convolution

简明释义

英[ˌkɒnvəˈluːʃn]美[ˌkɑːnvəˈluːʃn]

n. [数] 卷积；回旋；盘旋；卷绕

英英释义

单词用法

同义词

反义词

例句

1.The fourth audio signal is convolution of the second audio signal and the environment channel impulse response.

第四音频信号为第二音频信号与环境信道脉冲响应的回旋积分。

2.Using convolution model synthetic seismogram in time domain and depth domain is been contrast and the essence distinction is revealed.

利用褶积模型，将深度域和时间域合成的地震记录相对比，揭示两者之间的本质区别。

3.It's just a matter of convolution.

我以为那仅仅是个卷积上的问题。

4.Inner coding consists of punctured-convolution coding, bit interleaving and symbol interleaving.

内层包括删余卷积编码、位交织和符号交织。

5.However, the use of convolution model is to deduce recursion formula of the reflection for the converted wave.

而利用褶积模型理论解决转换波问题的关键是建立转换波反射系数递推公式。

6.What is sigma in convolution kernel?

在卷积内核西格玛是什么？

7.Our primate relatives show varying degrees of convolution in their brains, as do other intelligent creatures like elephants.

作为人类近亲们的灵长动物，他们的大脑也呈现出不同程度的褶皱，其它聪明的动物，如大象亦是如此。

8.The voice signal is convolution of an original voice signal and the environment channel impulse response.

声音信号为原始声音信号与环境信道脉冲响应的回旋积分。

9.The convolution (卷积) of two signals can be visualized as the way one signal modifies the other.

两个信号的卷积（卷积）可以被视为一个信号如何修改另一个信号。

10.In deep learning, neural networks use convolution (卷积) layers to automatically extract important patterns from data.

在深度学习中，神经网络使用卷积（卷积）层自动提取数据中的重要模式。

11.The process of image processing often involves a mathematical operation called convolution (卷积) to enhance features.

图像处理的过程通常涉及一种称为卷积（卷积）的数学运算，以增强特征。

12.The convolution (卷积) theorem is fundamental in both time and frequency domain analysis.

在时域和频域分析中，卷积（卷积）定理是基础。

13.To understand filtering in audio processing, you need to grasp the concept of convolution (卷积).

要理解音频处理中的滤波，你需要掌握卷积（卷积）的概念。

作文

In the realm of mathematics and signal processing, the term convolution refers to a specific operation that combines two functions to produce a third function. This process is fundamental in various fields such as engineering, physics, and even statistics. To understand convolution, one must first grasp its significance in the context of linear systems. When an input signal passes through a linear system, the output can be determined by the convolution of the input signal with the system's impulse response. This relationship is vital because it allows engineers to predict how systems will respond to different inputs.The mathematical definition of convolution involves integrating the product of two functions after one has been flipped and shifted. Mathematically, if we have two functions f(t) and g(t), their convolution is represented as (f * g)(t) = ∫ f(τ)g(t - τ)dτ. This integral essentially sums up the overlapping areas of the two functions, providing a new function that encapsulates their combined effects. Understanding this operation is crucial for anyone working with systems that require filtering or signal analysis.In practical applications, convolution is extensively used in image processing. For instance, when applying filters to images, such as blurring or sharpening, the filter itself can be considered as one of the functions in the convolution. The image data acts as the other function. By performing a convolution between the image and the filter, we can enhance or modify the image according to our needs. This technique is prevalent in software used for photo editing and computer graphics.Moreover, convolution plays a crucial role in machine learning, particularly in the design of convolutional neural networks (CNNs). CNNs are a class of deep learning algorithms that excel at processing data with a grid-like topology, such as images. In these networks, convolution layers apply multiple filters to the input data, allowing the model to learn spatial hierarchies of features. This ability to extract relevant features from images has revolutionized fields like computer vision, enabling advancements in facial recognition, object detection, and more.Despite its widespread use, the concept of convolution can be challenging to grasp initially. It requires a solid foundation in calculus and an understanding of how functions interact with one another. However, once mastered, it opens up a plethora of possibilities across various disciplines. From designing better communication systems to creating realistic graphics in video games, the implications of convolution are vast and significant.In conclusion, convolution is not merely a mathematical operation; it is a powerful tool that bridges the gap between theoretical concepts and practical applications. Whether in engineering, computer science, or data analysis, understanding convolution equips individuals with the knowledge to innovate and solve complex problems. As technology continues to evolve, the importance of mastering convolution will only increase, making it an essential topic for students and professionals alike.

在数学和信号处理领域，术语卷积指的是一种特定的操作，它将两个函数结合以产生第三个函数。这个过程在工程、物理甚至统计等多个领域都是基础性的。要理解卷积，首先必须掌握它在线性系统中的重要性。当输入信号通过线性系统时，输出可以通过输入信号与系统脉冲响应的卷积来确定。这种关系至关重要，因为它允许工程师预测系统对不同输入的响应。卷积的数学定义涉及在一个函数翻转和移动后，对两个函数的乘积进行积分。数学上，如果我们有两个函数f(t)和g(t)，它们的卷积表示为(f * g)(t) = ∫ f(τ)g(t - τ)dτ。这个积分本质上汇总了两个函数的重叠区域，提供了一个新的函数，概括了它们的综合效果。理解这一操作对于任何处理需要过滤或信号分析的系统的人来说都是至关重要的。在实际应用中，卷积在图像处理方面被广泛使用。例如，在对图像应用滤镜（如模糊或锐化）时，滤镜本身可以被视为卷积中的一个函数。图像数据则是另一个函数。通过在图像和滤镜之间进行卷积，我们可以根据需要增强或修改图像。这种技术在用于照片编辑和计算机图形的软件中非常普遍。此外，卷积在机器学习中也起着至关重要的作用，特别是在卷积神经网络（CNN）的设计中。卷积神经网络是一类深度学习算法，擅长处理具有网格状拓扑的数据，如图像。在这些网络中，卷积层将多个滤波器应用于输入数据，使模型能够学习特征的空间层次。这种从图像中提取相关特征的能力彻底改变了计算机视觉等领域，使面部识别、物体检测等方面取得了重大进展。尽管卷积的广泛应用，但最初掌握这一概念可能具有挑战性。它需要扎实的微积分基础以及对函数相互作用的理解。然而，一旦掌握，它将为各个学科打开无数可能性。从设计更好的通信系统到在视频游戏中创建逼真的图形，卷积的影响是广泛而重要的。总之，卷积不仅仅是一个数学操作；它是一个强大的工具，架起了理论概念与实际应用之间的桥梁。无论是在工程、计算机科学还是数据分析中，理解卷积使个人具备了创新和解决复杂问题的知识。随着技术的不断发展，掌握卷积的重要性只会增加，这使其成为学生和专业人士都必须掌握的主题。