GPU-based parallel Shadow Features generation at neural system for improving gait human activity recognition

Multimedia Tools and Applications - In this paper, we propose a new method for improving human activity recognition (HAR) datasets in order to increase their classification accuracy when trained...     

Gripper Design for Radio Base Station Autonomous Maintenance System

For an autonomous system to perform maintenance tasks in a networking device or a radio base station (RBS), it has to deal with a series of technological challenges ranging from identifying hardware-related problems to manipulating connectors. This paper describes the development of a robot maintenance system dedicated to detect and resolve faulty links caused by unplugged or poorly connected cables. Although the maintenance system relies on four subsystems, we significantly focus on our low-cost and efficient custom gripper solution developed to handle RJ45 Ethernet connectors. To examine our gripper, we conducted three experiments. First, a qualitative questionnaire was submitted to 30 users in the case of the teleoperated scenario of the gripper attached to a robotic arm. Similarly, we also tested the automatic operation mode. The results showed that our system is reliable and delivers a highly efficient maintenance tool in both teleoperated and autonomous operation modes. The practical experiment containing the removal or unplugging of connectors demonstrated our gripper’s ability to adequately handle these, whereas the feedback from the questionnaire pointed to a positive user experience. The automatic test assessed the gripper’s robustness against the continuous operation.

     

Mit zweierlei Maß: Die mangelnde Verbindlichkeit von Patientenverfügungen bei untergebrachten Personen mit psychischen Störungen in der Schweiz

Definition of the problem

Full age patients with mental disorders and preserved decision-making capacity are increasingly interested in drafting advance directives. Whereas in Switzerland (as well as in Germany) advance directives are binding in principle, an advance directive is not binding and has only to be considered in case of involuntary admission due to a mental disorder. Thereby, the virtual aim of an advance directive – the anticipated self-determination – is questioned.

Arguments

From an ethical perspective, patients with mental disorders have the same rights as patients with somatic diseases. Therefore, the question arises if there are good reasons for this unequal treatment. In the present contribution, four possible reasons are analyzed: (1) persons with mental disorders are “different”; (2) protection from self-harm; (3) often not present end-of-life context; (4) reduced bindingness as the more appropriate standard in general.

Conclusion

The authors come to the conclusion that patients with mental disorders are often capable of composing a valid advance directive, and just as any other human being, they have a principal and binding right for self-determination, even though they are particularly vulnerable in crisis situations.

     

Polyvinylidene fluoride nanofibers obtained by electrospinning and blowspinning: Electrospinning enhances the piezoelectric β-phase – myth or reality?

Considerable effort has been devoted to improving the properties of PVDF (polyvinylidene fluoride), arguably the most technologically important piezoelectric polymer. Electrospinning has been found to be a particularly effective method of producing PVDF nanofibers with superior piezoelectric properties due to the resulting exceptionally high fraction of the piezoelectrically active crystalline β-phase. It is typically assumed that the high external electric fields applied during electrospinning enhance the formation of this β-phase, with the confused literature offering various unsatisfactory mechanistic explanations. However, by comparing PVDF nanofibers produced by two different processes (electrospinning and blowspinning), we show that the electric field is entirely unnecessary; indeed, the crystallization dynamics are principally driven by the applied mechanical stress, as evidenced by structurally identical 200 nm diameter PVDF fibers produced with and without external electric fields.     

Biodegradability and improved mechanical performance of polyhydroxyalkanoates/agave fiber biocomposites compatibilized by different strategies

In this work, biocomposites made of polyhydroxyalkanoates (PHA) with natural fibers were produced via compression molding. In particular, polyhydroxybutyrate (PHB) and polyhydroxybutyrate-co-hydroxyvalerate (PHBV) were reinforced with 20 wt% of agave fibers. Different compatibilization strategies were investigated to improve the fiber-matrix interaction: fiber surface treatment in PHA solution, fiber surface treatment in maleated PHA solution, fiber propionylation, and extrusion with maleated PHA. The biocomposites were characterized in terms of morphology, mechanical properties, water absorption, and biodegradability by CO₂ production tracking. In general, fiber propionylation was the best strategy for mechanical properties enhancement and water uptake decreasing. Biocomposites with propionylated fibers showed improved flexural strength (170% for PHB and 84% for PHBV). The flexural modulus was also enhanced with propionylated fibers up to 19% and 18% compared to uncompatibilized biocomposites (PHB and PHBV, respectively). Tensile strength increased by 16% (PHB) and 14% (PHBV), and the water absorption was reduced using propionylated fibers going from 6.6% to 4.4% compared with biocomposites with untreated fibers. Most importantly, the impact strength was also improved for all biocomposites by up to 96% compared with the neat PHA matrices. Finally, it was found that the compatibilization did not negatively modify the PHA biodegradability.     

Experimental design to evaluate properties of electrospun fibers of zein/poly (ethylene oxide) for biomaterial applications

The production of polymer fibers from the combination of zein and PEO might have great potential in the field of biomaterial. Zein/PEO fibers were obtained in this work through solution electrospinning. An experimental design, 2^4-1, was used for evaluating the influences of PEO content in the blend, distance from the needle tip to the collector, applied electric voltage and solution flow for average fiber diameter and relative-yield process. Beyond this, the relationship between PEO content in the blend and the fiber properties were evaluated through FTIR, DSC, TG, tensile tests, and cytotoxic tests. The factor that exerts the greatest effect on the average fiber diameter response was the electrical voltage. The increase in PEO content in the blend decreased the thermal stability and increased the degree of the fibers' crystallinity. The mechanical tests showed that fibers with higher elongation were obtained at richer PEO blends. The fibers presented cytocompatible characteristics.     

Extended Reflexive Ontologies for the Generation of Clinical Recommendations

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process.     

On the evaluation of unsupervised outlier detection: measures,datasets, and an empirical study

The evaluation of unsupervised outlier detection algorithms is a constant challenge in data mining research. Little is known regarding the strengths and weaknesses of different standard outlier detection models, and the impact of parameter choices for these algorithms. The scarcity of appropriate benchmark datasets with ground truth annotation is a significant impediment to the evaluation of outlier methods. Even when labeled datasets are available, their suitability for the outlier detection task is typically unknown. Furthermore, the biases of commonly-used evaluation measures are not fully understood. It is thus difficult to ascertain the extent to which newly-proposed outlier detection methods improve over established methods. In this paper, we perform an extensive experimental study on the performance of a representative set of standard k nearest neighborhood-based methods for unsupervised outlier detection, across a wide variety of datasets prepared for this purpose. Based on the overall performance of the outlier detection methods, we provide a characterization of the datasets themselves, and discuss their suitability as outlier detection benchmark sets. We also examine the most commonly-used measures for comparing the performance of different methods, and suggest adaptations that are more suitable for the evaluation of outlier detection results.     

Stream fusion for multi-stream automatic speech recognition

Multi-stream automatic speech recognition (MS-ASR) has been confirmed to boost the recognition performance in noisy conditions. In this system, the generation and the fusion of the streams are the essential parts and need to be designed in such a way to reduce the effect of noise on the final decision. This paper shows how to improve the performance of the MS-ASR by targeting two questions; (1) How many streams are to be combined, and (2) how to combine them. First, we propose a novel approach based on stream reliability to select the number of streams to be fused. Second, a fusion method based on Parallel Hidden Markov Models is introduced. Applying the method on two datasets (TIMIT and RATS) with different noises, we show an improvement of MS-ASR.