dsp外文翻译.doc

_ 外文参考文献翻译英文题目 The Breadth and Depth of DSP 中文题目 DSP的广度和深度 学 院 自动化与电气工程学院 专 业 自 动 化 姓 名 白 学 文 学 号 201108536 指导教师 王 思 明 2015 年 04月 20日精品资料DSP的广度和深度数字信号处理是最强大的技术，将塑造二十一世纪的科学与工程之一。革命性的变化已经在广泛的领域：通信，医疗成像，雷达和声纳，高保真音乐再现，石油勘探，仅举几例。上述各领域已建立了深厚的DSP技术，用自己的算法，数学，和专门技术。这种呼吸和深度的结合，使得它不可能为任何一个人掌握所有已开发的DSP技术。 DSP教育包含两个任务：学习一般适用于作为一个整体领域的概念，并学习您感兴趣的特定领域的专门技术。本章开始描述DSP已在几个不同领域的戏剧性效果的数字信号处理的世界，我们的旅程。革命已经开始。1 DSP的根源独特的数据类型，它使用的信号，数字信号处理是区别于其他计算机科学领域。在大多数情况下，这些信号源于感觉来自现实世界的数据：地震的震动，视觉图像，声波等DSP是数学，算法，并用来操纵这些信号的技术后，他们已被转换成数字形式。这包括了各种目标，如：加强视觉图像识别和语音生成，存储和传输的数据压缩，等假设我们重视计算机模拟 - 数字转换器，并用它来获得一个现实世界的数据块。 DSP回答了这个问题：下一步怎么办？DSP的根是在20世纪60年代和70年代数字计算机时首次面世。电脑是昂贵的，在这个时代，DSP是有限的，只有少数关键应用。努力开拓，在四个关键领域：雷达和声纳，国家安全风险是石油勘探，可以大量资金;太空探索，其中的数据是不可替代的;和医疗成像，可节省生活。 20世纪80年代和90年代的个人电脑革命，引起新的应用DSP的爆炸。而不是由军方和政府的需求动机，DSP的突然被带动的商业市场。任何人士如认为他们可以使资金在迅速扩大的领域突然一个DSP供应商。 DSP的市民等产品达到：移动电话机，光盘播放器，电子语音邮件。这一技术革命，从自上而下的发生。在20世纪80年代初，DSP是研究生水平的课程，在电气工程教授。十年后，DSP已成为标准的本科课程的一部分。今天，DSP是一种在许多领域的科学家和工程师所需要的基本技能。作为一个比喻，DSP可以比以前的技术革命：电子。虽然仍是电气工程领域，几乎所有的科学家和工程师有一些基本的电路设计的背景。没有它，他们将失去在科技世界。 DSP具有相同的未来。这最近的历史是超过了好奇，它有一个巨大的影响你的学习能力和使用DSP。假设你遇到一个DSP的问题，并把课本或其他出版物，以找到一个解决方案。你通常会发现什么是页后页方程，模糊的数学符号，不熟悉的术语。这是一场恶梦！ DSP的文献多是令人费解，甚至在该领域经验丰富的。这并不是说有什么错用这种材料，它只是一个非常特殊的观众。国家的最先进的研究人员需要这种详细的数学理解的工作的理论意义。这本书的一个基本前提是，可以学到最实用的DSP技术，并没有详细的数学和理论的传统障碍。科学家和工程师的数字信号处理指南是写给那些想要使用DSP作为一种工具，而不是一个新的职业生涯。本章的其余部分说明，其中DSP已经产生了革命性的变化的地区。当你通过每个应用程序，请注意，DSP是非常跨学科，依托在许多相邻领域的技术工作。正如图。如果你想专注于DSP，这是多领域，则还需要研究。2 通信通信是信息传输从一个位置到另一个。这包括各种形式的信息：电话交谈，电视信号，计算机中的文件，和其他类型的数据。传输信息，你需要在两个地点之间的通道。这可能是一个线对无线电信号，光纤等电信公司接收他们的客户的信息转移支付，而他们一定要以建立和维护渠道。金融的底线很简单：信息越多，他们可以通过一个单一的通道，他们更多的钱。 DSP已彻底改变电信业在许多领域：信号音的产生和检测，频带的转移，过滤，除去电源线的嗡嗡声，从电话网络等具体的例子将在这里讨论：复用，压缩和回声控制。3 音频处理主要的两个人的感官是视觉和听觉。相应地，许多DSP的有关图像和音频处理。人们听音乐和语音。 DSP已经在这两个领域取得了革命性的变化。数字信号处理的一般方法的语音识别问题的两个步骤：特征提取的特征匹配。在输入音频信号的每个字都是孤立的，然后分析以确定激励型和共振频率。然后这些参数与以前的口语单词的例子相比，找出最接近的匹配。通常，这些系统是有限的只有几百字；只能接受与词之间明显的停顿的演讲；必须为每个扬声器的训练。虽然这是足够的许多商业应用，这些限制是震撼人心的相比，人类的听觉能力。有大量的这方面的工作要做，对于那些生产成功的商业产品，巨大的经济回报。4 回声定位一个常用的方法是获得远程对象的信息，超生波的关闭。例如，雷达通过发射无线电波脉冲，并从飞机回声检查接收到的信号。声纳，通过水传播的声波探测潜艇和其他水下物体。地球物理学家已经长探测地球所设置的爆炸和听回声从岩石层深埋。虽然这些应用都有一个共同的线程，每个人都有自己的具体问题和需求。数字信号处理，在所有这三个领域产生革命性的变化。5 影像处理图像信号特色。首先，他们是一个空间（距离）超过参数的措施，而大多数信号是随着时间的推移参数的措施。第二，它们包含了大量的信息。例如，超过10兆字节，可存储一秒钟的电视录像。这是一千倍以上，比类似长度的语音信号。第三，质量的最终判断往往是人类的主观评价，而不是一个客观的标准。这些特色使图像处理DSP内部的不同分组。The Breadth and Depth of DSPDigital Signal Processing is one of the most powerful technologies that will shape science and engineering in the twenty-first century. Revolutionary changes have already been made in a broad range of fields: communications, medical imaging, radar & sonar, high fidelity music reproduction, and oil prospecting, to name just a few. Each of these areas has developed a deep DSP technology, with its own algorithms, mathematics, and specialized techniques. This combination of breath and depth makes it impossible for any one individual to master all of the DSP technology that has been developed. DSP education involves two tasks: learning general concepts that apply to the field as a whole, and learning specialized techniques for your particular area of interest. This chapter starts our journey into the world of Digital Signal Processing by describing the dramatic effect that DSP has made in several diverse fields. The revolution has begun.1 The Roots of DSPDigital Signal Processing is distinguished from other areas in computer science by the unique type of data it uses: signals. In most cases, these signals originate as sensory data from the real world: seismic vibrations, visual images, sound waves, etc. DSP is the mathematics, the algorithms, and the techniques used to manipulate these signals after they have been converted into a digital form. This includes a wide variety of goals, such as: enhancement of visual images, recognition and generation of speech, compression of data for storage and transmission, etc. Suppose we attach an analog-to-digital converter to a computer and use it to acquire a chunk of real world data. DSP answers the question: What next? The roots of DSP are in the 1960s and 1970s when digital computers first became available. Computers were expensive during this era, and DSP was limited to only a few critical applications. Pioneering efforts were made in four key areas: radar & sonar, where national security was at risk; oil exploration, where large amounts of money could be made; space exploration, where the data are irreplaceable; and medical imaging, where lives could be saved. The personal computer revolution of the 1980s and 1990s caused DSP to explode with new applications. Rather than being motivated by military and government needs, DSP was suddenly driven by the commercial marketplace. Anyone who thought they could make money in the rapidly expanding field was suddenly a DSP vendor. DSP reached the public in such products as: mobile telephones, compact disc players, and electronic voice mail. Figure 1-1 illustrates a few of these varied applications. This technological revolution occurred from the top-down. In the early 1980s, DSP was taught as a graduate level course in electrical engineering. A decade later, DSP had become a standard part of the undergraduate curriculum. Today, DSP is a basic skill needed by scientists and engineers in many fields. As an analogy, DSP can be compared to a previous technological revolution: electronics. While still the realm of electrical engineering, nearly every scientist and engineer has some background in basic circuit design. Without it, they would be lost in the technological world. DSP has the same future.This recent history is more than a curiosity; it has a tremendous impact on your ability to learn and use DSP. Suppose you encounter a DSP problem, and turn to textbooks or other publications to find a solution. What you will typically find is page after page of equations, obscure mathematical symbols, and unfamiliar terminology. Its a nightmare! Much of the DSP literature is baffling even to those experienced in the field. Its not that there is anything wrong with this material, it is just intended for a very specialized audience. State-of-the-art researchers need this kind of detailed mathematics to understand the theoretical implications of the work.A basic premise of this book is that most practical DSP techniques can be learned and used without the traditional barriers of detailed mathematics and theory. The Scientist and Engineers Guide to Digital Signal Processing is written for those who want to use DSP as a tool, not a new career. The remainder of this chapter illustrates areas where DSP has produced revolutionary changes. As you go through each application, notice that DSP is very interdisciplinary, relying on the technical work in many adjacent fields. As Fig. 1-2 suggests, the borders between DSP and other technical disciplines are not sharp and well defined, but rather fuzzy and overlapping. If you want to specialize in DSP, these are the allied areas you will also need to study.2 TelecommunicationsTelecommunications is about transferring information from one location to another. This includes many forms of information: telephone conversations, television signals, computer files, and other types of data. To transfer the information, you need a channel between the two locations. This may be a wire pair, radio signal, optical fiber, etc. Telecommunications companies receive payment for transferring their customers information, while they must pay to establish and maintain the channel. The financial bottom line is simple: the more information they can pass through a single channel, the more money they make. DSP has revolutionized the telecommunications industry in many areas: signaling tone generation and detection, frequency band shifting, filtering to remove power line hum, etc. Three specific examples from the telephone network will be discussed here: multiplexing, compression, and echo control.3 Audio ProcessingThe two principal human senses are vision and hearing. Correspondingly, much of DSP is related to image and audio processing. People listen to both music and speech. DSP has made revolutionary changes in both these areas.Digital Signal Processing generally approaches the problem of voice recognition in two steps: feature extraction followed by feature matching. Each word in the incoming audio signal is isolated and then analyzed to identify the type of excitation and resonate frequencies. These parameters are then compared with previous examples of spoken words to identify the closest match. Often, these systems are limited to only a few hundred words; can only accept speech with distinct pauses between words; and must be retrained for each individual speaker. While this is adequate for many commercial applications, these limitations are humbling when compared to the abilities of human hearing. There is a great deal of work to be done in this area, with tremendous financial rewards for those that produce successful commercial products.4 Echo LocationA common method of obtaining information about a remote object is to bounce a wave off of it. For example, radar operates by transmitting pulses of radio waves, and examining the received signal for echoes from aircraft. In sonar, sound waves are transm