9 research outputs found

    Towards smarter Brain Computer Interface (BCI): study of electroencephalographic signal processing and classification techniques toward the use of intelligent and adaptive BCI

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-07-202

    Towards a Better Performance in Facial Expression Recognition: A Data-Centric Approach

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    Facial expression is the best evidence of our emotions. Its automatic detection and recognition are key for robotics, medicine, healthcare, education, psychology, sociology, marketing, security, entertainment, and many other areas. Experiments in the lab environments achieve high performance. However, in real-world scenarios, it is challenging. Deep learning techniques based on convolutional neural networks (CNNs) have shown great potential. Most of the research is exclusively model-centric, searching for better algorithms to improve recognition. However, progress is insufficient. Despite being the main resource for automatic learning, few works focus on improving the quality of datasets. We propose a novel data-centric method to tackle misclassification, a problem commonly encountered in facial image datasets. The strategy is to progressively refine the dataset by successive training of a CNN model that is fixed. Each training uses the facial images corresponding to the correct predictions of the previous training, allowing the model to capture more distinctive features of each class of facial expression. After the last training, the model performs automatic reclassification of the whole dataset. Unlike other similar work, our method avoids modifying, deleting, or augmenting facial images. Experimental results on three representative datasets proved the effectiveness of the proposed method, improving the validation accuracy by 20.45%, 14.47%, and 39.66%, for FER2013, NHFI, and AffectNet, respectively. The recognition rates on the reclassified versions of these datasets are 86.71%, 70.44%, and 89.17% and become state-of-the-art performance.This work was funded by grant CIPROM/2021/17 awarded by the Prometeo program from Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital of Generalitat Valenciana (Spain), and partially funded by the grant awarded by the Central University of Ecuador through budget certification no. 34 of March 25, 2022, for the development of the research project with code: DOCT-DI-2020-37

    A statistical multi-experts approach to image classification and segmentation

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    Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.Includes bibliographical references (leaves 170-177).by Lik Mui.M.Eng

    Organising and structuring a visual diary using visual interest point detectors

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    As wearable cameras become more popular, researchers are increasingly focusing on novel applications to manage the large volume of data these devices produce. One such application is the construction of a Visual Diary from an individual’s photographs. Microsoft’s SenseCam, a device designed to passively record a Visual Diary and cover a typical day of the user wearing the camera, is an example of one such device. The vast quantity of images generated by these devices means that the management and organisation of these collections is not a trivial matter. We believe wearable cameras, such as SenseCam, will become more popular in the future and the management of the volume of data generated by these devices is a key issue. Although there is a significant volume of work in the literature in the object detection and recognition and scene classification fields, there is little work in the area of setting detection. Furthermore, few authors have examined the issues involved in analysing extremely large image collections (like a Visual Diary) gathered over a long period of time. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. We present a number of approaches to setting detection based on the extraction of visual interest point detectors from the images. We also analyse the performance of two of the most popular descriptors - Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF).We present an implementation of a Visual Diary application and evaluate its performance via a series of user experiments. Finally, we also outline some techniques to allow the Visual Diary to automatically detect new settings, to scale as the image collection continues to grow substantially over time, and to allow the user to generate a personalised summary of their data

    Artificial Intelligence, Mathematical Modeling and Magnetic Resonance Imaging for Precision Medicine in Neurology and Neuroradiology

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    La tesi affronta la possibilità di utilizzare metodi matematici, tecniche di simulazione, teorie fisiche riadattate e algoritmi di intelligenza artificiale per soddisfare le esigenze cliniche in neuroradiologia e neurologia al fine di descrivere e prevedere i patterns e l’evoluzione temporale di una malattia, nonché di supportare il processo decisionale clinico. La tesi è suddivisa in tre parti. La prima parte riguarda lo sviluppo di un workflow radiomico combinato con algoritmi di Machine Learning al fine di prevedere parametri che favoriscono la descrizione quantitativa dei cambiamenti anatomici e del coinvolgimento muscolare nei disordini neuromuscolari, con particolare attenzione alla distrofia facioscapolo-omerale. Il workflow proposto si basa su sequenze di risonanza magnetica convenzionali disponibili nella maggior parte dei centri neuromuscolari e, dunque, può essere utilizzato come strumento non invasivo per monitorare anche i più piccoli cambiamenti nei disturbi neuromuscolari oltre che per la valutazione della progressione della malattia nel tempo. La seconda parte riguarda l’utilizzo di un modello cinetico per descrivere la crescita tumorale basato sugli strumenti della meccanica statistica per sistemi multi-agente e che tiene in considerazione gli effetti delle incertezze cliniche legate alla variabilità della progressione tumorale nei diversi pazienti. L'azione dei protocolli terapeutici è modellata come controllo che agisce a livello microscopico modificando la natura della distribuzione risultante. Viene mostrato come lo scenario controllato permetta di smorzare le incertezze associate alla variabilità della dinamica tumorale. Inoltre, sono stati introdotti metodi di simulazione numerica basati sulla formulazione stochastic Galerkin del modello cinetico sviluppato. La terza parte si riferisce ad un progetto ancora in corso che tenta di descrivere una porzione di cervello attraverso la teoria quantistica dei campi e di simularne il comportamento attraverso l'implementazione di una rete neurale con una funzione di attivazione costruita ad hoc e che simula la funzione di risposta del modello biologico neuronale. E’ stato ottenuto che, nelle condizioni studiate, l'attività della porzione di cervello può essere descritta fino a O(6), i.e, considerando l’interazione fino a sei campi, come un processo gaussiano. Il framework quantistico definito può essere esteso anche al caso di un processo non gaussiano, ovvero al caso di una teoria di campo quantistico interagente utilizzando l’approccio della teoria wilsoniana di campo efficace.The thesis addresses the possibility of using mathematical methods, simulation techniques, repurposed physical theories and artificial intelligence algorithms to fulfill clinical needs in neuroradiology and neurology. The aim is to describe and to predict disease patterns and its evolution over time as well as to support clinical decision-making processes. The thesis is divided into three parts. Part 1 is related to the development of a Radiomic workflow combined with Machine Learning algorithms in order to predict parameters that quantify muscular anatomical involvement in neuromuscular diseases, with special focus on Facioscapulohumeral dystrophy. The proposed workflow relies on conventional Magnetic Resonance Imaging sequences available in most neuromuscular centers and it can be used as a non-invasive tool to monitor even fine change in neuromuscular disorders and to evaluate longitudinal diseases’ progression over time. Part 2 is about the description of a kinetic model for tumor growth by means of classical tools of statistical mechanics for many-agent systems also taking into account the effects of clinical uncertainties related to patients’ variability in tumor progression. The action of therapeutic protocols is modeled as feedback control at the microscopic level. The controlled scenario allows the dumping of uncertainties associated with the variability in tumors’ dynamics. Suitable numerical methods, based on Stochastic Galerkin formulation of the derived kinetic model, are introduced. Part 3 refers to a still-on going project that attempts to describe a brain portion through a quantum field theory and to simulate its behavior through the implementation of a neural network with an ad-hoc activation function mimicking the biological neuron model response function. Under considered conditions, the brain portion activity can be expressed up to O(6), i.e., up to six fields interaction, as a Gaussian Process. The defined quantum field framework may also be extended to the case of a Non-Gaussian Process behavior, or rather to an interacting quantum field theory in a Wilsonian Effective Field theory approach

    A data mining approach for automated classification of Alzheimer’s disease

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    This work presents the creation of classifiers able to automatically diagnose Alzheimer’s disease from structural magnetic resonance images. Measurements of regions inside the human brain and how they relate to a diagnosis of Alzheimer’s disease are investigated. A genetic algorithm is used to extract a subset of brain measurements which have some of the highest predictive power when compared to the rest of the measurements. A descriptive classifier is created which uses the size of each of the hippocampal volumes of a subject and compares it to the distribution of other subjects. Based on how the individual subject’s measurements compares to the distribution, we can determine whether it is an Alzheimer’s disease predictive volume. This classifier achieves an accuracy equivalent to state-of-the-art approaches. A second descriptive classifier is created using a regression model to predict a healthy subject’s age and applying this to Alzheimer’s disease positive subjects to generate an apparent brain age. The classification accuracy of when the apparent brain age is compared to the subject’s real age is comparable to the state-of-the-art methods in the literature

    Using Ideation Tools for Face-to-face Collaboration Within Complex Design Problems

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    The focus of this research are ideation tools and their ability to catalyse ideas to address complex design problems. Complex design problems change over time and the interactions among the components of the problem and the interaction between the problem and its environment are of such that the system as a whole cannot be fully understood simply by analyzing its components (Cilliers 1998, pp. I). Ideation for this research is defined as a process of generating, developing and communicating ideas that are critical to the design process (Broadbent, in Fowles 1979, pp. 15). Based on Karni and Arciszewski, who stated that ideation tools should act more like an observer or suggester rather than controller or an expert, I defne design ideation tools as tools or methods that enhance, increase and improve the user's ability to generate ideas with the client (Karni and Arciszewski 1997; Reineg and Briggs 2007). Based on a survey of over 70 ideation tools, protocol analysis of design activities, a web survey and semistructured interviews, I conclude that designers and clients may not have sufficient knowledge of ideation or ideation tools in either testing or practice as a catalyst for generating possibilities and that measuring ideation tools based on how many ideas they generate is misleading because it relates creativity and idea generation but does not adequately reflect the participants' experience. This research suggests that participants' cultural perceptions of design ideation and the design process actively inhibit idea generation and that a shift from design outcome led ideation tool design to designing ideation tools that engage design contexts are necessary to effectively address complex design problems. This research identifed a gap in ideation tools for designers to collaborate with their clients during the ideation phase to catalyse possibilities to complex design problems as the contribution to new knowledge

    Spatial and temporal features of neutrophils in homeostasis from the perspective of computational biology

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Biología Molecular. Fecha de Lectura: 22-07-2022Neutrophils are myeloid cells that originate in the Bone Marrow and enter circulation to patrol for infectious agents. An important part of the “nonspecific” immune system consists on Neutrophils infiltrating challenged tissues, and the established belief was that they stay away from steady-state organs to avoid the risk of exposing them to their cytotoxic content. In the papers presented in this thesis, we show that neutrophils can in fact be found in almost all tissues under homeostasis. We further present proof that they undergo shifts in DNA accessibility, RNA expression and protein content in the infiltrated tissues. Using functional annotation we predict distinct roles depending on the tissue. While in hematopoietic organs the transcriptomic signatures of neutrophils align with canonical functions like immune response and migration, in other tissues such as the skin we find non-canonical functions i.e, epithelial and connective tissue growth or pro-angiogenic roles in the gut and the lung. This predicted pro-angiogenic role was indeed confirmed for the lung. We finally describe that infiltration in tissues follows circadian dynamics, and that once it has occurred, neutrophils experience changes in transcription depending on the time of the day. The analyses of circadian rhythms on mammalian models are often hindered by the inherent difficulty of performing exhaustive sampling (i.e.: every hour for at least 48h). Hence, I implemented CircaN as an R package, which outperforms existing tools in most scenarios. To provide the most complete analysis possible, we provide a full mode analysis option, in which we run CircaN and the two most used algorithms and provide integrated results. We present proof-of-concept results showing that combining various tools yields the best true positive to false positive ratio for most purposesEsta Tesis ha sido financiada por el Ministerio de Ciencia, Innovación y Universidades (MICINN

    Using ideation tools for face-to-face collaboration within complex design problems

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    The focus of this research are ideation tools and their ability to catalyse ideas to address complex design problems. Complex design problems change over time and the interactions among the components of the problem and the interaction between the problem and its environment are of such that the system as a whole cannot be fully understood simply by analyzing its components (Cilliers 1998, pp. I). Ideation for this research is defined as a process of generating, developing and communicating ideas that are critical to the design process (Broadbent, in Fowles 1979, pp. 15). Based on Karni and Arciszewski, who stated that ideation tools should act more like an observer or suggester rather than controller or an expert, I defne design ideation tools as tools or methods that enhance, increase and improve the user's ability to generate ideas with the client (Karni and Arciszewski 1997; Reineg and Briggs 2007). Based on a survey of over 70 ideation tools, protocol analysis of design activities, a web survey and semistructured interviews, I conclude that designers and clients may not have sufficient knowledge of ideation or ideation tools in either testing or practice as a catalyst for generating possibilities and that measuring ideation tools based on how many ideas they generate is misleading because it relates creativity and idea generation but does not adequately reflect the participants' experience. This research suggests that participants' cultural perceptions of design ideation and the design process actively inhibit idea generation and that a shift from design outcome led ideation tool design to designing ideation tools that engage design contexts are necessary to effectively address complex design problems. This research identifed a gap in ideation tools for designers to collaborate with their clients during the ideation phase to catalyse possibilities to complex design problems as the contribution to new knowledge.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
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