Signal Modality Characterization: from Phase Space Reconstruction to Real Applications

La caracterización de la modalidad de la señal es un nuevo concepto objeto de recientes trabajos de investigación cuyo principal propósito es identificar cambios en la naturaleza de señales reales. Con el término naturaleza de las señales se hace referencia al modelo subyacente que genera una señal desde el punto de vista de dos características principales: determinismo y linealidad. En esta tesis se emplea la modalidad de la señal para el procesado avanzado de señales acústicas, y en particular, en ensayos no destructivos de materiales no homogéneos como el hormigón. El problema de la caracterización de la modalidad comienza con la correcta reconstrucción del espacio de fases (Capítulo 2). Este nuevo dominio permite identificar los diferentes estados de una señal, recurrentes o no en función de su naturaleza determinista o aleatoria, respectivamente. En el ámbito de los ensayos no destructivos basados en ultrasonidos, el material se excita con una señal puramente determinista, sin embargo, la naturaleza de la señal recibida depende y es proporcional a la estructura interna del material. Esta hipótesis de trabajo permite plantear la medida del grado de determinismo como una alternativa complementaria a parámetros habituales de ultrasonidos como la atenuación y la velocidad. El nivel de determinismo ha resultado ser proporcional al nivel de porosidad en materiales cementantes (Capítulo 3). También permite la caracterización del nivel de daño de probetas de mortero sometidas a diferentes procesos de daño: ataque externo de sulfato y procesos de carga (Capítulo 4). El estudio de la no linealidad/ complejidad de una serie temporal se plantea inicialmente de forma ciega (sin tener información de la señal de entrada) mediante tests de hipótesis: generando datos surrogados y aplicando un test estadístico. Importantes avances se han logrado adaptando este enfoque a datos no estacionarios, característica habitual de señales no lineales reales. Los principales resultados en este sentido se han conseguido en la caracterización de la complejidad de señales oscilatorias de duración limitada (Capítulo 5). El concepto de la modalidad de la señal también se ha empleado para realizar un detallado estudio del fenómeno no lineal de espectroscopía acústica por impacto. Este análisis ha permitido entender las variables involucradas y plantear así un modelo matemático que caracterice el fenómeno. La comprensión del fenómeno y el modelo han permitido plantear un nuevo algoritmo de procesado equivalente a la técnica habitual NIRAS, pero óptimo en su aplicación. Esta alternativa de procesado puede suponer significativos avances sobre todo en aplicaciones industriales donde el tiempo y el esfuerzo son variables óptimas (Capítulo 6). Esta tesis demuestra que la caracterización de la modalidad de la señal no solo supone una alternativa a la caracterización de complejos fenómenos reales, sino que abre una nueva perspectiva de trabajo dentro del ámbito del procesado de señal. La medida del determinismo y el algoritmo FANSIRAS han demostrado que la modalidad de la señal es una interesante herramienta para futuros trabajos de caracterización de materiales cementantes.The characterization of the modality of a signal is a new concept, which has been the subject of recent research. Its main purpose is to identify any changes in the nature of a real signal. The term `nature of a signal' refers to the underlying model that generates the signal from the point of view of two main characteristics: determinism and linearity. In this thesis, the modality of a signal is used for the advanced processing of acoustic signals, and in particular, in non-destructive tests of non-homogeneous materials, such as concrete. The problem of the characterization of the modality begins with the correct reconstruction of the phase space (Chapter 2). This new domain allows identifying the different states of a signal, as to whether they are recurrent or not, depending on whether they are deterministic, respectively, random. In the field of non-destructive testing based on ultrasound, the material is excited with a purely deterministic signal. However, the nature of the received signal depends on the internal structure of the material. This working hypothesis allows us to propose measuring the degree of determinism as a complementary alternative to the usual ultrasound parameters such, as attenuation and speed. The level of determinism has been found to be proportional to the level of porosity in cementitious materials (Chapter 3). It also allows characterizing the level of damage of mortar test pieces subjected to different kinds of damaging processes: