TangiWheel: A Widget for Manipulating Collections on Tabletop Displays Supporting Hybrid Input Modality

In this paper we present TangiWheel,a collection manipulation widget for tabletop displays.Our implementation is flexible,allowing either multi-touch or interaction,or even a hybrid scheme to better suit user choice and convenience.Different TangiWheel aspects and features are compared with other existing widgets for collection manipulation.The study reveals that TangiWheel is the first proposal to support a hybrid input modality with large resemblance levels between touch and tangible interaction styles.Several experiments were conducted to evaluate the techniques used in each input scheme for a better understanding of tangible surface interfaces in complex tasks performed by a single user (e.g.,involving a typical master-slave exploration pattern).The results show that tangibles perform significantly better than fingers,despite dealing with a greater number of interactions,in situations that require a large number of acquisitions and basic manipulation tasks such as establishing location and orientation.However,when users have to perform multiple exploration and selection operations that do not require previous basic manipulation tasks,for instance when collections are fixed in the interface layout,touch input is significantly better in terms of required time and number of actions.Finally,when a more elastic collection layout or more complex additional insertion or displacement operations are needed,the hybrid and tangible approaches clearly outperform finger-based interactions.     

Methodological design and comparative evaluation of a MAS providing AmI

Researches on Ambient Intelligent and Ubiquitous Computing using wireless technologies have increased in the last years. In this work, we review several scenarios to define a multi-agent architecture that support the information needs of these new technologies, for heterogeneous domain. Our contribution consists of designing in a methodological way a Context Aware System (involving location services) using agents that can be used in very different domains. We describe all the steps followed in the design of the agent system. We apply a hybridizing methodology between GAIA and AUML. Additionally we propose a way to compare different agent architectures for Context Aware System using agent interactions. So, in this paper, we describe the assignment of weight values to agents interaction in two different MAS architectures for Context Aware problems solving different scenarios inspired in FIPA standard negotiation protocols.     

Intelligent system for time series classification using support vector machines applied to supply-chain

To be able of anticipate demand is a key factor for commercial success in the supply-chain sector. The benefits can be grouped around two main concepts: firstly the optimization of operations through the development of optimal strategies for procurement and secondly the stock reduction that reduces storage costs, handling, etc. There is currently a variety of methods for making predictions, these methods vary from pure statistical methods such as exponential smoothing Holt-Winters or ARIMA models, to those based on artificial intelligence techniques like neural networks or fuzzy systems. However, despite being able to build accurate models, in managing the supply chain based on forecasts there is a problem known as “Forrester effect” irrespective of the model chosen. To monitor the impact of this effect, given the volume of information handled in large corporations, is a very expensive task (often manual) for such corporations because it requires investigating issues such as the adequacy of the model, allocation of known models to the sales time series, discovery of new patterns of behavior, etc. This article proposes an intelligent system based on support vector machines to solve problems concerning the allocation and discovery of new models. With this focus in mind, the system objective is to build groups of time series that share the same forecasting model. For the identification of new models, the system will assign “virtual models” for those groups that do not have a predefined pattern. Using the proposed method, it has been possible to group a sample of more than 14,000 time series (real data taken from a store) in around 70 categories, of which only 12 of them already grouped over 98% of the total.     

Effect of Safety and Environmental Variables on Task Durations in Steel Erection

In spite of the efforts by government agencies, labor organizations, and researchers in the field of health and safety, injuries and fatalities continue to affect the construction industry. In 2002 the construction industry had the undesirable distinction of having two of the most dangerous occupations in the United States, with fatalities among structural steel workers at 58.2 per 100,000 workers (fourth highest rate) and among construction laborers at 27.7 per 100,000 workers (ninth highest rate). Costs associated with construction accidents, such as increased insurance premiums and medical expenses, and loss of productivity are also concerns in the industry. It has not been demonstrated how unsafe working conditions affect worker performance, and the impact of unsafe work practices on worker performance has not been quantified. This paper describes a methodology that included direct observation of steel erection activities and statistical analysis of task duration data. The data collected at steel erection sites included safety conditions such as the use of personal protective equipment (PPE), elevation of the work area, environmental conditions such as temperature and humidity, and worker performance in the form of task durations. Analysis of variance (ANOVA) analysis of 186 of steel erection task durations collected over a six-month period showed that the use of personal protective equipment (PPE), the time of day during which the operation was being performed, the elevation at which the work was being performed, and the presence of decking below the work area had statistically significant effects on the durations of steel erection tasks.     

