OpenAIREplus

Directions the outcomes of the OpenAIRE project, which implements the EC Open Access (OA) pilot. Capitalizing on the OpenAIRE infrastructure, built for managing FP7 and ERC funded articles, and the associated supporting mechanism of the European Helpdesk System, OpenAIREplus will “develop an open access, participatory infrastructure for scientific information”. It will significantly expand its base of harvested publications to also include all OA publications indexed by the DRIVER infrastructure (more than 270 validated institutional repositories) and any other repository containing “peer-reviewed literature” that complies with certain standards. It will also generically harvest and index the metadata of scientific datasets in selected diverse OA thematic data repositories. It will support the concept of linked publications by deploying novel services for “linking peer- reviewed literature and associated data sets and collections”, from link discovery based on diverse forms of mining (textual, usage, etc.), to storage, visual representation, and on-line exploration. It will offer both user-level services to experts and “non-scientists” alike as well as programming interfaces for “providers of value-added services” to build applications on its content. Deposited articles and data will be openly accessible through an enhanced version of the OpenAIRE portal, together with any available relevant information on associated project funding and usage statistics. OpenAIREplus will retain its European footprint, engaging people and scientific repositories in almost all 27 EU member states and beyond. The technical work will be complemented by a suite of studies and associated research efforts that will partly proceed in collaboration with “different European initiatives” and investigate issues of “intellectual property rights, efficient financing models, and standards”.

Self-adaptive volunteered services composition through stimulus-and time-awareness

Volunteered Service Composition (VSC) refers to the process of composing volunteered services and resources. These services are typically published to a pool of voluntary resources. Selection and composition decisions tend to encounter numerous uncertainties: service consumers and applications have little control of these services and tend to be uncertain about their level of support for the desired functionalities and non-functionalities. In this paper, we contribute to a self-awareness framework that implements two levels of awareness, Stimulus-awareness and Time-awareness. The former responds to basic changes in the environment while the latter takes into consideration the historical performance of the services. We have used volunteer service computing as an example to demonstrate the benefits that self-awareness can introduce to self-adaptation. We have compared the Stimulus-and Time-awareness approaches with a recent Ranking approach from the literature. The results show that the Time-awareness level has the advantage of satisfying higher number of requests with lower time cost.

Projected sequential Gaussian processes:a C++ tool for interpolation of large datasets with heterogeneous noise

Heterogeneous datasets arise naturally in most applications due to the use of a variety of sensors and measuring platforms. Such datasets can be heterogeneous in terms of the error characteristics and sensor models. Treating such data is most naturally accomplished using a Bayesian or model-based geostatistical approach; however, such methods generally scale rather badly with the size of dataset, and require computationally expensive Monte Carlo based inference. Recently within the machine learning and spatial statistics communities many papers have explored the potential of reduced rank representations of the covariance matrix, often referred to as projected or fixed rank approaches. In such methods the covariance function of the posterior process is represented by a reduced rank approximation which is chosen such that there is minimal information loss. In this paper a sequential Bayesian framework for inference in such projected processes is presented. The observations are considered one at a time which avoids the need for high dimensional integrals typically required in a Bayesian approach. A C++ library, gptk, which is part of the INTAMAP web service, is introduced which implements projected, sequential estimation and adds several novel features. In particular the library includes the ability to use a generic observation operator, or sensor model, to permit data fusion. It is also possible to cope with a range of observation error characteristics, including non-Gaussian observation errors. Inference for the covariance parameters is explored, including the impact of the projected process approximation on likelihood profiles. We illustrate the projected sequential method in application to synthetic and real datasets. Limitations and extensions are discussed. © 2010 Elsevier Ltd.

On the implementation of the L-Nash bargaining solution in two-person bargaining games

The “Nash program” initiated by Nash (Econometrica 21:128–140, 1953) is a research agenda aiming at representing every axiomatically determined cooperative solution to a game as a Nash outcome of a reasonable noncooperative bargaining game. The L-Nash solution first defined by Forgó (Interactive Decisions. Lecture Notes in Economics and Mathematical Systems, vol 229. Springer, Berlin, pp 1–15, 1983) is obtained as the limiting point of the Nash bargaining solution when the disagreement point goes to negative infinity in a fixed direction. In Forgó and Szidarovszky (Eur J Oper Res 147:108–116, 2003), the L-Nash solution was related to the solution of multiciteria decision making and two different axiomatizations of the L-Nash solution were also given in this context. In this paper, finite bounds are established for the penalty of disagreement in certain special two-person bargaining problems, making it possible to apply all the implementation models designed for Nash bargaining problems with a finite disagreement point to obtain the L-Nash solution as well. For another set of problems where this method does not work, a version of Rubinstein’s alternative offer game (Econometrica 50:97–109, 1982) is shown to asymptotically implement the L-Nash solution. If penalty is internalized as a decision variable of one of the players, then a modification of Howard’s game (J Econ Theory 56:142–159, 1992) also implements the L-Nash solution.

