Abstract
The large scale integration of electric vehicles and distributed energy resources on low voltage grids might cause serious problems related to, for instance, under/over voltages and line overloading. In order to cope with these problems, this paper presents a multi agent system (MAS) developed to dynamically schedule flexible loads on low voltage grids, preventing operation limit violations. Since different geographical positions of the loads in the grid will cause a different impact on the grid, load flow calculations are used to indicate operation limit violations. The application uses a decentralized algorithm which ensures similar chances of being scheduled to the customer loads using a priority scheme. A case study is carried out on a 70-bus feeder where electric vehicle loads are scheduled to prevent under voltages, showing the applicability of the approach.

Abstract
Interest in the MapReduce programming model has been rekindled by Google in the past 10 years; its popularity is mostly due to the convenient abstraction for parallelization details this framework provides. State-of-the-art systems such as Google's, Hadoop or SAGA often provide added features like a distributed file system, fault tolerance mechanisms, data redundancy and portability to the basic MapReduce framework. However, these features pose an additional overhead in terms of system performance. In this work, we present a MapReduce design for Prolog which can potentially take advantage of hybrid parallel environments; this combination allies the easy declarative syntax of logic programming with its suitability to represent and handle multi-relational data due to its first order logic basis. MapReduce for Prolog addresses efficiency issues by performing load balancing on data with different granularity and allowing for parallelization in shared memory, as well as across machines. In an era where multicore processors have become common, taking advantage of a cluster's full capabilities requires the hybrid use of parallelism.

Abstract
Optical fiber sensors based on the phenomenon of plasmonic resonance can be interrogated applying different methods, the most common one being the spectral approach where the measurand information is derived from the reading of the wavelength resonance dip. In principle, a far better performance can be achieved considering the reading of the phase of the light at a specific wavelength located within the spectral plasmonic resonance. This approach is investigated in this work for surface plasmon based fiber optic sensors with overlays which are combinations of bimetallic layers, permitting not only to tune the wavelength of the plasmon resonance but also the sensitivity associated with the phase interrogation of the sensors. The metals considered for the present analysis are silver, gold, copper, and aluminum.

Abstract
Data-flow is a natural approach to parallelism. However, describing dependencies and control between fine-grained data-flow tasks can be complex and present unwanted overheads. TALM (TALM is an Architecture and Language for Multi-threading) introduces a user-defined coarse-grained parallel data-flow model, where programmers identify code blocks, called super-instructions, to be run in parallel and connect them in a data-flow graph. TALM has been implemented as a hybrid Von Neumann/data-flow execution system: the Trebuchet. We have observed that TALM's usefulness largely depends on how programmers specify and connect super-instructions. Thus, we present Couillard, a full compiler that creates, based on an annotated C-program, a data-flow graph and C-code corresponding to each super-instruction. We show that our toolchain allows one to benefit from data-flow execution and explore sophisticated parallel programming techniques, with small effort. To evaluate our system we have executed a set of real applications on a large multi-core machine. Comparison with popular parallel programming methods shows competitive speedups, while providing an easier parallel programing approach. More specifically, for an application that follows the wavefront method, running with big inputs, Trebuchet achieved up to 4.7% speedup over Intel (R) TBB novel flow-graph approach and up to 44% over OpenMP.

Abstract
Counting the occurrences of small subgraphs in large networks is a fundamental graph mining metric with several possible applications. Computing frequencies of those subgraphs is also known as the subgraph census problem, which is a computationally hard task. In this paper we provide a parallel multicore algorithm for this purpose. At its core we use FaSE, an efficient network-centric sequential subgraph census algorithm, which is able to substantially decrease the number of isomorphism tests needed when compared to past approaches. We use one thread per core and employ a dynamic load balancing scheme capable of dealing with the highly unbalanced search tree induced by FaSE and effectively redistributing work during execution. We assessed the scalability of our algorithm on a varied set of representative networks and achieved near linear speedup up to 32 cores while obtaining a high efficiency for the total 64 cores of our machine.

Abstract
This paper presents a planar antenna using low temperature co-fired ceramics (LTCC) substrate for extreme environment applications. An ultra wideband (UWB) elliptical patch antenna was designed and fabricated using an LTCC Ceramtec GC substrate to demonstrate the capabilities of the technology for wideband applications. The simulated results were further validated experimentally. The fabricated antenna provides a peak gain of 5dB over a bandwidth of 4 GHz (3 GHz 7 GHz) with return loss better than -10dB. The radiation pattern is omni-directional in the horizontal plane (theta=90 degrees) over the whole frequency range.