external attack by sulphates, and loading processes (Chapter 4). The study of the non-linearity or complexity of a time series is initially presented blindly (without having information about the input signal) through hypothesis tests: generating surrogate data and applying a statistical test. Significant progress has been made in adapting this approach to nonstationary data, a common feature of real non-linear signals. The main results in this regard have been achieved in the characterization of the complexity of oscillatory signals of limited duration (Chapter 5). The concept of signal modality has also been used to perform a detailed study of the non-linear phenomenon of acoustic impact spectroscopy. This analysis has allowed understanding the variables involved, and thus, proposing a mathematical model that characterizes the phenomenon. The understanding of the phenomenon and the model have allowed proposing a new processing algorithm equivalent to the usual NIRAS technique, but optimal in its application. This processing alternative may mean significant advances, especially in industrial applications where time and e ort are variables to be optimized (Chapter 6). This thesis demonstrates that the characterization of the modality of a signal not only presents an alternative to the characterization of complicated real phenomena, but it also opens a new research perspective within the field of signal processing. The measure of determinism and the FANSIRAS algorithm have shown that the modality of a signal is an interesting tool for future research into the characterization of cementitious materials.La caracterització de la modalitat del senyal és un nou concepte, objecte de recents treballs de recerca amb el propòsit d'identificar canvis en la natura de senyals reals. Amb el terme natura dels senyals es fa referència al model subjacent que genera un senyal des del punt de vista de dues característiques principals: determinisme i linealitat. En aquesta tesi es fa servir la modalitat del senyal per al processament avançat de senyals acústics i, en particular, en assajos no destructius de materials no homogenis com ara el formigó. El problema de la caracterització de la modalitat comença amb la correcta reconstrucció de l'espai de fase (Capítol 2). Aquest nou domini permet identificar els diferents estats d'un senyal, recurrents o no en funció de la seva natura determinista o aleatòria, respectivament. Dins l'àmbit dels assajos no destructius basats en ultrasons, el material s'excita amb un senyal purament determinista, tanmateix, la natura del senyal rebut depèn i és proporcional a l'estructura interna del material. Aquesta hipòtesi de treball permet plantejar la mesura del grau de determinisme com una alternativa complementària a paràmetres habituals dels ultrasons com ara l'atenuació i la velocitat. El nivell de determinisme ha resultat ésser proporcional al nivell de porositat en materials cementants (Capítol 3). També permet la caracterització del nivell de dany de provetes de morter sotmeses a diferents processos de dany: atac extern de sulfat i processos de càrrega (Capítol 4). L'estudi de la no linealitat/ complexitat d'una sèrie temporal es planteja inicialment de forma cega (sense tindre cap informació del senyal d'entrada) mitjançant tests d'hipòtesi: generant dades subrogades i aplicant un test estadístic. Avanços importants s'han aconseguit adaptant aquest enfoc a dades no estacionàries, característica habitual de senyals no lineals reals. Els principals resultats en aquest sentit s'han aconseguit en la caracterització de la complexitat de senyals oscil·latoris de durada limitada (Capítol 5). El concepte de modalitat del senyal també s'ha emprat per realitzar un detallat estudi del fenomen no lineal d'espectroscòpia acústica per impacte. Aquesta anàlisi ha permet entendre les variables involucrades i plantejar llavors un nou algoritme de processament equivalent a la tècnica habitual NIRAS, però òptim en la seva aplicació. Aquesta alternativa de processament pot suposar significatius avanços sobretot en aplicacions industrials, on el temps i l'esforç són variables òptimes (Capítol 6). Aquesta tesi demostra que la caracterització de la modalitat del senyal no solament suposa una alternativa a la caracterització de complexes fenòmens reals, sinó que obri una nova perspectiva de treball dins l'àmbit del processament de senyal. La mesura del determinisme i l'algoritme FANSIRAS han demostrat que la modalitat del senyal és una ferramenta interessant per a futurs treballs de caracterització de materials cementants.Carrión García, A. (2018). Signal Modality Characterization: from Phase Space Reconstruction to Real Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/106366TESI

