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Speech Recognition
1 Defining the Problem
Speech recognition is the process of converting an acoustic signal, captured by a microphone or a telephone, to a set of words. The recognized words can be the final results, as for applications such as commands & control, data entry, and document preparation. They can also serve as the input to further linguistic processing in order to achieve speech understanding, a subject covered in section.
Speech recognition systems can be characterized by many parameters, some of the more important of which are shown in Figure. An isolated-word speech recognition system requires that the speaker pause briefly between words, whereas a continuous speech recognition system does not. Spontaneous, or extemporaneously generated, speech contains disfluencies, and is much more difficult to recognize than speech read from script. Some systems require speaker enrollment---a user must provide samples of his or her speech before using them, whereas other systems are said to be speaker-independent, in that no enrollment is necessary. Some of the other parameters depend on the specific task. Recognition is generally more difficult when vocabularies are large or have many similar-sounding words. When speech is produced in a sequence of words, language models or artificial grammars are used to restrict the combination of words.
The simplest language model can be specified as a finite-state network, where the
1
permissible words following each word are given explicitly. More general language models approximating natural language are specified in terms of a context-sensitive grammar.
One popular measure of the difficulty of the task, combining the vocabulary size and the language model, is perplexity, loosely defined as the geometric mean of the number of words that can follow a word after the language model has been applied (see section for a discussion of language modeling in general and perplexity in particular). Finally, there are some external parameters that can affect speech recognition system performance, including the characteristics of the environmental noise and the type and the placement of the microphone.
Table: Typical parameters used to characterize the capability of speech recognition systems Speech recognition is a difficult problem, largely because of the many sources of variability associated with the signal. First, the acoustic realizations of phonemes, the smallest sound units of which words are composed, are highly dependent on the context in which they appear. These phonetic variabilities are exemplified by the acoustic differences of the phoneme，At word boundaries, contextual variations can be quite dramatic---making gas shortage sound like gash shortage in American English, and devo andare sound like devandare in Italian.
Second, acoustic variabilities can result from changes in the environment as well as in the position and characteristics of the transducer. Third, within-speaker variabilities can result from changes in the speaker's physical and emotional state, speaking rate, or voice quality. Finally, differences in sociolinguistic background, dialect, and vocal tract size and shape can contribute to across-speaker variabilities.
Figure shows the major components of a typical speech recognition system. The digitized speech signal is first transformed into a set of useful measurements or features at a fixed rate, typically once every 10--20 msec (see sectionsand 11.3 for signal representation and digital signal processing, respectively). These measurements are then used to search for the most likely word candidate, making use of constraints imposed by the acoustic, lexical, and language models. Throughout this process, training data are used to determine the values of the model parameters.
Figure: Components of a typical speech recognition system.
Speech recognition systems attempt to model the sources of variability described above in several ways. At the level of signal representation, researchers have developed representations that emphasize perceptually important speaker-independent features of the signal, and de-emphasize speaker-dependent characteristics. At the acoustic phonetic level, speaker variability is typically modeled using statistical techniques applied to large amounts of data. Speaker adaptation algorithms have also been developed that adapt speaker-independent acoustic models to those of the current speaker during system use, (see section). Effects of linguistic context at the acoustic phonetic level are typically handled by training separate models for phonemes in different contexts; this is called context dependent acoustic modeling.
Word level variability can be handled by allowing alternate pronunciations of words in representations known as pronunciation networks. Common alternate pronunciations of words, as well as effects of dialect and accent are handled by allowing search algorithms to find alternate paths of phonemes through these networks. Statistical language models, based on estimates of the frequency of occurrence of word sequences, are often used to guide the search
through the most probable sequence of words.
The dominant recognition paradigm in the past fifteen years is known as hidden Markov models (HMM). An HMM is a doubly stochastic model, in which the generation of the underlying phoneme string and the frame-by-frame, surface acoustic realizations are both represented probabilistically as Markov processes, as discussed in sections,and 11.2. Neural networks have also been used to estimate the frame based scores; these scores are then integrated into HMM-based system architectures, in what has come to be known as hybrid systems, as described in section 11.5.
An interesting feature of frame-based HMM systems is that speech segments are identified during the search process, rather than explicitly. An alternate approach is to first identify speech segments, then classify the segments and use the segment scores to recognize words. This approach has produced competitive recognition performance in several tasks.
2 State of the Art
Comments about the state-of-the-art need to be made in the context of specific applications which reflect the constraints on the task. Moreover, different technologies are sometimes appropriate for different tasks. For example, when the vocabulary is small, the entire word can be modeled as a single unit. Such an approach is not practical for large vocabularies, where word models must be built up from subword units.
Performance of speech recognition systems is typically described in terms of word error rate E, defined as:
where N is the total number of words in the test set, and S, I, and D are the total number of substitutions, insertions, and deletions, respectively.
The past decade has witnessed significant progress in speech recognition technology. Word error rates continue to drop by a factor of 2 every two years. Substantial progress has been made in the basic technology, leading to the lowering of barriers to speaker independence, continuous speech, and large vocabularies. There are several factors that have contributed to this rapid progress. First, there is the coming of age of the HMM. HMM is powerful in that, with the availability of training data, the parameters of the model can be trained automatically to give
optimal performance.
Second, much effort has gone into the development of large speech corpora for system development, training, and testing. Some of these corpora are designed for acoustic phonetic research, while others are highly task specific. Nowadays, it is not uncommon to have tens of thousands of sentences available for system training and testing. These corpora permit researchers to quantify the acoustic cues important for phonetic contrasts and to determine parameters of the recognizers in a statistically meaningful way. While many of these corpora (e.g., TIMIT, RM, ATIS, and WSJ; see section 12.3) were originally collected under the sponsorship of the U.S. Defense Advanced Research Projects Agency (ARPA) to spur human language technology development among its contractors, they have nevertheless gained world-wide acceptance (e.g., in Canada, France, Germany, Japan, and the U.K.) as standards on which to evaluate speech recognition.
Third, progress has been brought about by the establishment of standards for performance evaluation. Only a decade ago, researchers trained and tested their systems using locally collected data, and had not been very careful in delineating training and testing sets. As a result, it was very difficult to compare performance across systems, and a system's performance typically degraded when it was presented with previously unseen data. The recent availability of a large body of data in the public domain, coupled with the specification of evaluation standards, has resulted in uniform documentation of test results, thus contributing to greater reliability in monitoring progress (corpus development activities and evaluation methodologies are summarized in chapters 12 and 13 respectively).
Finally, advances in computer technology have also indirectly influenced our progress. The availability of fast computers with inexpensive mass storage capabilities has enabled researchers to run many large scale experiments in a short amount of time. This means that the elapsed time between an idea and its implementation and evaluation is greatly reduced. In fact, speech recognition systems with reasonable performance can now run in real time using high-end workstations without additional hardware---a feat unimaginable only a few years ago.
One of the most popular, and potentially most useful tasks with low perplexity (PP=11) is the recognition of digits. For American English, speaker-independent recognition of digit strings spoken continuously and restricted to telephone bandwidth can achieve an error rate of 0.3% when the string length is known.
One of the best known moderate-perplexity tasks is the 1,000-word so-called Resource Management (RM) task, in which inquiries can be made concerning various naval vessels in the Pacific ocean. The best speaker-independent performance on the RM task is less than 4%, using a word-pair language model that constrains the possible words following a given word (PP=60). More recently, researchers have begun to address the issue of recognizing spontaneously generated speech. For example, in the Air Travel Information Service (ATIS) domain, word error rates of less than 3% has been reported for a vocabulary of nearly 2,000 words and a bigram language model with a perplexity of around 15.
High perplexity tasks with a vocabulary of thousands of words are intended primarily for the dictation application. After working on isolated-word, speaker-dependent systems for many years, the community has since 1992 moved towards very-large-vocabulary (20,000 words and more), high-perplexity (PP≈200), speaker-independent, continuous speech recognition. The best system in 1994 achieved an error rate of 7.2% on read sentences drawn from North America business news.
With the steady improvements in speech recognition performance, systems are now being deployed within telephone and cellular networks in many countries. Within the next few years, speech recognition will be pervasive in telephone networks around the world. There are tremendous forces driving the development of the technology; in many countries, touch tone penetration is low, and voice is the only option for controlling automated services. In voice dialing, for example, users can dial 10--20 telephone numbers by voice (e.g., call home) after having enrolled their voices by saying the words associated with telephone numbers. AT&T, on the other hand, has installed a call routing system using speaker-independent word-spotting technology that can detect a few key phrases (e.g., person to person, calling card) in sentences such as: I want to charge it to my calling card.
At present, several very large vocabulary dictation systems are available for document generation. These systems generally require speakers to pause between words. Their performance can be further enhanced if one can apply constraints of the specific domain such as dictating medical reports.
Even though much progress is being made, machines are a long way from recognizing conversational speech. Word recognition rates on telephone conversations in the Switchboard corpus are around 50%. It will be many years before unlimited vocabulary, speaker-independent
continuous dictation capability is realized.
3 Future Directions
In 1992, the U.S. National Science Foundation sponsored a workshop to identify the key research challenges in the area of human language technology, and the infrastructure needed to support the work. The key research challenges are summarized in. Research in the following areas for speech recognition were identified:
Robustness:
In a robust system, performance degrades gracefully (rather than catastrophically) as conditions become more different from those under which it was trained. Differences in channel characteristics and acoustic environment should receive particular attention.
Portability:
Portability refers to the goal of rapidly designing, developing and deploying systems for new applications. At present, systems tend to suffer significant degradation when moved to a new task. In order to return to peak performance, they must be trained on examples specific to the new task, which is time consuming and expensive.
Adaptation:
How can systems continuously adapt to changing conditions (new speakers, microphone, task, etc) and improve through use? Such adaptation can occur at many levels in systems, subword models, word pronunciations, language models, etc.
Language Modeling:
Current systems use statistical language models to help reduce the search space and resolve acoustic ambiguity. As vocabulary size grows and other constraints are relaxed to create more habitable systems, it will be increasingly important to get as much constraint as possible from language models; perhaps incorporating syntactic and semantic constraints that cannot be captured by purely statistical models.
Confidence Measures:
Most speech recognition systems assign scores to hypotheses for the purpose of rank ordering them. These scores do not provide a good indication of whether a hypothesis is correct or not, just that it is better than the other hypotheses. As we move to tasks that require actions,
we need better methods to evaluate the absolute correctness of hypotheses.
Out-of-Vocabulary Words:
Systems are designed for use with a particular set of words, but system users may not know exactly which words are in the system vocabulary. This leads to a certain percentage of out-of-vocabulary words in natural conditions. Systems must have some method of detecting such out-of-vocabulary words, or they will end up mapping a word from the vocabulary onto the unknown word, causing an error.
Spontaneous Speech:
Systems that are deployed for real use must deal with a variety of spontaneous speech phenomena, such as filled pauses, false starts, hesitations, ungrammatical constructions and other common behaviors not found in read speech. Development on the ATIS task has resulted in progress in this area, but much work remains to be done.
Prosody:
Prosody refers to acoustic structure that extends over several segments or words. Stress, intonation, and rhythm convey important information for word recognition and the user's intentions (e.g., sarcasm, anger). Current systems do not capture prosodic structure. How to integrate prosodic information into the recognition architecture is a critical question that has not yet been answered.
Modeling Dynamics:
Systems assume a sequence of input frames which are treated as if they were independent. But it is known that perceptual cues for words and phonemes require the integration of features that reflect the movements of the articulators, which are dynamic in nature. How to model dynamics and incorporate this information into recognition systems is an unsolved problem.
语音识别
一定义问题
语音识别是指音频信号的转换过程，被电话或麦克风的所捕获的一系列的消息。所识别的消息作为最后的结果，用于控制应用，如命令与数据录入，以及文件准备。它们也可以作为处理输入的语言，以便进一步实现语音理解，在第一个主题涵盖。
语音识别系统可以用多个参数来描述，一些更重要参数在图形中显示出来。一个孤立字语音识别系统要求词与词之间短暂停顿，而连续语音识别统对那些不自发的，或临时生成的，言语不流利的语音，比用讲稿更难以识别。有些系统要求发言者登记——即用户在使用系统前必须为系统提供演讲样本或发言底稿，而其他系统据说是独立扬声器，因为没有必要登记。一些参数特征依赖于特定的任务。当词汇量比较大或有较多象声词的时候，识别起来一般比较困难。当语音由有序的词语生成时，语言模型或特定语法便会限制词语的组合。
最简单的语言模型可以被指定为一个有限状态网络，每个语音所包含的所有允许的词语都能顾及到。更普遍的近似自然语言的语言模型在语法方面被指定为上下文相关联。
一种普及的任务的难度测量，词汇量和语言模型相结合的语音比较复杂，大量语音的几何意义可以按照语音模型的应用定义宽泛些（参见文章对语言模型普遍性与复杂性的详细讨论）。最后，还有一些其他参数，可以影响语音识别系统的性能，包括环境噪声和麦克风的类型和安置。
表格：特有参数用于表征语音识别系统的性能
语音识别是一个困难的问题，主要是因为与信号相关的变异有很多来源。首先，音素，作为组成词语的最小的语音单位，它的声学呈现是高度依赖于他们所出现的语境的。这些语音的变异性正好由音素的声学差异做出了验证。在词语的范围里，语境的变化会相当富有戏剧性---使得美国英语里的gas shortage听起来很像gash shortage，而意大利语中的devo andare听起来会很像devandare。
其次，声变异可能由环境变化，以及传输介质的位置和特征引起。第三，说话人的不同，演讲者身体和情绪上的差异可能导致演讲速度，质量和话音质量的差异。最后，社会语言学背景，方言的差异和声道的大小和形状更进一步促进了演讲者的差异性。
数字图形展示了语音识别系统的主要组成部分。数字化语音信号先转换成一系列有用的测量值或有特定速率的特征，通常每次间隔10 - 20毫秒（见第11.3章节，分别描述了模拟信号和数字信号的处理）。然后这些测量被用来寻找最有可能的备选词汇，使用被声学模型、词汇模型、和语言模型强加的限制因素。整个过程中，训练数据是用来确定模型参数值的。
图：一个典型语音识别系统的组成部分
语音识别系统尝试在上述变异的来源的某些方面做模型。在信号描述的层面上，研究人员已经开发出了感性地强调重要发言者独立语音信号的特征，以及忽略发言者依赖环境的语音信号特征。在声学语音层面上，说话人差异变化通常是参照使用大量的数据来做模型。语音改编法则还开发出适应说话人独立声学模型以适应那些目前在系统中使用的说
中文参考文献格式 参考文献（即引文出处）的类型以单字母方式标识： M——专著，C——论文集，N——报纸文章，J——期刊文章，D——学位论文，R——报告，S——标准，P——专利；对于不属于上述的文献类型，采用字母“Z”标识。 参考文献一律置于文末。其格式为： （一）专著 示例 [1] 张志建.严复思想研究[M]. 桂林：广西师范大学出版社，1989. [2] 马克思恩格斯全集：第1卷[M]. 北京：人民出版社，1956. [3] [英]蔼理士.性心理学[M]. 潘光旦译注.北京：商务印书馆，1997. （二）论文集 示例 [1] 伍蠡甫.西方文论选[C]. 上海：上海译文出版社，1979. [2] 别林斯基.论俄国中篇小说和果戈里君的中篇小说[A]. 伍蠡甫.西方文论选：下册[C]. 上海：上海译文出版社，1979. 凡引专著的页码，加圆括号置于文中序号之后。 （三）报纸文章 示例 [1] 李大伦.经济全球化的重要性[N]. 光明日报，1998-12-27，（3） （四）期刊文章 示例 [1] 郭英德.元明文学史观散论[J]. 北京师范大学学报（社会科学版），1995（3）. （五）学位论文 示例 [1] 刘伟.汉字不同视觉识别方式的理论和实证研究[D]. 北京：北京师范大学心理系，1998. （六）报告 示例 [1] 白秀水，刘敢，任保平. 西安金融、人才、技术三大要素市场培育与发展研究[R]. 西安：陕西师范大学西北经济发展研究中心，1998. （七）、对论文正文中某一特定内容的进一步解释或补充说明性的注释，置于本页地脚，前面用圈码标识。 参考文献的类型 根据GB3469-83《文献类型与文献载体代码》规定，以单字母标识： M——专著（含古籍中的史、志论著） C——论文集 N——报纸文章 J——期刊文章 D——学位论文 R——研究报告 S——标准 P——专利 A——专著、论文集中的析出文献 Z——其他未说明的文献类型 电子文献类型以双字母作为标识： DB——数据库 CP——计算机程序 EB——电子公告
指纹识别方法的综述 摘 要: 对在指纹的预处理和特征提取、指纹分类、指纹的匹配过程中的方向图、滤波器、神经网络等关 键性原理和技术做了详细的说明,并对在各个过程中用到的方法做了进一步的比较,讨论了各种方法的优越性。 0 引 言 自动指纹识别是上世纪六十年代兴起的,利用计算机取代人工来进行指纹识别的一种方法。近年 来,随着计算机技术的飞速发展,低价位指纹采集仪的出现以及高可靠算法的实现,更使得自动指纹识 别技术越来越多地进入到人们的生活和工作中,自动指纹识别系统的研究和开发正在成为国内外学术 界和商业界的热点。相对于其他生物特征鉴别技术例如语音识别及虹膜识别,指纹识别具有许多独到 的优点,更重要的是它具有很高的实用性和可行性,已经被认为是一种理想的身份认证技术,有着十分 广泛的应用前景,是将来生物特征识别技术的主流。 1 指纹取像 图 1 是一个自动指纹识别系统AFIS(Automated Fingerprint Identification System) 的简单流程。 → → → ↓ ↑ ———— 将一个人的指纹采集下来输入计算机进行处理是指纹自动识别的首要步骤。指纹图像的获取主要利用设备取像,方便实用,比较适合AFIS 。利用设备取像的主要方法又利用光学设备、晶体传感器和超声波来进行。光学取像设备是根据光的全反射原理来设计的。晶体传感器取像是根据谷线和脊线皮肤与传感器之间距离不同而产生的电容不同来设计的。超声波设备取像也是采用光波来取像,但由于超声波波长较短,抗干扰能力较强,所以成像的质量非常好。 2 图像的预处理与特征提取 无论采取哪种方法提取指纹,总会给指纹图像带来各种噪声。预处理的目的就是去除图像中的噪 音,把它变成一幅清晰的点线图,以便于提取正确的指纹特征。预处理是指纹自动识别过程的第一步, 它的好坏直接影响着指纹识别的效果。常用的预处理与特征提取( Image Preprocessing and Feature Ex 2 t raction) 方法的主要步骤包括方向图计算、图像滤波、二值化、细化、提取特征和后处理。当然这些步骤 可以根据系统和应用的具体情况再进行适当变化。文献[ 1 ]提出了基于脊线跟踪的方法能够指纹取像 图像预处理 特征提取 指纹识别 数据库管理
超详细中英文论文参考文献标准格式 1、参考文献和注释。按论文中所引用文献或注释编号的顺序列在论文正文之后，参考文献之前。图表或数据必须注明来源和出处。 （参考文献是期刊时，书写格式为： [编号]、作者、文章题目、期刊名（外文可缩写）、年份、卷号、期数、页码。参考文献是图书时，书写格式为： [编号]、作者、书名、出版单位、年份、版次、页码。） 2、附录。包括放在正文内过份冗长的公式推导，以备他人阅读方便所需的辅助性数学工具、重复性数据图表、论文使用的符号意义、单位缩写、程序全文及有关说明等。 参考文献（即引文出处）的类型以单字母方式标识，具体如下： [M]--专著,著作 [C]--论文集(一般指会议发表的论文续集，及一些专题论文集，如《***大学研究生学术论文集》[N]-- 报纸文章 [J]--期刊文章：发表在期刊上的论文，尽管有时我们看到的是从网上下载的（如知网），但它也是发表在期刊上的，你看到的电子期刊仅是其电子版 [D]--学位论文：不区分硕士还是博士论文 [R]--报告：一般在标题中会有"关于****的报告"字样 [S]-- 标准 [P]--专利 [A]--文章：很少用，主要是不属于以上类型的文章 [Z]--对于不属于上述的文献类型，可用字母"Z"标识，但这种情况非常少见 常用的电子文献及载体类型标识： [DB/OL] --联机网上数据(database online) [DB/MT] --磁带数据库(database on magnetic tape) [M/CD] --光盘图书(monograph on CDROM) [CP/DK] --磁盘软件(computer program on disk) [J/OL] --网上期刊(serial online) [EB/OL] --网上电子公告(electronic bulletin board online) 很显然，标识的就是该资源的英文缩写，/前面表示类型，/后面表示资源的载体，如OL表示在线资源 二、参考文献的格式及举例 1．期刊类 【格式】[序号]作者.篇名[J].刊名,出版年份,卷号(期号)起止页码. 【举例】 [1] 周融，任志国，杨尚雷，厉星星.对新形势下毕业设计管理工作的思考与实践[J].电气电子教学学报，2003(6):107-109. [2] 夏鲁惠.高等学校毕业设计(论文)教学情况调研报告[J].高等理科教育,2004(1):46-52. [3] Heider, E.R.& D.C.Oliver. The structure of color space in naming and memory of two languages [J]. Foreign Language Teaching and Research, 1999, (3): 62 67. 2．专著类
虹膜识别外文翻译文献 虹膜识别外文翻译文献 (文档含中英文对照即英文原文和中文翻译) 外文： The first chapter 1.1 The research background of iris recognition Biometrics is a technology for personal identification using physiological characteristics and behavior characteristics inherent in the human body. Can be used for the biological characteristics of biological recognition, fingerprint, hand type face, iris, retina, pulse, ear etc.. Behavior has the following characteristics: signature, voice, gait, etc.. Based on these characteristics, it has been the development of hand shape recognition, fingerprint recognition, facial recognition, iris recognition, signature recognition and other biometric technology, many techniques have been formed and mature to application of. Biological recognition technology in a , has a long history, the ancient Egyptians through identification of each part of the body size measure to carry out identity may be the earliest human based on the earliest history of biometrics. But the modern biological recognition technology began in twentieth Century 70 time metaphase, as biometric devices early is relatively expensive, so only a higher security level atomic test, production base.due to declining cost of microprocessor and various electronic components, precision gradually improve, control device of a biological recognition technology has been gradually applied to commerce authorized, such as access control, attendance management, management system, safety certification field etc.. All biometric technology, iris recognition is currently used as a convenient and accurate.
中英文参考文献格式! (細節也很重要啊。。）来源：李菲玥的日志 规范的参考文献格式 一、参考文献的类型 参考文献（即引文出处）的类型以单字母方式标识，具体如下： M——专著C——论文集N——报纸文章J——期刊文章 D——学位论文R——报告S——标准P——专利 A——文章 对于不属于上述的文献类型，采用字母“Z”标识。 常用的电子文献及载体类型标识： ［ＤＢ/OL］——联机网上数据(database online) ［DB/MT］——磁带数据库(database on magnetic tape) ［Ｍ/CD］——光盘图书(monograph on CD ROM) ［ＣＰ／DK］——磁盘软件(computer program on disk) ［Ｊ／ＯＬ］——网上期刊(serial online) ［ＥＢ/OL］——网上电子公告(electronic bulletin board online) 对于英文参考文献，还应注意以下两点： ①作者姓名采用“姓在前名在后”原则，具体格式是：姓，名字的首字母. 如：Malcolm R ichard Cowley 应为：Cowley, M.R.，如果有两位作者，第一位作者方式不变，&之后第二位作者名字的首字母放在前面，姓放在后面，如：Frank Norris 与Irving Gordon应为：Norri s, F. & I.Gordon.； ②书名、报刊名使用斜体字，如：Mastering English Literature，English Weekly。二、参考文献的格式及举例 1．期刊类 【格式】[序号]作者.篇名[J].刊名，出版年份，卷号（期号）：起止页码. 【举例】 [1] 周融，任志国，杨尚雷，厉星星.对新形势下毕业设计管理工作的思考与实践[J].电气电子教学学报，2003(6)：107-109.
