Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

744
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
744
Continuous -time Fourier Transform01:11

Continuous -time Fourier Transform

960
The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
960
Properties of Laplace Transform-I01:15

Properties of Laplace Transform-I

1.2K
The Laplace transform is a powerful mathematical tool used to convert functions from the time domain into the frequency domain, greatly simplifying the analysis and solution of linear time-invariant systems. This transformation is facilitated by several universal properties: Linearity, Time-Scaling, Time-Shifting, and Frequency Shifting.
The Linearity property is foundational to the Laplace transform. It states that the transform of a linear combination of functions is equivalent to the same...
1.2K
Discrete Fourier Transform01:15

Discrete Fourier Transform

961
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
961
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

396
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
396
Properties of Fourier series II01:21

Properties of Fourier series II

630
Time scaling of signals is a crucial concept in signal processing that affects the Fourier series representation without altering its coefficients. The process modifies the fundamental frequency, thereby changing how the series represents the signal over time. This principle is essential in various applications, including audio and image processing, where signal manipulation is frequent. Understanding function symmetries is fundamental to simplifying the Fourier series.
A function f(t) is...
630

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Maternal serum polyol levels associated with gestational diabetes mellitus and large for gestational age infants: a population-based nested case-control study.

Canadian journal of diabetes·2026
Same author

A roadmap for medical large language models: a review of foundations, applications, and challenges.

Military Medical Research·2026
Same author

Leveraging modality-guided pre-training for dual-prompt-driven multi-cancer PET-CT segmentation.

Medical image analysis·2026
Same author

SemanticST: Semantics-enhanced Spatio-Temporal Modeling for Ejection Fraction Estimation in Echocardiography.

IEEE journal of biomedical and health informatics·2026
Same author

Multimodal Distillation and Fusion for Enhanced Age-Related Macular Degeneration Classification.

IEEE journal of biomedical and health informatics·2026
Same author

PFN1 inhibits lytic replication of Kaposi sarcoma-associated herpesvirus through SQSTM1/p62-mediated selective autophagy targeting the KSHV helicase.

Autophagy·2026

Video Experimental Relacionado

Updated: Feb 20, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K

TF-LLM: Análisis de series temporales mejorado con modelos grandes de lenguaje tiempo-frecuencia

Yuhang Zhang1, Zitong Yu1, Mingtong Dai1

  • 1School of Computing and Information Technology, Great Bay University, China.

Neural networks : the official journal of the International Neural Network Society
|February 18, 2026
PubMed
Resumen

Este estudio presenta el marco TF-LLM, que mejora los modelos grandes de lenguaje (LLM) para el análisis de series temporales. TF-LLM mejora el pronóstico, la clasificación, la imputación y la detección de anomalías al integrar los dominios de tiempo y frecuencia con el aprendizaje de indicaciones.

Palabras clave:
Modelo grande de lenguajeEquilibrio del dominio tiempo-frecuenciaAnálisis de series temporales

Más Videos Relacionados

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.9K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.3K

Videos de Experimentos Relacionados

Last Updated: Feb 20, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.2K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.9K
Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans
08:25

Combined Invasive Subcortical and Non-invasive Surface Neurophysiological Recordings for the Assessment of Cognitive and Emotional Functions in Humans

Published on: May 19, 2016

11.3K

Área de la Ciencia:

  • Inteligencia Artificial
  • Ciencia de Datos
  • Procesamiento de Señales

Sus antecedentes:

  • Los modelos grandes de lenguaje (LLM) muestran potencial en el análisis de series temporales, particularmente para secuencias simbólicas complejas.
  • Aprovechar eficazmente el razonamiento contextual de los LLM para datos de series temporales presenta un desafío significativo.
  • Los métodos existentes luchan por utilizar plenamente los LLM para diversas tareas de series temporales.

Objetivo del estudio:

  • Proponer el marco TF-LLM para tareas avanzadas de análisis de series temporales.
  • Mejorar las capacidades de LLM en pronóstico, clasificación, imputación y detección de anomalías de series temporales.
  • Mejorar la comprensión y el manejo de datos complejos de series temporales utilizando LLM.

Principales métodos:

  • El marco TF-LLM integra representaciones de dominio de tiempo y frecuencia.
  • Las representaciones de frecuencia simplifican la complejidad de los datos y capturan patrones periódicos.
  • El modelado de tiempo aborda dependencias finas y no estacionariedad.
  • Se emplea el aprendizaje de indicaciones para enriquecer el contexto de entrada y mejorar la comprensión de LLM.

Principales resultados:

  • Se realizaron amplios experimentos en siete conjuntos de datos de referencia.
  • TF-LLM demostró un rendimiento superior en múltiples tareas de series temporales.
  • El marco propuesto superó a varios métodos existentes de vanguardia.

Conclusiones:

  • El marco TF-LLM aprovecha eficazmente los LLM para el análisis complejo de series temporales.
  • La integración de los dominios de tiempo y frecuencia mejora el rendimiento en pronóstico, clasificación, imputación y detección de anomalías.
  • El aprendizaje de indicaciones aumenta aún más las capacidades de razonamiento de los LLM para datos de series temporales.