Computational Intelligence in Electromyography Analysis

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles. EMG may be used clinically for the diagnosis of neuromuscular problems and for assessing biomechanical and motor control deficits and other functional disorders. Furthermore, it can be used as a control signal for interfacing with orthotic and/or prosthetic devices or other rehabilitation assists. This book presents an updated overview of signal processing applications and recent developments in EMG from a number of diverse aspects and various applications in clinical and experimental research. It will provide readers with a detailed introduction to EMG signal processing techniques and applications, while presenting several new results and explanation of existing algorithms. This book is organized into 18 chapters, covering the current theoretical and practical approaches of EMG research

Assessing Variability of EEG and ECG/HRV Time Series Signals Using a Variety of Non-Linear Methods

Time series signals, such as Electroencephalogram (EEG) and Electrocardiogram (ECG) represent the complex dynamic behaviours of biological systems. The analysis of these signals using variety of nonlinear methods is essential for understanding variability within EEG and ECG, which potentially could help unveiling hidden patterns related to underlying physiological mechanisms. EEG is a time varying signal, and electrodes for recording EEG at different positions on the scalp give different time varying signals. There might be correlation between these signals. It is important to know the correlation between EEG signals because it might tell whether or not brain activities from different areas are related. EEG and ECG might be related to each other because both of them are generated from one co-ordinately working body. Investigating this relationship is of interest because it may reveal information about the correlation between EEG and ECG signals. This thesis is about assessing variability of time series data, EEG and ECG, using variety of nonlinear measures. Although other research has looked into the correlation between EEGs using a limited number of electrodes and a limited number of combinations of electrode pairs, no research has investigated the correlation between EEG signals and distance between electrodes. Furthermore, no one has compared the correlation performance for participants with and without medical conditions. In my research, I have filled up these gaps by using a full range of electrodes and all possible combinations of electrode pairs analysed in Time Domain (TD). Cross-Correlation method is calculated on the processed EEG signals for different number unique electrode pairs from each datasets. In order to obtain the distance in centimetres (cm) between electrodes, a measuring tape was used. For most of our participants the head circumference range was 54-58cm, for which a medium-sized I have discovered that the correlation between EEG signals measured through electrodes is linearly dependent on the physical distance (straight-line) distance between them for datasets without medical condition, but not for datasets with medical conditions. Some research has investigated correlation between EEG and Heart Rate Variability (HRV) within limited brain areas and demonstrated the existence of correlation between EEG and HRV. But no research has indicated whether or not the correlation changes with brain area. Although Wavelet Transformations (WT) have been performed on time series data including EEG and HRV signals to extract certain features respectively by other research, so far correlation between WT signals of EEG and HRV has not been analysed. My research covers these gaps by conducting a thorough investigation of all electrodes on the human scalp in Frequency Domain (FD) as well as TD. For the reason of different sample rates of EEG and HRV, two different approaches (named as Method 1 and Method 2) are utilised to segment EEG signals and to calculate Pearson’s Correlation Coefficient for each of the EEG frequencies with each of the HRV frequencies in FD. I have demonstrated that EEG at the front area of the brain has a stronger correlation with HRV than that at the other area in a frequency domain. These findings are independent of both participants and brain hemispheres. Sample Entropy (SE) is used to predict complexity of time series data. Recent research has proposed new calculation methods for SE, aiming to improve the accuracy. To my knowledge, no one has attempted to reduce the computational time of SE calculation. I have developed a new calculation method for time series complexity which could improve computational time significantly in the context of calculating a correlation between EEG and HRV. The results have a parsimonious outcome of SE calculation by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain. Time series analysis method has been utilised to study complex systems that appear ubiquitous in nature, but limited to certain dynamic systems (e.g. analysing variables affecting stock values). In this thesis, I have also investigated the nature of the dynamic system of HRV. I have disclosed that Embedding Dimension could unveil two variables that determined HRV

