Symmetry in Electromagnetism

Electromagnetism plays a crucial role in basic and applied physics research. The discovery of electromagnetism as the unifying theory for electricity and magnetism represents a cornerstone in modern physics. Symmetry was crucial to the concept of unification: electromagnetism was soon formulated as a gauge theory in which local phase symmetry explained its mathematical formulation. This early connection between symmetry and electromagnetism shows that a symmetry-based approach to many electromagnetic phenomena is recurrent, even today. Moreover, many recent technological advances are based on the control of electromagnetic radiation in nearly all its spectra and scales, the manipulation of matter–radiation interactions with unprecedented levels of sophistication, or new generations of electromagnetic materials. This is a fertile field for applications and for basic understanding in which symmetry, as in the past, bridges apparently unrelated phenomena―from condensed matter to high-energy physics. In this book, we present modern contributions in which symmetry proves its value as a key tool. From dual-symmetry electrodynamics to applications to sustainable smart buildings, or magnetocardiography, we can find a plentiful crop, full of exciting examples of modern approaches to electromagnetism. In all cases, symmetry sheds light on the theoretical and applied works presented in this book

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Simultaneous EEG-fMRI at ultra-high field for the study of human brain function

Scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have highly complementary domains, and their combination has been actively sought within neuroscience research. The important gains in fMRI sensitivity achieved with higher field strengths open exciting perspectives for combined EEG-fMRI; however, simultaneous acquisitions are subject to highly undesirable interactions between the two modalities, which can strongly compromise data quality and subject safety, and most of these interactions are increased at higher fields. The work described in this thesis was centered on the development of simultaneous EEG-fMRI in humans at 7T, covering aspects of subject safety, signal quality assessment, and quality improvement. Additionally, given the potential value of high-field EEG-fMRI to study the neuronal correlates of so-called negative BOLD responses, an initial fMRI study was dedicated to these phenomena. The initial fMRI study aimed to characterize positive (PBR) and negative BOLD responses (NBR) to visual checkerboard stimulation of varying contrast and duration, focusing on NBRs occurring in visual and in auditory cortical regions. Results showed that visual PBRs and both visual and auditory NBRs significantly depend on stimulus contrast and duration, suggesting a dynamic system of visual-auditory interactions, sensitive to stimulus contrast and duration. The neuronal correlates of these interactions could not be addressed in higher detail with fMRI alone, yet could potentially be clarified in future work with combined EEG-fMRI. Moving on to simultaneous EEG-fMRI implementation, the first stage comprised an assessment of potential safety concerns at 7T. The safety tests comprised numerical simulations of RF power distribution and real temperature measurements on a phantom during acquisition. Overall, no significant safety concerns were found for the setup tested. A characterization of artifacts induced on MRI data due to the presence of EEG components was then performed. With the introduction of the EEG system, functional and anatomical images exhibited general losses in spatial SNR, with a smaller loss in temporal SNR in fMRI data. B0 and B1 field mapping pointed towards RF pulse disruption as the major degradation mechanism affecting MRI data. The main part of this work focused on EEG artifacts induced by MRI. The first step focused on optimizing signal transmission between the EEG cap and amplifiers, to minimize artifact contamination at this important stage. Along this line, adequate cable shortening and bundling effectively reduced environment noise in EEG recordings. Simultaneous acquisitions were then performed on humans using the optimized setup. On average, EEG data exhibited clear alpha modulation and average visual evoked potentials (VEP), with concomitant BOLD signal changes. In the second step, a novel approach for head motion artifact detection was developed, based on a simple modification of the EEG cap, and simultaneous acquisitions were performed in volunteers undergoing visual checkerboard stimulation. After gradient artifact correction, EEG signal variance was found to be largely dominated by pulse artifacts, but contributions from spontaneous motion were still comparable to those of neuronal activity. Using a combination of pulse artifact correction, motion artifact correction and ICA denoising, strong improvements in data quality could be obtained, especially at a single-trial level

Neuromagnetic Decomposition of Social Interaction

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Modeling and applications of the focus cue in conventional digital cameras

El enfoque en cámaras digitales juega un papel fundamental tanto en la calidad de la imagen como en la percepción del entorno. Esta tesis estudia el enfoque en cámaras digitales convencionales, tales como cámaras de móviles, fotográficas, webcams y similares. Una revisión rigurosa de los conceptos teóricos detras del enfoque en cámaras convencionales muestra que, a pasar de su utilidad, el modelo clásico del thin lens presenta muchas limitaciones para aplicación en diferentes problemas relacionados con el foco. En esta tesis, el focus profile es propuesto como una alternativa a conceptos clásicos como la profundidad de campo. Los nuevos conceptos introducidos en esta tesis son aplicados a diferentes problemas relacionados con el foco, tales como la adquisición eficiente de imágenes, estimación de profundidad, integración de elementos perceptuales y fusión de imágenes. Los resultados experimentales muestran la aplicación exitosa de los modelos propuestos.The focus of digital cameras plays a fundamental role in both the quality of the acquired images and the perception of the imaged scene. This thesis studies the focus cue in conventional cameras with focus control, such as cellphone cameras, photography cameras, webcams and the like. A deep review of the theoretical concepts behind focus in conventional cameras reveals that, despite its usefulness, the widely known thin lens model has several limitations for solving different focus-related problems in computer vision. In order to overcome these limitations, the focus profile model is introduced as an alternative to classic concepts, such as the near and far limits of the depth-of-field. The new concepts introduced in this dissertation are exploited for solving diverse focus-related problems, such as efficient image capture, depth estimation, visual cue integration and image fusion. The results obtained through an exhaustive experimental validation demonstrate the applicability of the proposed models

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise