SISO Decoding of Z4 Linear Kerdock and Preparata Codes

Some nonlinear codes, such as Kerdock and Preparata codes, can be represented as binary images under the Gray map of linear codes over rings. This paper introduces MAP decoding of Kerdock and Preparata codes by working with their quaternary representation (linear codes over Z4 ) with the complexity of O(N2log2N), where N is the code length in Z4. A sub-optimal bitwise APP decoder with good error-correcting performance and complexity of O(Nlog2N) that is constructed using the decoder lifting technique is also introduced. This APP decoder extends upon the original lifting decoder by working with likelihoods instead of hard decisions and is not limited to Kerdock and Preparata code families. Simulations show that our novel decoders significantly outperform several popular decoders in terms of error rate

Experimental investigation of a 16-dimensional modulation format for long-haul multi-core fiber transmission

We experimentally investigate a 16-dimensional modulation format applicable to multi-core fiber transmission, and demonstrate over 14,000 km transmission for a BER of 1E-3, a 55 % improvement in reach compared to DP-BPSK for the same spectral efficiency

Increasing the efficiency of extraction of residual reserves of hydrogen sulfide-containing deposits of the Uchkyr gas condensate field

This paper examines the low-sulfur gas deposit GKF Uchkyr, which has been preserved for an extended period due to the absence of sulfur removal facilities. The findings regarding the reasons for surpassing or falling behind the actual and projected development indicators are outlined. Additionally, the results of geological and geophysical studies, along with the factors contributing to the insufficient volume of research, are presented. To organize control over the development of a gas field, the correct choice of parameters characterizing the state of the deposit during its development is important. In this case, must be observed conditions that ensure systematic monitoring of changes in selected parameters and assessment of measurement error. These parameters are: reservoir pressure in the gas and aquifer parts of the deposit, gas saturation coefficient of the developed formation, ion-salt composition of the water produced by gas wells. Considering the information above, recommendations are provided to facilitate timely monitoring of changes in technological development indicators and to enhance the efficient extraction of hydrocarbons from the productive horizons of the GKF Uchkyr deposit

L'hexacode, le code de Golay et le réseau de Leech construction, décodage, application en quantification

Ce mémoire traite spécifiquement de l'utilisation du code quaternaire [6,3,4], l' hexacode , en quantification vectorielle. Celui-ci permet de construire et surtout de décoder très efficacement le code de Golay binaire étendu [24,12,8] et le réseau de Leech tourné R ? 24 . Ces objets sont exceptionnels; ils servent tout particulièrement de base de comparaison dans l'étude des algorithmes de décodage algébrique. Le sujet est inspiré de travaux de recherche sur le codage de canal et la modulation codée, mais les résultats sont appliqués ici à la quantification uniquement. Les algorithmes proposés dans la littérature (à distance minimale et à distance bornée) sont détaillés; de nouveaux al gorithmes, fondés sur une recherche en profondeur d'abord, sont proposés. Les algorithmes de décodage algébrique sont appliqués à la quantification d'une source gaussienne sans mémoire . En effet, de par la dualité source-canal, ce qui est décodage au sens du canal peut servir au codage au sens de la source. Il ressort qu'en 24 dimensions le décodage algébrique sous-optimal offre un meilleur compromis entre performance et complexité que le décodage algébrique à distance minimale. Ce résultat incite donc à explorer les dimensions élévées de quantification au moyen de techniques algébriques et d'algorithmes de décodage sous-optimaux

Brain Music : Sistema generativo para la creación de música simbólica a partir de respuestas neuronales afectivas