MANIFOLD REPRESENTATIONS OF MUSICAL SIGNALS AND GENERATIVE SPACES

Tra i diversi campi di ricerca nell\u2019ambito dell\u2019informatica musicale, la sintesi e la generazione di segnali audio incarna la pluridisciplinalita\u300 di questo settore, nutrendo insieme le pratiche scientifiche e musicale dalla sua creazione. Inerente all\u2019informatica dalla sua creazione, la generazione audio ha ispirato numerosi approcci, evolvendo colle pratiche musicale e gli progressi tecnologici e scientifici. Inoltre, alcuni processi di sintesi permettono anche il processo inverso, denominato analisi, in modo che i parametri di sintesi possono anche essere parzialmente o totalmente estratti dai suoni, dando una rappresentazione alternativa ai segnali analizzati. Per di piu\u300, la recente ascesa dei algoritmi di l\u2019apprendimento automatico ha vivamente interrogato il settore della ricerca scientifica, fornendo potenti data-centered metodi che sollevavano diversi epistemologici interrogativi, nonostante i sui efficacia. Particolarmente, un tipo di metodi di apprendimento automatico, denominati modelli generativi, si concentrano sulla generazione di contenuto originale usando le caratteristiche che hanno estratti dei dati analizzati. In tal caso, questi modelli non hanno soltanto interrogato i precedenti metodi di generazione, ma anche sul modo di integrare questi algoritmi nelle pratiche artistiche. Mentre questi metodi sono progressivamente introdotti nel settore del trattamento delle immagini, la loro applicazione per la sintesi di segnali audio e ancora molto marginale. In questo lavoro, il nostro obiettivo e di proporre un nuovo metodo di audio sintesi basato su questi nuovi tipi di generativi modelli, rafforazti dalle nuove avanzati dell\u2019apprendimento automatico. Al primo posto, facciamo una revisione dei approcci esistenti nei settori dei sistemi generativi e di sintesi sonore, focalizzando sul posto di nostro lavoro rispetto a questi disciplini e che cosa possiamo aspettare di questa collazione. In seguito, studiamo in maniera piu\u300 precisa i modelli generativi, e come possiamo utilizzare questi recenti avanzati per l\u2019apprendimento di complesse distribuzione di suoni, in un modo che sia flessibile e nel flusso creativo del utente. Quindi proponiamo un processo di inferenza / generazione, il quale rifletta i processi di analisi/sintesi che sono molto usati nel settore del trattamento del segnale audio, usando modelli latenti, che sono basati sull\u2019utilizzazione di un spazio continuato di alto livello, che usiamo per controllare la generazione. Studiamo dapprima i risultati preliminari ottenuti con informazione spettrale estratte da diversi tipi di dati, che valutiamo qualitativamente e quantitativamente. Successiva- mente, studiamo come fare per rendere questi metodi piu\u300 adattati ai segnali audio, fronteggiando tre diversi aspetti. Primo, proponiamo due diversi metodi di regolarizzazione di questo generativo spazio che sono specificamente sviluppati per l\u2019audio : una strategia basata sulla traduzione segnali / simboli, e una basata su vincoli percettivi. Poi, proponiamo diversi metodi per fronteggiare il aspetto temporale dei segnali audio, basati sull\u2019estrazione di rappresentazioni multiscala e sulla predizione, che permettono ai generativi spazi ottenuti di anche modellare l\u2019aspetto dinamico di questi segnali. Per finire, cambiamo il nostro approccio scientifico per un punto di visto piu\u301 ispirato dall\u2019idea di ricerca e creazione. Primo, descriviamo l\u2019architettura e il design della nostra libreria open-source, vsacids, sviluppata per permettere a esperti o non-esperti musicisti di provare questi nuovi metodi di sintesi. Poi, proponiamo una prima utilizzazione del nostro modello con la creazione di una performance in real- time, chiamata \ue6go, basata insieme sulla nostra libreria vsacids e sull\u2019uso di une agente di esplorazione, imparando con rinforzo nel corso della composizione. Finalmente, tramo dal lavoro presentato alcuni conclusioni sui diversi modi di migliorare e rinforzare il metodo di sintesi proposto, nonche\u301 eventuale applicazione artistiche.Among the diverse research fields within computer music, synthesis and generation of audio signals epitomize the cross-disciplinarity of this domain, jointly nourishing both scientific and artistic practices since its creation. Inherent in computer