convolutional

简明释义

英[ˌkɒnvəˈluːʃən(ə)l]美[kɑːnvəˈlʊʃənl]

adj. 卷积的；回旋的；脑回的

英英释义

单词用法

同义词

反义词

例句

1.Firstly the basic transmitter, including convolutional encoding, interleaving, scrambling, Walsh modulation, quadrature spreading and baseband filtering was studied.

首先研究基本的发射机，包括卷积编码、交织、扰码、沃尔什调制、正交扩频以及基带滤波。

2.A type of hybrid decoding (HD) algorithm for convolutional codes is proposed.

提出了一类级联的卷积码混合译码算法。

3.This method is suitable for all code rate punctured convolutional codes and practical for information interception and signal analyzing of satellite communication system.

本文给出的方法适应各种码率的删除卷积码，在卫星信号截获分析领域具有实用性。

4.Then it researches the different kinds of channel codec in DVB-H system, including pseudo random code, RS code, punctured convolutional code and interleaving.

论文重点研究了DVB - H系统中各个信道编码，包括伪随机码，RS码，压缩卷积码和内外交织的编解码算法。

5.Suitable for decoding convolutional codes with long constraint length, this architecture saves hardware resource without performance deterioration.

这种串行结构适合长约束度的卷积码译码，能在性能不下降的前提下有效地节省资源。

6.Unequal error method is done. Rate compatible punctured convolutional code—RCPC is adopted as the channel coding to carry out the UEP.

本文采用了一种可生成多码率码的特殊的删余卷积码——RCPC码作为信道编码技术，以实现不等差错保护机制（UEP）。

7.In this study, we implemented a convolutional 卷积的 model to enhance the accuracy of facial recognition.

在这项研究中，我们实施了一个卷积模型，以提高面部识别的准确性。

8.The convolutional 卷积的 architecture allows for the processing of visual information in a hierarchical manner.

卷积架构允许以分层方式处理视觉信息。

9.Using a convolutional 卷积的 approach, we improved the performance of our speech recognition system.

通过使用卷积的方法，我们提高了语音识别系统的性能。

10.The convolutional 卷积的 neural network is widely used for image classification tasks.

卷积神经网络广泛用于图像分类任务。

11.The convolutional 卷积的 layers help in extracting features from input data effectively.

卷积层有助于有效地从输入数据中提取特征。

作文

In recent years, the field of artificial intelligence has made significant strides, particularly in the area of image recognition. One of the key technologies driving this progress is the use of convolutional neural networks (CNNs). These networks have revolutionized how machines interpret visual data, enabling them to achieve remarkable accuracy in tasks such as object detection and facial recognition.To understand the importance of convolutional neural networks, it is essential to grasp how they operate. Unlike traditional neural networks, which treat input data as a flat array, convolutional networks take advantage of the spatial structure of images. This is achieved through the application of filters or kernels that slide over the input image, performing mathematical operations known as convolutions. As a result, the network can capture local patterns and features, such as edges, textures, and shapes, that are crucial for identifying objects within an image.The architecture of a convolutional neural network typically consists of several layers, including convolutional layers, pooling layers, and fully connected layers. The convolutional layers apply the filters to extract features, while the pooling layers reduce the dimensionality of the data, making the computation more efficient and less prone to overfitting. Finally, the fully connected layers combine the extracted features to make predictions about the input data.One of the most compelling aspects of convolutional neural networks is their ability to learn hierarchical representations of data. In the initial layers, the network may focus on simple features like edges and colors. As the data passes through deeper layers, the network begins to recognize more complex structures, such as shapes and patterns. This hierarchical learning process allows convolutional networks to achieve a level of abstraction that is often necessary for accurate image classification.The impact of convolutional neural networks extends far beyond mere image recognition. They have been successfully applied in various domains, including medical imaging, autonomous vehicles, and even video analysis. For instance, in the medical field, convolutional networks can assist radiologists in detecting anomalies in X-rays or MRIs with high precision, potentially leading to earlier diagnosis and treatment of diseases.Moreover, the rise of deep learning frameworks such as TensorFlow and PyTorch has made it easier for researchers and developers to implement convolutional networks. These tools provide pre-built functions and libraries that simplify the process of building and training models, allowing for rapid experimentation and innovation.Despite their success, convolutional neural networks are not without challenges. They require large amounts of labeled data for training, which can be a barrier in some applications. Additionally, the computational resources needed to train these models can be significant, often necessitating the use of powerful GPUs.In conclusion, convolutional neural networks represent a groundbreaking advancement in the field of artificial intelligence, particularly in image recognition tasks. Their unique architecture and ability to learn hierarchical representations have enabled machines to interpret visual data with unprecedented accuracy. As technology continues to evolve, the potential applications of convolutional networks will likely expand, opening new avenues for innovation across various industries. Understanding this technology is crucial for anyone interested in the future of AI and its impact on our world.

近年来，人工智能领域取得了显著进展，尤其是在图像识别方面。推动这一进展的关键技术之一是使用卷积神经网络（CNN）。这些网络彻底改变了机器解读视觉数据的方式，使其在物体检测和面部识别等任务中实现了惊人的准确性。要理解卷积神经网络的重要性，必须掌握它们的工作原理。与将输入数据视为平面数组的传统神经网络不同，卷积网络利用图像的空间结构。这是通过应用在输入图像上滑动的滤波器或核来实现的，执行称为卷积的数学运算。因此，网络能够捕捉局部模式和特征，如边缘、纹理和形状，这些对于识别图像中的物体至关重要。卷积神经网络的架构通常由几个层组成，包括卷积层、池化层和全连接层。卷积层应用滤波器以提取特征，而池化层则减少数据的维度，使计算更高效并降低过拟合的风险。最后，全连接层结合提取的特征，对输入数据进行预测。卷积神经网络最引人注目的方面之一是它们能够学习数据的层次表示。在初始层中，网络可能专注于简单特征，如边缘和颜色。随着数据通过更深层，网络开始识别更复杂的结构，如形状和模式。这种层次学习过程使得卷积网络能够达到通常在准确的图像分类中所需的抽象水平。卷积神经网络的影响远不止于图像识别。它们已成功应用于多个领域，包括医学成像、自动驾驶汽车，甚至视频分析。例如，在医疗领域，卷积网络可以帮助放射科医生以高精度检测X光或MRI中的异常，从而可能导致疾病的早期诊断和治疗。此外，TensorFlow和PyTorch等深度学习框架的兴起使研究人员和开发者更容易实现卷积网络。这些工具提供了预构建的函数和库，简化了构建和训练模型的过程，允许快速实验和创新。尽管取得了成功，卷积神经网络也不是没有挑战。它们需要大量标记数据进行训练，这在某些应用中可能是一个障碍。此外，训练这些模型所需的计算资源可能是巨大的，通常需要使用强大的GPU。总之，卷积神经网络代表了人工智能领域，特别是在图像识别任务中的突破性进展。它们独特的架构和学习层次表示的能力使机器能够以前所未有的准确性解读视觉数据。随着技术的不断发展，卷积网络的潜在应用可能会扩展，为各个行业的创新开辟新的途径。理解这项技术对于任何对人工智能未来及其对我们世界影响感兴趣的人来说都是至关重要的。