Dynamic Simulation of Multi-Body Systems on Internet Using CORBA,Java and XML

In this paper a prototype of a computer program for multi-bodysimulation based on the use of CORBA, Java and XML is presented. Thisprototype makes use of a recursive dynamic formalism which outperformsmany implementations based on global formulations. The prototypepresented has been implemented using distributed Object OrientedProgramming techniques. The definition of the multi-body system is donethrough a data file defined using XML (eXtendible Markup Language). Thisprototype implements a `simulation service' on a network of computers,following a client-server scheme. This `simulation service' can operatein two different ways: offering a remote simulation on the server, orproviding a Java compiled code for local simulation on the client. Thepaper describes the practical implementation of such a service. The useof recursive functions and OOP permits the implementation of ageneral-purpose simulation code, which is compact, clear, clean,re-usable and easy to maintain. Once this Java code is compiled, it isstored in less than 125 Kbytes (including the numerical integrators).The Java programming language is used in order to assure compatibilitybetween different platforms. Finally, practical examples which make useof this approach are shown.     

Statistical Pattern Modeling in Vision-Based Quality Control Systems

Machine vision technology improves productivity and quality management and provides a competitive advantage to industries that employ this technology. In this article, visual inspection and quality control theory are combined to develop a robust inspection system with manufacturing applications. The inspection process might be defined as the one used to determine if a given product fulfills a priori specifications, which are the quality standard. In the case of visual inspection, these specifications include the absence of defects, such as lack (or excess) of material, homogeneous visual aspect, required color, predetermined texture, etc. The characterization of the visual aspect of metallic surfaces is studied using quality control chars, which are a graphical technique used to compare on-line capabilities of a product with respect to these specifications. Original algorithms are proposed for implementation in automated visual inspection applications with on-line execution requirements. The proposed artificial vision method is a hybrid between the two usual methods of pattern comparison and theoretical decision. It incorporates quality control theory to statistically model the pattern for defect-free products. Specifically, individual control charts with 6-sigma limits are set so the inspection error is minimized. Experimental studies with metallic surfaces help demonstrate the efficacy and robustness of the proposed methodology.     

Boosting Search with Variable Elimination in Constraint Optimization and Constraint Satisfaction Problems

There are two main solving schemas for constraint satisfaction and optimization problems: i) search, whose basic step is branching over the values of a variables, and ii) dynamic programming, whose basic step is variable elimination. Variable elimination is time and space exponential in a graph parameter called induced width, which renders the approach infeasible for many problem classes. However, by restricting variable elimination so that only low arity constraints are processed and recorded, it can be effectively combined with search, because the elimination of variables may reduce drastically the search tree size.In this paper we introduce BE-BB(k), a hybrid general algorithm that combines search and variable elimination. The parameter k controls the tradeoff between the two strategies. The algorithm is space exponential in k. Regarding time, we show that its complexity is bounded by k and a structural parameter from the constraint graph. We provide experimental evidence that the hybrid algorithm can outperform state-of-the-art algorithms in constraint satisfaction, Max-CSP and Weighted CSP. Especially in optimization tasks, the advantage of our approach over plain search can be overwhelming.     

Nonlinear analysis for seismic upgrade studies of existing essential facilities

The need to perform cost-effective seismic upgrades of essential facilities has made the use of nonlinear finite-element techniques an attractive option. These techniques allow for the analysis of full buildings with large, nonlinear deformations. For reinforced concrete structures, this includes concrete crushing and cracking, and steel reinforcement yielding. These nonlinear models can predict the response of buildings subjected to strong motion earthquakes with a greater degree of accuracy than simple linear or ‘quasi’ nonlinear models.     

Measurement of three independent components in impedance sensors using a single square wave

Two-wire impedance-based sensors involving electrolytes add the impedance of the electrodes to the electrical impedance of the medium to measure. An equivalent circuit for the measured impedance is a resistance in series with the parallel combination of another resistance and a capacitance. If the two electrodes are modelled by equal impedances, the equivalent circuit for the complete set up consists of three impedance components, which can be determined from three independent measurements. This paper describes a novel method to obtain those three components using a single square wave voltage (period 2T) instead of several sine waves and provides the equations to calculate their value from the three current intensity amplitudes measured at T/8, 3T/8 and 5T/8. Other measurement times would need different equations, but the same procedure applies. Anyway, the proposed method keeps the advantages of synchronous detection and relies on analytical solutions instead of the customary curve fitting procedures. Computer simulation and experimental results obtained by measuring the conductivity of known electrolyte samples validate the proposed method.     

A Color Vision-Based Lane Tracking System for Autonomous Driving on Unmarked Roads

This work describes a color Vision-based System intended to perform stable autonomous driving on unmarked roads. Accordingly, this implies the development of an accurate road surface detection system that ensures vehicle stability. Although this topic has already been documented in the technical literature by different research groups, the vast majority of the already existing Intelligent Transportation Systems are devoted to assisted driving of vehicles on marked extra urban roads and highways. The complete system was tested on the BABIECA prototype vehicle, which was autonomously driven for hundred of kilometers accomplishing different navigation missions on a private circuit that emulates an urban quarter. During the tests, the navigation system demonstrated its robustness with regard to shadows, road texture, and weather and changing illumination conditions.