Dissertation – “Cross-Layer Approach Towards Robust and Efficient Internet Routing”
donas的附着流程
Donas的附着流程引言Donas是一种基于深度学习的图像风格转换算法,它可以将一张图像的风格转换为另一张图像的风格。
在实际应用中,Donas通常用于将普通照片转换为艺术作品风格的照片,例如将一张普通的照片转换为梵高的星夜或毕加索的画作风格。
本文将详细介绍Donas的附着流程,包括步骤和流程。
步骤1:数据准备需要准备两组数据:源图像集和目标图像集。
源图像集是待转换风格的原始图像集合,而目标图像集是期望转换后得到的目标风格图像集合。
这两组数据应该包含足够多样性和代表性,以确保算法能够学习到不同风格之间的映射关系。
步骤2:模型训练在进行附着之前,需要对Donas模型进行训练。
训练过程包括以下几个关键步骤:2.1 构建模型需要选择一个合适的深度学习模型作为Donas的基础模型。
常用的选择包括VGG、ResNet等。
选择模型时需要考虑模型的复杂度和计算资源的限制。
2.2 数据预处理在训练之前,需要对源图像集和目标图像集进行预处理。
预处理包括图像尺寸调整、归一化等操作,以便于输入到深度学习模型中进行训练。
2.3 模型定义在这一步骤中,需要定义深度学习模型的结构和参数。
根据Donas的要求,通常会选择一个卷积神经网络作为基础模型,并添加一些特定的层来实现风格转换。
2.4 损失函数定义在训练过程中,需要定义一个合适的损失函数来衡量源图像集和目标图像集之间的差异。
常用的损失函数包括内容损失和风格损失。
2.5 模型训练将预处理后的源图像集和目标图像集输入到深度学习模型中进行训练。
通过不断调整模型参数,使得模型能够准确地将源图像转换为目标风格。
步骤3:附着过程当Donas模型训练完成后,就可以进行附着过程了。
附着是将源图像的风格转换为目标风格的过程。
3.1 输入图像预处理在进行附着之前,首先需要对输入图像进行预处理。
预处理包括图像尺寸调整、归一化等操作,以便于输入到Donas模型中进行风格转换。
3.2 风格转换将预处理后的输入图像输入到训练好的Donas模型中进行风格转换。
Chapter 1 What is academic writing
CoheHale Waihona Puke ence:CREATING FOCUS
it is also important to give your writing a "focus" (coherence). You can do this by carefully choosing your topic at the beginning of each sentence. To understand why, look at the example paragraph below. Good cohesion, but no coherence: 1Romance languages descend from a Latin parent, and many words based on Latin are found in other modern languages such as English. 2English has become the lingua franca, the learned language of science and trade. 3Science is based on experimentation, description, and categorisation. 4Descriptions of the ‘northern lights’, or Aurora Borealis, often incude the words ‘twinkle’ or ‘flicker’ to explain the movement created when solar ions collide with the Earth’s atmosphere.
Cohesion
迁移学习中的无监督迁移和半监督迁移方法研究
迁移学习中的无监督迁移和半监督迁移方法研究迁移学习是一种通过将已学习的知识应用于新任务中的机器学习方法。
在实际应用中,由于数据的不完整性和不平衡性,以及标签的稀缺性等问题,传统的监督学习方法往往难以取得理想的效果。
为了解决这些问题,研究者们提出了无监督迁移和半监督迁移方法。
本文将对这两种方法进行深入研究。
无监督迁移是指在源领域和目标领域之间没有标签信息的情况下进行知识迁移。
无监督迁移通过挖掘源领域和目标领域之间的相似性来实现知识传递。
最常用的无监督迁移方法之一是领域自适应。
领域自适应通过对源领域和目标领域进行特征空间上的映射,使得两个领域能够在特征空间上具有相似性。
常见的映射方法有最大均值差异(MMD)和核最大均值差异(KMMD)等。
另一种常见的无监督迁移方法是主成分分析(PCA)和独立成分分析(ICA)。
PCA通过对源领域和目标领域的数据进行降维,找到它们之间的共享特征,从而实现知识迁移。
ICA则通过对源领域和目标领域进行独立分析,找到它们之间的独立特征,并进行知识迁移。
除了无监督迁移方法外,半监督迁移方法也是一种常用的迁移学习方法。
半监督迁移是指在源领域和目标领域之间只有部分标签信息的情况下进行知识迁移。
半监督迁移通过利用已有的标签信息和未标签信息来实现知识传递。
最常用的半监督迁移方法之一是自训练(self-training)。
自训练通过使用已有的标签信息来训练模型,并利用模型对未标签数据进行预测,从而获得更多的标签信息。
另一种常见的半监督迁移方法是共享分布式表示学习(SDRL)。
SDRL通过对源领域和目标领域进行表示学习,将它们映射到一个共享特征空间中,并利用已有的标签信息来训练模型。
在这个共享特征空间中,源领域和目标领域之间的相似性得到了保留,从而实现了知识迁移。
除了以上介绍的无监督迁移和半监督迁移方法,还有许多其他的方法被用于解决迁移学习问题。
例如,基于图的迁移学习方法通过构建源领域和目标领域之间的图结构来实现知识传递。
基于十字注意力机制改进U-Transformer_的新冠肺炎影像分割
第 22卷第 12期2023年 12月Vol.22 No.12Dec.2023软件导刊Software Guide基于十字注意力机制改进U-Transformer的新冠肺炎影像分割史爱武,高睿杨,黄晋,盛鐾,马淑然(武汉纺织大学计算机与人工智能学院,湖北武汉 430200)摘要:针对新冠肺炎CT片病灶部分分割检测困难、背景干扰多以及小病灶点易被忽略的问题,提出一种基于注意力机制改进U-Transformer的分割方法。
利用注意力机制提升分割精度,修改U-Transformer网络卷积层中间的注意力模块,并提出十字注意力机制,使网络对病灶边缘的分割更为精确。
在网络结构中添加全局—局部分割策略,使得对小病灶点的提取更加准确。
实验结果表明,改进方法较U-Transformer的精度提高了5.96%,召回率提高了7.11%,样本相似度提高了6.49%,说明改进方法对小病灶点提取具有较好效果。
拓展深度学习方法到医疗影像诊断中,有助于放射科医生更快捷、有效地进行病情诊断。
关键词:新冠肺炎;影像分割;U-Transformer;注意力机制;全局—局部策略DOI:10.11907/rjdk.222513开放科学(资源服务)标识码(OSID):中图分类号:TP391.4 文献标识码:A文章编号:1672-7800(2023)012-0209-06COVID-19 Image Segmentation by U-Transformer Improved by Criss-cross Attention MechanismSHI Aiwu, GAO Ruiyang, HUANG Jing, SHENG Bei, MA Shuran(School of Computer Science and Artificial Intelligence,Wuhan Textile University,Wuhan 430200,China)Abstract:Aiming at the problems of difficult partial segmentation detection, many background interferences and easy neglect of small lesions in new coronary pneumonia CT films, a segmentation method based on attention mechanism to improve U-Transformer is proposed. The atten‐tion mechanism is used to increase the accuracy of segmentation, and the attention module in the middle of the convolutional layer of the U-Transformer network is modified, and the cross-attention mechanism is used to realize the network to segment the lesion edge more accurately. The whole-local segmentation strategy is added to the network structure to achieve more accurate extraction of small lesion points. The experi‐mental results show that the improved method improves the accuracy by 5.96%, the recall rate by 7.11%, and the sample similarity by 6.49% compared to the U-Transformer, indicating that the improved method has a good effect on extracting small lesion points. Expanding deep learn‐ing methods to medical imaging diagnosis can help radiologists diagnose conditions more quickly and effectively.Key Words:COVID-19; image segmentation; U-Transformer; attention mechanism; global-local segmentation strategy0 引 言新冠肺炎近年来已成为全球热点话题,对新冠肺炎患者肺部病灶的准确识别与诊断,有助于患者得到及时治疗[1]。
特征抽取中的标签传播算法方法介绍
特征抽取中的标签传播算法方法介绍在机器学习和数据挖掘领域,特征抽取是一个非常重要的任务。
特征抽取的目的是将原始数据转换为可用于机器学习算法的特征向量。
标签传播算法是一种常用的特征抽取方法之一,它可以用于社交网络分析、图像处理、文本分类等领域。
标签传播算法是一种半监督学习方法,它利用已知标签的样本来预测未知标签的样本。
该算法基于以下假设:相似的样本具有相似的标签。
因此,通过将已知标签传播到相似的样本上,可以推断未知标签。
标签传播算法的基本思想是通过构建一个相似度图来表示样本之间的相似关系。
在图中,每个节点代表一个样本,边表示样本之间的相似度。
相似度可以根据不同的应用领域和任务来定义,比如欧氏距离、余弦相似度等。
标签传播算法的步骤如下:1. 构建相似度图:根据已知标签的样本计算样本之间的相似度,并构建相似度图。
2. 初始化标签:将已知标签传播到相似的样本上,初始化未知样本的标签。
3. 标签传播:通过迭代的方式,将已知标签传播到相邻的样本上。
传播的方式可以根据具体的算法不同而不同,比如基于概率的传播、基于距离的传播等。
4. 收敛判断:判断算法是否收敛,即未知样本的标签是否稳定不变。
如果未知样本的标签不再变化,则算法收敛。
5. 预测标签:根据传播后的标签,预测未知样本的标签。
标签传播算法的优点是可以利用未标记样本的信息来提高分类性能。
然而,该算法也存在一些问题。
首先,标签传播算法对初始标签的选择非常敏感,不同的初始标签会导致不同的结果。
其次,标签传播算法在处理噪声数据时表现较差,容易受到噪声样本的影响。
为了解决这些问题,研究者们提出了许多改进的标签传播算法。
例如,基于图割的标签传播算法可以通过最小化图割来提高分类性能。
另外,基于半监督聚类的标签传播算法可以结合聚类和标签传播的方法来进行特征抽取。
总结起来,标签传播算法是一种常用的特征抽取方法,其基本思想是通过将已知标签传播到相似的样本上来预测未知标签。
该算法可以应用于多个领域,但也存在一些问题。
对抗样本方法分类
对抗样本方法分类一、引言随着深度学习的广泛应用,其安全性问题也日益受到关注。
对抗样本(Adversarial Examples)作为深度学习领域的一个重要研究方向,旨在通过生成与原始样本相似但能使模型产生错误预测的样本,来评估和提高模型的鲁棒性。
本文将对对抗样本方法进行分类,并介绍各类方法的特点和应用场景。
二、对抗样本方法分类1. 基于梯度的方法基于梯度的方法是最早被提出的一类对抗样本生成方法,其核心思想是利用模型的梯度信息来生成对抗样本。
这类方法主要包括FGSM(Fast Gradient Sign Method)和PGD(Projected Gradient Descent)等。
FGSM是一种简单而有效的对抗样本生成方法,它通过计算模型对输入样本的梯度,并沿着梯度的反方向添加一个小的扰动来生成对抗样本。
这种方法生成的对抗样本具有较高的攻击成功率,但缺点是生成的对抗样本往往与原始样本差异较大,容易被人类识别出来。
PGD是一种更强大的对抗样本生成方法,它在FGSM的基础上进行了改进,通过多次迭代计算梯度并更新扰动来生成对抗样本。
这种方法可以生成更小且更难以被人类察觉的对抗样本,但需要较长的计算时间。
2. 基于优化的方法基于优化的方法是一类更为复杂的对抗样本生成方法,它们通过定义一个优化问题来求解对抗样本。
这类方法主要包括C&W(Carlini & Wagner)攻击和EAD (Elastic-net Attacks to Deep neural networks)等。
C&W攻击是一种基于优化的对抗样本生成方法,它通过定义一个包含模型预测概率和扰动大小的优化问题,并使用梯度下降等优化算法来求解对抗样本。
这种方法可以生成更小且更难以被检测的对抗样本,但需要较长的计算时间和较高的计算资源。
EAD是一种针对深度学习模型的基于优化的对抗样本生成方法,它结合了弹性网络正则化和C&W攻击的思想,通过同时优化模型的预测概率和扰动大小来生成对抗样本。
cross entropy loss的范围 -回复
cross entropy loss的范围-回复异中心学习(Cross-Entropy Loss)是一种常用的机器学习算法,用于衡量模型输出和实际标签之间的差异。
它是分类任务常用的一种损失函数,可以用于多分类问题。
本文将从什么是Cross-Entropy Loss开始,深入探讨它的作用、计算方法以及范围。
首先,让我们了解一下Cross-Entropy Loss的基本概念。
Cross-Entropy Loss用于衡量模型的输出概率分布与实际标签之间的差异。
在分类任务中,我们通常将模型的输出表示为一个概率分布,即每个类别的概率。
而实际标签是一个one-hot向量,只有一个位置上的值为1,表示该样本的真实类别。
Cross-Entropy Loss计算的是模型输出的概率分布与实际标签之间的交叉熵。
交叉熵(Cross-Entropy)是一种衡量两个概率分布之间差异的度量方法。
在分类任务中,我们希望模型的输出概率分布和实际标签的分布尽可能接近,因此交叉熵越小,表示模型的输出与实际标签越接近。
而Cross-Entropy Loss则是将交叉熵作为损失函数,在训练过程中最小化其值,从而使模型学习到更准确的概率分布。
接下来,我们将详细讨论Cross-Entropy Loss的计算方法。
假设有一个多分类任务,共有C个类别。
模型的输出概率分布表示为一个C维向量,记为y,其中每个位置上的值表示该类别的概率。
实际标签则是一个C维向量,记为t,其中只有一个位置上的值为1,表示真实类别。
Cross-Entropy Loss的计算公式如下:\[Loss = -\sum_{i=1}^{C} t_i \log(y_i)\]其中,\(t_i\)表示实际标签的第i个位置上的值,\(y_i\)表示模型输出的第i个位置上的值。
对于真实类别位置上的输出值,我们希望它越大越好,因为它表示模型判断这个类别的概率越大。
而对于其他位置上的输出值,我们希望它越小越好,因为它表示模型判断这些类别的概率越小。
基于多层特征嵌入的单目标跟踪算法
基于多层特征嵌入的单目标跟踪算法1. 内容描述基于多层特征嵌入的单目标跟踪算法是一种在计算机视觉领域中广泛应用的跟踪技术。
该算法的核心思想是通过多层特征嵌入来提取目标物体的特征表示,并利用这些特征表示进行目标跟踪。
该算法首先通过预处理步骤对输入图像进行降维和增强,然后将降维后的图像输入到神经网络中,得到不同层次的特征图。
通过对这些特征图进行池化操作,得到一个低维度的特征向量。
将这个特征向量输入到跟踪器中,以实现对目标物体的实时跟踪。
为了提高单目标跟踪算法的性能,本研究提出了一种基于多层特征嵌入的方法。
