在使用深度学习的振幅调制的对线全息数据存储中进行反噪声性能分析
Optics express
|November 22, 2024
概括
深度学习有效地减少了全息数据存储中的噪音. 卷积神经网络改善了信号噪声比率和更低的比特错误率,以获得可靠的数据检索.
科学领域:
- 光学和光子学 在光学和光子学.
- 数据存储技术 数据存储技术
- 在工程领域的人工智能.
背景情况:
- 全息数据存储系统面临的挑战是由于光学偏差和实验噪声,其比特错误率 (BER) 高,信号噪声比率 (SNR) 低.
- 振幅调制的直线全息存储中的直接检测方法易受各种噪声源的影响,影响数据完整性.
研究的目的:
- 提出和分析用于全息数据存储的深度学习方法的反噪声性能.
- 研究端到端卷积神经网络在减轻噪声和提高数据重建准确性的有效性.
主要方法:
- 利用端到端卷积神经网络 (CNN) 来分析探测器捕获的编码数据页中的噪声阻力.
- 应用深度学习模型来纠正系统成像偏差,检测器不均以及光学失焦噪声.
主要成果:
- 深度学习网络证明了对各种噪声源的有效校正,包括光学偏差和探测器响应不均.
- 重建的数据页显示了比特错误率 (BER) 的显著降低,降低到直接检测水平的1/10.
- 信号与噪声比 (SNR) 提高了五倍以上,这表明数据可读性有所改善.
结论:
- 深度学习,特别是CNN,为增强振幅全息数据存储系统的数据可靠性提供了强大的解决方案.
- 建议使用深度学习的反噪声分析显著提高了从全息存储介质检索数据的准确性和可靠性.
相关概念视频
Linear Approximation in Frequency Domain
85
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....
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....
85
¹H NMR: Interpreting Distorted and Overlapping Signals
1.0K
Spin systems where the difference in chemical shifts of the coupled nuclei is greater than ten times J are called first-order spin systems. These nuclei are weakly coupled, and their chemical shifts and coupling constant can generally be estimated from the well-separated signals in the spectrum.
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
As Δν decreases and the signals move closer, the doublets appear increasingly distorted. The intensities of the inner lines increase at the cost of those of the outer lines as the signals are...
1.0K
Deconvolution
132
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
132
Difference from Background: Limit of Detection
5.9K
The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
5.9K
Perceiving Loudness, Pitch, and Location
194
The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
Place theory, or place coding, suggests that different pitches are heard because various sound waves activate specific locations along the cochlea's basilar membrane. The brain determines the pitch of a sound by...
194
Linear Approximation in Time Domain
64
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,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
64


