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関連する概念動画

Upsampling01:22

Upsampling

309
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
309
Downsampling01:20

Downsampling

251
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
251
Fast Fourier Transform01:10

Fast Fourier Transform

465
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log⁡2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
465
Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

335
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
335
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

131
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....
131
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

348
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...
348

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音響記録データ圧縮アルゴリズムのDSPおよびFPGAプラットフォームの実装

Hang Hui1,2, Xiaolong Hao1,2, Fan Bai1,2

  • 1Downhole Measurement and Control Laboratory of National Engineering Laboratory of Oil and Gas Drilling Technology, Xi'an 710065, China.

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PubMed
まとめ

波形変換を用いたダウンホールデータの圧縮は,遠隔検出の音響記録を大幅に改善します. この方法は,最小の歪みで50%の圧縮比を達成し,データ伝送とログの効率を高めます.

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科学分野:

  • 地理学
  • シグナル処理
  • データ圧縮

背景:

  • リモート検出の音響ログは,リアルタイムでデータアップロードし,迅速なログに挑戦しています.
  • ダウンホールデータ圧縮は,これらの制限を克服するための重要な戦略です.

研究 の 目的:

  • 音響記録データのための波形変換ベースのデータ圧縮方法を体系的に分析する.
  • この圧縮アルゴリズムを実装するためのハードウェアプラットフォーム (DSPとFPGA) の開発と評価.

主な方法:

  • DSPとFPGAのハードウェアプラットフォームでウェーブレット変換ベースのデータ圧縮アルゴリズムを開発し実行しました.
  • 解圧アルゴリズムをホストコンピュータに実装した.
  • 圧縮比,歪み,実行時間に焦点を当てた実際の音響記録データを用いて性能を評価した.

主要な成果:

  • 信号の形状と波の抽出に最小限の影響を及ぼす約50%の圧縮比を達成しました.
  • アルゴリズムは,圧縮比と歪みに関して,特定のハードウェアプラットフォーム (DSP vs. FPGA) との最小の関係を示しました.
  • FPGAの実装は,DSP (ミリ秒) よりも著しく速く (42 μs) で,メモリも少ない.

結論:

  • 波形変換ベースのデータ圧縮は,重要な波形情報を保存して,遠隔検出の音響記録に有効です.
  • FPGAは,DSPと比較してリアルタイム処理に優れた性能を提供します.
  • このアプローチは,ログの効率を高め,コントローラーの作業量を軽減し,将来のツール設計のための参照を提供します.