Pattern recognition in stained HEp-2 cells: Where are we now?

Indirect Immunofluorescence (IIF) images are increasingly being used for the diagnosis of autoimmune diseases. However, the analysis of this kind of images has until now reached a comparatively low level of automation, if compared with other medical imaging techniques. The Special Issue on the Analysis and Recognition of Indirect Immunofluorescence Images of the Pattern Recognition journal aims at providing a comprehensive evaluation of the state of the art for the staining pattern classification problem, through the adoption of a common experimental protocol and the testing of all the methods on a publicly available dataset.     

A metaheuristic-based downlink power allocation for LTE/LTE-A cellular deployments

The explosive growth of cellular networks makes their deployment and maintenance more and more complex, time consuming, and expensive. Self-Organizing Networks have been recognized as a promising way to alleviate this problem by minimizing human intervention in such processes. This paper introduces a novel multiobjective framework, based on evolutionary optimization, aiming at improving network performance and users Quality of Service. By tuning the transmitted power at each cell, average intercell interference levels are minimized. The design of the proposed scheme is feasible for distributed implementations in Long Term Evolution (LTE) and LTE-Advanced networks and its operation is compatible with current specifications. The framework is able to provide effective network-specific optimization and obtained results show that gains in terms of network capacity and cell edge performance are 5 and 10 %, respectively. Energy savings always accompanied such enhancements with reductions up to 35 %.     

Non-saturation Throughput of S-ALOHA Using the Time-Scale Decomposition Technique

S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.     

SWTRACK: An intelligent model for cargo tracking based on off-the-shelf mobile devices

Logistics represent the biggest cost for transportation companies. Companies are investing in monitoring and tracking systems aiming at improving services, reducing costs and ensuring the safety in cargos transports. This article presents an intelligent model to obtain the position of vehicles, using off-the-shelf mobile devices named SWTRACK. The proposal allows distributors companies to track loads and to have real-time information about their whereabouts during a travel. SWTRACK presents an automatic travel management to initialize and finish travels without user interaction. Furthermore, it provides a mechanism to monitor detours in planned routes and to send alarms notification through mobile devices. To build that mechanism, we studied the Geofence concept and existing techniques to prepare two solutions based on continuous monitoring. Those solutions were compared and discussed. We also conducted a model evaluation using off-the-shelf mobile devices. Moreover, we provide an impact analysis regarding GPS precision and battery power consumption. In the end, we show that the proposed model generates a reliable system for tracking vehicles.     

A multi-expert system for ranking patents: An approach based on fuzzy pay-off distributions and a TOPSIS–AHP framework

The aim of this paper is to introduce a decision support system that ranks patents based on multiple expert evaluations. The presented approach starts with the creation of three value scenarios for each considered patent by each expert. These are used for the construction of individual fuzzy pay-off distribution functions for the patent value; a consensual fuzzy pay-off distribution is then determined starting from the individual distributions. Possibilistic moments are calculated from the consensus pay-off distribution for each patent and used in ranking them with TOPSIS. It is further showed how the analytic hierarchy process (AHP) can be used to include additional decision variables into the patent selection, thus allowing for a two-tier decision making process. The system is illustrated with a numerical example and the usability of the system and the combination of methods it includes for patent portfolio selection in the real world context is discussed.     

A semantic role labelling-based framework for learning ontologies from Spanish documents

Currently, most of the information available in the Web is adapted primarily for human consumption, but there is so much information that can no longer be processed by a person in a reasonable time, either in digital or physical formats. To solve this problem, the idea of the Semantic Web arose. The Semantic Web deals with adding machine-readable information to Web pages. Ontologies represent a very important element of this web, as they provide a valid and robust structure to represent knowledge based on concepts, relations, axioms, etc. The need for overcoming the bottleneck provoked by the manual construction of ontologies has generated several studies and research on obtaining semiautomatic methods to learn ontologies. In this sense, this paper proposes a new ontology learning methodology based on semantic role labeling from digital Spanish documents. The method makes it possible to represent multiple semantic relations specially taxonomic and partonomic ones in the standardized OWL 2.0. A set of experiments has been performed with the approach implemented in educational domain that show promising results.     

Reputation-based decisions for logic-based cognitive agents

Computational trust and reputation models have been recognized as one of the key technologies required to design and implement agent systems. These models manage and aggregate the information needed by agents to efficiently perform partner selection in uncertain situations. For simple applications, a game theoretical approach similar to that used in most models can suffice. However, if we want to undertake problems found in socially complex virtual societies, we need more sophisticated trust and reputation systems. In this context, reputation-based decisions that agents make take on special relevance and can be as important as the reputation model itself. In this paper, we propose a possible integration of a cognitive reputation model, Repage, into a cognitive BDI agent. First, we specify a belief logic capable to capture the semantics of Repage information, which encodes probabilities. This logic is defined by means of a two first-order languages hierarchy, allowing the specification of axioms as first-order theories. The belief logic integrates the information coming from Repage in terms if image and reputation, and combines them, defining a typology of agents depending of such combination. We use this logic to build a complete graded BDI model specified as a multi-context system where beliefs, desires, intentions and plans interact among each other to perform a BDI reasoning. We conclude the paper with an example and a related work section that compares our approach with current state-of-the-art models.     

Are energy statistics useful for making energy scenarios?

One can measure “energy quantities” – e.g. joules, BTU, quads – but only at a given scale and within a specific narrative about energy conversions at the time. Therefore, at the moment of generating aggregate indicators, the arithmetic summing of assessments of energy quantities referring to different scales and narratives is meaningless. This paper addresses epistemological problems typical of energy accounting, which are at the moment tackled by acknowledging the existence of unspecified “qualitative differences” among different energy forms – e.g. a joule of electricity has more “value” than a joule of coal. Three energy forms referring to different scales and narratives about energy conversions are relevant for national accounting: Primary Energy Sources (PES), Energy Carriers (EC), and End Uses (EU). We critically examine the usefulness of current energy statistics in relation to this distinction. The conventional linear representation – flow chart – based on a single scale and a single quantitative accounting confuses the three semantic categories and entails an important loss of information. Finally, we illustrate an innovative scheme for energy accounting across hierarchical levels that: (i) addresses the autocatalytic nature of energy transformations; (ii) provides a multi-scale quantitative representation; and (iii) preserves the semantic distinction between relevant energy forms (PES, EC, and EU).     

Investigation on PEM water electrolysis cell design and components for a HyCon solar hydrogen generator

Hydrogen as a secondary energy carrier promises a large potential as a long term storage for fluctuating renewable energies. In this sense a highly efficient solar hydrogen generation is of great interest especially in southern countries having high solar irradiation. The patented Hydrogen Concentrator (HyCon) concept yields high efficiencies combining multi-junction solar cells with proton exchange (PEM) membrane water electrolysis. In this work, a special PEM electrolysis cell for the HyCon concept was developed and investigated. It is shown that the purpose-made PEM cell shows a high performance using a titanium hybrid fiber sinter function both as a porous transport layer and flow field. The electrolysis cell shows a high performance with 1.83 V at 1 A/cm² and 24 °C working under natural convection with a commercially available catalyst coated membrane. A theoretical examination predicts a total efficiency for the HyCon module from sunlight to hydrogen of approximately 19.5% according to the higher heating value.