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.

Abstract
This paper presents a new generation of man portable acoustic navigation buoys. The aim of these buoys is to facilitate the deployment of an underwater acoustic positioning system for the operation of Autonomous Underwater Vehicles. Each buoy includes only the vital modules required for the most typical schemes of underwater acoustic navigation, packed in a small but dynamically stable platform for one day long operations in coastal waters. We will present an overview of the systems hardware and electronics, and also the key features of the deployment and operation of the beacons.

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This paper examines how firms should allocate their advertising budgets between consumers who have a high preference for their products (i.e., strong segment) and those who prefer competing products (i.e., weak segment). Targeted advertising transmits relevant information to otherwise uninformed consumers and it is used as a price discrimination device. With targeted advertising and price discrimination, we find that, when the attractiveness of the weak segment is low, each firm advertises more intensively in its strong segment. The same result arises when the attractiveness of the weak segment is high and advertising is sufficiently expensive. Interestingly, when the attractiveness of the weak segment is high but advertising costs are sufficiently low, it is optimal for each firm to advertise more intensively in its weak segment. The paper also investigates how advertising strategies and equilibrium profits are affected by price discrimination. Compared with uniform pricing, firms can increase or reduce the intensity of advertising targeted to each segment when price discrimination is allowed. Furthermore, when the attractiveness of the weak market is high, price discrimination boosts firms' profits provided that advertising costs are sufficiently low. The reverse happens when advertising costs are high.

Abstract