joining or repair method has a wide application in many industries. Repairs with bonded patches are often carried out to re-establish the stiffness at critical regions or spots of corrosion and/or fatigue cracks. Single and double-strap repairs (SS and DS, respectively) are a viable option for repairing. For the SS repairs, a patch is adhesively-bonded on one of the structure faces. SS repairs are easy to execute, but the load eccentricity leads to peel peak stresses at the overlap edges. DS repairs involve the use of two patches, one on each face of the structure. These are more efficient than SS repairs, due to the doubling of the bonding area and suppression of the transverse deflection of the adherends. Shear stresses also become more uniform as a result of smaller differential straining. The experimental and Finite Element (FE) study presented here for strength prediction and design optimization of bonded repairs includes SS and DS solutions with different values of overlap length (LO). The examined values of LO include 10, 20 and 30 mm. The failure strengths of the SS and DS repairs were compared with FE results by using the Abaqus® FE software. A Cohesive Zone Model (CZM) with a triangular shape in pure tensile and shear modes, including the mixed-mode possibility for crack growth, was used to simulate fracture of the adhesive layer. A good agreement was found between the experiments and the FE simulations on the failure modes, elastic stiffness and strength of the repairs, showing the effectiveness and applicability of the proposed FE technique in predicting strength of bonded repairs. Furthermore, some optimization principles were proposed to repair structures with adhesively-bonded patches that will allow repair designers to effectively design bonded repairs.

A hybrid genetic algorithm for the multi-mode resource-constrained project scheduling

A construction project is a group of discernible tasks or activities that are conduct-ed in a coordinated effort to accomplish one or more objectives. Construction projects re-quire varying levels of cost, time and other resources. To plan and schedule a construction project, activities must be defined sufficiently. The level of detail determines the number of activities contained within the project plan and schedule. So, finding feasible schedules which efficiently use scarce resources is a challenging task within project management. In this context, the well-known Resource Constrained Project Scheduling Problem (RCPSP) has been studied during the last decades. In the RCPSP the activities of a project have to be scheduled such that the makespan of the project is minimized. So, the technological precedence constraints have to be observed as well as limitations of the renewable resources required to accomplish the activities. Once started, an activity may not be interrupted. This problem has been extended to a more realistic model, the multi-mode resource con-strained project scheduling problem (MRCPSP), where each activity can be performed in one out of several modes. Each mode of an activity represents an alternative way of combining different levels of resource requirements with a related duration. Each renewable resource has a limited availability for the entire project such as manpower and machines. This paper presents a hybrid genetic algorithm for the multi-mode resource-constrained pro-ject scheduling problem, in which multiple execution modes are available for each of the ac-tivities of the project. The objective function is the minimization of the construction project completion time. To solve the problem, is applied a two-level genetic algorithm, which makes use of two separate levels and extend the parameterized schedule generation scheme. It is evaluated the quality of the schedules and presents detailed comparative computational re-sults for the MRCPSP, which reveal that this approach is a competitive algorithm.

Using a cohesive damage model to predict the tensile behaviour of CFRP single-strap repairs

This work addresses both experimental and numerical analyses regarding the tensile behaviour of CFRP single-strap repairs. Two fundamental geometrical parameters were studied: overlap length and patch thickness. The numerical model used ABAQUS® software and a developed cohesive mixed-mode damage model adequate for ductile adhesives, and implemented within interface finite elements. Stress analyses and strength predictions were carried out. Experimental and numerical comparisons were performed on failure modes, failure load and equivalent stiffness of the repair. Good correlation was found between experimental and numerical results, showing that the proposed model can be successfully applied to bonded joints or repairs.

A genetic algorithm for the resource constrained multi-project scheduling problem

This paper presents a genetic algorithm for the resource constrained multi-project scheduling problem. The chromosome representation of the problem is based on random keys. The schedules are constructed using a heuristic that builds parameterized active schedules based on priorities, delay times, and release dates defined by the genetic algorithm. The approach is tested on a set of randomly generated problems. The computational results validate the effectiveness of the proposed algorithm.

Multi-criteria optimization manipulator trajectory planning

In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control, system identification, robotics, planning and scheduling, image processing, and pattern and speech recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance and manipulator structure design considering a single criteria has been solved using several techniques (Alander, 2003). Most engineering applications require the optimization of several criteria simultaneously. Often the problems are complex, include discrete and continuous variables and there is no prior knowledge about the search space. These kind of problems are very more complex, since they consider multiple design criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective) optimization, that has been addressed successfully through GAs (Deb, 2001). The overall aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible and iii) to achieve a Pareto front uniformly spread. Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area. In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA) (Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello, 1998). In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ), ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance, end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at destination point. Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development of an algorithm to find a continuous motion that takes the manipulator from a given starting configuration up to a desired end position without colliding with any obstacle in the workspace. The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches proposed in the literature. Section 3 formulates the problem, namely the representation adopted to solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links) when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for the 3R redundant manipulator with five goals and for other complementary experiments are described, respectively. Finally, section 8 draws the main conclusions.

Analysis of the domain of applicability of an algorithm for a resources system selection problem for Distributed/Agile/Virtual Enterprises integration

The process of resources systems selection takes an important part in Distributed/Agile/Virtual Enterprises (D/A/V Es) integration. However, the resources systems selection is still a difficult matter to solve in a D/A/VE, as it is pointed out in this paper. Globally, we can say that the selection problem has been equated from different aspects, originating different kinds of models/algorithms to solve it. In order to assist the development of a web prototype tool (broker tool), intelligent and flexible, that integrates all the selection model activities and tools, and with the capacity to adequate to each D/A/V E project or instance (this is the major goal of our final project), we intend in this paper to show: a formulation of a kind of resources selection problem and the limitations of the algorithms proposed to solve it. We formulate a particular case of the problem as an integer programming, which is solved using simplex and branch and bound algorithms, and identify their performance limitations (in terms of processing time) based on simulation results. These limitations depend on the number of processing tasks and on the number of pre-selected resources per processing tasks, defining the domain of applicability of the algorithms for the problem studied. The limitations detected open the necessity of the application of other kind of algorithms (approximate solution algorithms) outside the domain of applicability founded for the algorithms simulated. However, for a broker tool it is very important the knowledge of algorithms limitations, in order to, based on problem features, develop and select the most suitable algorithm that guarantees a good performance.