Over the past few years, multicore systems have become increasingly powerful and thereby very useful in high-performance computing. However, many applications, such as some linear algebra algorithms, still cannot take full advantage of these systems. This is mainly due to the shortage of optimization techniques dealing with irregular control structures. In particular, the well-known polyhedral model fails to optimize loop nests whose bounds and/or array references are not affine functions. This is more likely to occur when handling sparse matrices in their packed formats. In this article, we propose using 2d-packed layouts and simple affine transformations to enable optimization of triangular and banded matrix operations. The benefit of our proposal is shown through an experimental study over a set of linear algebra benchmarks.

In this work, an optimization study of the delivered power density by the solid oxide fuel cells is presented according to a thermodynamic model. The power density is defined by the current density, the Nernst potential and the three losses: concentration, activation and ohmic. A comparison between the Tafel and Butler-Volmer formulations was performed to quantify the activation loss. A program in FORTRAN language was developed locally for the resolution of the mathematical equations representing the used physical model. The obtained results show that the SOFCs power density is inversely proportional to the anode thickness, electrolyte thickness and cathode thickness. The optimum fuel water content ensures the maximum power density is 6.25%. The cell power density is proportional to the oxygen concentration in the oxidant, the operating temperature and the operating pressure.

Zn(1–x)MgxO thin films with various concentrations of magnesium were deposited using the spray pyrolysis method. The transmittance spectra recorded for all films exhibit maxima exceeding 90%. The band gap energy of the films with wurtzite structure increases from 3.22 up to 3.60 eV by incorporating Mg into ZnO. However, when the atomic ratio of Mg exceeded 0.4, a second crystalline phase (assigned to cubic MgO) became discernable in XRD patterns, a compressive strain was observed in the wurtzite lattice, and crystallite sizes decreased significantly. In accordance with these observations, finer grains with a pronounced columnar growth were observed in 3D AFM representations and the surface roughness decreases significantly. Finally, selective etching in water yields to porous films with a great surface-to-volume ratio, a lower refractive index and a better light transmission. These porous films with tunable band gap seem to be excellent candidates to various interesting applications.

Over the last few years, different traffic sign recognition systems were proposed. The present paper introduces an overview of some recent and efficient methods in the traffic sign detection and classification. Indeed, the main goal of detection methods is localizing regions of interest containing traffic sign, and we divide detection methods into three main categories: color-based (classified according to the color space), shape-based, and learning-based methods (including deep learning). In addition, we also divide classification methods into two categories: learning methods based on hand-crafted features (HOG, LBP, SIFT, SURF, BRISK) and deep learning methods. For easy reference, the different detection and classification methods are summarized in tables along with the different datasets. Furthermore, future research directions and recommendations are given in order to boost TSR’s performance.

The path-planning problem is commonly formulated to handle the obstacle avoidance constraints. This problem becomes more complicated when further restrictions are added. It often requires efficient algorithms to be solved. In this paper, a new approach is proposed where the path is described by means of Non Uniform Rational B-Splines (NURBS for short) with more additional constraints. An evolutionary technique called Particle Swarm Optimization (PSO) with three options of particles velocity updating offering three alternatives namely the PSO with inertia weight (PSO-W), the constriction factor PSO (PSO-C) and the combination of the two(PSO-WC); are used to optimize the weights of the control points that serve as parameters of the algorithm describing the path. Simulation results show how the mixture of the first two options produces a powerful algorithm, specifically (PSO-WC), in producing a compromise between fast convergence and large number of potential solution. In addition, the whole approach seems to be flexible, powerful and useful for the generation of successful smooth trajectories for robot manipulator which are independent from environment conditions.

In wireless sensor networks, one of the most important constraints is the low power consumption requirement. For that reason, several hierarchical or cluster-based routing methods have been proposed to provide an efficient way to save energy during communication. However, their main challenge is to have efficient mechanisms to achieve the trade-off between increasing the network lifetime and accomplishing acceptable transmission latency. In this paper, we propose a novel protocol for cluster-based wireless sensor networks called PEAL (Power Efficient and Adaptive Latency). Our simulation results show that PEAL can extend the network lifetime about 47% compared to the classic protocol LEACH (Low-Energy Adaptive Clustering Hierarchy) and introduces an acceptable transmission latency compared to the energy conservation gain.