music since its genesis, audio generation has inspired numerous approaches, evolving both with musical practices and scientific/technical advances. Moreover, some syn- thesis processes also naturally handle the reverse process, named analysis, such that synthesis parameters can also be partially or totally extracted from actual sounds, and providing an alternative representation of the analyzed audio signals. On top of that, the recent rise of machine learning algorithms earnestly questioned the field of scientific research, bringing powerful data-centred methods that raised several epistemological questions amongst researchers, in spite of their efficiency. Especially, a family of machine learning methods, called generative models, are focused on the generation of original content using features extracted from an existing dataset. In that case, such methods not only questioned previous approaches in generation, but also the way of integrating this methods into existing creative processes. While these new generative frameworks are progressively introduced in the domain of image generation, the application of such generative techniques in audio synthesis is still marginal. In this work, we aim to propose a new audio analysis-synthesis framework based on these modern generative models, enhanced by recent advances in machine learning. We first review existing approaches, both in sound synthesis and in generative machine learning, and focus on how our work inserts itself in both practices and what can be expected from their collation. Subsequently, we focus a little more on generative models, and how modern advances in the domain can be exploited to allow us learning complex sound distributions, while being sufficiently flexible to be integrated in the creative flow of the user. We then propose an inference / generation process, mirroring analysis/synthesis paradigms that are natural in the audio processing domain, using latent models that are based on a continuous higher-level space, that we use to control the generation. We first provide preliminary results of our method applied on spectral information, extracted from several datasets, and evaluate both qualitatively and quantitatively the obtained results. Subsequently, we study how to make these methods more suitable for learning audio data, tackling successively three different aspects. First, we propose two different latent regularization strategies specifically designed for audio, based on and signal / symbol translation and perceptual constraints. Then, we propose different methods to address the inner temporality of musical signals, based on the extraction of multi-scale representations and on prediction, that allow the obtained generative spaces that also model the dynamics of the signal. As a last chapter, we swap our scientific approach to a more research & creation-oriented point of view: first, we describe the architecture and the design of our open-source library, vsacids, aiming to be used by expert and non-expert music makers as an integrated creation tool. Then, we propose an first musical use of our system by the creation of a real-time performance, called aego, based jointly on our framework vsacids and an explorative agent using reinforcement learning to be trained during the performance. Finally, we draw some conclusions on the different manners to improve and reinforce the proposed generation method, as well as possible further creative applications.A\u300 travers les diffe\u301rents domaines de recherche de la musique computationnelle, l\u2019analysie et la ge\u301ne\u301ration de signaux audio sont l\u2019exemple parfait de la trans-disciplinarite\u301 de ce domaine, nourrissant simultane\u301ment les pratiques scientifiques et artistiques depuis leur cre\u301ation. Inte\u301gre\u301e a\u300 la musique computationnelle depuis sa cre\u301ation, la synthe\u300se sonore a inspire\u301 de nombreuses approches musicales et scientifiques, e\u301voluant de pair avec les pratiques musicales et les avance\u301es technologiques et scientifiques de son temps. De plus, certaines me\u301thodes de synthe\u300se sonore permettent aussi le processus inverse, appele\u301 analyse, de sorte que les parame\u300tres de synthe\u300se d\u2019un certain ge\u301ne\u301rateur peuvent e\u302tre en partie ou entie\u300rement obtenus a\u300 partir de sons donne\u301s, pouvant ainsi e\u302tre conside\u301re\u301s