该方法首先引入了一个自适应的学习率策略,使得神经网络能够根据当前训练状态自动调整学习率。
通过引入注意力机制,使得神经网络能够更加关注重要的特征信息。
为了进一步提高跟踪器的鲁棒性,本研究还采用了一种多目标融合的方法,将多个跟踪器的结果进行加权融合,从而得到更加准确的目标位置估计。
通过实验验证,本研究提出的方法在多种数据集上均取得了显著的性能提升,证明了其在单目标跟踪领域的有效性和可行性。
1.1 研究背景随着计算机视觉和深度学习技术的快速发展,目标跟踪在许多领域(如安防、智能监控、自动驾驶等)中发挥着越来越重要的作用。
单目标跟踪(MOT)算法是一种广泛应用于视频分析领域的技术,它能够实时跟踪视频序列中的单个目标物体,并将其位置信息与相邻帧进行比较,以估计目标的运动轨迹。
传统的单目标跟踪算法在处理复杂场景、遮挡、运动模糊等问题时表现出较差的鲁棒性。
为了解决这些问题,研究者们提出了许多改进的单目标跟踪算法,如基于卡尔曼滤波的目标跟踪、基于扩展卡尔曼滤波的目标跟踪以及基于深度学习的目标跟踪等。
这些方法在一定程度上提高了单目标跟踪的性能,但仍然存在一些局限性,如对多目标跟踪的支持不足、对非平稳运动的适应性差等。
开发一种既能有效跟踪单个目标物体,又能应对多种挑战的单目标跟踪算法具有重要的理论和实际意义。
1.2 研究目的本研究旨在设计一种基于多层特征嵌入的单目标跟踪算法,以提高目标跟踪的准确性和鲁棒性。
categorical_crossentropy计算方法
categorical_crossentropy计算方法交叉熵(cross-entropy)是常用的一种损失函数,常被用于类别分类问题中。
交叉熵通过衡量两个概率之间的差异性,可以量化一个分类模型的预测结果与真实结果之间的差异性。
Categorical_crossentropy是针对分类问题而言的交叉熵损失函数。
它的计算方法可以分为以下几个步骤:1. 定义真实标签 y_true 和预测标签 y_pred在分类问题中,我们通常使用 One-Hot 编码的方式表示样本的真实标签 y_true。
例如,在一个 4 类分类问题中,如果某个样本属于第二类,则其对应的 One-Hot 编码为 [0, 1, 0, 0]。
模型的预测结果 y_pred 则是模型对于这个样本在各个类别上得到的概率分布。
例如,模型预测一个样本在 4 个类别上的概率分别为[0.2, 0.5, 0.1, 0.2]。
2. 将真实标签 y_true 和预测标签 y_pred 代入交叉熵公式交叉熵的公式为:```H(p, q) = Σ(p * log(1/q))```其中,p 是真实概率分布,q 是预测概率分布。
在分类问题中,真实概率分布 p 就是 One-Hot 编码,而预测概率分布 q 就是模型对于各个类别的概率分布。
将这两者代入公式中,可以得到交叉熵损失函数的数学表达式:```loss = -Σ(y_true * log(y_pred))```在实际计算中,由于对数函数在 x=0 处不定义,因此通常会加上一个极小值 eps,避免计算中出现无穷大值:```loss = -Σ(y_true * log(y_pred + eps))```3. 对样本交叉熵进行平均一个批次(batch)的交叉熵损失通常是由 individual losses 的平均值计算得到的。
这个平均值可以用如下代码实现:```loss =tf.reduce_mean(tf.keras.losses.categorical_crossentropy(y_tru e, y_pred))```交叉熵损失函数是深度学习中最常用的损失函数之一,它的优点在于它可以充分考虑分类的置信程度,有效地处理分类问题中的不确定性。
k近邻分类法的步骤
k近邻分类法的步骤
4. 确定k值:选择一个合适的k值,表示在分类时考虑的最近邻样本的数量。k值的选择需 要根据具体问题和数据集进行调整。一般来说,较小的k值会使分类结果更敏感,而较大的k 值会使分类结果更平滑。
5. 选择最近邻:根据计算得到的距离,选择与未知样本最近的k个已知样本作为最近邻。
6. 进行投票:对于这k个最近邻样本,根据它们的类别标签进行投票。一般采用多数表决 的方式,将得票最多的类别作为未知样本的预测类别。
7. 输出结果:根据投票结果,将未知样本分类到预测的分类法是一种常用的机器学习算法,用于对未知样本进行分类。其步骤如下:
1. 数据准备:首先,需要准备一个已知类别的训练数据集,其中包含了已知样本的特征和 对应的类别标签。同时,还需要准备一个未知样本的测试数据集,用于进行分类预测。
2. 特征选择:根据问题的需求和数据的特点,选择合适的特征进行分类。特征应该具有区 分不同类别的能力,并且能够提供足够的信息用于分类。
3GPP TS 36.331 V13.2.0 (2016-06)
3GPP TS 36.331 V13.2.0 (2016-06)Technical Specification3rd Generation Partnership Project;Technical Specification Group Radio Access Network;Evolved Universal Terrestrial Radio Access (E-UTRA);Radio Resource Control (RRC);Protocol specification(Release 13)The present document has been developed within the 3rd Generation Partnership Project (3GPP TM) and may be further elaborated for the purposes of 3GPP. The present document has not been subject to any approval process by the 3GPP Organizational Partners and shall not be implemented.This Specification is provided for future development work within 3GPP only. The Organizational Partners accept no liability for any use of this Specification. Specifications and reports for implementation of the 3GPP TM system should be obtained via the 3GPP Organizational Partners' Publications Offices.KeywordsUMTS, radio3GPPPostal address3GPP support office address650 Route des Lucioles - Sophia AntipolisValbonne - FRANCETel.: +33 4 92 94 42 00 Fax: +33 4 93 65 47 16InternetCopyright NotificationNo part may be reproduced except as authorized by written permission.The copyright and the foregoing restriction extend to reproduction in all media.© 2016, 3GPP Organizational Partners (ARIB, ATIS, CCSA, ETSI, TSDSI, TTA, TTC).All rights reserved.UMTS™ is a Trade Mark of ETSI registered for the benefit of its members3GPP™ is a Trade Mark of ETSI registered for the benefit of its Members and of the 3GPP Organizational PartnersLTE™ is a Trade Mark of ETSI currently being registered for the benefit of its Members and of the 3GPP Organizational Partners GSM® and the GSM logo are registered and owned by the GSM AssociationBluetooth® is a Trade Mark of the Bluetooth SIG registered for the benefit of its membersContentsForeword (18)1Scope (19)2References (19)3Definitions, symbols and abbreviations (22)3.1Definitions (22)3.2Abbreviations (24)4General (27)4.1Introduction (27)4.2Architecture (28)4.2.1UE states and state transitions including inter RAT (28)4.2.2Signalling radio bearers (29)4.3Services (30)4.3.1Services provided to upper layers (30)4.3.2Services expected from lower layers (30)4.4Functions (30)5Procedures (32)5.1General (32)5.1.1Introduction (32)5.1.2General requirements (32)5.2System information (33)5.2.1Introduction (33)5.2.1.1General (33)5.2.1.2Scheduling (34)5.2.1.2a Scheduling for NB-IoT (34)5.2.1.3System information validity and notification of changes (35)5.2.1.4Indication of ETWS notification (36)5.2.1.5Indication of CMAS notification (37)5.2.1.6Notification of EAB parameters change (37)5.2.1.7Access Barring parameters change in NB-IoT (37)5.2.2System information acquisition (38)5.2.2.1General (38)5.2.2.2Initiation (38)5.2.2.3System information required by the UE (38)5.2.2.4System information acquisition by the UE (39)5.2.2.5Essential system information missing (42)5.2.2.6Actions upon reception of the MasterInformationBlock message (42)5.2.2.7Actions upon reception of the SystemInformationBlockType1 message (42)5.2.2.8Actions upon reception of SystemInformation messages (44)5.2.2.9Actions upon reception of SystemInformationBlockType2 (44)5.2.2.10Actions upon reception of SystemInformationBlockType3 (45)5.2.2.11Actions upon reception of SystemInformationBlockType4 (45)5.2.2.12Actions upon reception of SystemInformationBlockType5 (45)5.2.2.13Actions upon reception of SystemInformationBlockType6 (45)5.2.2.14Actions upon reception of SystemInformationBlockType7 (45)5.2.2.15Actions upon reception of SystemInformationBlockType8 (45)5.2.2.16Actions upon reception of SystemInformationBlockType9 (46)5.2.2.17Actions upon reception of SystemInformationBlockType10 (46)5.2.2.18Actions upon reception of SystemInformationBlockType11 (46)5.2.2.19Actions upon reception of SystemInformationBlockType12 (47)5.2.2.20Actions upon reception of SystemInformationBlockType13 (48)5.2.2.21Actions upon reception of SystemInformationBlockType14 (48)5.2.2.22Actions upon reception of SystemInformationBlockType15 (48)5.2.2.23Actions upon reception of SystemInformationBlockType16 (48)5.2.2.24Actions upon reception of SystemInformationBlockType17 (48)5.2.2.25Actions upon reception of SystemInformationBlockType18 (48)5.2.2.26Actions upon reception of SystemInformationBlockType19 (49)5.2.3Acquisition of an SI message (49)5.2.3a Acquisition of an SI message by BL UE or UE in CE or a NB-IoT UE (50)5.3Connection control (50)5.3.1Introduction (50)5.3.1.1RRC connection control (50)5.3.1.2Security (52)5.3.1.2a RN security (53)5.3.1.3Connected mode mobility (53)5.3.1.4Connection control in NB-IoT (54)5.3.2Paging (55)5.3.2.1General (55)5.3.2.2Initiation (55)5.3.2.3Reception of the Paging message by the UE (55)5.3.3RRC connection establishment (56)5.3.3.1General (56)5.3.3.1a Conditions for establishing RRC Connection for sidelink communication/ discovery (58)5.3.3.2Initiation (59)5.3.3.3Actions related to transmission of RRCConnectionRequest message (63)5.3.3.3a Actions related to transmission of RRCConnectionResumeRequest message (64)5.3.3.4Reception of the RRCConnectionSetup by the UE (64)5.3.3.4a Reception of the RRCConnectionResume by the UE (66)5.3.3.5Cell re-selection while T300, T302, T303, T305, T306, or T308 is running (68)5.3.3.6T300 expiry (68)5.3.3.7T302, T303, T305, T306, or T308 expiry or stop (69)5.3.3.8Reception of the RRCConnectionReject by the UE (70)5.3.3.9Abortion of RRC connection establishment (71)5.3.3.10Handling of SSAC related parameters (71)5.3.3.11Access barring check (72)5.3.3.12EAB check (73)5.3.3.13Access barring check for ACDC (73)5.3.3.14Access Barring check for NB-IoT (74)5.3.4Initial security activation (75)5.3.4.1General (75)5.3.4.2Initiation (76)5.3.4.3Reception of the SecurityModeCommand by the UE (76)5.3.5RRC connection reconfiguration (77)5.3.5.1General (77)5.3.5.2Initiation (77)5.3.5.3Reception of an RRCConnectionReconfiguration not including the mobilityControlInfo by theUE (77)5.3.5.4Reception of an RRCConnectionReconfiguration including the mobilityControlInfo by the UE(handover) (79)5.3.5.5Reconfiguration failure (83)5.3.5.6T304 expiry (handover failure) (83)5.3.5.7Void (84)5.3.5.7a T307 expiry (SCG change failure) (84)5.3.5.8Radio Configuration involving full configuration option (84)5.3.6Counter check (86)5.3.6.1General (86)5.3.6.2Initiation (86)5.3.6.3Reception of the CounterCheck message by the UE (86)5.3.7RRC connection re-establishment (87)5.3.7.1General (87)5.3.7.2Initiation (87)5.3.7.3Actions following cell selection while T311 is running (88)5.3.7.4Actions related to transmission of RRCConnectionReestablishmentRequest message (89)5.3.7.5Reception of the RRCConnectionReestablishment by the UE (89)5.3.7.6T311 expiry (91)5.3.7.7T301 expiry or selected cell no longer suitable (91)5.3.7.8Reception of RRCConnectionReestablishmentReject by the UE (91)5.3.8RRC connection release (92)5.3.8.1General (92)5.3.8.2Initiation (92)5.3.8.3Reception of the RRCConnectionRelease by the UE (92)5.3.8.4T320 expiry (93)5.3.9RRC connection release requested by upper layers (93)5.3.9.1General (93)5.3.9.2Initiation (93)5.3.10Radio resource configuration (93)5.3.10.0General (93)5.3.10.1SRB addition/ modification (94)5.3.10.2DRB release (95)5.3.10.3DRB addition/ modification (95)5.3.10.3a1DC specific DRB addition or reconfiguration (96)5.3.10.3a2LWA specific DRB addition or reconfiguration (98)5.3.10.3a3LWIP specific DRB addition or reconfiguration (98)5.3.10.3a SCell release (99)5.3.10.3b SCell addition/ modification (99)5.3.10.3c PSCell addition or modification (99)5.3.10.4MAC main reconfiguration (99)5.3.10.5Semi-persistent scheduling reconfiguration (100)5.3.10.6Physical channel reconfiguration (100)5.3.10.7Radio Link Failure Timers and Constants reconfiguration (101)5.3.10.8Time domain measurement resource restriction for serving cell (101)5.3.10.9Other configuration (102)5.3.10.10SCG reconfiguration (103)5.3.10.11SCG dedicated resource configuration (104)5.3.10.12Reconfiguration SCG or split DRB by drb-ToAddModList (105)5.3.10.13Neighbour cell information reconfiguration (105)5.3.10.14Void (105)5.3.10.15Sidelink dedicated configuration (105)5.3.10.16T370 expiry (106)5.3.11Radio link failure related actions (107)5.3.11.1Detection of physical layer problems in RRC_CONNECTED (107)5.3.11.2Recovery of physical layer problems (107)5.3.11.3Detection of radio link failure (107)5.3.12UE actions upon leaving RRC_CONNECTED (109)5.3.13UE actions upon PUCCH/ SRS release request (110)5.3.14Proximity indication (110)5.3.14.1General (110)5.3.14.2Initiation (111)5.3.14.3Actions related to transmission of ProximityIndication message (111)5.3.15Void (111)5.4Inter-RAT mobility (111)5.4.1Introduction (111)5.4.2Handover to E-UTRA (112)5.4.2.1General (112)5.4.2.2Initiation (112)5.4.2.3Reception of the RRCConnectionReconfiguration by the UE (112)5.4.2.4Reconfiguration failure (114)5.4.2.5T304 expiry (handover to E-UTRA failure) (114)5.4.3Mobility from E-UTRA (114)5.4.3.1General (114)5.4.3.2Initiation (115)5.4.3.3Reception of the MobilityFromEUTRACommand by the UE (115)5.4.3.4Successful completion of the mobility from E-UTRA (116)5.4.3.5Mobility from E-UTRA failure (117)5.4.4Handover from E-UTRA preparation request (CDMA2000) (117)5.4.4.1General (117)5.4.4.2Initiation (118)5.4.4.3Reception of the HandoverFromEUTRAPreparationRequest by the UE (118)5.4.5UL handover preparation transfer (CDMA2000) (118)5.4.5.1General (118)5.4.5.2Initiation (118)5.4.5.3Actions related to transmission of the ULHandoverPreparationTransfer message (119)5.4.5.4Failure to deliver the ULHandoverPreparationTransfer message (119)5.4.6Inter-RAT cell change order to E-UTRAN (119)5.4.6.1General (119)5.4.6.2Initiation (119)5.4.6.3UE fails to complete an inter-RAT cell change order (119)5.5Measurements (120)5.5.1Introduction (120)5.5.2Measurement configuration (121)5.5.2.1General (121)5.5.2.2Measurement identity removal (122)5.5.2.2a Measurement identity autonomous removal (122)5.5.2.3Measurement identity addition/ modification (123)5.5.2.4Measurement object removal (124)5.5.2.5Measurement object addition/ modification (124)5.5.2.6Reporting configuration removal (126)5.5.2.7Reporting configuration addition/ modification (127)5.5.2.8Quantity configuration (127)5.5.2.9Measurement gap configuration (127)5.5.2.10Discovery signals measurement timing configuration (128)5.5.2.11RSSI measurement timing configuration (128)5.5.3Performing measurements (128)5.5.3.1General (128)5.5.3.2Layer 3 filtering (131)5.5.4Measurement report triggering (131)5.5.4.1General (131)5.5.4.2Event A1 (Serving becomes better than threshold) (135)5.5.4.3Event A2 (Serving becomes worse than threshold) (136)5.5.4.4Event A3 (Neighbour becomes offset better than PCell/ PSCell) (136)5.5.4.5Event A4 (Neighbour becomes better than threshold) (137)5.5.4.6Event A5 (PCell/ PSCell becomes worse than threshold1 and neighbour becomes better thanthreshold2) (138)5.5.4.6a Event A6 (Neighbour becomes offset better than SCell) (139)5.5.4.7Event B1 (Inter RAT neighbour becomes better than threshold) (139)5.5.4.8Event B2 (PCell becomes worse than threshold1 and inter RAT neighbour becomes better thanthreshold2) (140)5.5.4.9Event C1 (CSI-RS resource becomes better than threshold) (141)5.5.4.10Event C2 (CSI-RS resource becomes offset better than reference CSI-RS resource) (141)5.5.4.11Event W1 (WLAN becomes better than a threshold) (142)5.5.4.12Event W2 (All WLAN inside WLAN mobility set becomes worse than threshold1 and a WLANoutside WLAN mobility set becomes better than threshold2) (142)5.5.4.13Event W3 (All WLAN inside WLAN mobility set becomes worse than a threshold) (143)5.5.5Measurement reporting (144)5.5.6Measurement related actions (148)5.5.6.1Actions upon handover and re-establishment (148)5.5.6.2Speed dependant scaling of measurement related parameters (149)5.5.7Inter-frequency RSTD measurement indication (149)5.5.7.1General (149)5.5.7.2Initiation (150)5.5.7.3Actions related to transmission of InterFreqRSTDMeasurementIndication message (150)5.6Other (150)5.6.0General (150)5.6.1DL information transfer (151)5.6.1.1General (151)5.6.1.2Initiation (151)5.6.1.3Reception of the DLInformationTransfer by the UE (151)5.6.2UL information transfer (151)5.6.2.1General (151)5.6.2.2Initiation (151)5.6.2.3Actions related to transmission of ULInformationTransfer message (152)5.6.2.4Failure to deliver ULInformationTransfer message (152)5.6.3UE capability transfer (152)5.6.3.1General (152)5.6.3.2Initiation (153)5.6.3.3Reception of the UECapabilityEnquiry by the UE (153)5.6.4CSFB to 1x Parameter transfer (157)5.6.4.1General (157)5.6.4.2Initiation (157)5.6.4.3Actions related to transmission of CSFBParametersRequestCDMA2000 message (157)5.6.4.4Reception of the CSFBParametersResponseCDMA2000 message (157)5.6.5UE Information (158)5.6.5.1General (158)5.6.5.2Initiation (158)5.6.5.3Reception of the UEInformationRequest message (158)5.6.6 Logged Measurement Configuration (159)5.6.6.1General (159)5.6.6.2Initiation (160)5.6.6.3Reception of the LoggedMeasurementConfiguration by the UE (160)5.6.6.4T330 expiry (160)5.6.7 Release of Logged Measurement Configuration (160)5.6.7.1General (160)5.6.7.2Initiation (160)5.6.8 Measurements logging (161)5.6.8.1General (161)5.6.8.2Initiation (161)5.6.9In-device coexistence indication (163)5.6.9.1General (163)5.6.9.2Initiation (164)5.6.9.3Actions related to transmission of InDeviceCoexIndication message (164)5.6.10UE Assistance Information (165)5.6.10.1General (165)5.6.10.2Initiation (166)5.6.10.3Actions related to transmission of UEAssistanceInformation message (166)5.6.11 Mobility history information (166)5.6.11.1General (166)5.6.11.2Initiation (166)5.6.12RAN-assisted WLAN interworking (167)5.6.12.1General (167)5.6.12.2Dedicated WLAN offload configuration (167)5.6.12.3WLAN offload RAN evaluation (167)5.6.12.4T350 expiry or stop (167)5.6.12.5Cell selection/ re-selection while T350 is running (168)5.6.13SCG failure information (168)5.6.13.1General (168)5.6.13.2Initiation (168)5.6.13.3Actions related to transmission of SCGFailureInformation message (168)5.6.14LTE-WLAN Aggregation (169)5.6.14.1Introduction (169)5.6.14.2Reception of LWA configuration (169)5.6.14.3Release of LWA configuration (170)5.6.15WLAN connection management (170)5.6.15.1Introduction (170)5.6.15.2WLAN connection status reporting (170)5.6.15.2.1General (170)5.6.15.2.2Initiation (171)5.6.15.2.3Actions related to transmission of WLANConnectionStatusReport message (171)5.6.15.3T351 Expiry (WLAN connection attempt timeout) (171)5.6.15.4WLAN status monitoring (171)5.6.16RAN controlled LTE-WLAN interworking (172)5.6.16.1General (172)5.6.16.2WLAN traffic steering command (172)5.6.17LTE-WLAN aggregation with IPsec tunnel (173)5.6.17.1General (173)5.7Generic error handling (174)5.7.1General (174)5.7.2ASN.1 violation or encoding error (174)5.7.3Field set to a not comprehended value (174)5.7.4Mandatory field missing (174)5.7.5Not comprehended field (176)5.8MBMS (176)5.8.1Introduction (176)5.8.1.1General (176)5.8.1.2Scheduling (176)5.8.1.3MCCH information validity and notification of changes (176)5.8.2MCCH information acquisition (178)5.8.2.1General (178)5.8.2.2Initiation (178)5.8.2.3MCCH information acquisition by the UE (178)5.8.2.4Actions upon reception of the MBSFNAreaConfiguration message (178)5.8.2.5Actions upon reception of the MBMSCountingRequest message (179)5.8.3MBMS PTM radio bearer configuration (179)5.8.3.1General (179)5.8.3.2Initiation (179)5.8.3.3MRB establishment (179)5.8.3.4MRB release (179)5.8.4MBMS Counting Procedure (179)5.8.4.1General (179)5.8.4.2Initiation (180)5.8.4.3Reception of the MBMSCountingRequest message by the UE (180)5.8.5MBMS interest indication (181)5.8.5.1General (181)5.8.5.2Initiation (181)5.8.5.3Determine MBMS frequencies of interest (182)5.8.5.4Actions related to transmission of MBMSInterestIndication message (183)5.8a SC-PTM (183)5.8a.1Introduction (183)5.8a.1.1General (183)5.8a.1.2SC-MCCH scheduling (183)5.8a.1.3SC-MCCH information validity and notification of changes (183)5.8a.1.4Procedures (184)5.8a.2SC-MCCH information acquisition (184)5.8a.2.1General (184)5.8a.2.2Initiation (184)5.8a.2.3SC-MCCH information acquisition by the UE (184)5.8a.2.4Actions upon reception of the SCPTMConfiguration message (185)5.8a.3SC-PTM radio bearer configuration (185)5.8a.3.1General (185)5.8a.3.2Initiation (185)5.8a.3.3SC-MRB establishment (185)5.8a.3.4SC-MRB release (185)5.9RN procedures (186)5.9.1RN reconfiguration (186)5.9.1.1General (186)5.9.1.2Initiation (186)5.9.1.3Reception of the RNReconfiguration by the RN (186)5.10Sidelink (186)5.10.1Introduction (186)5.10.1a Conditions for sidelink communication operation (187)5.10.2Sidelink UE information (188)5.10.2.1General (188)5.10.2.2Initiation (189)5.10.2.3Actions related to transmission of SidelinkUEInformation message (193)5.10.3Sidelink communication monitoring (195)5.10.6Sidelink discovery announcement (198)5.10.6a Sidelink discovery announcement pool selection (201)5.10.6b Sidelink discovery announcement reference carrier selection (201)5.10.7Sidelink synchronisation information transmission (202)5.10.7.1General (202)5.10.7.2Initiation (203)5.10.7.3Transmission of SLSS (204)5.10.7.4Transmission of MasterInformationBlock-SL message (205)5.10.7.5Void (206)5.10.8Sidelink synchronisation reference (206)5.10.8.1General (206)5.10.8.2Selection and reselection of synchronisation reference UE (SyncRef UE) (206)5.10.9Sidelink common control information (207)5.10.9.1General (207)5.10.9.2Actions related to reception of MasterInformationBlock-SL message (207)5.10.10Sidelink relay UE operation (207)5.10.10.1General (207)5.10.10.2AS-conditions for relay related sidelink communication transmission by sidelink relay UE (207)5.10.10.3AS-conditions for relay PS related sidelink discovery transmission by sidelink relay UE (208)5.10.10.4Sidelink relay UE threshold conditions (208)5.10.11Sidelink remote UE operation (208)5.10.11.1General (208)5.10.11.2AS-conditions for relay related sidelink communication transmission by sidelink remote UE (208)5.10.11.3AS-conditions for relay PS related sidelink discovery transmission by sidelink remote UE (209)5.10.11.4Selection and reselection of sidelink relay UE (209)5.10.11.5Sidelink remote UE threshold conditions (210)6Protocol data units, formats and parameters (tabular & ASN.1) (210)6.1General (210)6.2RRC messages (212)6.2.1General message structure (212)–EUTRA-RRC-Definitions (212)–BCCH-BCH-Message (212)–BCCH-DL-SCH-Message (212)–BCCH-DL-SCH-Message-BR (213)–MCCH-Message (213)–PCCH-Message (213)–DL-CCCH-Message (214)–DL-DCCH-Message (214)–UL-CCCH-Message (214)–UL-DCCH-Message (215)–SC-MCCH-Message (215)6.2.2Message definitions (216)–CounterCheck (216)–CounterCheckResponse (217)–CSFBParametersRequestCDMA2000 (217)–CSFBParametersResponseCDMA2000 (218)–DLInformationTransfer (218)–HandoverFromEUTRAPreparationRequest (CDMA2000) (219)–InDeviceCoexIndication (220)–InterFreqRSTDMeasurementIndication (222)–LoggedMeasurementConfiguration (223)–MasterInformationBlock (225)–MBMSCountingRequest (226)–MBMSCountingResponse (226)–MBMSInterestIndication (227)–MBSFNAreaConfiguration (228)–MeasurementReport (228)–MobilityFromEUTRACommand (229)–Paging (232)–ProximityIndication (233)–RNReconfiguration (234)–RNReconfigurationComplete (234)–RRCConnectionReconfiguration (235)–RRCConnectionReconfigurationComplete (240)–RRCConnectionReestablishment (241)–RRCConnectionReestablishmentComplete (241)–RRCConnectionReestablishmentReject (242)–RRCConnectionReestablishmentRequest (243)–RRCConnectionReject (243)–RRCConnectionRelease (244)–RRCConnectionResume (248)–RRCConnectionResumeComplete (249)–RRCConnectionResumeRequest (250)–RRCConnectionRequest (250)–RRCConnectionSetup (251)–RRCConnectionSetupComplete (252)–SCGFailureInformation (253)–SCPTMConfiguration (254)–SecurityModeCommand (255)–SecurityModeComplete (255)–SecurityModeFailure (256)–SidelinkUEInformation (256)–SystemInformation (258)–SystemInformationBlockType1 (259)–UEAssistanceInformation (264)–UECapabilityEnquiry (265)–UECapabilityInformation (266)–UEInformationRequest (267)–UEInformationResponse (267)–ULHandoverPreparationTransfer (CDMA2000) (273)–ULInformationTransfer (274)–WLANConnectionStatusReport (274)6.3RRC information elements (275)6.3.1System information blocks (275)–SystemInformationBlockType2 (275)–SystemInformationBlockType3 (279)–SystemInformationBlockType4 (282)–SystemInformationBlockType5 (283)–SystemInformationBlockType6 (287)–SystemInformationBlockType7 (289)–SystemInformationBlockType8 (290)–SystemInformationBlockType9 (295)–SystemInformationBlockType10 (295)–SystemInformationBlockType11 (296)–SystemInformationBlockType12 (297)–SystemInformationBlockType13 (297)–SystemInformationBlockType14 (298)–SystemInformationBlockType15 (298)–SystemInformationBlockType16 (299)–SystemInformationBlockType17 (300)–SystemInformationBlockType18 (301)–SystemInformationBlockType19 (301)–SystemInformationBlockType20 (304)6.3.2Radio resource control information elements (304)–AntennaInfo (304)–AntennaInfoUL (306)–CQI-ReportConfig (307)–CQI-ReportPeriodicProcExtId (314)–CrossCarrierSchedulingConfig (314)–CSI-IM-Config (315)–CSI-IM-ConfigId (315)–CSI-RS-Config (317)–CSI-RS-ConfigEMIMO (318)–CSI-RS-ConfigNZP (319)–CSI-RS-ConfigNZPId (320)–CSI-RS-ConfigZP (321)–CSI-RS-ConfigZPId (321)–DMRS-Config (321)–DRB-Identity (322)–EPDCCH-Config (322)–EIMTA-MainConfig (324)–LogicalChannelConfig (325)–LWA-Configuration (326)–LWIP-Configuration (326)–RCLWI-Configuration (327)–MAC-MainConfig (327)–P-C-AndCBSR (332)–PDCCH-ConfigSCell (333)–PDCP-Config (334)–PDSCH-Config (337)–PDSCH-RE-MappingQCL-ConfigId (339)–PHICH-Config (339)–PhysicalConfigDedicated (339)–P-Max (344)–PRACH-Config (344)–PresenceAntennaPort1 (346)–PUCCH-Config (347)–PUSCH-Config (351)–RACH-ConfigCommon (355)–RACH-ConfigDedicated (357)–RadioResourceConfigCommon (358)–RadioResourceConfigDedicated (362)–RLC-Config (367)–RLF-TimersAndConstants (369)–RN-SubframeConfig (370)–SchedulingRequestConfig (371)–SoundingRS-UL-Config (372)–SPS-Config (375)–TDD-Config (376)–TimeAlignmentTimer (377)–TPC-PDCCH-Config (377)–TunnelConfigLWIP (378)–UplinkPowerControl (379)–WLAN-Id-List (382)–WLAN-MobilityConfig (382)6.3.3Security control information elements (382)–NextHopChainingCount (382)–SecurityAlgorithmConfig (383)–ShortMAC-I (383)6.3.4Mobility control information elements (383)–AdditionalSpectrumEmission (383)–ARFCN-ValueCDMA2000 (383)–ARFCN-ValueEUTRA (384)–ARFCN-ValueGERAN (384)–ARFCN-ValueUTRA (384)–BandclassCDMA2000 (384)–BandIndicatorGERAN (385)–CarrierFreqCDMA2000 (385)–CarrierFreqGERAN (385)–CellIndexList (387)–CellReselectionPriority (387)–CellSelectionInfoCE (387)–CellReselectionSubPriority (388)–CSFB-RegistrationParam1XRTT (388)–CellGlobalIdEUTRA (389)–CellGlobalIdUTRA (389)–CellGlobalIdGERAN (390)–CellGlobalIdCDMA2000 (390)–CellSelectionInfoNFreq (391)–CSG-Identity (391)–FreqBandIndicator (391)–MobilityControlInfo (391)–MobilityParametersCDMA2000 (1xRTT) (393)–MobilityStateParameters (394)–MultiBandInfoList (394)–NS-PmaxList (394)–PhysCellId (395)–PhysCellIdRange (395)–PhysCellIdRangeUTRA-FDDList (395)–PhysCellIdCDMA2000 (396)–PhysCellIdGERAN (396)–PhysCellIdUTRA-FDD (396)–PhysCellIdUTRA-TDD (396)–PLMN-Identity (397)–PLMN-IdentityList3 (397)–PreRegistrationInfoHRPD (397)–Q-QualMin (398)–Q-RxLevMin (398)–Q-OffsetRange (398)–Q-OffsetRangeInterRAT (399)–ReselectionThreshold (399)–ReselectionThresholdQ (399)–SCellIndex (399)–ServCellIndex (400)–SpeedStateScaleFactors (400)–SystemInfoListGERAN (400)–SystemTimeInfoCDMA2000 (401)–TrackingAreaCode (401)–T-Reselection (402)–T-ReselectionEUTRA-CE (402)6.3.5Measurement information elements (402)–AllowedMeasBandwidth (402)–CSI-RSRP-Range (402)–Hysteresis (402)–LocationInfo (403)–MBSFN-RSRQ-Range (403)–MeasConfig (404)–MeasDS-Config (405)–MeasGapConfig (406)–MeasId (407)–MeasIdToAddModList (407)–MeasObjectCDMA2000 (408)–MeasObjectEUTRA (408)–MeasObjectGERAN (412)–MeasObjectId (412)–MeasObjectToAddModList (412)–MeasObjectUTRA (413)–ReportConfigEUTRA (422)–ReportConfigId (425)–ReportConfigInterRAT (425)–ReportConfigToAddModList (428)–ReportInterval (429)–RSRP-Range (429)–RSRQ-Range (430)–RSRQ-Type (430)–RS-SINR-Range (430)–RSSI-Range-r13 (431)–TimeToTrigger (431)–UL-DelayConfig (431)–WLAN-CarrierInfo (431)–WLAN-RSSI-Range (432)–WLAN-Status (432)6.3.6Other information elements (433)–AbsoluteTimeInfo (433)–AreaConfiguration (433)–C-RNTI (433)–DedicatedInfoCDMA2000 (434)–DedicatedInfoNAS (434)–FilterCoefficient (434)–LoggingDuration (434)–LoggingInterval (435)–MeasSubframePattern (435)–MMEC (435)–NeighCellConfig (435)–OtherConfig (436)–RAND-CDMA2000 (1xRTT) (437)–RAT-Type (437)–ResumeIdentity (437)–RRC-TransactionIdentifier (438)–S-TMSI (438)–TraceReference (438)–UE-CapabilityRAT-ContainerList (438)–UE-EUTRA-Capability (439)–UE-RadioPagingInfo (469)–UE-TimersAndConstants (469)–VisitedCellInfoList (470)–WLAN-OffloadConfig (470)6.3.7MBMS information elements (472)–MBMS-NotificationConfig (472)–MBMS-ServiceList (473)–MBSFN-AreaId (473)–MBSFN-AreaInfoList (473)–MBSFN-SubframeConfig (474)–PMCH-InfoList (475)6.3.7a SC-PTM information elements (476)–SC-MTCH-InfoList (476)–SCPTM-NeighbourCellList (478)6.3.8Sidelink information elements (478)–SL-CommConfig (478)–SL-CommResourcePool (479)–SL-CP-Len (480)–SL-DiscConfig (481)–SL-DiscResourcePool (483)–SL-DiscTxPowerInfo (485)–SL-GapConfig (485)。
纹理物体缺陷的视觉检测算法研究--优秀毕业论文
摘 要
在竞争激烈的工业自动化生产过程中,机器视觉对产品质量的把关起着举足 轻重的作用,机器视觉在缺陷检测技术方面的应用也逐渐普遍起来。与常规的检 测技术相比,自动化的视觉检测系统更加经济、快捷、高效与 安全。纹理物体在 工业生产中广泛存在,像用于半导体装配和封装底板和发光二极管,现代 化电子 系统中的印制电路板,以及纺织行业中的布匹和织物等都可认为是含有纹理特征 的物体。本论文主要致力于纹理物体的缺陷检测技术研究,为纹理物体的自动化 检测提供高效而可靠的检测算法。 纹理是描述图像内容的重要特征,纹理分析也已经被成功的应用与纹理分割 和纹理分类当中。本研究提出了一种基于纹理分析技术和参考比较方式的缺陷检 测算法。这种算法能容忍物体变形引起的图像配准误差,对纹理的影响也具有鲁 棒性。本算法旨在为检测出的缺陷区域提供丰富而重要的物理意义,如缺陷区域 的大小、形状、亮度对比度及空间分布等。同时,在参考图像可行的情况下,本 算法可用于同质纹理物体和非同质纹理物体的检测,对非纹理物体 的检测也可取 得不错的效果。 在整个检测过程中,我们采用了可调控金字塔的纹理分析和重构技术。与传 统的小波纹理分析技术不同,我们在小波域中加入处理物体变形和纹理影响的容 忍度控制算法,来实现容忍物体变形和对纹理影响鲁棒的目的。最后可调控金字 塔的重构保证了缺陷区域物理意义恢复的准确性。实验阶段,我们检测了一系列 具有实际应用价值的图像。实验结果表明 本文提出的纹理物体缺陷检测算法具有 高效性和易于实现性。 关键字: 缺陷检测;纹理;物体变形;可调控金字塔;重构
Keywords: defect detection, texture, object distortion, steerable pyramid, reconstruction
II
基于解耦学习的图像跨域迁移算法
基于解耦学习的图像跨域迁移算法
## 一、算法介绍
跨域图像迁移(Cross-Domain Image Transfer,CIT)是指在源
域和目标域之间进行图像迁移的过程,以便在目标域中更好地推理图像。
解耦学习(Decoupled Learning,DL)是一种新型
的跨域图像迁移算法,它通过将源域和目标域中的特征分解为空间特征和属性特征,从而解耦源域和目标域之间的特征表示,并分别训练空间特征和属性特征的网络模型,从而实现跨域图像迁移。
解耦学习算法的主要思想是,将源域和目标域中的特征表示分解为空间特征和属性特征,并分别训练空间特征和属性特征的网络模型,从而实现跨域图像迁移。
空间特征模型用于学习源域和目标域之间的空间特征映射,属性特征模型用于学习源域和目标域之间的属性特征映射,最后,通过联合空间特征和属性特征的映射,实现跨域图像迁移。
## 二、算法流程
解耦学习算法的算法流程如下:
(1)收集源域和目标域的图像数据集;
(2)提取源域和目标域图像数据集中的空间特征和属性特征;
(3)训练空间特征模型和属性特征模型;
(4)利用空间特征模型和属性特征模型,对源域和目标域图像进行跨域迁移;
(。
Chapter 1 What is academic writing
Examples:1. scope
This paper presents a new method to improve the reliability of roll bearings in paper machines. This thesis aims to produce a complete design specification for a remotely operated
Chapter 1What is academic writing?
Definition Features: Cohesion and coherence; grammar, STYLE Metatext
RESEARCH FUNCTIONS
Citing other researchers' work VOCABULARY CHOICE
Coherence:CREATING FOCUS
it is also important to give your writing a "focus" (coherence). You can do this by carefully choosing your topic at the beginning of each sentence. To understand why, look at the example paragraph below. Good cohesion, but no coherence: 1Romance languages descend from a Latin parent, and many words based on Latin are found in other modern languages such as English. 2English has become the lingua franca, the learned language of science and trade. 3Science is based on experimentation, description, and categorisation. 4Descriptions of the ‘northern lights’, or Aurora Borealis, often incude the words ‘twinkle’ or ‘flicker’ to explain the movement created when solar ions collide with the Earth’s atmosphere.
博士生 英语、
