Rede neural de função de base radial de transmissão de fase complexa para decodificação mimo-ofdm massiva

Orientador: Dalton Soares ArantesDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Os esquemas de transmissão MIMO (multiple-input multiple-output) se tornaram as técnicas escolhidas para aumentar a eficiência espectral em áreas congestionadas. No entanto, o projeto de receptores de baixo custo para canais MIMO continua sendo uma tarefa desafiadora. O detector de máxima verossimilhança pode atingir um desempenho excelente, geralmente o melhor, mas sua complexidade computacional é um fator limitante na implementação prática. Neste trabalho, um novo esquema MIMO é proposto com um algoritmo de decodificação pratico e viável baseado na PTRBFNN (rede neural de função de base radial de transmitância de fase). O esquema proposto atinge uma complexidade computacional bastante competitiva em relação à decodificação de Máxima Verossimilhança, aumentando substancialmente a aplicabilidade do algoritmo. Os resultados da simulação são apresentados para MIMO-OFDM sob desvanecimento Rayleigh em canais sem fio, para que uma comparação de desempenho justa com outras técnicas de referência possa ser estabelecidaAbstract: Multi-Input Multi-Output (MIMO) transmission schemes have become the techniques of choice for increasing spectral efficiency in bandwidth-congested areas. However, the design of cost-effective receivers for MIMO channels remains a challenging task. The maximum likelihood detector can achieve excellent performance, usually the best, but its computational complexity is a limiting factor in practical implementation. In this work, a new MIMO scheme is proposed with a practically feasible decoding algorithm based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed scheme achieves a computational complexity that is quite competitive relative to the Maximum Likelihood decoding, thus substantially increasing the applicability of the algorithm. Simulation results are presented for MIMO-OFDM under wireless Rayleigh fading channels so that a fair performance comparison with other reference techniques can be establishedMestradoTelecomunicações e TelemáticaMestre em Engenharia Elétrica132545/2019-5CNP

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Full Proceedings, 2018

Full conference proceedings for the 2018 International Building Physics Association Conference hosted at Syracuse University

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world