Abstract
In this paper it is proposed an interrogation system based on OTDR for fiber loop mirror intensity sensors. The system has been characterized in order to obtain its maximum dynamic range. The technique demonstrated good linearity with a - 13.3 dB/mm slope. A 0.027 mm resolution was achieved. The proposed interrogation system permits multiplexing of around 10 sensors and showed to be an alternative technique for multiplexing and remote sensing.

Abstract
Automated tracking of axonal neurotransmitter vesicles is a challenging problem in neuroscience. The present vesicle tracking is typically performed manually over confocal microscopy images. NeuronDynamics is a method designed to automate and speed-up the characterization of global vesicle movement in neurons while yielding high accuracy and precision results (similar or better than expert clinicians). For a set of fluorescent-marked vesicles "films", NeuronDynamics performs a two stage approach: 1) Training: the system asks the user to mark a set of vesicles and the position of the cellular body; 2) Detection & tracking: based on the previous training, the system runs a Bayesian classifier over the image sequence to detect and classify vesicles and their movements (speed and direction). The obtained results were compared to another state-of-the-art method (FluoTracker), and were found greatly higher in accuracy, sensitivity, specificity and precision. Although NeuronDynamics is a semi-automated process, it is significantly faster than manual tracking and can be adapted to be used for similar approaches for other biological samples.

Abstract
While Cluster-Tree network topologies look promising for WSN applications with timeliness and energy-efficiency requirements, we are yet to witness its adoption in commercial and academic solutions. One of the arguments that hinder the use of these topologies concerns the lack of flexibility in adapting to changes in the network, such as in traffic flows. This paper presents a solution to enable these networks with the ability to self-adapt their clusters' duty-cycle and scheduling, to provide increased quality of service to multiple traffic flows. Importantly, our approach enables a network to change its cluster scheduling without requiring long inaccessibility times or the re-association of the nodes. We show how to apply our methodology to the case of IEEE 802.15.4/ZigBee cluster-tree WSNs without significant changes to the protocol. Finally, we analyze and demonstrate the validity of our methodology through a comprehensive simulation and experimental validation using commercially available technology on a Structural Health Monitoring application scenario.

Abstract
This paper describes a study developed with the goal to understand the panorama of the metadata Application Profiles (AP): (i) what AP have been developed so far; (ii) what type of institutions have developed these AP; (iii) what are the application domains of these AP; (iv) what are the Metadata Schemes (MS) used by these AP; (v) what application domains have been producing MS; (vi) what are the Syntax Encoding Schemes (SES) and the Vocabulary Encoding Schemes (VES) used by these AP; and finally (vii) if these AP have followed the Singapore Framework (SF). We found (i) 74 AP; (ii) the AP are mostly developed by the scientific community, (iii) the 'Learning Objects' domain is the most intensive producer; (iv) Dublin Core metadata vocabularies are the most used and are being used in all domains of application and IEEE LOM is the second most used but only inside the 'Learning Objects' application domain; (v) the most intensive producer of MS is the domain of 'Libraries and Repositories'; (vi) 13 distinct SES and 90 distinct VES were used; (vi) five of the 74 AP found follow the SF. Copyright © 2014 Inderscience Enterprises Ltd.

Abstract

Abstract
High-maturity software development processes, making intensive use of metrics and quantitative methods, such as the Team Software Process (TSP) and the accompanying Personal Software Process (PSP), can generate a significant amount of data that can be periodically analyzed to identify performance problems, determine their root causes and devise improvement actions. However, there is a lack of tool support for automating the data analysis and the recommendation of improvement actions, and hence diminish the manual effort and expert knowledge required. So, we propose in this paper a comprehensive performance model, addressing time estimation accuracy, quality and productivity, to enable the automated (tool based) analysis of performance data produced in the context of the PSP, namely, identify performance problems and their root causes, and subsequently recommend improvement actions. Performance ranges and dependencies in the model were calibrated and validated, respectively, based on a large PSP data set referring to more than 30,000 finished projects. © 2014 ACM.