An ArcGIS Tool for Modeling the Climate Envelope with Feed-Forward ANN

This paper is about the development and the application of an ESRI ArcGIS tool which implements multi-layer, feed-forward artificial neural network (ANN) to study the climate envelope of species. The supervised learning is achieved by backpropagation algorithm. Based on the distribution and the grids of the climate (and edaphic data) of the reference and future periods the tool predicts the future potential distribution of the studied species. The trained network can be saved and loaded. A modeling result based on the distribution of European larch (Larix decidua Mill.) is presented as a case study.

An agent-based model of team coordination and performance

This research is based on the premises that teams can be designed to optimize its performance, and appropriate team coordination is a significant factor to team outcome performance. Contingency theory argues that the effectiveness of a team depends on the right fit of the team design factors to the particular job at hand. Therefore, organizations need computational tools capable of predict the performance of different configurations of teams. This research created an agent-based model of teams called the Team Coordination Model (TCM). The TCM estimates the coordination load and performance of a team, based on its composition, coordination mechanisms, and job’s structural characteristics. The TCM can be used to determine the team’s design characteristics that most likely lead the team to achieve optimal performance. The TCM is implemented as an agent-based discrete-event simulation application built using JAVA and Cybele Pro agent architecture. The model implements the effect of individual team design factors on team processes, but the resulting performance emerges from the behavior of the agents. These team member agents use decision making, and explicit and implicit mechanisms to coordinate the job. The model validation included the comparison of the TCM’s results with statistics from a real team and with the results predicted by the team performance literature. An illustrative 26-1 fractional factorial experimental design demonstrates the application of the simulation model to the design of a team. The results from the ANOVA analysis have been used to recommend the combination of levels of the experimental factors that optimize the completion time for a team that runs sailboats races. This research main contribution to the team modeling literature is a model capable of simulating teams working on complex job environments. The TCM implements a stochastic job structure model capable of capturing some of the complexity not capture by current models. In a stochastic job structure, the tasks required to complete the job change during the team execution of the job. This research proposed three new types of dependencies between tasks required to model a job as a stochastic structure. These dependencies are conditional sequential, single-conditional sequential, and the merge dependencies.

Improving Storage with Stackable Extensions

Storage is a central part of computing. Driven by exponentially increasing content generation rate and a widening performance gap between memory and secondary storage, researchers are in the perennial quest to push for further innovation. This has resulted in novel ways to "squeeze" more capacity and performance out of current and emerging storage technology. Adding intelligence and leveraging new types of storage devices has opened the door to a whole new class of optimizations to save cost, improve performance, and reduce energy consumption. In this dissertation, we first develop, analyze, and evaluate three storage extensions. Our first extension tracks application access patterns and writes data in the way individual applications most commonly access it to benefit from the sequential throughput of disks. Our second extension uses a lower power flash device as a cache to save energy and turn off the disk during idle periods. Our third extension is designed to leverage the characteristics of both disks and solid state devices by placing data in the most appropriate device to improve performance and save power. In developing these systems, we learned that extending the storage stack is a complex process. Implementing new ideas incurs a prolonged and cumbersome development process and requires developers to have advanced knowledge of the entire system to ensure that extensions accomplish their goal without compromising data recoverability. Futhermore, storage administrators are often reluctant to deploy specific storage extensions without understanding how they interact with other extensions and if the extension ultimately achieves the intended goal. We address these challenges by using a combination of approaches. First, we simplify the storage extension development process with system-level infrastructure that implements core functionality commonly needed for storage extension development. Second, we develop a formal theory to assist administrators deploy storage extensions while guaranteeing that the given high level goals are satisfied. There are, however, some cases for which our theory is inconclusive. For such scenarios we present an experimental methodology that allows administrators to pick an extension that performs best for a given workload. Our evaluation demostrates the benefits of both the infrastructure and the formal theory.

