Nuno Horta – författare

This book presents a novel logarithmic conversion architecture based on cross-coupled inverter. An overview of the current state of the art of logarithmic converters is given where most conventional logarithmic analog-to-digital converter architectures are derived or adapted from linear analog-to-digital converter architectures, implying the use of analog building blocks such as amplifiers. The conversion architecture proposed in this book differs from the conventional logarithmic architectures. Future possible studies on integrating calibration in the voltage to time conversion element and work on an improved conversion architecture derived from the architecture are also presented in this book.

This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder.

In the experimental results chapter, the trained ANNs are used to produce a variety of valid placement solutions even beyond the scope of the training/validation sets, demonstrating the model’s effectiveness in terms of identifying common components between newer topologies and reutilizing the acquired knowledge. Lastly, the methodology used can readily adapt to the given problem’s context (high label production cost), resulting in an efficient, inexpensive and fast model.                           

This book consists of the research, design and implementation, from sizing to layout with parasitic extraction and yield estimation, of a low-power, low-noise amplifier for biomedical and healthcare applications of bio-potential signals, particularly focusing on the electromyography and electrooculography. These signals usually operate in different broadbands, yet follow an impulse-shape transmission, hence being suitable to be applied and detected by the same receiver.

This book introduces readers to a variety of tools for analog layout design automation. After discussing the placement and routing problem in electronic design automation (EDA), the authors overview a variety of automatic layout generation tools, as well as the most recent advances in analog layout-aware circuit sizing. The discussion includes different methods for automatic placement (a template-based Placer and an optimization-based Placer), a fully-automatic Router and an empirical-based Parasitic Extractor. The concepts and algorithms of all the modules are thoroughly described, enabling readers to reproduce the methodologies, improve the quality of their designs, or use them as starting point for a new tool. All the methods described are applied to practical examples for a 130nm design process, as well as placement and routing benchmark sets.