A fully pipelined RLS-based array for channel equalization

It is shown that several recursive least squares (RLS) type equalization algorithms such as, e.g., decisiondirected schemes and orthogonalized constant modulus algorithms, possess a common algorithmic structure and are therefore rather straightforwardly implemented on an triangular array (filter structure) for RLS estimation with inverse updating. While the computational complexity for such algorithms isO(N ²), whereN is the problem size, the throughput rate for the array implementation isO(1), i.e., independent of the problem size. Such a throughput rate cannot be achieved with standard (Gentleman-Kung-type) RLS/QR-updating arrays because of feedback loops in the computational schemes.     

Diseases of the digestive system and a system for patient classification: diagnostic-related groups (DRG)

In the last years different classification systems for hospitalized patients have been developed in the United States, that allow to evaluate different aspects of any Hospital: The quality, the efficacy and the efficiency. The classification of "The Diagnosis Related Groups" (DRG) relates the hospitalized patients with the expenses that they generate, and it is the most used in Europe. In the version DRG-All Patient, all the diagnoses and procedures of the hospitalized patients, using the International Classification of Diseases, are divided in to 25 Major Diagnostic Categories (MDC) where each one excludes all the others. We describe the behavior with respect to the age, sex and stay of all the GDR integrated in the CDM 6 and 7: diseases and disorders of the digestive tract, and diseases and disorders of the hepatobiliary system and the pancreas. The study has been carried out with a national data base of two millions of cases seen from 214 acute-care Hospitals. The MDC number 6 is the most frequent, with an important number of patients in the pediatric age; it is more frequent in males; the average stay is 8.59 days and 19 GDR (36.5%) have a variation coefficient greater than one. The other MDC, number 7 is less frequent, the majority of the patients are between 60-80 years of age, the average stay is 12.2 days with a coefficient of variation greater than one in 4 GDR (17.4%). Clinical Services should know the characteristics and behavior of their patients, as well as the comparison with the national data bases. This may allow a control of the quality and the costs by using a common "language" with the managers.     

Controlled counter-flow motion of magnetic bead chains rolling along microchannels

We demonstrate controlled transport of superparamagnetic beads in the opposite direction of a laminar flow. A permanent magnet assembles 200 nm magnetic particles into about 200 μm long bead chains that are aligned in parallel to the magnetic field lines. Due to a magnetic field gradient, the bead chains are attracted towards the wall of a microfluidic channel. A rotation of the permanent magnet results in a rotation of the bead chains in the opposite direction to the magnet. Due to friction on the surface, the bead chains roll along the channel wall, even in counter-flow direction, up to at a maximum counter-flow velocity of 8 mm s⁻¹. Based on this approach, magnetic beads can be accurately manoeuvred within microfluidic channels. This counter-flow motion can be efficiently be used in Lab-on-a-Chip systems, e.g. for implementing washing steps in DNA purification.     

Cost optimization of a hybrid composite flywheel rotor with a split-type hub using combined analytical/numerical models

A procedure to find the optimal design of a flywheel with a split-type hub is presented. Since cost plays a decisive role in stationary flywheel energy storage applications, a trade-off between energy and cost is required. Applying a scaling technique, the multi-objective design problem is reduced to the maximization of the energy-per-cost ratio as the single objective. Both an analytical and a finite element model were studied. The latter was found to be more than three orders of magnitude more computationally expensive than the analytical model, while the analytical model can only be regarded as a coarse approximation. Multifidelity approaches were examined to reduce the computational expense while retaining the high accuracy and large modeling depth of the finite element model. Using a surrogate-based optimization strategy, the computational cost was reduced to only one third in comparison to using only the finite element model. A nonlinear interior-point method was employed to find the optimal rim thicknesses and rotational speed. The benefits of the split-type hub architecture were demonstrated.     

Learning state machine-based string edit kernels

During the past few years, several works have been done to derive string kernels from probability distributions. For instance, the Fisher kernel uses a generative model M (e.g. a hidden Markov model) and compares two strings according to how they are generated by M. On the other hand, the marginalized kernels allow the computation of the joint similarity between two instances by summing conditional probabilities. In this paper, we adapt this approach to edit distance-based conditional distributions and we present a way to learn a new string edit kernel. We show that the practical computation of such a kernel between two strings x and x^′ built from an alphabet Σ requires (i) to learn edit probabilities in the form of the parameters of a stochastic state machine and (ii) to calculate an infinite sum over Σ^* by resorting to the intersection of probabilistic automata as done for rational kernels. We show on a handwritten character recognition task that our new kernel outperforms not only the state of the art string kernels and string edit kernels but also the standard edit distance used by a neighborhood-based classifier.     