gráficas, tablasEsta tesis de maestría presenta una metodología de aprendizaje profundo multimodal innovadora que fusiona un modelo de clasificación de emociones con un generador musical, con el propósito de crear música a partir de señales de electroencefalografía, profundizando así en la interconexión entre emociones y música. Los resultados alcanzan tres objetivos específicos: Primero, ya que el rendimiento de los sistemas interfaz cerebro-computadora varía considerablemente entre diferentes sujetos, se introduce un enfoque basado en la transferencia de conocimiento entre sujetos para mejorar el rendimiento de individuos con dificultades en sistemas de interfaz cerebro-computadora basados en el paradigma de imaginación motora. Este enfoque combina datos de EEG etiquetados con datos estructurados, como cuestionarios psicológicos, mediante un método de "Kernel Matching CKA". Utilizamos una red neuronal profunda (Deep&Wide) para la clasificación de la imaginación motora. Los resultados destacan su potencial para mejorar las habilidades motoras en interfaces cerebro-computadora. Segundo, proponemos una técnica innovadora llamada "Labeled Correlation Alignment"(LCA) para sonificar respuestas neurales a estímulos representados en datos no estructurados, como música afectiva. Esto genera características musicales basadas en la actividad cerebral inducida por las emociones. LCA aborda la variabilidad entre sujetos y dentro de sujetos mediante el análisis de correlación, lo que permite la creación de envolventes acústicos y la distinción entre diferente información sonora. Esto convierte a LCA en una herramienta prometedora para interpretar la actividad neuronal y su reacción a estímulos auditivos. Finalmente, en otro capítulo, desarrollamos una metodología de aprendizaje profundo de extremo a extremo para generar contenido musical MIDI (datos simbólicos) a partir de señales de actividad cerebral inducidas por música con etiquetas afectivas. Esta metodología abarca el preprocesamiento de datos, el entrenamiento de modelos de extracción de características y un proceso de emparejamiento de características mediante Deep Centered Kernel Alignment, lo que permite la generación de música a partir de señales EEG. En conjunto, estos logros representan avances significativos en la comprensión de la relación entre emociones y música, así como en la aplicación de la inteligencia artificial en la generación musical a partir de señales cerebrales. Ofrecen nuevas perspectivas y herramientas para la creación musical y la investigación en neurociencia emocional. Para llevar a cabo nuestros experimentos, utilizamos bases de datos públicas como GigaScience, Affective Music Listening y Deap Dataset (Texto tomado de la fuente)This master’s thesis presents an innovative multimodal deep learning methodology that combines an emotion classification model with a music generator, aimed at creating music from electroencephalography (EEG) signals, thus delving into the interplay between emotions and music. The results achieve three specific objectives: First, since the performance of brain-computer interface systems varies significantly among different subjects, an approach based on knowledge transfer among subjects is introduced to enhance the performance of individuals facing challenges in motor imagery-based brain-computer interface systems. This approach combines labeled EEG data with structured information, such as psychological questionnaires, through a "Kernel Matching CKA"method. We employ a deep neural network (Deep&Wide) for motor imagery classification. The results underscore its potential to enhance motor skills in brain-computer interfaces. Second, we propose an innovative technique called "Labeled Correlation Alignment"(LCA) to sonify neural responses to stimuli represented in unstructured data, such as affective music. This generates musical features based on emotion-induced brain activity. LCA addresses variability among subjects and within subjects through correlation analysis, enabling the creation of acoustic envelopes and the distinction of different sound information. This makes LCA a promising tool for interpreting neural activity and its response to auditory stimuli. Finally, in another chapter, we develop an end-to-end deep learning methodology for generating MIDI music content (symbolic data) from EEG signals induced by affectively labeled music. This methodology encompasses data preprocessing, feature extraction model training, and a feature matching process using Deep Centered Kernel Alignment, enabling music generation from EEG signals. Together, these achievements represent significant advances in understanding the relationship between emotions and music, as well as in the application of artificial intelligence in musical generation from brain signals. They offer new perspectives and tools for musical creation and research in emotional neuroscience. To conduct our experiments, we utilized public databases such as GigaScience, Affective Music Listening and Deap DatasetMaestríaMagíster en Ingeniería - Automatización IndustrialInvestigación en Aprendizaje Profundo y señales BiológicasEléctrica, Electrónica, Automatización Y Telecomunicaciones.Sede Manizale

Development, validation, qualification, and dissemination of quantitative MR methods: Overview and recommendations by the ISMRM quantitative MR study group

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton-density fat fraction, and cardiac T1. These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic