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Signal Flow Graphs01:18

Signal Flow Graphs

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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
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Basic Discrete Time Signals01:16

Basic Discrete Time Signals

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The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
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Classification of Signals01:30

Classification of Signals

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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
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Energy and Power Signals01:17

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In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
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Basic Continuous Time Signals01:22

Basic Continuous Time Signals

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Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
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Video Experimental Relacionado

Updated: Jan 18, 2026

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
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Diagramas de Persistencia de Picos para Estimación de Señales Basada en Forma

Woo Min Kim1, Sutanoy Dasgupta2, Pavan Turaga3

  • 1Department of Statistics, Florida State University, Tallahassee, FL 32306, USA.

IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society
|January 16, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un nuevo método de estimación de señales que utiliza características topológicas y geométricas de los datos. El enfoque de alineación elástica penalizada (PESA) mejora la precisión para señales con ruido aditivo y de deformación.

Palabras clave:
deformación temporal dinámicaalineación elásticapersistencia de picosestimación de formaseñal con restricción de formaestimación de señal

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Área de la Ciencia:

  • Procesamiento de Señales
  • Análisis de Datos
  • Topología Computacional

Sus antecedentes:

  • La estimación de señales a partir de datos ruidosos es un desafío central.
  • Los métodos existentes se basan en elecciones de modelos y criterios específicos.
  • Existe la necesidad de estimadores robustos que manejen tipos de ruido complejos.

Objetivo del estudio:

  • Desarrollar un marco innovador de estimación de señales utilizando características de datos topológicas y geométricas.
  • Introducir un diagrama de persistencia de picos (PPD) para el análisis de la forma de la señal.
  • Proporcionar un estimador robusto para señales con ruido aditivo y de deformación.

Principales métodos:

  • Aprovechamiento del marco de alineación elástica penalizada (PESA).
  • Utilización de diagramas de persistencia de picos (PPD) para estimar la forma de la señal (picos/valles).
  • Empleo de optimización con restricciones de forma para la estimación de señales.

Principales resultados:

  • El enfoque PESA equilibra el promediado de señales y la alineación elástica.
  • Se presenta un procedimiento computacionalmente eficiente para el método propuesto.
  • Demostró un rendimiento superior frente a técnicas de vanguardia en simulaciones y datos del mundo real.

Conclusiones:

  • El marco PESA propuesto ofrece un avance significativo en la estimación de señales.
  • Efectivo para analizar conjuntos de datos complejos como las curvas de tasas de COVID y consumo de electricidad.
  • Destaca la utilidad de las características topológicas en el procesamiento de señales.