博士生英语、Pursuing a doctoral degree in English can be a challenging yet rewarding endeavor. As a graduate student, one embarks on a journey of intellectual exploration, honing their research skills, and contributing to the scholarly discourse within the field of English studies. This essay will delve into the various aspects of doctoral studies in English, highlighting the benefits, challenges, and the transformative impact it can have on an individual's academic and professional trajectory.One of the primary draws of a doctoral program in English is the opportunity to delve deeply into a specific area of interest. Whether it's literary analysis, linguistic research, or the intersection of English and other disciplines, the doctoral program allows students to become experts in their chosen field. This level of specialization not only enhances one's knowledge and understanding but also positions them as authorities in their respective areas of study.The rigorous coursework and comprehensive exams that are integral to doctoral programs in English serve to sharpen critical thinking,analytical, and writing skills. Students are expected to engage with complex theoretical frameworks, interpret and synthesize scholarly literature, and articulate their ideas in a clear and compelling manner. These skills are not only invaluable within the academic realm but also have significant transferable value in various professional settings.Moreover, the dissertation process, which is the culmination of the doctoral journey, offers an unparalleled opportunity for independent research and intellectual exploration. Doctoral candidates are tasked with identifying a significant research question, conducting an in-depth literature review, designing and implementing a methodological approach, and ultimately, presenting their original contributions to the field. This process not only hones research skills but also fosters a deep sense of ownership and pride in one's work.The mentorship and guidance provided by faculty members during the doctoral program are another crucial aspect of the experience. Doctoral students have the privilege of working closely with renowned scholars in their respective fields, who serve as advisors, collaborators, and advocates. This mentorship not only shapes the student's academic development but also provides valuable insights into the world of academia, including navigating the publication process, securing funding, and building a professional network.In addition to the academic benefits, pursuing a doctoral degree in English can also have a profound impact on personal growth and self-discovery. The journey of doctoral studies often involves grappling with complex ideas, challenging one's assumptions, and developing a deeper understanding of the human experience as reflected in literary and linguistic expressions. This process can lead to personal transformation, fostering a greater appreciation for diverse perspectives and a heightened sense of intellectual curiosity.However, it would be remiss to overlook the challenges that come with doctoral studies in English. The path is often marked by periods of intense pressure, self-doubt, and even isolation. The demands of coursework, comprehensive exams, and the dissertation can be overwhelming, requiring exceptional time management, resilience, and self-discipline. Moreover, the competitive nature of the academic job market can add an additional layer of stress and uncertainty for those seeking to transition from the doctoral program to a professional career.Despite these challenges, the rewards of a doctoral degree in English are immense. Graduates emerge as highly skilled researchers, critical thinkers, and effective communicators, equipped to contribute to the advancement of knowledge and the enrichment of intellectual discourse. They may pursue careers in academia, as professors, researchers, or administrators, or they may leverage their expertise invarious other fields, such as publishing, government, or the nonprofit sector.In conclusion, pursuing a doctoral degree in English is a transformative journey that offers unparalleled opportunities for intellectual growth, scholarly contribution, and personal development. While the path may be arduous, the rewards are substantial, both in terms of academic and professional achievements, as well as the profound impact it can have on one's worldview and understanding of the human experience. For those who are passionate about the power of language, literature, and the exploration of ideas, a doctoral program in English may be the perfect avenue to realize their academic and professional aspirations.。
数据挖掘第三版第十章课后习题答案
10.1 简略介绍如下聚类方法:划分方法、层次方法。
每种给出两个例子。
(1)划分方法:给定一个有N个对象的集合,划分方法构造数据的K个分区,每一个分区表示一个簇,且K≤N。
而且这K个分组满足下列条件:第一,每一个分组至少包含一条记录;第二,每一条记录属于且仅属于一个分组(注意:这个要求在某些模糊聚类算法中可以放宽);对于给定的K,算法首先给出一个初始的分组方法,以后通过反复迭代的方法改变分组,使得每一次改进之后的分组方案都较前一次好,而所谓好的标准就是:同一分组中的记录越近越好,而不同分组中的记录越远越好。
使用这个基本思想的算法有:K-MEANS 算法、K-MEDOIDS 算法、CLARANS 算法。
(2)层次方法:这种方法对给定的数据集进行层次似的分解,直到某种条件满足为止。
具体又可分为“自底向上”和“自顶向下”两种方案。
例如在“自底向上”方案中,初始时每一个数据记录都组成一个单独的组,在接下来的迭代中,它把那些相互邻近的组合并成一个组,直到所有的记录组成一个分组或者某个条件满足为止。
代表算法有:BIRCH 算法、CURE 算法、CHAMELEON 算法等。
10.2 假设数据挖掘的任务是将如下的8个点(用(x, y)代表位置)聚类为3个簇。
A1(2,10), A2(2,5), A3(8,4), B1(5,8), B2(7,5), B3(6,4), C1(1,2), C2(4,9)距离函数是欧氏距离。
假设初始我们选择A1、B1和C1分别为每个簇的中心,用k-均值算法给出:(a)在第一轮执行后的3个簇中心。
(b)最后的3个簇。
(a)第一轮后, 三个新的簇为(1){A1}(2){B1,A3,B2,B3,C2}(3){C1,A2}簇中心分别为(1) (2, 10), (2) (6, 6), (3) (1.5, 3.5).(b)最后3个簇为(1) {A1,C2,B1}, (2) {A3,B2,B3}, (3) {C1,A2}.10.6 k-均值和k-中心点算法都可以进行有效的聚类。
人工智能岗位招聘笔试题与参考答案(某大型集团公司)
招聘人工智能岗位笔试题与参考答案(某大型集团公司)(答案在后面)一、单项选择题(本大题有10小题,每小题2分,共20分)1、以下哪个算法不属于监督学习算法?A、决策树B、支持向量机C、K最近邻D、朴素贝叶斯2、在深度学习中,以下哪个概念指的是通过调整网络中的权重和偏置来最小化损失函数的过程?A、过拟合B、欠拟合C、反向传播D、正则化3、以下哪个技术不属于深度学习中的卷积神经网络(CNN)组件?A. 卷积层B. 激活函数C. 池化层D. 反向传播算法4、在自然语言处理(NLP)中,以下哪种模型通常用于文本分类任务?A. 决策树B. 朴素贝叶斯C. 支持向量机D. 长短期记忆网络(LSTM)5、题干:以下哪项不属于人工智能的核心技术?A. 机器学习B. 深度学习C. 数据挖掘D. 计算机视觉6、题干:以下哪个算法在处理大规模数据集时,通常比其他算法更具有效率?A. K最近邻(K-Nearest Neighbors, KNN)B. 支持向量机(Support Vector Machines, SVM)C. 决策树(Decision Tree)D. 随机森林(Random Forest)7、以下哪个技术不属于深度学习领域?A. 卷积神经网络(CNN)B. 支持向量机(SVM)C. 递归神经网络(RNN)D. 随机梯度下降(SGD)8、以下哪个算法不是用于无监督学习的?A. K-均值聚类(K-means)B. 决策树(Decision Tree)C. 主成分分析(PCA)D. 聚类层次法(Hierarchical Clustering)9、以下哪个技术不属于深度学习中的神经网络层?A. 卷积层(Convolutional Layer)B. 循环层(Recurrent Layer)C. 线性层(Linear Layer)D. 随机梯度下降法(Stochastic Gradient Descent)二、多项选择题(本大题有10小题,每小题4分,共40分)1、以下哪些技术或方法通常用于提升机器学习模型的性能?()A、特征工程B、数据增强C、集成学习D、正则化E、迁移学习2、以下关于深度学习的描述,哪些是正确的?()A、深度学习是一种特殊的机器学习方法,它通过多层神经网络来提取特征。
英语五年级上册短作文很短很短很短
英语五年级上册短作文很短很短很短全文共3篇示例,供读者参考篇1Here's a very short essay titled "English Grade 5 Vol. 1 Very Very Very Short" written from a student's perspective, around 2000 words in length:My Favorite SubjectEnglish is my favorite subject in school. I know it's weird for a kid to like English class, but I really do enjoy it a lot. I find the lessons interesting, and I love learning new words and grammar rules. It's like solving little puzzles every day!One of the things I like most about English class is reading the stories and passages we go through. Some of them are really funny, and others are sad or inspiring. I remember last year we read a story about a boy who wanted to become a famous chef, and it made me want to try cooking more at home. My mom wasn't too happy when I accidentally set off the fire alarm while trying to make scrambled eggs, but at least I gave it a shot!Writing is another part of English class that I enjoy. I know a lot of my classmates groan when we have to write essays orstories, but I find it really satisfying to take the jumbled thoughts in my head and organize them into clear sentences and paragraphs. It's like fitting puzzle pieces together, except the puzzle pieces are words.Of course, English class isn't all fun and games. There are definitely times when I struggle with some of the more complex grammar rules or when I can't figure out the meaning of a particular word or phrase. But that's all part of the challenge, and I like challenging myself to improve.One of the highlights of English class for me is when we get to do group activities or projects. I love working with my classmates and bouncing ideas off each other. It's amazing how much more creative our ideas can be when we collaborate instead of working alone.I'll never forget the time we had to create a short skit based on a story we