Abstract
A common problem in mobile networking research and development is the cost related to deploying and running real-world mobile testbeds. Due to cost and operational constraints, these testbeds usually run for short time periods but generate very unique and relevant results that are hard to reproduce. We propose the use of ns-3 as a solution to successfully reproduce real-world mobile testbed experiments. This is accomplished by feeding ns-3 with real testbed traces including node positions and radio link quality only. In order to validate our approach, the network throughput between a fixed Base Station and a Unmanned Aerial Vehicle (UAV) was measured in a real-world testbed. The experimental results were compared to the network throughput achieved using the ns-3 trace-based simulation and a plain ns-3 simulation. The obtained results show the high accuracy of the trace-based simulation, thus validating our approach. © 2017 ACM.

Abstract
This work presents a multimode interference-based fiber sensor in a cavity ring-down system for sensing temperature-induced refractive index (RI) changes of water. The sensing head is based in multimodal interference (MMI) and it was placed inside the fiber loop cavity of the CRD system. A modulated laser source was used to send pulses down into the fiber loop cavity and an erbium-doped fiber amplifier (EDFA) was placed in the fiber ring to provide an observable signal with a reasonable decay time. The behavior of the sensing head to temperature was studied due to its intrinsic sensitivity to said parameter - a sensitivity of -1.6 10(-9) s/C was attained. This allowed eliminating the temperature component from RI measurement of water and a linear sensitivity of 580 mu s/RIU in the RI range of 1.324-1.331 was obtained.

Abstract

Abstract
In this work we present a study addressing the load modulation for wireless power transfer in underwater applications. A voltage-mode class-D in a series-series resonance topology is analyzed with simplified equations provided to describe the operation of the wireless power system, including the influence of the load on the primary side. The analysis is validated through simulation results provided for a resistive load modulation using component SPICE models, including the characterization of coupling coils in salt water.

Abstract
OpenCL is an open standard for parallel programming of heterogeneous compute devices, such as GPUs, CPUs, DSPs or FPGAs. However, the verbosity of its C host API can hinder application development. In this paper we present cf4ocl, a software library for rapid development of OpenCL programs in pure C. It aims to reduce the verbosity of the OpenCL API, offering straightforward memory management, integrated profiling of events (e.g., kernel execution and data transfers), simple but extensible device selection mechanism and user-friendly error management. We compare two versions of a conceptual application example, one based on cf4ocl, the other developed directly with the OpenCL host API. Results show that the former is simpler to implement and offers more features, at the cost of an effectively negligible computational overhead. Additionally, the tools provided with cf4ocl allowed for a quick analysis on how to optimize the application.

Abstract
The optimization of software to be (more) energy efficient is becoming a major concern for the software industry. Although several techniques have been presented to measure energy consumption for software, none has addressed software product lines (SPLs). Thus, to measure energy consumption of a SPL, the products must be generated and measured individually, which is too costly. In this paper, we present a technique and a prototype tool to statically estimate the worst case energy consumption for SPL. The goal is to provide developers with techniques and tools to reason about the energy consumption of all products in a SPL, without having to produce, run and measure the energy in all of them. Our technique combines static program analysis techniques and worst case execution time prediction with energy consumption analysis. This technique analyzes all products in a feature-sensitive manner, that is, a feature used in several products is analyzed only once, while the energy consumption is estimated once per product. We implemented our technique in a tool called Serapis. We did a preliminary evaluation using a product line for image processing implemented in C. Our experiments considered 7 products from such line and our initial results show that the tool was able to estimate the worst-case energy consumption with a mean error percentage of 9.4% and standard deviation of 6.2% when compared with the energy measured when running the products.