Bio-Inspired Optimization of Ultra-Wideband Patch Antennas Using Graphics Processing Unit Acceleration

Ultra-wideband (UWB) wireless systems have recently gained considerable attention as effective communications platforms with the properties of low power and high data rates. Applications of UWB such as wireless USB put size constraints on the antenna, however, which can be very dicult to meet using typical narrow band antenna designs. The aim of this thesis is to show how bio-inspired evolutionary optimization algorithms, in particular genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO) can produce novel UWB planar patch antenna designs that meet a size constraint of a 10 mm 10 mm patch. Each potential antenna design is evaluated with the nite dierence time domain (FDTD) technique, which is accurate but time-consuming. Another aspect of this thesis is the modication of FDTD to run on a graphics processing unit (GPU) to obtain nearly a 20 speedup. With the combination of GA, PSO, BBO and GPU-accelerated FDTD, three novel antenna designs are produced that meet the size and bandwidth requirements applicable to UWB wireless USB system

Topology optimization of freeform large-area metasurfaces

We demonstrate optimization of optical metasurfaces over $10^5$--$10^6$ degrees of freedom in two and three dimensions, 100--1000+ wavelengths ($\lambda$) in diameter, with 100+ parameters per $\lambda^2$. In particular, we show how topology optimization, with one degree of freedom per high-resolution "pixel," can be extended to large areas with the help of a locally periodic approximation that was previously only used for a few parameters per $\lambda^2$. In this way, we can computationally discover completely unexpected metasurface designs for challenging multi-frequency, multi-angle problems, including designs for fully coupled multi-layer structures with arbitrary per-layer patterns. Unlike typical metasurface designs based on subwavelength unit cells, our approach can discover both sub- and supra-wavelength patterns and can obtain both the near and far fields

FPGA Acceleration of Domain-specific Kernels via High-Level Synthesis

L'abstract è presente nell'allegato / the abstract is in the attachmen

Number of Iteration Analysis for Complex FSS Shape Using GA for Efficient ESG

ESG stand for Energy-Saving Glass is a special shielded glass with a metallic oxide layer to abuse undesirable of infrared and ultraviolet radiation into construction assemblies like our home. Firstly, different number of the iteration is the main thing to study a performance of the frequency selective surface shape using genetic algorithm (GA) for efficient energy saving glass (ESG). Three different values for the number of iterations were taken that is 1500, 2000 1nd 5000. Before that, the response of this complex FSS shape on incident electromagnetic wave with different symmetry shape are investigating. Three of them are no symmetrical shape, ¼ symmetrical shape, and 1/8 symmetrical shape. The 1500 number simulation considered about 89.000 per second, compared with 2000 iteration and 5000 iterations had consumed 105.09 per second and 196.00 per second, respectively. For 1/8 symmetry complex FSS shape, it demonstrations the improved performance of transmission loss at 1.2 GHz with - 40 dB. A 2 dB of transmission loss is achieved at WLAN application of 2.45 GHz with 0°, 30°, and 45° incidence angle shows

Number of iteration analysis for complex fss shape using GA for efficient ESG

ESG stand for Energy-Saving Glass is a special shielded glass with a metallic oxide layer to abuse undesirable of infrared and ultraviolet radiation into construction assemblies like our home. Firstly, different number of the iteration is the main thing to study a performance of the frequency selective surface shape using genetic algorithm (GA) for efficient energy saving glass (ESG). Three different values for the number of iterations were taken that is 1500, 2000 1nd 5000. Before that, the response of this complex FSS shape on incident electromagnetic wave with different symmetry shape are investigating. Three of them are no symmetrical shape, ¼ symmetrical shape, and 1/8 symmetrical shape. The 1500 number simulation considered about 89.000 per second, compared with 2000 iteration and 5000 iterations had consumed 105.09 per second and 196.00 per second, respectively. For 1/8 symmetry complex FSS shape, it demonstrations the improved performance of transmission loss at 1.2 GHz with - 40 dB. A 2 dB of transmission loss is achieved at WLAN application of 2.45 GHz with 0°, 30°, and 45° incidence angle shows

