Field Programmable Gate Arrays (FPGAs) II

This Edited Volume Field Programmable Gate Arrays (FPGAs) II is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Computer and Information Science. The book comprises single chapters authored by various researchers and edited by an expert active in the Computer and Information Science research area. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on Computer and Information Science, and open new possible research paths for further novel developments

Optics for AI and AI for Optics

Artificial intelligence is deeply involved in our daily lives via reinforcing the digital transformation of modern economies and infrastructure. It relies on powerful computing clusters, which face bottlenecks of power consumption for both data transmission and intensive computing. Meanwhile, optics (especially optical communications, which underpin today’s telecommunications) is penetrating short-reach connections down to the chip level, thus meeting with AI technology and creating numerous opportunities. This book is about the marriage of optics and AI and how each part can benefit from the other. Optics facilitates on-chip neural networks based on fast optical computing and energy-efficient interconnects and communications. On the other hand, AI enables efficient tools to address the challenges of today’s optical communication networks, which behave in an increasingly complex manner. The book collects contributions from pioneering researchers from both academy and industry to discuss the challenges and solutions in each of the respective fields

Diseño de estrategias de sincronización y estimación de canal para la mejora de comunicaciones en redes inteligentes de energía

La presente tesis contribuye en el desarrollo de estrategias eficientes de sincronización y estimación de canal para sistemas de comunicaciones por la red eléctrica (Power-Line Communications – PLC), que utilizan modulación multiportadora por división de frecuencias ortogonales (Orthogonal Frequency Division Multiplexing – OFDM). El principal objetivo es disminuir la complejidad asociada respecto a variantes existentes en la literatura, y a su vez mantener un desempeño competitivo. Para ello, se realiza el diseño de un símbolo piloto construido a partir de pares de secuencias complementarias y se definen algoritmos de sincronización y estimación de canal. Se analizan las técnicas de sincronización gruesa por Autocorrelación (AC) y Correlación Cruzada (CC) en sistemas PLC, y se define un algoritmo de sincronización fina y estimación de canal a partir de la reutilización de la CC. La propuesta se evalúa por simulaciones estudiando el efecto en cada etapa de: el canal PLC, el ruido de fondo coloreado y las diversas fuentes de ruido impulsivo. Adicionalmente, se determina la degradación en el desempeño de cada etapa y se proponen soluciones en un escenario con restricción en la cantidad de subportadoras habilitadas para la transmisión del símbolo piloto, al aplicar una máscara espectral de compatibilidad electromagnética.Universidad Nacional de La PlataUniversidad de Alcal

Machine Learning in Digital Signal Processing for Optical Transmission Systems

The future demand for digital information will exceed the capabilities of current optical communication systems, which are approaching their limits due to component and fiber intrinsic non-linear effects. Machine learning methods are promising to find new ways of leverage the available resources and to explore new solutions. Although, some of the machine learning methods such as adaptive non-linear filtering and probabilistic modeling are not novel in the field of telecommunication, enhanced powerful architecture designs together with increasing computing power make it possible to tackle more complex problems today. The methods presented in this work apply machine learning on optical communication systems with two main contributions. First, an unsupervised learning algorithm with embedded additive white Gaussian noise (AWGN) channel and appropriate power constraint is trained end-to-end, learning a geometric constellation shape for lowest bit-error rates over amplified and unamplified links. Second, supervised machine learning methods, especially deep neural networks with and without internal cyclical connections, are investigated to combat linear and non-linear inter-symbol interference (ISI) as well as colored noise effects introduced by the components and the fiber. On high-bandwidth coherent optical transmission setups their performances and complexities are experimentally evaluated and benchmarked against conventional digital signal processing (DSP) approaches. This thesis shows how machine learning can be applied to optical communication systems. In particular, it is demonstrated that machine learning is a viable designing and DSP tool to increase the capabilities of optical communication systems