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相关概念视频

Convolution Properties I01:20

Convolution Properties I

136
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
136
Convolution Properties II01:17

Convolution Properties II

168
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
168
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

223
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
223
Network Function of a Circuit01:25

Network Function of a Circuit

255
Frequency response analysis in electrical circuits provides vital insights into a circuit's behavior as the frequency of the input signal changes. The transfer function, a mathematical tool, is instrumental in understanding this behavior. It defines the relationship between phasor output and input and comes in four types: voltage gain, current gain, transfer impedance, and transfer admittance. The critical components of the transfer function are the poles and zeros.
255
Non-ohmic Devices00:51

Non-ohmic Devices

1.0K
In most substances, the current flow is proportional to the voltage applied to it. A simple relationship between the values of current, voltage, and resistance is known as Ohm's law. Nonohmic devices do not exhibit a linear relationship between voltage and current. One such device is the semiconducting circuit element known as a diode. A diode is a circuit device that allows current flow in only one direction.
Consider a simple circuit consisting of a battery, a diode, and a resistor. A...
1.0K
MOSFET: Enhancement Mode01:22

MOSFET: Enhancement Mode

282
Enhancement-mode MOSFETs are pivotal components in electronics, distinguished by their capacity to act as highly efficient switches. They are part of the larger family of metal-oxide Semiconductor Field-Effect Transistors (MOSFETs). They are available in two types: p-channel and n-channel, each tailored to specific polarity operations.
In their basic form, enhancement-mode MOSFETs are typically non-conductive when the gate-source voltage (Vgs) is zero. This default 'off' state means no...
282

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Updated: Jun 2, 2025

Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
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光子卷积操作员利用芯片上的非线性激活功能.

Zilong Tao, Jie You, Hao Ouyang

    Optics letters
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    概括
    此摘要是机器生成的。

    我们引入了一种使用光子学的新型非线性激活函数 (NAF),增强光学神经网络. 这种综合方法显著提高了无线电机器学习分类准确性,并减少了延迟.

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    相关实验视频

    Last Updated: Jun 2, 2025

    Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station
    05:57

    Characterization of SiN Integrated Optical Phased Arrays on a Wafer-Scale Test Station

    Published on: April 1, 2020

    8.0K
    Measurement of Quantum Interference in a Silicon Ring Resonator Photon Source
    12:19

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    Published on: April 4, 2017

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    Generation and Coherent Control of Pulsed Quantum Frequency Combs
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    科学领域:

    • 光子学和光学计算技术
    • 人工智能和机器学习

    背景情况:

    • 非线性激活函数 (NAF) 对人工神经网络 (ANN) 至关重要,但通常需要单独的光电子或数字组件,阻碍光学计算的效率.
    • 现有的NAF实现通过引入延迟和系统复杂性来限制光学计算的优势.

    研究的目的:

    • 提出和演示使用集成到光子芯片上的电光IQ调制器的新型NAF的首次实现.
    • 在现实世界的机器学习应用中验证这个集成NAF的有效性.

    主要方法:

    • 在光子卷积运算符芯片上使用电光IQ调制器开发了一种新的非线性调制过程.
    • 构建了一个卷积神经网络 (CNN),将集成的NAF用于无线电机器学习分类任务.

    主要成果:

    • 在无线电机器学习分类中实现了92.5%的准确性,与没有NAF的CNN相比,提高了27%.
    • 证明了NAF与卷积操作的近同步执行,显著降低了延迟.
    • 展示了由于完全集成的光子芯片而降低了外围控制系统的复杂性.

    结论:

    • 集成的光子NAF为光学神经网络计算提供了重大进步.
    • 这种方法可以在芯片上进行高精度,低延迟的机器学习分类,为大规模的光学神经网络铺平了道路.