comme une repre\u301sentation alternative des signaux analyse\u301s. Paralle\u300lement, l\u2019inte\u301re\u302t croissant souleve\u301 par les algorithmes d\u2019apprentissage automatique a vivement questionne\u301 le monde scientifique, apportant de puissantes me\u301thodes d\u2019analyse de donne\u301es suscitant de nombreux questionnements e\u301piste\u301mologiques chez les chercheurs, en de\u301pit de leur effectivite\u301 pratique. En particulier, une famille de me\u301thodes d\u2019apprentissage automatique, nomme\u301e mode\u300les ge\u301ne\u301ratifs, s\u2019inte\u301ressent a\u300 la ge\u301ne\u301ration de contenus originaux a\u300 partir de caracte\u301ristiques extraites directement des donne\u301es analyse\u301es. Ces me\u301thodes n\u2019interrogent pas seulement les approches pre\u301ce\u301dentes, mais aussi sur l\u2019inte\u301gration de ces nouvelles me\u301thodes dans les processus cre\u301atifs existants. Pourtant, alors que ces nouveaux processus ge\u301ne\u301ratifs sont progressivement inte\u301gre\u301s dans le domaine la ge\u301ne\u301ration d\u2019image, l\u2019application de ces techniques en synthe\u300se audio reste marginale. Dans cette the\u300se, nous proposons une nouvelle me\u301thode d\u2019analyse-synthe\u300se base\u301s sur ces derniers mode\u300les ge\u301ne\u301ratifs, depuis renforce\u301s par les avance\u301es modernes dans le domaine de l\u2019apprentissage automatique. Dans un premier temps, nous examinerons les approches existantes dans le domaine des syste\u300mes ge\u301ne\u301ratifs, sur comment notre travail peut s\u2019inse\u301rer dans les pratiques de synthe\u300se sonore existantes, et que peut-on espe\u301rer de l\u2019hybridation de ces deux approches. Ensuite, nous nous focaliserons plus pre\u301cise\u301ment sur comment les re\u301centes avance\u301es accomplies dans ce domaine dans ce domaine peuvent e\u302tre exploite\u301es pour l\u2019apprentissage de distributions sonores complexes, tout en e\u301tant suffisamment flexibles pour e\u302tre inte\u301gre\u301es dans le processus cre\u301atif de l\u2019utilisateur. Nous proposons donc un processus d\u2019infe\u301rence / g\ue9n\ue9ration, refle\u301tant les paradigmes d\u2019analyse-synthe\u300se existant dans le domaine de ge\u301ne\u301ration audio, base\u301 sur l\u2019usage de mode\u300les latents continus que l\u2019on peut utiliser pour contro\u302ler la ge\u301ne\u301ration. Pour ce faire, nous e\u301tudierons de\u301ja\u300 les re\u301sultats pre\u301liminaires obtenus par cette me\u301thode sur l\u2019apprentissage de distributions spectrales, prises d\u2019ensembles de donne\u301es diversifie\u301s, en adoptant une approche a\u300 la fois quantitative et qualitative. Ensuite, nous proposerons d\u2019ame\u301liorer ces me\u301thodes de manie\u300re spe\u301cifique a\u300 l\u2019audio sur trois aspects distincts. D\u2019abord, nous proposons deux strate\u301gies de re\u301gularisation diffe\u301rentes pour l\u2019analyse de signaux audio : une base\u301e sur la traduction signal/ symbole, ainsi qu\u2019une autre base\u301e sur des contraintes perceptives. Nous passerons par la suite a\u300 la dimension temporelle de ces signaux audio, proposant de nouvelles me\u301thodes base\u301es sur l\u2019extraction de repre\u301sentations temporelles multi-e\u301chelle et sur une ta\u302che supple\u301mentaire de pre\u301diction, permettant la mode\u301lisation de caracte\u301ristiques dynamiques par les espaces ge\u301ne\u301ratifs obtenus. En dernier lieu, nous passerons d\u2019une approche scientifique a\u300 une approche plus oriente\u301e vers un point de vue recherche & cre\u301ation. Premie\u300rement, nous pre\u301senterons notre librairie open-source, vsacids, visant a\u300 e\u302tre employe\u301e par des cre\u301ateurs experts et non-experts comme un outil inte\u301gre\u301. Ensuite, nous proposons une premie\u300re utilisation musicale de notre syste\u300me par la cre\u301ation d\u2019une performance temps re\u301el, nomme\u301e \ue6go, base\u301e a\u300 la fois sur notre librarie et sur un agent d\u2019exploration appris dynamiquement par renforcement au cours de la performance. Enfin, nous tirons les conclusions du travail accompli jusqu\u2019a\u300 maintenant, concernant les possibles ame\u301liorations et de\u301veloppements de la me\u301thode de synthe\u300se propose\u301e, ainsi que sur de possibles applications cre\u301atives