英语优秀论文参考文献格式要求
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英语专业本科毕业论文 参考文献格式要求 I．文内引用 （一）直接引用 1．引用中的省略 原始资料的引用：在正文中直接引用时，应给出作者、年份，并用带括号的数字标出页码。若有任何资料省略，使用英文时，应用3个省略号在句中标出（…），中文用6个（……）；若两句间的资料省略，英文应用4个省略号标出（‥‥），中文用6个（……）。若要在直接引用插入自己的解释，应使用方括号[ ]。若在资料中有什么错误拼写、错误语法或标点错误会使读者糊涂，应在引用后立即插入[sic]，中文用[原文如此]。下面是一些示例： 例一：The DSM IV defines the disorder [dysthymic] as being in a chronically depressed mood that occurs for "most of the day more days than not for at least two years (Criterion A) .... In children, the mood may be irritable rather than depressed, and the required minimum duration is only one year" (APA, 1994, p. 345). 例二：Issac (1995) states that bipolar disorder "is not only uncommon but may be the most diagnostic entity in children and adolescents in similar settings .... and may be the most common diagnosis in adolescents who are court-remanded to such settings" (p.275). 2．大段落引用 当中文引用超过160字时，不使用引号，而使用“块”的形式（引用起于新的一行，首行缩进4个空格，两端对齐，之后每行都缩进）。 当英文引用超过40字时，不使用引号，而使用“块”的形式（引用起于新的一行，首行缩进5个空格，左对齐，之后每行都缩进）。 Elkind (1978) states:
英文引用及参考文献格式要求 一、参考文献的类型 参考文献（即引文出处）的类型以单字母方式标识，具体如下： M——专著C——论文集N——报纸文章 J——期刊文章D——学位论文R——报告 对于不属于上述的文献类型，采用字母“Z”标识。 对于英文参考文献，还应注意以下两点： ①作者姓名采用“姓在前名在后”原则，具体格式是：姓，名字的首字母.如：MalcolmRichardCowley应为：Cowley,M.R.，如果有两位作者，第一位作者方式不变，&之后第二位作者名字的首字母放在前面，姓放在后面，如：FrankNorris与IrvingGordon应为：Norris,F.&I.Gordon.； ②书名、报刊名使用斜体字，如：MasteringEnglishLiterature，EnglishWeekly。 二、参考文献的格式及举例 1.期刊类 【格式】[序号]作者.篇名[J].刊名，出版年份，卷号（期号）：起止页码. 【举例】 [1]王海粟.浅议会计信息披露模式[J].财政研究，2004,21(1)：56-58. [2]夏鲁惠.高等学校毕业论文教学情况调研报告[J].高等理科教育，2004(1):46-52. [3]Heider,E.R.&D.C.Oliver.Thestructureofcolorspaceinnamingandmemo ryoftwolanguages[J].ForeignLanguageTeachingandResearch,1999,(3):62–6 7. 2.专著类 【格式】[序号]作者.书名[M].出版地：出版社，出版年份：起止页码. 【举例】[4]葛家澍，林志军.现代西方财务会计理论[M].厦门：厦门大学出版社，2001：42. [5]Gill,R.MasteringEnglishLiterature[M].London:Macmillan,1985:42-45. 3.报纸类 【格式】[序号]作者.篇名[N].报纸名，出版日期（版次）. 【举例】 [6]李大伦.经济全球化的重要性[N].光明日报，1998-12-27(3). [7]French,W.BetweenSilences:AVoicefromChina[N].AtlanticWeekly,198 715(33). 4.论文集 【格式】[序号]作者.篇名[C].出版地：出版者，出版年份：起始页码. 【举例】 [8]伍蠡甫.西方文论选[C].上海：上海译文出版社，1979：12-17. [9]Spivak,G.“CantheSubalternSpeak?”[A].InC.Nelson&L.Grossberg(e ds.).VictoryinLimbo:Imigism[C].Urbana:UniversityofIllinoisPress,1988, pp.271-313.
曲阜师范大学 杏坛学院 指纹识别技术综述 课题名称指纹识别技术 专业班级通信工程310班 学号 201112302022 姓名张喆 指导老师周崇波
目录 1.1 课题背景 (5) 1.2 目的和意义 (6) 1.3 理论基础 (7) 1.4 指纹识别技术的具体表现 (7) 1.4.1 在涉及国家刑事领域的应用 (7) 1.4.2 在经济生活方面 (7) 1.4.3 在公共事务管理方面 (8) 2.1 本课题目标 (8) 2.2 功能需求 (8) 2.3性能需求 (9) 2.4 开发工具的选择 (9) 2.5 系统设计原则 (9) 指纹识别系统总体设计 (10) 2.6系统总体设计 (10) 2.6.1指纹图像的获取 (10) 2.6.2 指纹图像预处理 (11) 2.6.3 特征的提取 (12) 2.6.4模板匹配 (12) 2.6.5 本章小结 (12) 指纹图像预处理之一 (13) 2.7.1 引言 (13) 2.7.2 系统算法描述 (13) 2.7.3 归一化 (14) 2.7.4 产生方向图 (14) 2.7.5 图像增强算法 (17) 2.7.6 图像分割 (19) 2.7.7 二值化 (24) 2.7.8细化算法 (25) 2.7.9指纹的匹配 (26) 2.8 本章小结 (26) 第三章实验结果与分析 (28) 3.1 评估标准 (28)
3.2 实验结果 (28) 3.2.1 图像分割算法结果比较 (29) 3.2.2图像增强算法结果比较 (30) 3.3 本章小结 (30) 第四章结论 (31) 致谢 (31) 参考文献 (32)
Structure and function of the MCS-51series Structure and function of the MCS-51series one-chip computer MCS-51is a name of a piece of one-chip computer series which Intel Company produces.This company introduced8top-grade one-chip computers of MCS-51series in1980after introducing8one-chip computers of MCS-48series in1976.It belong to a lot of kinds this line of one-chip computer the chips have,such as8051,8031,8751, 80C51BH,80C31BH,etc.,their basic composition,basic performance and instruction system are all the same.8051daily representatives-51serial one-chip computers. An one-chip computer system is made up of several following parts:(1)One microprocessor of8(CPU).(2)At slice data memory RAM(128B/256B),it use not depositting not can reading/data that write,such as result not middle of operation, final result and data wanted to show,etc.(3)Procedure memory ROM/EPROM (4KB/8KB),is used to preserve the procedure,some initial data and form in slice. But does not take ROM/EPROM within some one-chip computers,such as8031, 8032,80C,etc..(4)Four8run side by side I/O interface P0four P3,each mouth can use as introduction,may use as exporting too.(5)Two timer/counter,each timer/ counter may set up and count in the way,used to count to the external incident,can set up into a timing way too,and can according to count or result of timing realize the control of the computer.(6)Five cut off cutting off the control system of the source. (7)One all duplexing serial I/O mouth of UART(universal asynchronous receiver/transmitter(UART)),is it realize one-chip computer or one-chip computer and serial communication of computer to use for.(8)Stretch oscillator and clock produce circuit,quartz crystal finely tune electric capacity need outer.Allow oscillation frequency as12megahertas now at most.Every the above-mentioned part was joined through the inside data bus.Among them,CPU is a core of the one-chip computer,it is the control of the computer and command centre,made up of such parts as arithmetic unit and controller,etc..The arithmetic unit can carry on8persons of arithmetic operation and unit ALU of logic operation while including one,the1 storing device temporarilies of8,storing device2temporarily,8's accumulation
https://www.doczj.com/doc/4d2867215.html, 中英文论文参考文献 一、中英文论文期刊参考文献 [1].面向中英文混合环境的多模式匹配算法. 《软件学报》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2008年3期.孙钦东.黄新波.王倩. [2].基于自适应特征与多级反馈模型的中英文混排文档分割. 《自动化学报》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2006年3期.夏勇.王春恒.戴汝为. [3].基于最大熵方法的中英文基本名词短语识别. 《计算机研究与发展》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI 收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2003年3期.周雅倩.郭以昆.黄萱菁.吴立德. [4].中英文指代消解中待消解项识别的研究. 《计算机研究与发展》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI 收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2012年5期.孔芳.朱巧明.周国栋. [5].基于树核函数的中英文代词消解?. 《软件学报》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2013年5期.孔芳.周国栋. [6].基于树核函数的中英文代词消解. 《软件学报》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2012年5期.孔芳.周国栋. [7].一种并行中英文混合多模式匹配算法. 《计算机工程》.被中信所《中国科技期刊引证报告》收录ISTIC.被北京大学《中文核心期刊要目总览》收录PKU.2014年4期.王震.李仁发.李彦彪.田峥. [8].中英文混合文章识别问题. 《软件学报》.被中信所《中国科技期刊引证报告》收录ISTIC.被EI收录EI.被北京大学《中文核心期刊要目总览》收录PKU.2005年5期.王恺.王庆人.
中英文论文参考文献标准格式 参考文献（即引文出处）的类型以单字母方式标识，具体如下：? [M]--专着,着作? [C]--论文集(一般指会议发表的论文续集，及一些专题论文集，如《***大学研究生学术论文集》? [N]-- 报纸文章? [J]--期刊文章：发表在期刊上的论文，尽管有时我们看到的是从网上下载的（如知网），但它也是发表在期刊上的，你看到的电子期刊仅是其电子版? [D]--学位论文：不区分硕士还是博士论文? [R]--报告：一般在标题中会有"关于****的报告"字样? [S]-- 标准? [P]--专利? [A]--文章：很少用，主要是不属于以上类型的文章? [Z]--对于不属于上述的文献类型，可用字母"Z"标识，但这种情况非常少见? 常用的电子文献及载体类型标识：? [DB/OL] --联机网上数据(database online)? [DB/MT] --磁带数据库(database on magnetic tape)? [M/CD] --光盘图书(monograph on CDROM)? [CP/DK] --磁盘软件(computer program on disk)? [J/OL] --网上期刊(serial online)? [EB/OL] --网上电子公告(electronic bulletin board online)? 很显然，标识的就是该资源的英文缩写，/前面表示类型，/后面表示资源的载体，如OL表示在线资源? 二、参考文献的格式及举例? 1．期刊类? 【格式】[序号]作者.篇名[J].刊名,出版年份,卷号(期号)起止页码.? 【举例】? [1] 周融，任志国，杨尚雷，厉星星.对新形势下毕业设计管理工作的思考与实践[J].电气电子教学学报，2003(6):107-109.? [2] 夏鲁惠.高等学校毕业设计(论文)教学情况调研报告[J].高等理科教育,2004(1):46-52.? ? 2．专着类? 【格式】[序号]作者.书名[M].出版地:出版社,出版年份:起止页码.? 【举例】? [4] 刘国钧,王连成.图书馆史研究[M].北京:高等教育出版社,1979:15-18,31.? [5] Gill, R. Mastering English Literature [M]. London: Macmillan, 1985: 42-45.? 3．报纸类? 【格式】[序号]作者.篇名[N].报纸名,出版日期(版次).? 【举例】? [6] 李大伦.经济全球化的重要性[N]. 光明日报，1998-12-27(3).? [7] French, W. Between Silences: A Voice from China[N]. Atlantic Weekly, 1987-8-15(33).?
参考文献中英文人名的缩写规则 参考文献是科技论文的重要组成部分，也是编辑加工和重要内容。温哥华格式要求，著录文后参考文献时，英文刊名和人名一律用缩写。这一规则也是众多检索系统在人名著录时的首选规则。 下面我们先看一个例子： 在文章发表时，由于西方人士名在前姓在后，一般也采用名+姓的格式书写，如下题名、作者及正文的书写： Bis-pyranoside Alkenes: Novel Templates for the Synthesis of Adjacently Linked Tetrahydrofurans Zheming Ruan, Phyllis Wilson and David R. Mootoo 而上述文章若作为参考文献为他人引用，则需写成 Bis-pyranoside Alkenes: Novel Templates for the Synthesis of Adjacently Linked Tetrahydrofurans Tetrahedron Letters V olume: 37, Issue: 21, May 2, 1996, pp. 3619-3622 Ruan, Zheming; Wilson, Phyllis; Mootoo, David R. 由于东西方姓与名排列的差异，有的国外杂志在人名后还给出作者学位或参加的学会，因此很多人不知道如何区别姓、名、学位单位，如何缩写。 下面我们将著者姓名缩写规则的几个要点摘录如下： 1 姓名缩写只缩写名而不缩写姓； 2 无论东西方人，缩写名的书写形式都是姓在前、名在后； 3 杂志作者名中，全大写一定是姓； 4 省略所有缩写点 如R. Brain Haynes缩写为Haynes RB, Edward J. Huth缩写为Huth EJ等。 但有些特殊情况： (1)Maeve O'Conner, 正确缩写应为O'Conner M, 有人会按英文的构词习惯认为是印刷错误，认为Oconner M (2)国外也有复姓，如Julie C. Fanbury-Smith, Hartly Lorberboum-Galski等分别缩写为Fanbury-Smith JC, Lorbertoum-Galski HL (3)姓名中含前缀De,Des,Du,La,Dal,La,V on,Van,den,der等，将前缀和姓作为一个整体，按字顺排列，词间空格和大小写字母不影响排列，如Kinder V on Werder缩写为Von W erder K,不可写为Werder KV.
Integration of modeling in solidworks and matlab/simulink environments Abstract In the paper, the authors present construction stages of simulation models worked out using SolidWorks and Matlab/Simulink environments. As examples of simulation models, a laboratory truck crane and a forest crane have been shown. These models allow for visualization of movements, tracking of the trajectory, velocity and acceleration of any point of the system. 1. Introduction Current technological development has caused an increase in customers’requirements as for designed products. Different conditions and competition on the market mean that new products must be characterized by high quality and functionality. This situation forces engineers to design machines which are characterized by great flexibility and a variety of applications. However, the construction of prototypes of all kinds of devices, which are subjected to experimental research, is impossible because of both economy and time. Therefore, modeling problem [1, 2] is useful and it plays a fundamental role in the design stage of a new structure as well as during the modification of an existing construction. The development of software used to computer- aided design causes that the geometrical models of objects not only can be built, but also one can perform kinematic, dynamic, and strength analysis on the basis of these models in the professional CAD system. However, it is complicated or even impossible to add the control in these applications. Therefore, very often modeling of control systems is performed in Matlab environment with Simulink module [3-6]. Other alternative, but rarely used method of visualization and control of movements of the working machines [7, 8], may be the Python programming language with appropriate libraries [9]. However, in this case, the preparation of simulation models is much more laborious and requires programming skills in Python. Matlab is intended to solve complex mathematical problems and generate a graphical visualization of the
中英文参考文献格式 中文参考文献格式 参考文献（即引文出处）的类型以单字母方式标识： M——专著，C——论文集，N——报纸文章，J——期刊文章，D——学位论文，R——报告，S——标准，P——专利；对于不属于上述的文献类型，采用字母“Z”标识。 参考文献一律置于文末。其格式为： （一）专著 示例 [1] 张志建.严复思想研究[M]. 桂林：广西师范大学出版社，1989. [2] 马克思恩格斯全集：第1卷[M]. 北京：人民出版社，1956. [3] [英]蔼理士.性心理学[M]. 潘光旦译注.北京：商务印书馆，1997. （二）论文集 示例 [1] 伍蠡甫.西方文论选[C]. 上海：上海译文出版社，1979. [2] 别林斯基.论俄国中篇小说和果戈里君的中篇小说[A]. 伍蠡甫.西方文论选：下册[C]. 上海：上海译文出版社，1979. 凡引专著的页码，加圆括号置于文中序号之后。 （三）报纸文章 示例 [1] 李大伦.经济全球化的重要性[N]. 光明日报，1998-12-27，（3） （四）期刊文章 示例 [1] 郭英德.元明文学史观散论[J]. 北京师范大学学报（社会科学版），1995（3）. （五）学位论文 示例 [1] 刘伟.汉字不同视觉识别方式的理论和实证研究[D]. 北京：北京师范大学心理系，1998. （六）报告 示例 [1] 白秀水，刘敢，任保平. 西安金融、人才、技术三大要素市场培育与发展研究[R]. 西安：陕西师范大学西北经济发展研究中心，1998. （七）、对论文正文中某一特定内容的进一步解释或补充说明性的注释，置于本页地脚，前面用圈码标识。 参考文献的类型 根据GB3469-83《文献类型与文献载体代码》规定，以单字母标识： M——专著（含古籍中的史、志论著） C——论文集 N——报纸文章 J——期刊文章 D——学位论文 R——研究报告 S——标准 P——专利 A——专著、论文集中的析出文献 Z——其他未说明的文献类型 电子文献类型以双字母作为标识： DB——数据库
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