Meyer wavelets with multiple scale factors N > 2

Определяются вейвлеты Мейера с произвольным натуральным коэффициентом масштабирования N > 2 с использованием вейвлетов Мейера с кратными коэффициентами масштабирования MN > 2. Получены выражения частотных функций вейвлетов и соответствующих фильтров. Рассмотрен пример при M = 3 и N = 2

Sleep Stage Classification: A Deep Learning Approach

Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity

Feature Extraction and Selection in Automatic Sleep Stage Classification

Sleep stage classification is vital for diagnosing many sleep related disorders and Polysomnography (PSG) is an important tool in this regard. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, researchers have been trying to develop automatic classification algorithms. The automatic sleep stage classification mainly consists of three steps: pre-processing, feature extraction and classification. In this research work, we focused on feature extraction and selection steps. The main goal of this thesis was identifying a robust and reliable feature set that can lead to efficient classification of sleep stages. For achieving this goal, three types of contributions were introduced in feature selection, feature extraction and feature vector quality enhancement. Several feature ranking and rank aggregation methods were evaluated and compared for finding the best feature set. Evaluation results indicated that the decision on the precise feature selection method depends on the system design requirements such as low computational complexity, high stability or high classification accuracy. In addition to conventional feature ranking methods, in this thesis, novel methods such as Stacked Sparse AutoEncoder (SSAE) was used for dimensionality reduction. In feature extration area, new and effective features such as distancebased features were utilized for the first time in sleep stage classification. The results showed that these features contribute positively to the classification performance. For signal quality enhancement, a loss-less EEG artefact removal algorithm was proposed. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Wearable and Nearable Biosensors and Systems for Healthcare

Biosensors and systems in the form of wearables and “nearables” (i.e., everyday sensorized objects with transmitting capabilities such as smartphones) are rapidly evolving for use in healthcare. Unlike conventional approaches, these technologies can enable seamless or on-demand physiological monitoring, anytime and anywhere. Such monitoring can help transform healthcare from the current reactive, one-size-fits-all, hospital-centered approach into a future proactive, personalized, decentralized structure. Wearable and nearable biosensors and systems have been made possible through integrated innovations in sensor design, electronics, data transmission, power management, and signal processing. Although much progress has been made in this field, many open challenges for the scientific community remain, especially for those applications requiring high accuracy. This book contains the 12 papers that constituted a recent Special Issue of Sensors sharing the same title. The aim of the initiative was to provide a collection of state-of-the-art investigations on wearables and nearables, in order to stimulate technological advances and the use of the technology to benefit healthcare. The topics covered by the book offer both depth and breadth pertaining to wearable and nearable technology. They include new biosensors and data transmission techniques, studies on accelerometers, signal processing, and cardiovascular monitoring, clinical applications, and validation of commercial devices

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Contribuciones al problema de clasificación en machine learning

El problema de clasificación es un tema muy estudiado en la ciencia de datos, en concreto en el campo del aprendizaje automático o “machine learning”. En la actualidad cada vez hay más información y los agentes económicos y sociales quieren extraer conclusiones relevantes de los datos que les ayuden a tomar mejores decisiones. El problema de clasificación es muy importante en la toma de decisiones en una gran variedad de campos, de hecho, en la literatura se puede encontrar un gran número de métodos que son capaces de realizar las tareas propias de la clasificación. La clasificación es una metodología de aprendizaje supervisado en la ciencia de datos, cuyo propósito es predecir la clase correcta, entre un conjunto de clases conocidas, de una nueva observación dada en base al conocimiento proporcionado por un conjunto de datos previo, también llamado datos de entrenamiento. En esta tesis doctoral se trabaja el problema de la clasificación en los aspectos siguientes: Se hace una revisión bibliográfica exhaustiva del problema de clasificación. Se compara el análisis discriminante y el método de selección de características, RBS. Se estudia el desempeño de dos conceptos de la teoría de juegos, como técnicas para la selección de características, comparándolos con distintos métodos de selección de características implementados en Weka. Y se definen tres medidas de desempeño para evaluar el rendimiento de un clasificador. A continuación, se desarrolla cada uno de los aspectos anteriores. En esta tesis se realiza una revisión bibliográfica muy amplia, que queda reflejada a lo largo de toda la memoria por estar estrechamente vinculada con la revisión de la literatura relacionada con el problema de clasificación y en particular, con la selección de características. Todo ello ha servido para elaborar un estado del arte del tema que ha sido muy útil como punto de partida para establecer diferentes problemas abiertos pendientes de estudiar. Se sabe que una de las dificultades en el análisis de un conjunto de datos es su alta dimensionalidad, lo que puede implicar un peor rendimiento de los clasificadores utilizados. La respuesta más eficaz es reducir la dimensión transformando los datos o la otra alternativa puede ser la selección de características. En esta tesis se lleva a cabo un estudio computacional en el que se comparan los resultados obtenidos mediante un método de reducción de la dimensión como es el análisis discriminante y un método de selección de características, incorporado en RBS. En dicho estudio se obtiene que en tiempo computacional el análisis discriminante es ligeramente mejor que el método RBS. Sin embargo, en términos de precisión para conjuntos de 1,000,000 de registros, el método de selección de características RBS ofrece mejores resultados. Además, en esta memoria se lleva a cabo un estudio computacional comparando la selección de características mediante los valores de Shapley y Banzhaf con varios algoritmos de selección de características implementados en Weka. Lo que se hace es definir un juego cooperativo asociado a un problema de clasificación y se calculan los valores de Shapley y Banzhaf asociados a ese juego, seleccionando aquellas características con un mayor valor por considerarse que tienen una mayor influencia en la precisión de la predicción. Finalmente, se compara, para diversos conjuntos de datos, la selección de características obtenidas con los métodos basados en teoría de juegos y los métodos implementados en Weka. Resaltar que, dado el mismo conjunto de datos, no todos los clasificadores son igualmente precisos en sus predicciones. La precisión conseguida por un modelo de clasificación depende de varios factores. Por lo tanto, el análisis del desempeño de los clasificadores es relevante para determinar cuál funciona mejor. Asimismo, en esta tesis se definen tres medidas de desempeño para evaluar el rendimiento de un clasificador. Se consideran tres clasificadores de referencia, en concreto, dos intuitivos y uno aleatorio. Para evaluar un clasificador se determina la reducción proporcional del error de clasificación cuando se utiliza el clasificador a evaluar con respecto a emplear uno de referencia. Este también es un enfoque interesante de la evaluación del desempeño de los clasificadores porque se puede medir lo ventajoso que es un nuevo clasificador con respecto a los tres de referencia simples, que pueden verse como las mejores opciones basadas en el sentido común. Además, también se analiza la relación entre los tres clasificadores de referencia y diferentes aspectos de la entropía del conjunto de datos. Se lleva a cabo un experimento intensivo para exponer cómo funcionan las medidas de rendimiento propuestas y cómo la entropía puede afectar el rendimiento de un clasificador. Para validar lo observado en el experimento anterior, se realiza un experimento extensivo utilizando 11 conjuntos de datos y cuatro clasificadores implementados en Weka