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u

Modelling and quantifying brain connectivity and dynamics with applications in aging and ADHD

Human brain is a complex organ and made up of integrative networks encompassing a large number of regions. These regions communicate with each other to share information involved in complex cognitive processes. Functional connectivity (FC) represents the level of synchronization between different brain regions/networks. Studying functional interactions of the brain creates a platform for understanding functional architecture of the brain as an integrative network and has implications for understanding human cognition. Furthermore, there is evidence that FC patterns are sensitive to different diseases. In addition, age is a significant determinant of intra-/inter-individual variability in the FC patterns. Therefore, key aims for the studies included in this thesis were to apply and develop novel resting-state FC methodologies, with applications in healthy aging and ADHD. Indeed, measures of the brain’s FC may serve as a useful tool to diagnose and predict the course of disease, and useful in developing individualized therapies. Age- or disease-related alterations in the FC could reflect a multitude of factors, including changes in structural connectivity. However, we still have limited knowledge of the emergence of brain dynamics from the underlying anatomy. The interplay between the brain’s structure and dynamics underlies all brain functions. Therefore, in the last study we focused on the systematic modeling of the brain network dynamics. Large-scale computational models are uniquely suited to address difficult questions related to the role of brain’s structural network in shaping functional interactions. In addition, computational modeling of the brain enables us to test different hypotheses without any experimental complication while it provides us with a platform for improving our understanding of different brain mechanisms. A new macroscopic computational model of the brain oscillations for resting-state fMRI was introduced in this thesis, which outperforms previous model in the same class. Then, the effects of malfunctions in different brain regions were simulated and subsequently predicted perturbation patterns were recruited for local vulnerability mapping as well as quantification of hazard rates induced after perturbing any brain regio

Epilepsy

With the vision of including authors from different parts of the world, different educational backgrounds, and offering open-access to their published work, InTech proudly presents the latest edited book in epilepsy research, Epilepsy: Histological, electroencephalographic, and psychological aspects. Here are twelve interesting and inspiring chapters dealing with basic molecular and cellular mechanisms underlying epileptic seizures, electroencephalographic findings, and neuropsychological, psychological, and psychiatric aspects of epileptic seizures, but non-epileptic as well

Strategies for Devising Automatic Signal Recognition Algorithms in a Shared Radio Environment

In an increasingly congested and complex radio environment interference is to be expected, which poses problems for Automatic Signal Recognition (ASR) systems. This thesis explores strategies for improving ASR performance in the presence of interference. The thesis breaks the overall research question down into a number of subquestions and explores each of these in turn. A Phase-symmetric Cross Recurrence Plot is developed and used to show how a radio signal can be manipulated to separate information about the modulation from the information being carried. The Logarithmic Cyclic frequency Domain Profile is introduced to illustrate how a logarithmic representation can be used for analysing mixtures of signals with very different cyclic frequencies. After defining a canonical ASR system architecture, the concepts of an Ideal Feature and Interference Selectivity are introduced and applied to typical features used in ASR processing. Finally it is shown how these algorithmic developments can be combined in a Bayesian chain implementation that can accommodate a wide variety of feature extraction algorithms. It is concluded that future ASR systems will require features that can handle a wide range of signal types with much higher levels of interference selectivity if they are to achieve acceptable performance in shared spectrum bands. Intelligent segmentation is shown to be a requirement for future ASR systems unless features can be developed that have near ideal performance

International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio