Abstract

Abstract
A silicone rubber-coated Mach-Zehnder interferometer (MZI) is proposed and applied to temperature measurement. The MZI is fabricated by splicing single mode fiber between a short section of no-core fiber (NCF) and the ultra-abrupt taper region. The sensing length of MZI is coated with liquid silicone rubber to enhance the temperature sensitivity. Here, NCF is used to excite the higher order cladding mode, the ultra-abrupt taper region acts as a optical fiber coupler, and the silicone rubber coating on sensing length is used as solid cladding material instead of liquid. The enhancement of the sensitivity of a device is due to the high refractive index (1.42) and thermo-optic coefficient (-1.4×10-4/°C) of silicone rubber as compared to liquid cladding temperature sensors. The experiment was performed for both coated and uncoated MZI and the results were compared. The MZI exhibits a high temperature sensitivity of 253.75 and 121.26 pm/°C for coated and uncoated sensing probes, respectively, in the temperature range from 30°C to 75°C. © 2016 Society of Photo-Optical Instrumentation Engineers (SPIE).

Abstract
Eco-routing has been shown as a promising strategy to reduce emissions. However, during peak periods, with limited additional capacity, the eco-friendliness of various routes may change. We have explored this issue empirically by covering about 13,300 km, in three different areas, using GPS-equipped vehicles to record second-by-second vehicle dynamics. This study has confirmed the importance of the eco-routing concept given that the selection of eco-friendly routes can lead to significant emissions savings. Furthermore, these savings are expected to be practically unchanged during the peak period. However, some potential negative externalities may arise from purely dedicated eco-friendly navigation systems.

Abstract
The Container Loading Problem (CLP) literature has traditionally guaranteed cargo static stability by imposing the full support constraint for the base of the box. Used as a proxy for real-world static stability, this constraint excessively restricts the container space utilization and has conditioned the algorithms developed for this problem. In this paper we propose a container loading algorithm with static stability constraints based on the static mechanical equilibrium conditions applied to rigid bodies, which derive from Newton's laws of motion. The algorithm is a multi-population biased random-key genetic algorithm, with a new placement procedure that uses the maximal-spaces representation to manage empty spaces, and a layer building strategy to fill the maximal-spaces. The new static stability criterion is embedded in the placement procedure and in the evaluation function of the algorithm. The new algorithm is extensively tested on well-known literature benchmark instances using three variants: no stability constraint, the classical full base support constraint and with the new static stability constraint a comparison is then made with the state-of-the-art algorithms for the CLP. The computational experiments show that by using the new stability criterion it is always possible to achieve a higher percentage of space utilization than with the classical full base support constraint, for all classes of problems, while still guaranteeing static stability. Moreover, for highly heterogeneous cargo the new algorithm with full base support constraint outperforms the other literature approaches, improving the best solutions known for these classes of problems.

Abstract
A large number of compiler optimizations are nowadays available to users. These optimizations interact with each other and with the input code in several and complex ways. The sequence of application of optimization passes can have a significant impact on the performance achieved. The effect of the optimizations is both platform and application dependent. The exhaustive exploration of all viable sequences of compiler optimizations for a given code fragment is not feasible. As this exploration is a complex and time-consuming task, several researchers have focused on Design Space Exploration (DSE) strategies both to select optimization sequences to improve the performance of each function of the application and to reduce the exploration time. In this article, we present a DSE scheme based on a clustering approach for grouping functions with similarities and exploration of a reduced search space resulting from the combination of optimizations previously suggested for the functions in each group. The identification of similarities between functions uses a data mining method that is applied to a symbolic code representation. The data mining process combines three algorithms to generate clusters: the Normalized Compression Distance, the Neighbor Joining, and a new ambiguity-based clustering algorithm. Our experiments for evaluating the effectiveness of the proposed approach address the exploration of optimization sequences in the context of the ReflectC compiler, considering 49 compilation passes while targeting a Xilinx MicroBlaze processor, and aiming at performance improvements for 51 functions and four applications. Experimental results reveal that the use of our clustering-based DSE approach achieves a significant reduction in the total exploration time of the search space (20x over a Genetic Algorithm approach) at the same time that considerable performance speedups (41% over the baseline) were obtained using the optimized codes. Additional experiments were performed considering the LLVM compiler, considering 124 compilation passes, and targeting a LEON3 processor. The results show that our approach achieved geometric mean speedups of 1.49x, 1.32x, and 1.24x for the best 10, 20, and 30 functions, respectively, and a global improvement of 7% over the performance obtained when compiling with -O2.

Abstract
The Physical layer architectures for the next generation of wireless devices will be characterized by a high degree of flexibility for real-time adaptation to communication conditions variability. OFDM-based architectures are strong candidates for the Physical layer implementation in 5G systems and one of the most important baseband processing operations required by this waveform is the Fast Fourier Transform (FFT). This paper proposes a dynamically reconfigurable FFT processor supporting FFT sizes and throughputs required by the most widely used wireless standards. The FFT reconfiguration was achieved by means of FPGA-based Dynamic Partial Reconfiguration (DPR) techniques, which enables run-time FFT size adaptation according to communication requirements and better resource utilization. The impact of DPR in terms of reconfiguration time and power consumption overhead was evaluated. The obtained results encourage the exploitation of DPR techniques to implement reconfigurable hardware infrastructures for OFDM baseband processing engines.