Development of safety performance functions for SafetyAnalyst applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

Self-tuning algorithm for dsp-based motion control in cnc applications

This thesis describes the development of an adaptive control algorithm for Computerized Numerical Control (CNC) machines implemented in a multi-axis motion control board based on the TMS320C31 DSP chip. The adaptive process involves two stages: Plant Modeling and Inverse Control Application. The first stage builds a non-recursive model of the CNC system (plant) using the Least-Mean-Square (LMS) algorithm. The second stage consists of the definition of a recursive structure (the controller) that implements an inverse model of the plant by using the coefficients of the model in an algorithm called Forward-Time Calculation (FTC). In this way, when the inverse controller is implemented in series with the plant, it will pre-compensate for the modification that the original plant introduces in the input signal. The performance of this solution was verified at three different levels: Software simulation, implementation in a set of isolated motor-encoder pairs and implementation in a real CNC machine. The use of the adaptive inverse controller effectively improved the step response of the system in all three levels. In the simulation, an ideal response was obtained. In the motor-encoder test, the rise time was reduced by as much as 80%, without overshoot, in some cases. Even with the larger mass of the actual CNC machine, decrease of the rise time and elimination of the overshoot were obtained in most cases. These results lead to the conclusion that the adaptive inverse controller is a viable approach to position control in CNC machinery.

Perceiving Spanish in Miami: The Interaction of Dialect and National Labeling

The current study implements a speech perception experiment that interrogates local perceptions of Spanish varieties in Miami. Participants (N=292) listened to recordings of three Spanish varieties (Peninsular, Highland Colombian, and Post-Castro Cuban) and were given background information about the speakers, including the parents’ country of origin. In certain cases, the parents’ national-origin label matched the country of origin of the speaker, but otherwise the background information and voices were mismatched. The manipulation distinguishes perceptions determined by bottom-up cues (dialect) from top-down ones (social information). Participants then rated each voice for a range of personal characteristics and answered hypothetical questions about the speakers’ employment, family, and income. Results show clear top-down effects of the social information that often drive perceptions up or down depending on the traits themselves. Additionally, the data suggest differences in perceptions between Hispanic/non-Hispanic and Cuban/non-Cuban participants, although the Cuban participants do not drive the Hispanic participants’ perceptions.

Autonomic hardware manager: uma arquitetura de hardware autonômico usando a solução de repositório ativo de componentes

This Thesis main objective is to implement a supporting architecture to Autonomic Hardware systems, capable of manage the hardware running in reconfigurable devices. The proposed architecture implements manipulation, generation and communication functionalities, using the Context Oriented Active Repository approach. The solution consists in a Hardware-Software based architecture called "Autonomic Hardware Manager (AHM)" that contains an Active Repository of Hardware Components. Using the repository the architecture will be able to manage the connected systems at run time allowing the implementation of autonomic features such as self-management, self-optimization, self-description and self-configuration. The proposed architecture also contains a meta-model that allows the representation of the Operating Context for hardware systems. This meta-model will be used as basis to the context sensing modules, that are needed in the Active Repository architecture. In order to demonstrate the proposed architecture functionalities, experiments were proposed and implemented in order to proof the Thesis hypothesis and achieved objectives. Three experiments were planned and implemented: the Hardware Reconfigurable Filter, that consists of an application that implements Digital Filters using reconfigurable hardware; the Autonomic Image Segmentation Filter, that shows the project and implementation of an image processing autonomic application; finally, the Autonomic Autopilot application that consist of an auto pilot to unmanned aerial vehicles. In this work, the applications architectures were organized in modules, according their functionalities. Some modules were implemented using HDL and synthesized in hardware. Other modules were implemented kept in software. After that, applications were integrated to the AHM to allow their adaptation to different Operating Context, making them autonomic.

Cloud query manager: uso de web semântica para evitar o problema de aprisionamento em IaaS

Cloud computing can be defined as a distributed computational model by through resources (hardware, storage, development platforms and communication) are shared, as paid services accessible with minimal management effort and interaction. A great benefit of this model is to enable the use of various providers (e.g a multi-cloud architecture) to compose a set of services in order to obtain an optimal configuration for performance and cost. However, the multi-cloud use is precluded by the problem of cloud lock-in. The cloud lock-in is the dependency between an application and a cloud platform. It is commonly addressed by three strategies: (i) use of intermediate layer that stands to consumers of cloud services and the provider, (ii) use of standardized interfaces to access the cloud, or (iii) use of models with open specifications. This paper outlines an approach to evaluate these strategies. This approach was performed and it was found that despite the advances made by these strategies, none of them actually solves the problem of lock-in cloud. In this sense, this work proposes the use of Semantic Web to avoid cloud lock-in, where RDF models are used to specify the features of a cloud, which are managed by SPARQL queries. In this direction, this work: (i) presents an evaluation model that quantifies the problem of cloud lock-in, (ii) evaluates the cloud lock-in from three multi-cloud solutions and three cloud platforms, (iii) proposes using RDF and SPARQL on management of cloud resources, (iv) presents the cloud Query Manager (CQM), an SPARQL server that implements the proposal, and (v) comparing three multi-cloud solutions in relation to CQM on the response time and the effectiveness in the resolution of cloud lock-in.

An Empirically Based Stochastic Turbulence Simulator with Temporal Coherence for Wind Energy Applications

In this dissertation, we develop a novel methodology for characterizing and simulating nonstationary, full-field, stochastic turbulent wind fields.

In this new method, nonstationarity is characterized and modeled via temporal coherence, which is quantified in the discrete frequency domain by probability distributions of the differences in phase between adjacent Fourier components.

The empirical distributions of the phase differences can also be extracted from measured data, and the resulting temporal coherence parameters can quantify the occurrence of nonstationarity in empirical wind data.

This dissertation (1) implements temporal coherence in a desktop turbulence simulator, (2) calibrates empirical temporal coherence models for four wind datasets, and (3) quantifies the increase in lifetime wind turbine loads caused by temporal coherence.

The four wind datasets were intentionally chosen from locations around the world so that they had significantly different ambient atmospheric conditions.

The prevalence of temporal coherence and its relationship to other standard wind parameters was modeled through empirical joint distributions (EJDs), which involved fitting marginal distributions and calculating correlations.

EJDs have the added benefit of being able to generate samples of wind parameters that reflect the characteristics of a particular site.

Lastly, to characterize the effect of temporal coherence on design loads, we created four models in the open-source wind turbine simulator FAST based on the \windpact turbines, fit response surfaces to them, and used the response surfaces to calculate lifetime turbine responses to wind fields simulated with and without temporal coherence.

The training data for the response surfaces was generated from exhaustive FAST simulations that were run on the high-performance computing (HPC) facilities at the National Renewable Energy Laboratory.

This process was repeated for wind field parameters drawn from the empirical distributions and for wind samples drawn using the recommended procedure in the wind turbine design standard \iec.

The effect of temporal coherence was calculated as a percent increase in the lifetime load over the base value with no temporal coherence.

Testing of Interposer-Based 2.5D Integrated Circuits

The unprecedented and relentless growth in the electronics industry is feeding the demand for integrated circuits (ICs) with increasing functionality and performance at minimum cost and power consumption. As predicted by Moore's law, ICs are being aggressively scaled to meet this demand. While the continuous scaling of process technology is reducing gate delays, the performance of ICs is being increasingly dominated by interconnect delays. In an effort to improve submicrometer interconnect performance, to increase packing density, and to reduce chip area and power consumption, the semiconductor industry is focusing on three-dimensional (3D) integration. However, volume production and commercial exploitation of 3D integration are not feasible yet due to significant technical hurdles.

At the present time, interposer-based 2.5D integration is emerging as a precursor to stacked 3D integration. All the dies and the interposer in a 2.5D IC must be adequately tested for product qualification. However, since the structure of 2.5D ICs is different from the traditional 2D ICs, new challenges have emerged: (1) pre-bond interposer testing, (2) lack of test access, (3) limited ability for at-speed testing, (4) high density I/O ports and interconnects, (5) reduced number of test pins, and (6) high power consumption. This research targets the above challenges and effective solutions have been developed to test both dies and the interposer.

The dissertation first introduces the basic concepts of 3D ICs and 2.5D ICs. Prior work on testing of 2.5D ICs is studied. An efficient method is presented to locate defects in a passive interposer before stacking. The proposed test architecture uses e-fuses that can be programmed to connect or disconnect functional paths inside the interposer. The concept of a die footprint is utilized for interconnect testing, and the overall assembly and test flow is described. Moreover, the concept of weighted critical area is defined and utilized to reduce test time. In order to fully determine the location of each e-fuse and the order of functional interconnects in a test path, we also present a test-path design algorithm. The proposed algorithm can generate all test paths for interconnect testing.

In order to test for opens, shorts, and interconnect delay defects in the interposer, a test architecture is proposed that is fully compatible with the IEEE 1149.1 standard and relies on an enhancement of the standard test access port (TAP) controller. To reduce test cost, a test-path design and scheduling technique is also presented that minimizes a composite cost function based on test time and the design-for-test (DfT) overhead in terms of additional through silicon vias (TSVs) and micro-bumps needed for test access. The locations of the dies on the interposer are taken into consideration in order to determine the order of dies in a test path.

To address the scenario of high density of I/O ports and interconnects, an efficient built-in self-test (BIST) technique is presented that targets the dies and the interposer interconnects. The proposed BIST architecture can be enabled by the standard TAP controller in the IEEE 1149.1 standard. The area overhead introduced by this BIST architecture is negligible; it includes two simple BIST controllers, a linear-feedback-shift-register (LFSR), a multiple-input-signature-register (MISR), and some extensions to the boundary-scan cells in the dies on the interposer. With these extensions, all boundary-scan cells can be used for self-configuration and self-diagnosis during interconnect testing. To reduce the overall test cost, a test scheduling and optimization technique under power constraints is described.

In order to accomplish testing with a small number test pins, the dissertation presents two efficient ExTest scheduling strategies that implements interconnect testing between tiles inside an system on chip (SoC) die on the interposer while satisfying the practical constraint that the number of required test pins cannot exceed the number of available pins at the chip level. The tiles in the SoC are divided into groups based on the manner in which they are interconnected. In order to minimize the test time, two optimization solutions are introduced. The first solution minimizes the number of input test pins, and the second solution minimizes the number output test pins. In addition, two subgroup configuration methods are further proposed to generate subgroups inside each test group.

Finally, the dissertation presents a programmable method for shift-clock stagger assignment to reduce power supply noise during SoC die testing in 2.5D ICs. An SoC die in the 2.5D IC is typically composed of several blocks and two neighboring blocks that share the same power rails should not be toggled at the same time during shift. Therefore, the proposed programmable method does not assign the same stagger value to neighboring blocks. The positions of all blocks are first analyzed and the shared boundary length between blocks is then calculated. Based on the position relationships between the blocks, a mathematical model is presented to derive optimal result for small-to-medium sized problems. For larger designs, a heuristic algorithm is proposed and evaluated.

In summary, the dissertation targets important design and optimization problems related to testing of interposer-based 2.5D ICs. The proposed research has led to theoretical insights, experiment results, and a set of test and design-for-test methods to make testing effective and feasible from a cost perspective.

Coding Strategies and Implementations of Compressive Sensing

This dissertation studies the coding strategies of computational imaging to overcome the limitation of conventional sensing techniques. The information capacity of conventional sensing is limited by the physical properties of optics, such as aperture size, detector pixels, quantum efficiency, and sampling rate. These parameters determine the spatial, depth, spectral, temporal, and polarization sensitivity of each imager. To increase sensitivity in any dimension can significantly compromise the others.

This research implements various coding strategies subject to optical multidimensional imaging and acoustic sensing in order to extend their sensing abilities. The proposed coding strategies combine hardware modification and signal processing to exploiting bandwidth and sensitivity from conventional sensors. We discuss the hardware architecture, compression strategies, sensing process modeling, and reconstruction algorithm of each sensing system.

Optical multidimensional imaging measures three or more dimensional information of the optical signal. Traditional multidimensional imagers acquire extra dimensional information at the cost of degrading temporal or spatial resolution. Compressive multidimensional imaging multiplexes the transverse spatial, spectral, temporal, and polarization information on a two-dimensional (2D) detector. The corresponding spectral, temporal and polarization coding strategies adapt optics, electronic devices, and designed modulation techniques for multiplex measurement. This computational imaging technique provides multispectral, temporal super-resolution, and polarization imaging abilities with minimal loss in spatial resolution and noise level while maintaining or gaining higher temporal resolution. The experimental results prove that the appropriate coding strategies may improve hundreds times more sensing capacity.

Human auditory system has the astonishing ability in localizing, tracking, and filtering the selected sound sources or information from a noisy environment. Using engineering efforts to accomplish the same task usually requires multiple detectors, advanced computational algorithms, or artificial intelligence systems. Compressive acoustic sensing incorporates acoustic metamaterials in compressive sensing theory to emulate the abilities of sound localization and selective attention. This research investigates and optimizes the sensing capacity and the spatial sensitivity of the acoustic sensor. The well-modeled acoustic sensor allows localizing multiple speakers in both stationary and dynamic auditory scene; and distinguishing mixed conversations from independent sources with high audio recognition rate.