read in class. My group decided to do a twist on the classic fairy tale "Goldilocks and the Three Bears," except in our version, Goldilocks was a secret agent on a mission to infiltrate the bears' secret hideout. It was completely ridiculous, but we had so much fun coming up with the idea and practicing our lines.Overall, I just really enjoy the variety and creativity that English class offers. One day we might be analyzing a poem, the next day we're learning about different types of figurative language, and the day after that we're playing a vocabulary game. It never gets boring!Of course, I know that not everyone shares my enthusiasm for English class. Some of my friends would much rather be doing math or science. But for me, English is where it's at. It's a subject that challenges me in different ways every day, and I always feel a sense of accomplishment when I've mastered a new concept or written a great essay.Who knows, maybe I'll even become a writer or a journalist someday. Or maybe I'll end up doing something completely different. But no matter what career path I choose, I know that the skills I've learned in English class – critical thinking, effective communication, creativity, and problem-solving – will serve me well in whatever I do.So here's to many more years of English class adventures, puzzles, and challenges! I can't wait to see what's in store for me in the years ahead.篇2English Grade 5 Volume 1 is So So So Short!Woohoo, a new English textbook for 5th grade! I was so excited to get the new books for this year. English is my favorite subject and I couldn't wait to see what adventures awaited. I ripped off the plastic wrapping and there it was - the thinnest, tiniest book I've ever seen! "English Grade 5 Volume 1" was printed on the cover in big, bold letters. But the book itself was definitely not big and bold.I flipped it open in disbelief. Side to side, the pages were the normal size. But top to bottom, they were maybe 4 inches tall at most! My little sister's picture books are bigger than this. I rapidly fanned the pages watching them blur into a sliver of color - it only took a second because there barely were any pages! I started counting out of curiosity: 1...2...3...26 pages? No way, this can't be right!I rushed to show my mom, laughing at the ridiculously petite English book. "Maybe it's a disclaimer and the actual book is coming later?" she joked, struggling to understand how an entire textbook could be so small. We inspected it closer - yep, those were word lessons, reading passages, even exercises crammed into those vertically-challenged pages.The next day, I couldn't wait to bring it to class and see if my friends' books were similar tiny tomes. Sure enough, everyone was marveling at the compact size, flipping through the biblical-thin editions. Our teacher started cracking up when she pulled out the teacher's edition, which looked equally diminutive."Don't worry guys, I've been teaching for years and this is pretty normal for 5th grade English volumes. We'll be breezing through this one at lightning speed!" she announced. Lightning is right - at this rate we'll be zipping through the curriculum at warp speed! Maybe they made it mini as a fun novelty size?Or maybe the publishers erred on the eco-friendly side, printing on a smaller scale to reduce paper usage? Or perhaps the content gets progressively harder each year, so 5th grade only requires a condensed overview of concepts before expanding in more depth for middle school grades? Whatever the reason, it's both amusing and somewhat underwhelming to have such a pocket-sized book as our primary English text.In a world of thick, heavy tomes overflowing with chapters, this compact little number is quite the opposite. Instead of getting weighted down by a behemoth book, I can breezily carry it between classes without throwing my back out! And ratherthan dragging my feet through tedious dissertation-length lessons, the truncated size guarantees I'll be zipping through units at record pace.There's something delightfully novel about an English book that barely exists. A book so insignificantly thin, you could virtually sneeze and blow it clear across the classroom! A book that doesn't require inching it layer-by-layer into an overstuffed backpack, but tucks neatly into even the smallest pocket or pencil case. It's like we're getting an excerpt, a preview chapter, a chance to sample the subjects before the full epic editions hit in later grades.For now, I'll enjoy breezing through this English course in a blissful blink. Assignments will be check-boxed in a breath. Units will go zipping by. We'll be on to the next book before you can say "Wow, that was short!" Yes, English Grade 5 Volume 1 is deceptively eensy - but sometimes, good things come in small packages. Or in this case, very very very small!篇3English Grade 5 Volume 1 Very Short Very Short Very ShortWhew, English class is no joke this year! We just started the new English textbook for 5th grade, volume 1, and I've got to behonest, some of these lessons and exercises are pretty tricky. I guess that's why they call it the "very short, very short, very short" edition - because it will feel very short if I don't put in the work!The vocabulary sections are intense. We're learning all these new words like "conscientious", "diligent", and "perseverance". My brain hurts just trying to remember how to spell them, let alone what they mean! But my teacher says building up our word power is super important for doing well on tests and writing amazing compositions. Easy for her to say...Then there are the grammar lessons - yikes! We're diving deep into stuff like perfect verb tenses, relative clauses, and conditionals. It's like they've taken the simple grammar rules I learned in younger grades and raised them to the next level. I've got to make tons of charts and do tons of practice to wrap my head around when to use "had been" versus "has been" versus "will have been". Low-key feeling stressed just thinking about it!Don't even get me started on the reading comprehension passages. They've gotten significantly longer and more complex compared to last year. I'm not just reading fun stories about adventures anymore. Now the texts are analyzing concepts like renewable energy, ancient civilizations, and global economics.Half the words are new vocabulary for me, and the ideas are super abstract. Why couldn't they pick easier topics I'm actually interested in, like video games or my favorite athletes?At least the writing sections let me be a bit more creative and share my thoughts. Though they expect a lot more skill and detail than just "cat sat hat" sentences now. We have to plan and organize persuasive essays, research-based reports, and detailed narratives while perfectly applying everything we've learned about grammar, vocabulary, and paragraph structure. No pressure!I know it's all challenging me to improve my English abilities before heading to middle school. And yeah, "conscientious" does mean showing sustained effort and care. "Diligent" is working hard and staying focused. "Perseverance" is persisting through the struggles instead of giving up. Clearly the textbook writers had a fun time picking vocab words that describe exactly the mindset I need!Maybe if I "persevere" with "diligence" and put in "conscientious" effort, I can master this "very short, very short, very short" book. Because honestly, it feels pretty darn long right now! At least summer break is around the corner so I can forgetEnglish for a few months. But then it's back to the grind with English 5th grade volume 2. Wish me luck, fam!。