The SHARC framework for data quality in Web archiving

Web archives preserve the history of born-digital content and offer great potential for sociologists, business analysts, and legal experts on intellectual property and compliance issues. Data quality is crucial for these purposes. Ideally, crawlers should gather coherent captures of entire Web sites, but the politeness etiquette and completeness requirement mandate very slow, long-duration crawling while Web sites undergo changes. This paper presents the SHARC framework for assessing the data quality in Web archives and for tuning capturing strategies toward better quality with given resources. We define data quality measures, characterize their properties, and develop a suite of quality-conscious scheduling strategies for archive crawling. Our framework includes single-visit and visit?Crevisit crawls. Single-visit crawls download every page of a site exactly once in an order that aims to minimize the ??blur?? in capturing the site. Visit?Crevisit strategies revisit pages after their initial downloads to check for intermediate changes. The revisiting order aims to maximize the ??coherence?? of the site capture(number pages that did not change during the capture). The quality notions of blur and coherence are formalized in the paper. Blur is a stochastic notion that reflects the expected number of page changes that a time-travel access to a site capture would accidentally see, instead of the ideal view of a instantaneously captured, ??sharp?? site. Coherence is a deterministic quality measure that counts the number of unchanged and thus coherently captured pages in a site snapshot. Strategies that aim to either minimize blur or maximize coherence are based on prior knowledge of or predictions for the change rates of individual pages. Our framework includes fairly accurate classifiers for change predictions. All strategies are fully implemented in a testbed and shown to be effective by experiments with both synthetically generated sites and a periodic crawl series for different Web sites.     

Automatic metro map layout using multicriteria optimization

This paper describes an automatic mechanism for drawing metro maps. We apply multicriteria optimization to find effective placement of stations with a good line layout and to label the map unambiguously. A number of metrics are defined, which are used in a weighted sum to find a fitness value for a layout of the map. A hill climbing optimizer is used to reduce the fitness value, and find improved map layouts. To avoid local minima, we apply clustering techniques to the map-the hill climber moves both stations and clusters when finding improved layouts. We show the method applied to a number of metro maps, and describe an empirical study that provides some quantitative evidence that automatically-drawn metro maps can help users to find routes more efficiently than either published maps or undistorted maps. Moreover, we have found that, in these cases, study subjects indicate a preference for automatically-drawn maps over the alternatives.     

The relation between EA effectiveness and stakeholder satisfaction

Enterprise Architecture (EA) is increasingly being used by large organizations to get a grip on the complexity of their business processes, information systems and technical infrastructure. Although seen as an important instrument to help solve major organizational problems, effectively applying EA seems no easy task. Active participation of EA stakeholders is one of the main critical success factors for EA. This participation depends on the degree in which EA helps stakeholders achieve their individual goals. A highly related topic is effectiveness of EA, the degree in which EA helps to achieve the collective goals of the organization. In this article we present our work regarding EA stakeholder satisfaction and EA effectiveness, and compare these two topics. We found that, regarding EA, the individual goals of stakeholders map quite well onto the collective goals of the organization. In a case study we conducted, we found that the organization is primarily concerned with the final results of EA, while individual stakeholders also worry about the way the architects operate.     

TRIMS,a privacy-aware trust and reputation model for identity management systems

Electronic transactions are becoming more important everyday. Several tasks like buying goods, booking flights or hotel rooms, or paying for streaming a movie, for instance, can be carried out through the Internet. Nevertheless, they are still some drawbacks due to security threats while performing such operations. Trust and reputation management rises as a novel way of solving some of those problems. In this paper we present our work TRIMS (a privacy-aware trust and reputation model for identity management systems), which applies a trust and reputation model to guarantee an acceptable level of security when deciding if a different domain might be considered reliable when receiving certain sensitive user’s attributes. Specifically, we will address the problems which surfaces when a domain needs to decide whether to exchange some information with another possibly unknown domain to effectively provide a service to one of its users. This decision will be determined by the trust deposited in the targeting domain. As far as we know, our proposal is one of the first approaches dealing with trust and reputation management in a multi-domain scenario. Finally, the performed experiments have demonstrated the robustness and accuracy of our model in a wide variety of scenarios.