The rise of immersive technologies has led to an increase in the number of use cases that adapt this type of technology within the telecommunications area. Some examples are: industrial training, multimedia content consumption and tele-training. Among all the immersive technologies, eXtended Reality through the use of Head-Mounted Displays (HMD) is the one that focuses…
Surface electromyography (sEMG) has become an indispensable tool, extensively used across various fields such as medical diagnosis, rehabilitation, sports science, and prosthetic control. Among these applications, the study of neuromuscular adaptations related to muscle fatigue stands out due to its complexity and the intricate physiological processes underlying muscle activity. This PhD thesis aims to address…
In the era of artificial intelligence, there has been a growing consensus that solutions to complex science and engineering problems require novel methodologies that can integrate interpretable physics-based modeling approaches with machine learning techniques, from stochastic optimization to deep neural networks. This thesis aims to develop new methodological and applied frameworks for combining the advantages…
The scoring of a subject’s sleep stages from electroencephalographic (EEG) signals is a costly process. As such, many approaches to its automation have been proposed, including ones based on Deep Learning. However, said approaches have yet to attain a level of performance good enough for use in clinical settings, in part due to the high…
In multiple-input multiple-output (MIMO) communications systems, the channel state information (CSI) is typically estimated at the receiver side by sending a few known pilots and then used for decoding at the receiver and/or for precoding at the transmitter. These are known as coherent schemes. However, in scenarios dominated by fast fading or massive MIMO systems…
This thesis is focused on the basis function method for the identification of nonstationary processes. The first chapter describes a group of models that can be identified using the basis function method. The next chapter describes the basic version of the basis function method, including its algebraic and statistical properties. The following section introduces the…
Nonlinear parameter estimation for targets is one of the fundamental problem in statistical signal processing, and has attracted a lot of research interest in the past few decades, where higher estimation accuracy is one of the key objectives. Since there is no general closed-form expression on minimum mean-squared error (MSE the lower bounds on MSE…
This dissertation is concerned with the design and analysis of approximation-based methods for nonconvex nonsmooth optimization problems. The main idea behind those methods is to solve a difficult optimization problem by converting it into a sequence of simpler surrogate/approximate problems. In the two widely-used optimization frameworks, namely, the majorization-minimization (MM) framework and the successive convex…
Post-selection estimation refers to the scenario where a preliminary data-based selection stage determines the specific estimation problem. In this work, we are researching two fundamental problems of this framework: estimation after model selection and estimation after parameter selection. In estimation after model selection, the observation model is unknown. Therefore, prior to estimation, a model selection…
System designers across all disciplines of technology face the need to develop machines capable of independently processing and analyzing data and predicting future data. This is the fundamental problem of interest in ?estimation theory,? wherein probabilistic analyses are used to isolate relationships between variables, and in ?statistical inference,? wherein those variables are used to make…