Topology optimization of dispersive plasmonic nanostructures in the time-domain

Topology optimization techniques have been applied in integrated optics and nanophotonics for the inverse design of devices with shapes that cannot be conceived by human intuition. At optical frequencies, these techniques have only been utilized to optimize nondispersive materials using frequency-domain methods. However, a time-domain formulation is more efficient to optimize materials with dispersion. We introduce such a formulation for the Drude model, which is widely used to simulate the dispersive properties of metals, conductive oxides, and conductive polymers. Our topology optimization algorithm is based on the finite-difference time-domain (FDTD) method, and we introduce a time-domain sensitivity analysis that enables the evaluation of the gradient information by using one additional FDTD simulation. The existence of dielectric and metallic structures in the design space produces plasmonic field enhancement that causes convergence issues. We employ an artificial damping approach during the optimization iterations that, by reducing the plasmonic effects, solves the convergence problem. We present several design examples of 2D and 3D plasmonic nanoantennas with optimized field localization and enhancement in frequency bands of choice. Our method has the potential to speed up the design of wideband optical nanostructures made of dispersive materials for applications in nanoplasmonics, integrated optics, ultrafast photonics, and nonlinear optics

Multi-Objective Optimization of Wire Antennas: Genetic Algorithms Versus Particle Swarm Optimization

The paper is aimed to the multi-objective optimization of wire multi-band antennas. Antennas are numerically modeled using time-domain integral-equation method. That way, the designed antennas can be characterized in a wide band of frequencies within a single run of the analysis. Antennas are optimized to reach the prescribed matching, to exhibit the omni-directional constant gain and to have the satisfactory polarization purity. Results of the design are experimentally verified. The multi-objective cost function is minimized by the genetic algorithm and by the particle swarm optimization. Results of the optimization by both the multi-objective methods are in detail compared. The combination of the time domain analysis and global optimization methods for the broadband antenna design and the detailed comparison of the multi-objective particle swarm optimization with the multi-objective genetic algorithm are the original contributions of the paper

Iterative Schedule Optimization for Parallelization in the Polyhedron Model

In high-performance computing, one primary objective is to exploit the performance that the given target hardware can deliver to the fullest. Compilers that have the ability to automatically optimize programs for a specific target hardware can be highly useful in this context. Iterative (or search-based) compilation requires little or no prior knowledge and can adapt more easily to concrete programs and target hardware than static cost models and heuristics. Thereby, iterative compilation helps in situations in which static heuristics do not reflect the combination of input program and target hardware well. Moreover, iterative compilation may enable the derivation of more accurate cost models and heuristics for optimizing compilers. In this context, the polyhedron model is of help as it provides not only a mathematical representation of programs but, more importantly, a uniform representation of complex sequences of program transformations by schedule functions. The latter facilitates the systematic exploration of the set of legal transformations of a given program. Early approaches to purely iterative schedule optimization in the polyhedron model do not limit their search to schedules that preserve program semantics and, thereby, suffer from the need to explore numbers of illegal schedules. More recent research ensures the legality of program transformations but presumes a sequential rather than a parallel execution of the transformed program. Other approaches do not perform a purely iterative optimization. We propose an approach to iterative schedule optimization for parallelization and tiling in the polyhedron model. Our approach targets loop programs that profit from data locality optimization and coarse-grained loop parallelization. The schedule search space can be explored either randomly or by means of a genetic algorithm. To determine a schedule's profitability, we rely primarily on measuring the transformed code's execution time. While benchmarking is accurate, it increases the time and resource consumption of program optimization tremendously and can even make it impractical. We address this limitation by proposing to learn surrogate models from schedules generated and evaluated in previous runs of the iterative optimization and to replace benchmarking by performance prediction to the extent possible. Our evaluation on the PolyBench 4.1 benchmark set reveals that, in a given setting, iterative schedule optimization yields significantly higher speedups in the execution of the program to be optimized. Surrogate performance models learned from training data that was generated during previous iterative optimizations can reduce the benchmarking effort without strongly impairing the optimization result. A prerequisite for this approach is a sufficient similarity between the training programs and the program to be optimized