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相关概念视频

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Differential Leveling01:12

Differential Leveling

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Differential leveling is a precise method in surveying used to determine the elevation difference between two points. Its primary goal is to establish accurate vertical measurements to create level surfaces or grade lines critical for designing and constructing infrastructures such as roads, bridges, and buildings.The procedure for differential leveling begins with setting up and leveling the instrument at a point where the benchmark can be seen. The level rod is held on the benchmark (BM), and...
137
Deconvolution01:20

Deconvolution

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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...
132
Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
295
Downsampling01:20

Downsampling

130
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...
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Transfer Function in Control Systems01:21

Transfer Function in Control Systems

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The transfer function is a fundamental concept in the analysis and design of linear time-invariant (LTI) systems. It offers a concise way to understand how a system responds to different inputs in the frequency domain. It serves as a bridge between the time-domain differential equations that describe system dynamics and the frequency-domain representation that facilitates easier manipulation and analysis.
To derive the transfer function, consider a general nth-order linear time-invariant...
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基于IMDD系统中的多源域转移学习的低复杂度深度神经网络等级器.

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    此摘要是机器生成的。

    一个新的多源域转移学习 (MST) 方案大大降低了强度调制和直接检测 (IMDD) 系统中深度神经网络 (DNN) 均等器的培训成本. 这种方法提高了模型的概括性和稳定性,以更少的数据和更少的训练时代实现目标位误差率.

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    科学领域:

    • 光学通信是指光学通信.
    • 机器学习 机器学习
    • 信号处理 信号处理

    背景情况:

    • 深度神经网络 (DNN) 均衡器对于减轻强度调制和直接检测 (IMDD) 系统中的信号损害至关重要.
    • 训练基于DNN的等分器可能是计算上昂贵和数据密集型,限制了它们的实际应用.
    • 现有的转移学习方法可能无法在不同的道条件下完全解决泛化和稳定性挑战.

    研究的目的:

    • 开发一种新的多源域转移学习 (MST) 方案,以降低IMDD系统基于DNN的等分器的培训成本.
    • 为了提高DNN等分器在各种通道参数上的概括能力和稳定性.
    • 在实用的高速IMDD系统中验证拟议的MST等效器的有效性.

    主要方法:

    • 设计了一个多源域转移学习 (MST) 方案,利用来自不同道参数的数据.
    • 通过比例选择具有不同道特征的数据,构建了一个多源域数据集.
    • 在单个任务中训练源域,以增强模型的概括性和稳定性.

    主要成果:

    • 拟议的MST等级器在80Gb/s的PAM-4 IMDD短距离系统中证明了其有效性.
    • 实现了符合硬决策前错误纠正门的位错误率.
    • 与传统的DNN等分器相比,减少了87%的代时代和65%的训练数据.

    结论:

    • 在IMDD系统中,MST方案为基于DNN的均衡器提供了显著的培训成本降低.
    • 拟议的方法确保了模型的概括性和稳定性,这对于现实世界的光通信系统至关重要.
    • MST提供了一种更有效,更实用的方法来部署先进的均等化技术.