Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation01:26

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

296
Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy  (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....
296
Aliasing01:18

Aliasing

227
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
227
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

356
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]...
356
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

124
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
124
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

353
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...
353
Properties of Fourier series II01:21

Properties of Fourier series II

269
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...
269

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Enhancing data compatibility in an evolving landscape: Medical cannabis and polysubstance use protocols in the PhenX Toolkit.

Drug and alcohol dependence·2025
Same author

An Innovative Method of Singular Spectrum Analysis to Conduct Gap-filling and Denoising on Time Series Data.

Journal of data science : JDS·2025
Same author

Cannabis use among adults who smoke tobacco: Relations with switching from combusted cigarettes to e-cigarettes or very low nicotine cigarettes.

Drug and alcohol dependence·2025
Same author

Adaptive Sequential Singular Spectrum Analysis: Effective Signal Extraction with Application to Heart Rate Signals Related to E-cigarette Use.

Data science in science·2025
Same author

Bidirectional Relationships Between Sleep Quality and Nicotine Vaping: Studying Young Adult e-cigarette Users in Real Time and Real Life.

Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco·2025
Same author

Applied statistical methods for identifying features of heart rate that are associated with nicotine vaping.

The American journal of drug and alcohol abuse·2025

関連する実験動画

Updated: Sep 10, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.6K

タイムシリーズ分析のための単数スペクトル解析における自動パラメータ選択

James J Yang1, Anne Buu2

  • 1Department of Biostatistics and Data Science, University of Texas Health Science Center, Houston, Texas, U.S.A.

Communications in statistics: Simulation and computation
|August 26, 2025
PubMed
まとめ

この研究は,時系列のノイズ削減のための単数スペクトル解析 (SSA) の新しい幾何学的な見方を導入します. 心拍数モニタリングのような複雑なデータの精度と適応性を改善します

科学分野:

  • タイムシリーズ分析
  • 信号処理
  • データサイエンス

背景:

  • Singular Spectrum Analysis (SSA) は広く使用されていますが,タイムシリーズ再構築とノイズ除去のための複雑なメカニズムはよく理解されていません.
  • 従来のSSAは,ウィンドウの長さやグループの値のような固定されたパラメータに依存し,特定のデータタイプに適用することを制限します.

研究 の 目的:

  • SSAの根本的なメカニズムの解明のための新しい幾何学的な視点を提供する.
  • 従来のSSAの限界を克服する連続的な再構築アプローチを提案する.
  • 異なる構造を持つ時間系列へのSSAの適用性を高める.

主な方法:

  • 様々な窓の長さから再構築を平均するSSAの連続再構築アプローチを開発しました.
  • グループ数を決定するために対称的なテストに基づいた停止ルールを実装しました.
  • シミュレーションと実際の7日間の心拍数データを分析して 検証した.

主要な成果:

  • 提案された方法は,窓の長さやグループ番号の事前の知識を必要としません.
  • 従来のSSAと比較して,より小さな平方根平均誤差 (RMSE) を達成しました.
  • 心拍数データにおける局所的な特徴と突然の変化を 明らかにし,イベントに関連したパターンを示した.
キーワード:
心拍数単数スペクトル分析タイムシリーズウェアラブル

さらに関連する動画

A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

6.0K
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

11.3K

関連する実験動画

Last Updated: Sep 10, 2025

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis
07:11

ARL Spectral Fitting as an Application to Augment Spectral Data via Franck-Condon Lineshape Analysis and Color Analysis

Published on: August 19, 2021

2.6K
A Computational Method to Quantify Fly Circadian Activity
13:05

A Computational Method to Quantify Fly Circadian Activity

Published on: October 28, 2017

6.0K
Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research
08:12

Author Spotlight: Exploring Light-Driven Chemical Reactions and Energy-Harnessing Devices in Photochemical Research

Published on: February 16, 2024

11.3K

結論:

  • 新しい幾何学的な展望は,SSAの再構築とノイズ除去プロセスを明確にします.
  • 連続的なSSAアプローチは,従来の方法よりも高い精度と適応性を提供します.
  • この強化されたSSAは,スマートウォッチの心拍数モニタリングなどのダイナミックタイムシリーズデータに特に適しています.