mmsegmentation中解决类别不平衡的损失函数
mmsegmentation中解决类别不平衡的损失函数在mmsegmentation中,可以使用Dice Loss(SoftDiceLoss)来解决类别不平衡的问题。
Dice Loss是一种基于交叉熵的损失函数。
对于每个像素点,它首先计算预测分割结果和真实分割标签之间的交叉熵损失,然后再计算预测的前景和背景之间的Dice系数,将交叉熵损失和Dice系数进行加权求和,得到最终的损失函数。
Dice Loss的计算公式如下:$\text{DiceLoss}(p, t) = 1 - \frac{2 \cdot \sum_{i=0}^{N-1} p_i \cdot t_i}{\sum_{i=0}^{N-1} p_i^2 + \sum_{i=0}^{N-1} t_i^2}$ 其中,$p$表示预测的概率图,在mmsegmentation中一般会经过Softmax函数得到,$t$表示真实的分割标签。
由于Dice Loss是通过计算Dice系数来量化前景的重要性,因此对于类别不平衡的问题,Dice Loss可以自动调整不同类别之间的权重,将更多的关注放在少数类别上,从而缓解类别不平衡问题。
在mmsegmentation中,可以通过在配置文件中的loss项设置损失函数为DiceLoss来使用Dice Loss。
例如:```pythonloss=dict(type='DiceLoss',loss_weight=1.0,class_weight=[1, 2] # 设置前景类别的权重为2,背景类别的权重为1,可以根据具体情况进行调整)```需要注意的是,mmsegmentation还提供了其他的损失函数,如CrossEntropyLoss、LovaszLoss等,可以根据具体场景选择合适的损失函数来解决类别不平衡的问题。
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R A M K E R A L A P U R A412 Russell Park, #6, Davis, CA 95616Phone: (530) 792-8132Email: rkeralapura@Web: /~rkeralapRESEARCH INTERESTSNetwork Management – traffic engineering, graceful network upgrade, fault tolerance, and securityIntra-domain and Inter-domain routing – robustness, routing dynamics, protocol convergence, protocol interactions and modeling, and failure restorationOverlay Networks (like content delivery networks) and Peer-to-Peer NetworksDistributed Networked Systems – algorithm design, security, monitoring, and managementCross-layer interactions between networks in different layers of the network protocol stack (like overlay and IP networks)EDUCATION2002–present University of California, Davis, CAPhD Candidate, Electrical and Computer EngineeringDissertation – “Cross-Layer Approach Towards Robust and Efficient Internet Routing”Advisor: Prof. Chen-Nee ChuahGPA: 3.97/4.01999–2000 University of Alabama, Huntsville, ALMS, Computer ScienceProject – “Computer-based Diagnosis of Heart Diseases due to Reduced Blood Flow”Advisor: Prof. Heggere RanganathGPA: 4.0/4.01994–1998 Bangalore University, Bangalore, IndiaBE, Electronics and CommunicationsProject – “Spacecraft Attitude Estimation using Adaptive Kalman Filter Technique”GPA: 3.71/4.0RESEARCH EXPERIENCEJun 2006–present Narus, Mountain View, CAResearch InternInvolved in designing novel algorithms for real-time traffic classification by monitoring only network-layer data on high-speed linksSummer 2005 Bell Labs, Bangalore, IndiaResearch InternReseach involved developing static and adaptive algorithms for efficient distributed monitoringSummer 2004 Intel Research Labs, Berkeley, CAResearch InternResearch involved qualifying, quantifying, and modeling the interactions between protocols that control overlay networks and the underlying IP networksDeveloped an analytical model and a control plane simulator to explain race conditions between multiple coexisting overlay networks2002–present ECE Department, University of California, DavisGraduate Student ResearcherReal-Time Traffic Classification – In collaboration with Narus, designing novel algorithms for real-time traffic classification by monitoring only network-layer data on high-speed linksCross Layer Interactions – In collaboration with Intel Research Labs, identified interactions between IP and overlay layers and evaluated their impact on the performance of data forwarding planeDistributed Monitoring – In collaboration with Bell Labs, developed distributed monitoring algorithms that minimize the communication overheadService Availability in IP Networks – In collaboration with Sprint ATL, developed a novel concept of ‘Service Availability’ for characterizing networks, based on IGP routing dynamicsInterface Specific Forwarding – Proposed a novel approach to avoid routing loops in networks by constructing forwarding tables based on the incoming interface of a packetNetwork Layer Feedback for Multimedia Coding and Streaming– Proposed and simulated a Network Layer Feedback Architecture to enhance the performance of adaptive multimedia servers in the InternetTraffic Matrix Based Admission Control– Proposed a measurement-based admission control algorithm for packet based networks to help ISPs provide effective QoS1999–2000 CS Department, University of Alabama, HuntsvilleGraduate ResearcherDiagnosis of Cardiac Diseases– Designed algorithms to correlate SPECT images of the heart in space and time, and built a system (implemented in Java) for fast diagnosis of several cardiac diseases due to reduced blood flowREFEREED PUBLICATIONSRam Keralapura, Chen-Nee Chuah, and Yueyue Fan, “Optimal Strategy for Graceful Network Upgrade”, in ACM SIGCOMM Workshop on Internet Network Management (INM), Sep, 2006Ram Keralapura, Graham Cormode, and Jai Ramamirtham, “Communication-Efficient Distributed Monitoring of Thresholded Counts”, in ACM Special Interest Group on Management of Data (SIGMOD),Jun 2006Ram Keralapura, Adam Moerchell, Chen-Nee Chuah, Gianluca Iannaccone, and Supratik Bhattacharyya, “A Case for using Service Availability to Characterize IP Backbone Topologies”, Journal of Communications and Networks (JCN), Jun 2006Ram Keralapura, Graham Cormode, and Jai Ramamirtham, “Communication-Efficient Distributed Monitoring of Thresholded Counts”, filed in Jun 2006WORK EXPERIENCE2001–2002 Sycamore Networks, Wallingford, CTSenior Software EngineerProject Lead in the ‘System Software Integration’ team involved in the development of ‘Automated Self-Test Environment’ for optical switchesInvolved in various projects like the Gigabit Ethernet over SONET/SDH, Protection Switching system design, OC192 interface design, etc.2000–2001 Lucent Technologies, Holmdel, NJSoftware DeveloperInvolved in the development of a graphical user interface for the Sub-Network Management System (SNMS) in the Optical Networking Group (ONG)Responsible for developing configuration, administration and security features for optical nodes in the WaveStar family like Lambda Router, OLS400G, etc.1997–1998 Indian Space Research Organization, Bangalore, IndiaProject InternDesigned and developed a real-time system, using adaptive Kalman filter estimation technique, to track2002 ECE Department, University of California, DavisGraduate Teaching Assistant‘Digital Systems’, ECE180ADuties included classroom teaching, organizing lab sessions, and grading exams1999–2000 CS Department, University of Alabama, HuntsvilleGraduate Teaching Assistant‘Logic Design’, CS309 and ‘Computer Architecture’, CS513Developed new lab assignments starting from the design of simple counters using flip-flops to the design of a simple microprocessorHONORSProfessors for the Future (PFTF) Fellowship, UC-Davis, 2005-2006Non-Resident Tuition Fellowship (NRTF), UC-Davis, 2003-2006HP-CITRIS Fellowship Award, 2003-2004Dean’s List, UAH, 1999-2000Selected to the Indian National Mathematics Olympiad, 1993-1994National Merit Scholarship, 1992-1994National Talent Search Examination (NTSE) Scholarship, 1992-1998Gold medals in all 6 levels of ‘Sanskrit Scholar’ exams, 1987-1990EXTRA-CURRICULAR ACTIVITIESStudent Co-Chair, Networks Lab Workshop, CS/CE Dept, UC-Davis, April 2005Graduate Student Mentor, 2004-2006ECE Graduate Student Association (ECE-GSA) Representative, 2004-2005Student Member – IEEE, ACM, USENIXPoster presentation (invited), Intel Research Lab-Berkeley Open House, Oct 2004Reviewer – IEEE Infocom 2005/06, IEEE ICC-2004/05, IEEE IWQoS-2004/05, IEEE ICON-2004 Training in Undergraduate and Graduate Classroom Teaching, 2005Training in Research Ethics and Professionalism, 2006Project in the Professors for the Future (PFTF) program on Training Graduate Students to be Successful Tenure-Track Faculty Members, 2005-2006REFERENCESAvailable upon request。