计算机标准用户英文名称,标准计算机专业英文简历范文

标准计算机专业英文简历范文

2019-07-28

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1、Position Sought: ****Programmer with a **** in **** 2、Qualifications: Four years’ work experience operating computers extensively, coupled with educational preparation. 3、Professional Experience: Computer Programmer,*****Computer Company, ****, from ****to date. Operate flow-charts, collect business infromation fro management, update methods of operation. Adept at operating ****8 and Compact computers. 4、Educational Background: ***** University of *****.. in Computer Science, ******. 5、Courses included: Computer Science, Systems Design and Analysis, PASCAL Programming, Operating Systems, COBOL Programming, D-BASE Programming, FORTRAN Programming. 6、English Proficiency: Fluent in speaking, reading and writing. 7、Hobbies: Bridge, computer games, boating, swimming. 8、Personal Data: Born: ****, **** in *****; Health: Excellent; Marital Status: Single; Height: ***cm; Weight: **kg 9、References: will be supplied upon request. Sep.xxxx—Jul.xxxx Computer Science Dept. of Computer College, Northeast Normal University. Courses taken include: data structure, principle of database, principle of compiling, principle of computers, operating systems, C++, Java, computer architecture, computer networks, , photoshop Sep.xxxx—Jul.xxxx No.1 Middle School of Qingyuan City, Guangdong Computer Abilities A good understanding and working knowledge of computers. Have hands-on experience in PC operation. Database programming and (networking knowledge)or (knowledge of networks.) Good at computer operation of Windows. =? ("computer operation of windows"=? Guess: Good at computers operating with Windows.) Knowledge of php and asp programming. English Skills Have a good command on both spoken and written English; passed CET-4. Scholarship and rewards xxxx-6 won the third place in Jilin's in "challenge cup " competition of scientifical and technological works ( competition between universities/university students, or what?) ...e.g. Jilin's "Challenge cup" for science and technology competition among university students. (Not clear as to what the "works" are. What is the name of that competition in Chinese ?) xxxx-5 won the title of Excellent League Member in the university. xxxx-12 won the title of Excellent president in the dept. xxxx-9 won the first place in Jilin in college student in national of math model compentition (If this is a national competition, it cannot be a "Jilian prize") --> won first place in the National Math Model competition as the college student representative from Jilin. xxxx-12 won the title of Excellent Leader in dept. xxxx-1 Awarded the “an Excellent Learner” scholarship from the university Activities xxxx-now Leader of a web studio in the university xxxx-now leader of a web studio in the dept. xxxx-xxxx class president xxxx Vice President of English club in the university xxxx president of a volunteer association in Computer Science dept. xxxx won the third place in web design contest from school Work Experiences xxxx-9—now work as a administratorin the Job Direction Center of University xxxx-2—xxxx-5 used Asp + Access to design a web page for the Physics College of Physics xxxx-10—xxxx-12 used Php + Mysql to design a web for The Student Office of the University xxxx-2—xxxx-5 used php+ Mysql to design a web for The Job Direction Center of the University References will be supplied upon request.

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被举报文档标题:标准计算机专业英文简历范文

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本文旨在系统阐述利用MATLAB平台执行多模态语音分离任务的方法,重点围绕LRS3数据集的数据生成流程展开。LRS3(长时RGB+音频语音数据集)作为一个规模庞大的视频与音频集合,整合了丰富的视觉与听觉信息,适用于语音识别、语音分离及情感分析等多种研究场景。MATLAB凭借其高效的数值计算能力与完备的编程环境,成为处理此类多模态任务的适宜工具。 多模态语音分离的核心在于综合利用视觉与听觉等多种输入信息来解析语音信号。具体而言,该任务的目标是从混合音频中分离出不同说话人的声音,并借助视频中的唇部运动信息作为辅助线索。LRS3数据集包含大量同步的视频与音频片段,提供RGB视频、单声道音频及对应的文本转录,为多模态语音处理算法的开发与评估提供了重要平台。其高质量与大容量使其成为该领域的关键资源。 在相关资源包中,主要包含以下两部分内容: 1. 说明文档:该文件详细阐述了项目的整体结构、代码运行方式、预期结果以及可能遇到的问题与解决方案。在进行数据处理或模型训练前,仔细阅读此文档对正确理解与操作代码至关重要。 2. 专用于语音分离任务的LRS3数据集版本:解压后可获得原始的视频、音频及转录文件,这些数据将由MATLAB脚本读取并用于生成后续训练与测试所需的数据。 基于MATLAB的多模态语音分离通常遵循以下步骤: 1. 数据预处理:从LRS3数据集中提取每段视频的音频特征与视觉特征。音频特征可包括梅尔频率倒谱系数、感知线性预测系数等;视觉特征则涉及唇部运动的检测与关键点定位。 2. 特征融合:将提取的音频特征与视觉特征相结合,构建多模态表示。融合方式可采用简单拼接、加权融合或基于深度学习模型的复杂方法。 3. 模型构建:设计并实现用于语音分离的模型。传统方法可采用自适应滤波器或矩阵分解,而深度学习方法如U-Net、Transformer等在多模态学习中表现优异。 4. 训练与优化:使用预处理后的数据对模型进行训练,并通过交叉验证与超参数调整来优化模型性能。 5. 评估与应用:采用信号失真比、信号干扰比及信号伪影比等标准指标评估模型性能。若结果满足要求,该模型可进一步应用于实际语音分离任务。 借助MATLAB强大的矩阵运算功能与信号处理工具箱,上述步骤得以有效实施。需注意的是,多模态任务常需大量计算资源,处理大规模数据集时可能需要对代码进行优化或借助GPU加速。所提供的MATLAB脚本为多模态语音分离研究奠定了基础,通过深入理解与运用这些脚本,研究者可更扎实地掌握语音分离的原理,从而提升其在实用场景中的性能表现。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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