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

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

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

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

関連する実験動画

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:タイム周波数の大型言語モデルによる強化されたタイムシリーズ分析.

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
まとめ

この研究は,TF-LLMフレームワークを導入し,タイムシリーズ分析のための大型言語モデル (LLM) を強化します. TF-LLMは,時間と周波数の領域を迅速な学習と統合することにより,予測,分類,帰算,および異常検出を改善します.

キーワード:
大型言語モデルタイム・周波数・ドメインバランスタイムシリーズの分析

さらに関連する動画

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

関連する実験動画

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

科学分野:

  • 人工知能 (AI) とは,人工知能 (AI) のことです.
  • データサイエンス データサイエンス
  • シグナル処理 信号処理

背景:

  • 大型言語モデル (LLM) は,特に複雑なシンボリックシーケンスの場合,タイムシリーズ分析の可能性を示しています.
  • タイムシリーズデータに対するLLMの文脈的推論を効果的に活用することは,大きな課題です.
  • 既存の方法では,さまざまなタイムシリーズのタスクに対してLLMを完全に活用するのに苦労しています.

研究 の 目的:

  • 先進的なタイムシリーズ分析タスクのためのTF-LLMフレームワークを提案する.
  • タイムシリーズ予測,分類,帰算,および異常検出におけるLLMの能力を高めるため.
  • LLMを使用して複雑なタイムシリーズデータの理解と処理を改善する.

主な方法:

  • TF-LLMのフレームワークは,時間と周波数領域の表現を統合しています.
  • 周波数表現はデータの複雑さを簡素化し,周期的なパターンを捉えます.
  • 時間モデリングは,微細な依存関係と非静止性を扱います.
  • 迅速な学習は,入力文脈を豊かにし,LLMの理解を改善するために使用されます.

主要な成果:

  • 7つのベンチマークデータセットで広範な実験が行われました.
  • TF-LLMは,複数のタイムシリーズタスクで優れたパフォーマンスを示しました.
  • 提案された枠組みは,いくつかの既存の最先端の方法を上回った.

結論:

  • TF-LLMフレームワークは,複雑なタイムシリーズ分析のためのLLMを効果的に活用します.
  • 時間と周波数の領域を統合することで,予測,分類,帰算,および異常検出の性能が向上します.
  • 迅速な学習は,タイムシリーズデータに対するLLMの推論能力をさらに高めます.