数字信号处理外文翻译
数字信号处理 名词解释-概述说明以及解释
数字信号处理名词解释-概述说明以及解释1.引言1.1 概述数字信号处理（Digital Signal Processing，简称DSP）是一种广泛应用于信号处理领域的技术，它利用数字化的方式对连续时间信号进行处理和分析。
数字信号处理可以实现信号的滤波、频谱分析、模拟与数字信号的转换、信息编码解码等功能，是现代通信、音视频处理、生物医学领域等各个领域中不可或缺的技术手段。
通过数字信号处理技术，我们可以更加精确和高效地处理各种类型的信号，包括声音、图像、视频等。
数字信号处理可以使信号的处理过程更加稳定可靠，同时也可以方便地与计算机等数字系统进行集成，实现更多复杂功能。
在本篇文章中，我们将深入探讨数字信号处理的定义、应用领域以及基本原理，以期让读者对这一重要领域有更加全面的认识和理解。
1.2 文章结构本文将分为三个主要部分，分别是引言、正文和结论。
在引言部分，我们将对数字信号处理进行简要的概述，并介绍文章的结构和目的。
正文部分将详细讨论数字信号处理的定义、应用领域和基本原理。
最后，在结论部分，我们将总结数字信号处理的重要性，探讨未来数字信号处理的发展趋势，并做出最终的结论。
通过这样的结构安排，读者能够清晰地了解数字信号处理的基本概念、应用以及未来发展方向。
1.3 目的：本文旨在介绍数字信号处理的概念、应用领域和基本原理，旨在帮助读者更深入了解数字信号处理的重要性和作用。
通过对数字信号处理的定义和应用领域的介绍，读者可以了解数字信号处理在各个领域中的广泛应用和重要性。
同时，通过对数字信号处理的基本原理的讲解，读者可以更好地理解数字信号处理的工作原理和技术特点。
通过本文的阐述，希望读者能够全面了解数字信号处理的基本概念和工作原理，进而认识到数字信号处理在现代科学技术中的重要性和必要性。
同时，本文也将展望未来数字信号处理的发展趋势，希望能够启发读者对数字信号处理领域的进一步研究和探索。
最终，通过本文的阐述，读者可以更加深入地理解数字信号处理这一重要的科学技术领域。
专业英语翻译之数字信号处理
Signal processingSignal processing is an area of electrical engineering and applied mathematics that deals with operations on or analysis of signals, in either discrete or continuous time, to perform useful operations on those signals. Signals of interest can include sound, images, time-varying measurement values and sensor data, for example biological data such as electrocardiograms, control system signals, telecommunication transmission signals such as radio signals, and many others. Signals are analog or digital electrical representations of time-varying or spatial-varying physical quantities. In the context of signal processing, arbitrary binary data streams and on-off signalling are not considered as signals, but only analog and digital signals that are representations of analog physical quantities.HistoryAccording to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal processing can be found in the classical numerical analysis techniques of the 17th century. They further state that the "digitalization" or digital refinement of these techniques can be found in the digital control systems of the 1940s and 1950s.[2]Categories of signal processingAnalog signal processingAnalog signal processing is for signals that have not been digitized, as in classical radio, telephone, radar, and television systems. This involves linear electronic circuits such as passive filters, active filters, additive mixers, integrators and delay lines. It also involves non-linear circuits such ascompandors, multiplicators (frequency mixers and voltage-controlled amplifiers), voltage-controlled filters, voltage-controlled oscillators andphase-locked loops.Discrete time signal processingDiscrete time signal processing is for sampled signals that are considered as defined only at discrete points in time, and as such are quantized in time, but not in magnitude.Analog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals.The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.Digital signal processingDigital signal processing is for signals that have been digitized. Processing is done by general-purpose computers or by digital circuits such as ASICs, field-programmable gate arrays or specialized digital signal processors (DSP chips). Typical arithmetical operations include fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Other typical operations supported by the hardware are circular buffers and look-up tables. Examples of algorithms are the Fast Fourier transform (FFT), finite impulseresponse (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters1.Digital signal processingDigital signal processing (DSP) is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc.The goal of DSP is usually to measure, filter and/or compress continuousreal-world analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analog converter (DAC). Even if this process is more complex than analog processing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.[1]DSP algorithms have long been run on standard computers, on specialized processors called digital signal processors (DSPs), or on purpose-built hardware such as application-specific integrated circuit (ASICs). Today thereare additional technologies used for digital signal processing including more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial apps such as motor control), and stream processors, among others.[2]2. DSP domainsIn DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. Autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space.3. Signal samplingMain article: Sampling (signal processing)With the increasing use of computers the usage of and need for digital signal processing has increased. In order to use an analog signal on a computer it must be digitized with an analog-to-digital converter. Sampling is usually carried out in two stages, discretization and quantization. In the discretization stage, the space of signals is partitioned into equivalence classes and quantization is carried out by replacing the signal with representative signal of the corresponding equivalence class. In the quantization stage the representative signal values are approximated by values from a finite set.The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal; but requires an infinite number of samples . In practice, the sampling frequency is often significantly more than twice that required by the signal's limited bandwidth.A digital-to-analog converter is used to convert the digital signal back to analog. The use of a digital computer is a key ingredient in digital control systems. 4. Time and space domainsMain article: Time domainThe most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:∙ A "linear" filter is a linear transformation of input samples; other filters are "non-linear". Linear filters satisfy the superposition condition, i.e. if an input is a weighted linear combination of different signals, the output is an equally weighted linear combination of the corresponding output signals.∙ A "causal" filter uses only previous samples of the input or output signals; while a "non-causal" filter uses future input samples. A non-causal filter can usually be changed into a causal filter by adding a delay to it.∙ A "time-invariant" filter has constant properties over time; other filters such as adaptive filters change in time.∙Some filters are "stable", others are "unstable". A stable filter produces an output that converges to a constant value with time, or remains bounded within a finite interval. An unstable filter can produce an output that grows without bounds, with bounded or even zero input.∙ A "finite impulse response" (FIR) filter uses only the input signals, while an "infinite impulse response" filter (IIR) uses both the input signal and previous samples ofthe output signal. FIR filters are always stable, while IIR filters may be unstable.Filters can be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response.The output of a digital filter to any given input may be calculated by convolving the input signal with the impulse response.5. Frequency domainMain article: Frequency domainSignals are converted from time or space domain to the frequency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum todetermine which frequencies are present in the input signal and which are missing.In addition to frequency information, phase information is often needed. This can be obtained from the Fourier transform. With some applications, how the phase varies with frequency can be a significant consideration.Filtering, particularly in non-realtime work can also be achieved by converting to the frequency domain, applying the filter and then converting back to the time domain. This is a fast, O(n log n) operation, and can give essentially any filter shape including excellent approximations to brickwall filters.There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components.Frequency domain analysis is also called spectrum- or spectral analysis. 6. Z-domain analysisWhereas analog filters are usually analysed on the s-plane; digital filters are analysed on the z-plane or z-domain in terms of z-transforms.Most filters can be described in Z-domain (a complex number superset of the frequency domain) by their transfer functions. A filter may be analysed in the z-domain by its characteristic collection of zeroes and poles.7. ApplicationsThe main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, RADAR, SONAR, seismology, and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room matching equalization of sound in Hifi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, computer-generated animations in movies, medical imaging such as CAT scans and MRI, MP3 compression, image manipulation, high fidelity loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers8. ImplementationDigital signal processing is often implemented using specialised microprocessors such as the DSP56000, the TMS320, or the SHARC. These often process data using fixed-point arithmetic, although some versions are available which use floating point arithmetic and are more powerful. For faster applications FPGAs[3] might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge from companies including Freescale and Stream Processors, Inc. For faster applications with vast usage, ASICs might be designed specifically. For slow applications, a traditional slower processor such as a microcontroller may be adequate. Also a growing number of DSP applications are now being implemented on Embedded Systems using powerful PCs with a Multi-core processor.（翻译）信号处理信号处理是电气工程与应用数学领域，在离散的或连续时间域处理和分析信号，以对这些信号进行所需的有用的处理。
数字信号处理
数字信号处理数字信号处理（Digital Signal Processing，简称DSP）是一种通过算法对数字信号进行处理和分析的技术方法。
它广泛应用于音频、图像、视频、通信等领域，在现代科技发展中扮演重要角色。
本文将从数字信号处理技术的定义、应用领域、基本原理等角度进行探讨。
一、定义数字信号处理是指利用数字技术方法来处理和分析信号的过程。
相较于模拟信号处理，数字信号处理能够通过采样、量化和编码将连续时间信号转换为离散时间信号，然后利用计算机等设备对离散时间信号进行处理。
在数字信号处理中，信号被表示为数字序列，通过算法进行运算和处理。
二、应用领域数字信号处理在众多领域中都有着广泛的应用，下面列举几个典型的应用领域。
1. 音频处理音频处理是数字信号处理的重要应用之一。
通过对音频信号进行采样和处理，可以实现音频增强、噪声消除、音频编码等功能。
在音频设备、通信系统以及音乐制作等领域都离不开数字信号处理的技术支持。
2. 图像处理数字图像处理是应用数字信号处理技术处理图像的方法。
通过对图像进行采样和处理，可以实现图像增强、边缘检测、图像压缩等功能。
在计算机视觉、医学影像、卫星图像等领域得到广泛应用。
3. 视频处理视频处理是对视频信号进行处理和分析的过程。
通过对视频信号进行采样、编码和压缩，可以实现视频压缩、移动视频传输等功能。
在监控系统、视频会议等领域都离不开数字信号处理技术的支持。
4. 通信处理数字信号处理技术在通信领域中起到了至关重要的作用。
通过对数字信号进行调制、编解码、信道均衡等处理，可以提高通信系统的可靠性和传输效率。
在移动通信、卫星通信等领域都广泛应用了数字信号处理技术。
三、基本原理数字信号处理的基本原理包括信号采样、量化、编码、运算和重构等步骤。
1. 信号采样信号采样是将连续时间信号转换为离散时间信号的过程。
通过按照一定的时间间隔对信号进行采样，得到一系列取样值，用来表示原始信号。
2. 量化和编码信号量化是将连续时间信号中的幅度值转换为离散值的过程。
数字信号处理(英文版)0-引言
Introduction
Digital Signal Processing
Theory, method, algorithm
Digital Signal Processor
A kind of microprocessor used to implement digital signal processing algorithm
processor speed.
We still need analog processing
（2）Processing very high frequency signals
Analog system：may process microwave,
mini-meter-wave, even light wave signals.
Radar/Sonar
Cruise missile
Smart bomb from F117
Pattern recognization
Fingerprint distinguish
Why digital?
（1）Programmability
Analog system：Modify
hardware design.
MIPS TECHNOLOGIES, INC. AND TI ANNOUNCE A STRATEGIC
RELATIONSHIP
MOUNTAIN VIEW, CALIF. and DALLAS, TX (Feb. 16, 1999) -- MIPS Technologies, Inc. (NASDAQ: MIPS) and Texas Instruments (NYSE: TXN) announced today a relationship to provide TI access to MIPS Technologies' processor cores in support of TI's digital signal processor (DSP) System Level Integration (SLI) solutions.
数字信号处理 英语
数字信号处理英语Digital Signal Processing (DSP) is an essential technology used in various fields such as communication, media, control systems and audio signal processing. This technology uses algorithms to transform digital signals (numbers) into specific applications. In this article, wewill explore some common terminologies used in DSP in English.1. SamplingSampling is the process of converting a continuoussignal into a discrete signal. The sampled signal represents the original signal at specific intervals, known as the sampling frequency. The number of samples taken per unit time is called the sample rate. For example, in audio signal processing, the standard sample rate is 44.1 kilohertz (kHz), which means that the signal is sampled 44,100 times per second.2. QuantizationQuantization is the process of assigning a discretevalue to each sample. Each sample is rounded to the nearest value in a given set of discrete values. The interval between each value is known as the quantization step size. For example, in audio signal processing, the quantization stepsize is measured in bits. The most common quantization bitsize is 16 bits, which means that each sample can be represented by a 16-bit binary number.3. FilteringFiltering is the process of removing or attenuating specific frequencies in a signal. The filter can be designedto pass only the desired frequency range or to eliminate unwanted frequencies. There are two types of filters –analog filters and digital filters. Analog filters usepassive components such as capacitors and resistors, while digital filters use mathematical algorithms to process the signal.4. Fast Fourier Transform (FFT)The Fourier Transform is a mathematical technique usedto analyze signals in the frequency domain. FFT is aparticular algorithm that efficiently calculates the Fourier Transform of a discrete signal. It is widely used in digital signal processing to analyze and process signals in the frequency domain.5. Digital Signal Processors (DSPs)Digital Signal Processors (DSPs) are specialized microprocessors used to perform DSP operations. DSPs are used in devices such as cellphones, wireless modems, televisions, and audio devices. They are optimized for performing complex mathematical operations required in digital signal processing.In conclusion, digital signal processing has become an essential technology in many fields, from communications to audio signal processing. Understanding the terminologies used in DSP is vital in learning and applying this technology. The above-mentioned terminologies are some of the most common terms used in DSP, and having a good understanding of thesewill help you to get started in this exciting field.。
数字信号处理
数字信号处理数字信号处理（Digital Signal Processing）数字信号处理是指将连续时间的信号转换为离散时间信号，并对这些离散时间信号进行处理和分析的过程。
随着计算机技术的飞速发展，数字信号处理在各个领域得到了广泛应用，如通信、医学影像、声音处理等。
本文将介绍数字信号处理的基本概念和原理，以及其在不同领域的应用。
一、数字信号处理的基本概念数字信号处理是建立在模拟信号处理基础之上的一种新型信号处理技术。
在数字信号处理中，信号是用数字形式来表示和处理的，因此需要进行模数转换和数模转换。
数字信号处理的基本原理包括采样、量化和编码这三个步骤。
1. 采样：采样是将连续时间信号在时间上进行离散化的过程，通过一定的时间间隔对信号进行取样。
采样的频率称为采样频率，一般以赫兹（Hz）为单位表示。
采样频率越高，采样率越高，可以更准确地表示原始信号。
2. 量化：量化是指将连续的幅度值转换为离散的数字值的过程。
在量化过程中，需要确定一个量化间隔，将信号分成若干个离散的级别。
量化的级别越多，表示信号的精度越高。
3. 编码：编码是将量化后的数字信号转换为二进制形式的过程。
在数字信号处理中，常用的编码方式有PCM（脉冲编码调制）和DPCM （差分脉冲编码调制）等。
二、数字信号处理的应用1. 通信领域：数字信号处理在通信领域中具有重要的应用价值。
在数字通信系统中，信号需要经过调制、解调、滤波等处理，数字信号处理技术可以提高信号传输的质量和稳定性。
2. 医学影像：医学影像是数字信号处理的典型应用之一。
医学影像技术如CT、MRI等需要对采集到的信号进行处理和重建，以获取患者的影像信息，帮助医生进行诊断和治疗。
3. 声音处理：数字信号处理在音频处理和语音识别领域也有广泛的应用。
通过数字滤波、噪声消除、语音识别等技术，可以对声音信号进行有效处理和分析。
总结：数字信号处理作为一种新兴的信号处理技术，已经深入到各个领域中，并取得了显著的进展。
数字信号处理论文
数字信号处理论文引言数字信号处理（Digital Signal Processing，DSP）是利用数字技术对连续时间信号进行采样、量化和处理的一种信号处理方法。
随着计算机技术的发展，数字信号处理在多个领域得到了广泛应用，包括音频和视频处理、通信系统、雷达和成像等。
本文旨在通过介绍数字信号处理的基本概念、原理和应用，为读者提供一个全面了解数字信号处理的框架。
数字信号处理的基本概念1. 数字信号与模拟信号数字信号是以离散值表示的信号，而模拟信号是以连续值表示的信号。
数字信号可以通过采样和量化从模拟信号中获得。
2. 采样和量化采样是将连续时间信号转换为离散时间信号的过程，采样定理指出采样频率要大于信号最高频率的2倍，以避免采样失真。
量化是将连续幅度信号转换为离散幅度信号的过程，通过将信号幅度划分成有限个级别来实现。
3. 信号的时域和频域表示信号的时域表示了信号在时间上的变化情况，可以通过时域图像展示。
频域表示了信号在频率上的变化情况，可以通过傅里叶变换将信号从时域转换为频域表示。
数字信号处理的原理1. 傅里叶变换和逆变换傅里叶变换是将信号从时域转换为频域的一种数学工具。
通过傅里叶变换，我们可以将信号的频域特性分析出来，以便进行后续的处理。
逆变换则是将频域信号重新转换回时域信号。
2. 滤波器设计滤波器是数字信号处理中常用的一种工具，用于增强或抑制信号的特定频率成分。
滤波器可以分为低通滤波器、高通滤波器、带通滤波器和带阻滤波器等不同类型。
滤波器设计的目标是使得滤波器在频域上满足一定的要求，通常使用巴特沃斯、切比雪夫等方法来实现。
3. 时域和频域处理算法数字信号处理中有许多常见的时域和频域处理算法，如加法、减法、乘法、卷积、相关等。
这些算法可以对信号进行的处理包括增加、减少、平滑、增强等各种操作。
数字信号处理的应用1. 音频和视频处理数字信号处理在音频和视频处理中有着广泛的应用。
例如，音频信号处理可以用于音频的音质改进、语音识别、音频压缩等。
数字信号处理(DSP)专业词汇
系统：system 信号：signal模拟信号：analog signal 数字信号：digital signal模/数转换：analog-to-digital conversion 频谱：spectrum数字滤波：digital filtering 滤波器：filter采样：sample 保持：hold数字代码：digital code 量化电平：quantization level时域：time domain 频域：frequency domain低频：low frequency 高频：high frequency低通滤波器：low pass filter 高通滤波器：high pass filter带通滤波器：band pass filter 带阻滤波器：band stop filter零阶保持信号:zero order hold signal 平滑：smooth采样周期：sampling period 频率分量:frequency elements图像处理：image processing 传感器：sensor电压：voltage 电流：current•anti-aliasing filter 抗混叠滤波器•anti-imaging filter 抗镜像滤波器•sampling interval 采样间隔•=sampling period 采样周期•sampling frequency 采样频率•=sampling rate 采样速率•sampling theorem 采样定理•Nyquist sampling rate 奈奎斯特采样率•Nyquist frequency 奈奎斯特频率•Nyquist range 奈奎斯特范围•oversampling 过采样undersampling 欠采样•quantization step 量化步长quantization noise量化噪声•bit rate 比特率•数字函数：digital function 合成函数：composite function •二维数字信号：two-dimensional digital signal•语音信号：speech signal 量化方案：quantization scheme •脉冲函数：impulse function 单位脉冲函数：unit impulse function •阶跃函数：step function 幂函数:power function •指数函数: exponential function 正弦函数：sine function•余弦函数：cosine function 复平面：complex plain•欧拉恒等式：Euler’s identity 模拟频率：analog frequency •数字频率:digital frequency 采样间隔：sampling interval •相移：phase shift 像素：pixel•灰度级：gray scale•roll-off 滚降gain 增益•pass band 通带stop band 阻带•bandwidth 带宽linear system 线性系统•superposition 叠加原理time-invariant 时不变•causal system因果系统difference equation差分方程•filter coefficient滤波器系数recursive filter 递归滤波器•nonrecursive filter 非递归滤波器finite word length effect有限字长效应•impulse response 脉冲响应infinite impulse response （IIR)无限脉冲响应•finite impulse response (FIR)有限脉冲响应•moving average filter 滑动平均滤波器•step response 阶跃响应•z transform z变换•region of convergence 收敛域•inverse z transform 逆z变换•transfer function 传输函数•partial fraction expansion 部分分式展开•cover-up method 覆盖法•zero 零点pole 极点•marginally stable 临界稳定unstable 不稳定•傅立叶变换：Fourier Transform•滤波器形状：filter shape•频率响应:frequency response•频率特性：frequency characteristics•离散时间傅立叶变换：Discrete Time Fourier Transform•幅度响应：magnitude response•相位响应：phase response•传输函数：transfer function•相位差：phase difference•采样频率：sampling frequency•white noise 白噪声•magnitude spectrum 幅度频谱•phase spectrum 相位频谱•discrete Fourier series（DFS)离散傅里叶级数•有限脉冲响应滤波器：finite impulse response filter•无限脉冲响应滤波器：infinite impulse response filter•相位失真：phase distortion•理想低通滤波器：idle low pass filter•窗函数：window function 稳定性：stability•通带波纹：pass band ripple•阻带波纹：stop band ripple•通带边缘频率:pass band edge frequency•过渡带宽度：transition width•矩形窗：Rectangular Window•汉宁窗：Hanning Window•哈明窗:Hamming Window•布莱克曼窗:Blackman Window•凯塞窗:Kaiser Window•项数:number of terms 衰减:attenuation•增益:gain•采样频率:sampling frequency•infinite impulse response filter(IIR)无限脉冲响应滤波器•bilinear transformation 双线性变换•prewarping equation 预扭曲方程•Butterworth filter 巴特沃斯滤波器•Chebyshev Type I filter 切比雪夫I 型滤波器•Chebyshev Type II filter 切比雪夫II 型滤波器•elliptic filter 椭圆滤波器•Impulse invariance method 脉冲响应不变法•discrete Fourier transform (DFT) 离散傅里叶变换•inverse DFT 逆离散傅里叶变换•phase spectrum 相位频谱•frequency spacing频率间隔•resolution分辨率•smear模糊•spectral leakage 频谱泄漏•spectrogram频谱图•fast Fourier transform (FFT) 快速傅里叶变换•butterfly 蝶形。
数字信号处理中的英文缩写
数字信号处理中的英文缩写在数字信号处理领域中，有许多常用的英文缩写，以下是一些常见的缩写及其含义：1. DSP：数字信号处理（Digital Signal Processing）2. FFT：快速傅里叶变换（Fast Fourier Transform）3. FIR：有限脉冲响应（Finite Impulse Response）4. IIR：无限脉冲响应（Infinite Impulse Response）5. DFT：离散傅里叶变换（Discrete Fourier Transform）6. IDFT：离散傅里叶逆变换（Inverse Discrete Fourier Transform）7. ADC：模数转换器（Analog-to-Digital Converter）8. DAC：数模转换器（Digital-to-Analog Converter）9. LTI：线性时不变（Linear Time-Invariant）10. SNR：信噪比（Signal-to-Noise Ratio）11. MSE：均方误差（Mean Squared Error）12. PDF：概率密度函数（Probability Density Function）13. CDF：累积分布函数（Cumulative Distribution Function）14. PSD：功率谱密度（Power Spectral Density）15. FIR filter：有限脉冲响应滤波器16. IIR filter：无限脉冲响应滤波器17. AWGN：加性白噪声（Additive White Gaussian Noise）18. QAM：正交振幅调制（Quadrature Amplitude Modulation）19. BPSK：二进制相移键控（Binary Phase-Shift Keying）20. FSK：频移键控（Frequency-Shift Keying）这些缩写在数字信号处理的理论、算法、实现中都有广泛应用，了解这些缩写有助于更好地理解和掌握数字信号处理相关知识。
数字信号处理
数字信号处理数字信号处理（Digital Signal Processing，简称DSP）是一门研究数字信号的获取、处理和分析的学科。
数字信号处理在各个领域都有着广泛的应用，例如通信、音频和视频处理、图像处理等。
本文将从数字信号的获取、数字信号处理的基本原理以及数字信号处理的应用等几个方面进行论述。
一、数字信号的获取在数字信号处理中，数字信号的获取是非常重要的一步。
通常，我们通过模拟信号转换成数字信号进行处理。
这个过程包括了模拟信号的采样和量化两个步骤。
1. 采样采样是指将连续的模拟信号转换成离散的数字信号。
在采样过程中，我们将连续的信号在时间上进行等间隔地取样，得到一系列离散的采样值。
采样定理告诉我们，采样频率必须大于信号最高频率的两倍，这样才能保证信号在采样后的恢复。
2. 量化量化是指将连续的采样值转换成离散的数字量。
在量化过程中，我们对每个采样值进行近似处理，将其量化为离散的取值，通常使用有限个取值来表示连续的信号强度。
二、数字信号处理的基本原理数字信号处理的基本原理包括离散信号的表示和离散信号的处理。
1. 离散信号的表示离散信号是指在时间上是离散的，并且在幅值上也是离散的。
常用的离散信号表示方法包括时间序列和频率谱。
- 时间序列是离散信号在时间上的表示，通常由一系列采样值组成，可以看作是一个序列。
- 频率谱是离散信号在频率上的表示，可以将离散信号分解成一系列不同频率的正弦波成分。
2. 离散信号处理离散信号处理是指对离散信号进行一系列运算和变换，常见的包括滤波、频谱分析和信号重建等。
- 滤波是指对信号进行滤波器的作用，通常用于去除信号中的噪声或者增强希望的信号成分。
- 频谱分析是指对信号的频谱进行分析，常用的方法包括傅里叶变换和快速傅里叶变换等。
- 信号重建是指将经过处理的离散信号恢复成连续信号，常用的方法包括插值和重采样等。
三、数字信号处理的应用数字信号处理在多个领域都有着广泛的应用，下面以通信领域和音频处理领域为例进行介绍。
数字信号处理
数字信号处理数字信号处理（Digital Signal Processing，DSP）是一种利用数字计算机对连续或离散信号进行处理的技术。
它在现代通信、音频、图像、视频以及其他领域中得到广泛应用。
本文将介绍数字信号处理的基本概念、应用领域以及发展趋势。
一、基本概念数字信号处理是将连续信号转换为离散信号，并利用数字计算机对其进行处理和分析的过程。
它的基本原理是将连续信号进行采样、量化和编码，得到离散信号后通过算法进行处理。
数字信号处理可以实现信号的滤波、锐化、压缩等功能，从而提高信号的质量和传输效率。
二、应用领域1. 通信系统：数字信号处理在通信系统中发挥着重要作用。
通过数字信号处理技术，可以实现信号的编码、调制、解调、信道均衡等功能，提高通信质量和系统性能。
2. 音频处理：数字音频处理是将模拟音频信号转换为数字形式，并对其进行处理的过程。
数字音频处理可以实现音频的录制、混音、均衡、降噪等功能，广泛应用于音乐制作、电影制作、语音识别等领域。
3. 图像处理：数字图像处理是将模拟图像信号转换为数字形式，并对其进行处理的过程。
通过数字图像处理技术，可以实现图像的增强、去噪、压缩、分割等功能，广泛应用于医学影像、遥感图像、安全监控等领域。
4. 视频处理：数字视频处理是将模拟视频信号转换为数字形式，并对其进行处理的过程。
数字视频处理可以实现视频的压缩、解码、编辑、特效处理等功能，广泛应用于视频会议、视频监控、数字电视等领域。
5. 生物医学信号处理：数字信号处理在医学领域有着重要的应用价值。
通过对生物医学信号进行处理，可以实现心电图分析、脑电图分析、血压信号处理等功能，对疾病的诊断和治疗具有重要意义。
三、发展趋势随着计算机技术的不断进步，数字信号处理领域也在不断发展。
未来的发展趋势主要包括以下几个方面：1. 实时性能提升：随着计算机处理能力的提高，数字信号处理系统的实时性能将得到显著提升。
这将为实时语音、视频通信等领域带来更好的用户体验。
数字信号处理
数字信号处理数字信号处理（Digital signal processing，DSP）是一门广泛应用于信号处理领域的技术。
传统的信号处理技术是指将连续信号进行分析和处理，而数字信号处理则是指将连续信号通过采样和量化的方式转化为离散信号，然后对这些离散信号进行数字化的运算和处理。
数字信号处理的基本原理是将模拟信号转换为数字信号，然后按照数学模型进行数字信号的处理，最后再通过数字信号转换回模拟信号。
数字信号处理在现代通信、音频、视频、图像、控制等领域得到了广泛的应用，几乎每个人都在日常生活中体验到了数字信号处理的便捷性和高效性。
一、数字信号处理的基础1.离散时间系统：数字信号处理中的离散时间系统（discrete time system）是指使用离散的时序来描述的系统，该系统输入和输出的信号都是离散信号。
离散时间系统有多种类型，包括差分方程系统、线性时不变系统（LTI）和非线性时变系统（NLTV）等。
2.数字信号：数字信号是时域离散和幅度量化的信号，可以通过采样和量化的方式将连续信号转变为离散信号。
数字信号可以用一系列的数字来表示，由于数字信号处于离散状态，因此操作数域也是离散的。
3.频域：频域是指信号在频率上的展示，包括信号的功率谱、频谱和相位谱等等。
数字信号处理中，频域变换是一种将时域信号转换为频域信号的变换，常见的频域变换包括傅里叶变换、快速傅里叶变换和Z变换等。
4.量化：量化是将模拟信号转化为数字信号的必要步骤，它将连续和无限的模拟信号转化为离散和有限的数字信号。
量化方法包括线性量化和非线性量化两种，其中非线性量化更适用于高动态范围（HDR）信号等应用场合。
二、数字信号处理的应用数字信号处理在通讯、音频、视频、图像等领域得到广泛应用。
下面是其中几个应用领域的浅析。
1.通信：数字信号处理在通信领域中最广泛的应用之一是数字调制和解调。
数字调制将数字信号转化为模拟信号，然后发送到接收端。
在接收端，通过数字解调将模拟信号转化为数字信号。
数字信号处理 名词解释
数字信号处理是一种重要的信号处理技术，广泛应用于各个领域。随着科技的不断发展，数字信号处理的应用范围将会更加广泛，为人类的生活和工作带来更多便利和效益。希望未来能够有更多的科学家和工程师投入数字信号处理的研究与开发，推动这一领域的不断进步和完善。【1391字】
第四篇示例：
数字信号处理是指以数字方式对信号进行处理和分析的技术领域，其在许多领域都有着广泛的应用，如通信、控制系统、医学成像、音频处理等。在数字信号处理中，信号通常以数字形式进行表示和处理，包括对信号进行采样、量化和编码等步骤，通过数字滤波、变换、解调等算法来实现对信号的处理和分析。
数字信号处理的基本概念包括采样、量化和编码。采样是将连续时间信号转换为离散时间信号的过程，即以一定的时间间隔对信号进行采样。量化是将信号的幅度值转换为离散的幅度级别，为了方便数字表示和处理。编码是将量化后的信号转换为数字形式，通常使用二进制数来表示。
另一个常见的数字信号处理技术是信号变换，如傅里叶变换、离散傅里叶变换、小波变换等。这些变换技术可以将信号从时域转换到频域，分析信号的频谱特性。傅里叶变换可以将信号分解为不同频率的正弦波成分，用于频谱分析和滤波。小波变换则可以提供更好的时频局部性能，适用于信号的多尺度分析和处理。
数字信号处理还广泛应用于音频处理、图像处理、视频处理等领。在图像处理中，数字信号处理技术被用于图像压缩、图像滤波、图像增强等。在视频处理中，数字信号处理技术被用于视频编码、视频增强、视频分析等。
2. 采样：采样是将连续信号转换成离散信号的过程。通过在连续信号中取样一定时间间隔内的数值，并把这些数值转换为数字形式，就可以得到离散信号。
数字信号处理
数字信号处理数字信号处理（Digital Signal Processing，简称DSP）是指通过数学运算和算法实现对数字信号的分析、处理和改变的技术。
它广泛应用于通信、音频、视频、雷达、医学图像等领域，并且在现代科技发展中发挥着重要作用。
本文将介绍数字信号处理的基本原理和应用，以及相关的算法和技术。
一、数字信号处理的基本原理数字信号处理的基本原理是将连续的模拟信号转换为离散的数字信号，再通过算法对数字信号进行处理。
这个过程主要包括信号采样、量化和编码三个步骤。
1. 信号采样：信号采样是指以一定的时间间隔对连续的模拟信号进行离散化处理，得到一系列的采样点。
通过采样，将连续的信号转换为离散的信号，方便进行后续的处理和分析。
2. 量化：量化是指对采样得到的信号进行幅度的离散化处理，将连续的幅度变为离散的幅度级别。
量化可以采用线性量化或非线性量化的方式，通过确定幅度级别的个数来表示信号的幅度。
3. 编码：编码是指对量化后的信号进行编码处理，将其转换为数字形式的信号。
常用的编码方式包括二进制编码、格雷码等，在信息传输和存储过程中起到重要作用。
二、数字信号处理的应用领域数字信号处理被广泛应用于各个领域，以下介绍几个主要的应用领域：1. 通信领域：在通信领域中，数字信号处理用于信号的调制、解调、编码、解码等处理过程。
通过数字信号处理，可以提高通信系统的性能和可靠性，实现高速、高质量的数据传输。
2. 音频和视频处理：在音频和视频处理领域，数字信号处理可以用于音频和视频的压缩、解压、滤波、增强等处理过程。
通过数字信号处理，可以实现音频和视频信号的高保真传输和高质量处理。
3. 医学图像处理：在医学图像处理领域，数字信号处理可以用于医学图像的增强、分割、识别等处理过程。
通过数字信号处理，可以提高医学图像的质量和准确性，帮助医生进行疾病的诊断和治疗。
4. 雷达信号处理：在雷达领域，数字信号处理可以用于雷达信号的滤波、目标检测、跟踪等处理过程。
数字信号处理英文翻译
英文原文The simulation and the realization of the digital filter With the information age and the advent of the digital world, digital signal processing has become one of today's most important disciplines and door technology. Digital signal processing in communications, voice, images, automatic control, radar, military, aerospace, medical and household appliances, and many other fields widely applied. In the digital signal processing applications, the digital filter is important and has been widely applied.1、figures Unit on :Analog and digital filtersIn signal processing, the function of a filter is to remove unwanted parts of the signal, such as random noise, or to extract useful parts of the signal, such as the components lying within a certain frequency range.There are two main kinds of filter, analog and digital. They are quite different in their physical makeup and in how they work. An analog filter uses analog electronic circuits made up from components such as resistors, capacitors and op amps to produce the required filtering effect. Such filter circuits are widely used in such applications as noise reduction, video signal enhancement, graphic equalisers in hi-fi systems, and many other areas. There are well-established standard techniques for designing an analog filter circuit for a given requirement. At all stages, the signal being filtered is an electrical voltage or current which is the direct analogue of the physical quantity (e.g. a sound or video signal or transducer output) involved. A digital filter uses a digital processor to perform numerical calculations on sampled values of the signal. The processor may be a general-purpose computer such as a PC, or a specialised DSP (Digital Signal Processor) chip. The analog input signal must first be sampled and digitised using an ADC (analog to digital converter). The resulting binary numbers, representing successive sampled values of the input signal, are transferred to the processor, which carries out numerical calculations on them. These calculations typically involve multiplying the input values by constants and adding the products together. If necessary, the results of these calculations, which now represent sampled values of the filtered signal, are output through a DAC (digital to analog converter) to convert the signal back to analog form.Note that in a digital filter, the signal is represented by a sequence of numbers, rather than a voltage or current.Unit refers to the input signals used to filter hardware or software. If the filter input, output signals are separated, they are bound to respond to the impact of the Unit is separated, such as digital filters filter definition. Digital filter function, which was to import sequences X transformation into export operations through a series Y.According to figures filter function 24-hour live response characteristics, digital filters can be divided into two, namely, unlimited long live long live the corresponding IIR filter and the limited response to FIR filters. IIR filters have the advantage of the digital filter design can use simulation results, and simulation filter design of a large number of tables may facilitate simple. It is the shortcomings of the nonlinear phase; Linear phase if required, will use the entire network phase-correction. Image processing and transmission of data collection is required with linear phase filters identity. And FIR linear phase digital filter to achieve, but an arbitrary margin characteristics. Impact from the digital filter response of the units can be divided into two broad categories : the impact of the limited response (FIR) filters, and unlimited number of shocks to (IIR) digital filters.FIR filters can be strictly linear phase, but because the system FIR filter function extremity fixed at the original point, it can only use the higher number of bands to achieve their high selectivity for the same filter design indicators FIR filter called band than a few high-IIR 5-10 times, the cost is higher, Signal delay is also larger. But if the same linear phase, IIR filters must be network-wide calibration phase, the same section also increase the number of filters and network complexity. FIR filters can be used to achieve non-Digui way, not in a limited precision of a shock, and into the homes and quantitative factors of uncertainty arising from the impact of errors than IIR filter small number, and FIR filter can be used FFT algorithms, the computational speed. But unlike IIR filter can filter through the simulation results, there is no ready-made formula FIR filter must use computer-aided design software (such as MATLAB) to calculate. So, a broader application of FIR filters, and IIR filters are not very strict requirements on occasions.2、MATLAB introducedMATLAB is a matrix laboratory (Matrix Laboratory) is intended. In addition to anexcellent value calculation capability, it also provides professional symbols terms, word processing, visualization modeling, simulation and real-time control functions. MATLAB as the world's top mathematical software applications, with a strong engineering computing, algorithms research, engineering drawings, applications development, data analysis and dynamic simulation, and other functions, in aerospace, mechanical manufacturing and construction fields playing an increasingly important role. And the C language function rich, the use of flexibility, high-efficiency goals procedures. High language both advantages as well as low level language features. Therefore, C language is the most widely used programming language. Although MATLAB is a complete, fully functional programming environment, but in some cases, data and procedures with the external environment of the world is very necessary and useful. Filter design using Matlab, could be adjusted with the design requirements and filter characteristics of the parameters, visual simple, greatly reducing the workload for the filter design optimization.In the electricity system protection and secondary computer control, many signal processing and analysis are based on are certain types Yeroskipou and the second harmonics of the system voltage and current signals (especially at D process), are mixed with a variety of complex components, the filter has been installed power system during the critical components. Current computer protection and the introduction of two digital signal processing software main filter. Digital filter design using traditional cumbersome formula, the need to change the parameters after recalculation, especially in high filters, filter design workload. Uses MATLAB signal processing boxes can achieve rapid and effective digital filter design and simulation.MATLAB is the basic unit of data matrix, with its directives Biaodashi mathematics, engineering, commonly used form is very similar, it is used to solve a problem than in MATLAB C, Fortran and other languages End precision much the same thing. The popular MATLAB 5.3/Simulink3.0 including hundreds of internal function with the main pack and 30 types of tool kits (Toolbox). kits can be divided into functional tool kits and disciplines toolkit. MATLAB tool kit used to expand the functional symbols terms, visualization simulation modelling, word processing and real-time control functions. professional disciplines toolkit is a stronger tool kits, tool kits control, signal processing tool kit, tool kits, etc. belonging to such communicationsMATLAB users to open widely welcomed. In addition to the internal function, all thepackages MATLAB tool kits are readable document and the document could be amended, modified or users through Yuanchengxu the construction of new procedures to prepare themselves for kits.3、Digital filter designDigital filter design of the basic requirementsDigital filter design must go through three steps :(1) Identification of indicators : In the design of a filter, there must be some indicators. These indicators should be determined on the basis of the application. In many practical applications, digital filters are often used to achieve the frequency operation. Therefore, indicators in the form of general jurisdiction given frequency range and phase response. Margins key indicators given in two ways. The first is absolute indicators. It provides a function to respond to the demands of the general application of FIR filter design. The second indicator is the relative indicators. Its value in the form of answers to decibels. In engineering practice, the most popular of such indicators. For phase response indicators forms, usually in the hope that the system with a linear phase frequency bands human. Using linear phase filter design with the following response to the indicators strengths：①it only contains a few algorithms, no plural operations；②there is delay distortion, only a fixed amount of delay; ③the filter length N (number of bands for N-1), the volume calculation for N/2 magnitude.(2) Model approach : Once identified indicators can use a previous study of the basic principles and relationships, a filter model to be closer to the target system.(3) Achieved : the results of the above two filters, usually by differential equations, system function or pulse response to describe. According to this description of hardware or software used to achieve it.4、Introduced FPGAProgrammable logic device is a generic logic can use a variety of chips, which is to achieve ASIC ASIC (Application Specific Integrated Circuit) semi-customized device, Its emergence and development of electronic systems designers use CAD tools to design their own laboratory in the ASIC device. Especially FPGA (Field Programmable Gate Array) generated and development, as a microprocessor, memory, the figures for electronic system design and set a new industry standard (that is based on standard product sales catalogue in the market to buy).Is a digital system for microprocessors, memories, FPGA or three standard building blocks constitute their integration direction.Digital circuit design using FPGA devices, can not only simplify the design process and can reduce the size and cost of the entire system, increasing system reliability. They do not need to spend the traditional sense a lot of time and effort required to create integrated circuits, to avoid the investment risk and become the fastest-growing industries of electronic devices group. Digital circuit design system FPGA devices using the following main advantages(1)Design flexibleUse FPGA devices may not in the standard series device logic functional limitations. And changes in system design and the use of logic in any one stage of the process, and only through the use of re-programming the FPGA device can be completed, the system design provides for great flexibility.(2) Increased functional densityFunctional density in a given space refers to the number of functional integration logic. Programmable logic chip components doors several high, a FPGA can replace several films, film scores or even hundreds of small-scale digital IC chip illustrated in the film. FPGA devices using the chip to use digital systems in small numbers, thus reducing the number of chips used to reduce the number of printed size and printed, and will ultimately lead to a reduction in the overall size of the system.(3) Improve reliabilityPrinting plates and reduce the number of chips, not only can reduce system size, but it greatly enhanced system reliability. A higher degree of integration than systems in many low-standard integration components for the design of the same system, with much higher reliability. FPGA device used to reduce the number of chips required to achieve the system in the number printed on the cord and joints are reduced, the reliability of the system can be improved.(4) Shortening the design cycleAs FPGA devices and the programmable flexibility, use it to design a system for longer than traditional methods greatly shortened. FPGA device master degrees high, use printed circuit layout wiring simple. At the same time, success in the prototype design, the development of advanced tools, a high degree of automation, their logic is very simple changes quickly.Therefore, the use of FPGA devices can significantly shorten the design cycle system, and speed up the pace of product into the market, improving product competitiveness.(5) Work fastFPGA/CPLD devices work fast, generally can reach several original Hertz, far larger than the DSP device. At the same time, the use of FPGA devices, the system needed to achieve circuitclasses and small, and thus the pace of work of the entire system will be improved.(6)Increased system performance confidentialityFPGA is the English abbreviation Field of Programmable Gate Array for the site programmable gate array, which is in Pal, Gal, Epld, programmable device basis to further develop the product. It is as ASIC (ASIC) in the field of a semi-customized circuit and the emergence of both a customized solution to the shortage circuit, but overcome the original programmable devices doors circuit few limited shortcomings.FPGA logic module array adopted home (Logic Cell Array), a new concept of internal logic modules may include CLB (Configurable Logic Block), export import module IOB (Input Output Block) and internal links (Interconnect) 3. FPGA basic features are :(1) Using FPGA ASIC design ASIC using FPGA circuits, the chip can be used,while users do not need to vote films production.(2) FPGA do other customized or semi-customized ASIC circuits throughout the Chinese specimen films.3) FPGA internal capability and rich I/O Yinjue.4) FPGA is the ASIC design cycle, the shortest circuit, the lowest development costs, risks among the smallest device5) FPGA using high-speed Chmos crafts, low consumption, with CMOS, TTL low-power compatibleIt can be said that the FPGA chip is for small-scale systems to improve system integration, reliability one of the bestCurrently FPGA many varieties, the Revenue software series, TI companies TPC series, the fiex ALTERA company seriesFPGA is stored in films from the internal RAM procedures for the establishment of the state of its work, therefore, need to programmed the internal Ram. Depending on the differentconfiguration, users can use a different programming methodsPlus electricity, FPGA, EPROM chips will be read into the film, programming RAM中data, configuration is completed, FPGA into working order. Diaodian, FPGA resume into white films, the internal logic of relations disappear, FPGA to repeated use. FPGA's programming is dedicated FPGA programming tool, using generic EPROM, prom programming device can. When the need to modify functional FPGA, EPROM can only change is. Thus, with a FPGA, different programming data to produce different circuit functions. Therefore, the use of FPGA very flexible.There are a variety of FPGA model : the main model for a parallel FPGA plus a EPROM manner; From the model can support a number of films FPGA; serial prom programming model could be used serial prom FPGA programming FPGA; The external model can be engineered as microprocessors from its programming microprocessors.Verilog HDL is a hardware description language for the algorithm level, doors at the level of abstract level to switch-level digital system design modelling. Modelling of the target figure by the complexity of the system can be something simple doors and integrity of electronic digital systems. Digital system to the levels described, and in the same manner described in Hin-time series modelling.Verilog HDL language with the following description of capacity : design behaviour characteristics, design data flow characteristics, composition and structure designed to control and contain the transmission and waveform design a certification mechanism. All this with the use of a modelling language. In addition, Verilog HDL language programming language interface provided by the interface in simulation, design certification from the external design of the visit, including specific simulation control and operation.Verilog HDL language grammar is not only a definition, but the definition of each grammar structure are clear simulation, simulation exercises. Therefore, the use of such language to use Verilog simulation models prepared by a certification. From the C programming language, the language inherited multiple operating sites and structures. Verilog HDL provides modelling capacity expansion, many of the initial expansion would be difficult to understand. However, the core subsets of Verilog HDL language very easy to learn and use, which is sufficient for most modelling applications. Of course, the integrity of the hardware description language is the most complex chips from the integrity of the electronic systems described.5、In troduction of DSPToday, DSP is w idely used in the modern techno logy and it has been the key part of many p roducts and p layed more and mo re impo rtant ro le in our daily life.Recent ly, Northw estern Po lytechnica lUniversity Aviation Microelect ronic Center has comp leted the design of digital signal signal p rocesso r co re NDSP25, w h ich is aim ing at TM S320C25 digital signal p rocesso r of Texas Inst rument TM S320 series. By using top 2dow n design flow , NDSP25 is compat ible w ith inst ruct ion and interface t im ing of TM S320C25.Digital signal processors (DSP) is a fit for real-time digital signal processing for high-speed dedicated processors, the main variety used for real-time digital signal processing to achieve rapid algorithms. In today's digital age background, the DSP has become the communications, computer, and consumer electronics products, and other fields based device.Digital signal processors and digital signal processing is inseparably, we usually say "DSP" can also mean the digital signal processing (Digital Signal Processing), is that in this digital signal processors Lane. Digital signal processing is a cover many disciplines applied to many areas and disciplines, refers to the use of computers or specialized processing equipment, the signals in digital form for the collection, conversion, recovery, valuation, enhancement, compression, identification, processing, the signals are compliant form. Digital signal processors for digital signal processing devices, it is accompanied by a digital signal processing to produce. DSP development process is broadly divided into three phases : the 20th century to the 1970s theory that the 1980s and 1990s for the development of products. Before the emergence of the digital signal processing in the DSP can only rely on microprocessors (MPU) to complete. However, the advantage of lower high-speed real-time processing can not meet the requirements. Therefore, until the 1970s, a talent made based DSP theory and algorithms. With LSI technology development in 1982 was the first recipient of the world gave birth to the DSP chip. Years later, the second generation based on CMOS工艺DSP chips have emerged. The late 1980s, the advent of the third generation of DSP chips. DSP is the fastest-growing 1990s, there have been four successive five-generation and the generation DSP devices. After 20 years of development, the application of DSP products has been extended to people's learning, work and all aspects of life and gradually become electronics products determinants.中文翻译数字滤波器的仿真与实现随着信息时代和数字世界的到来，数字信号处理已成为当今一门极其重要的学科和技术领域。
数字信号处理
数字信号处理什么是数字信号处理？数字信号处理（Digital Signal Processing，DSP）是一种广泛应用于信息处理的技术领域。
它涉及对以离散时间表示的信号进行获取、分析、变换和合成。
数字信号处理技术可以应用于音频、视频、图像、通信和控制等领域，从而提高信号质量、提取有用信息、实现实时控制等多种功能。
数字信号处理的基本原理数字信号处理的基本原理可以总结为以下几个步骤：1.信号获取：通过传感器、麦克风、摄像头等设备获取模拟信号或数字信号。
2.采样：将连续的模拟信号转换为离散时间信号，即将模拟信号在时间上进行等间隔采样。
3.量化：将采样后的信号的幅度值转换为有限数量的离散值。
4.编码：对量化后的信号进行编码，将其表示为二进制形式，方便在计算机中处理和存储。
5.数字信号处理算法：对编码后的数字信号进行一系列算法处理，包括滤波、频谱分析、变换等。
6.逆变换和解码：将处理后的数字信号转换回模拟信号，以便输出和使用。
数字信号处理的算法和技术在数字信号处理领域，有许多常用的算法和技术。
下面介绍几种常见的算法和技术：1. 滤波器滤波器是数字信号处理中常用的一种算法。
它用于改变信号的频率响应，滤除不需要的频率分量或增强需要的频率分量。
低通滤波器用于滤除高频成分，高通滤波器用于滤除低频成分，带通滤波器用于保留某一频率范围的信号成分。
2. 快速傅里叶变换（FFT）快速傅里叶变换是一种高效的频谱分析算法，它可以将信号从时域转换为频域。
通过傅里叶变换，可以对信号的频率分量进行分析，从而实现频谱分析、频域滤波等操作。
3. 信号压缩信号压缩是一种将信号表示为更紧凑形式的技术。
通过去除冗余信息和利用信号的统计特性，可以实现对信号的压缩和恢复。
4. 语音处理语音处理是数字信号处理中的一个重要应用领域。
它涉及到语音信号的获取、分析、合成和识别等方面。
语音处理技术可以用于语音识别、语音合成、语音增强等场景。
数字信号处理的应用数字信号处理技术在许多领域得到了广泛的应用，下面介绍几个典型的应用领域：1. 通信数字信号处理在通信领域中发挥了重要作用。
科技英语 5数字信号处理器原文与翻译
Words and Expressionsfollow v.遵循memory n.存储器register n.寄存器access v.访问overlap v. 重叠pipelining n. 流水线操作multiplier n. 乘法器accumulator n. 累加器shifter n.移位器reference n. 寻址mantissa n.尾数exponent n. 指数cycle n. 机器周期customize v.定制，用户化package v.封装digital signal processor 数字信号处理器von Neumann architecture 冯·诺伊曼结构shared single memory 单一共享存储器program instruction 程序指令harvard architecture 哈佛结构fetch from 从…获取circular buffer 循环缓冲区，环形缓冲区address generator 地址产生器fixed point 定点floating point 浮点binary point 二进制小数点available precision 可用精度dynamic range 动态范围scale range 量程smallest Resolvable Difference 最小分辨率scientific notation 科学计数法assembly language 汇编语言multi-function instructions 多功能指令parallel architecture 并行结构looping scheme 循环机制sampling frequency 采样频率on-chip memory 片内存储器well-matched 非常匹配software tools 软件开发工具low level programming language 低级编程语言high level programming language 高级编程语言third party software 第三方软件board level product 板级产品data register 数据寄存器ALU=Arithmetic Logical Unit 运算逻辑单元program sequencer 程序定序器peripheral sections 外设single integrated circuit 单片集成电路cellular telephone 蜂窝电话printed circuit board 印刷电路板licensing agreement 专利使用权转让协定custom devices 定制器件extra memory 附加存储器stand alone 单机third party developer 第三方开发商multimedia operations 多媒体操作merged into 融合calculation-intensive algorithm运算密集型算法Unit 5 Digital Signal ProcessorsDigital signal processing tasks can be performed by all processors. Specialized digital signal processors(DSPs), however, perform these tasks most efficiently and most quickly. While traditional processors follow the Von Neumann architecture[]1model, which assumes a shared single memory to be used for both program instructions and data, DSPs use the Harvard or modified Harvard architecture []2, which includes multiple program and data memories, along with multiple buses to access them. This arrangement means that much less waiting is required when instructions or numbers are fetched from memory. In fact at least one of each can be fetched simultaneously. Such overlapping of tasks is called pipelining. In addition to multiple memories and buses, all DSPs have fast multipliers, accumulators, and shifters, and many have hardware support for circular buffers. Address generators can speed up accesses to memory locations referenced by registers.DSPs are available in two major classes: fixed point and floating point. The fixed point class represents real numbers in a fixed number of bits. The position of the binary point (similar to the decimal point) can be controlled by the programmer, and determines the range of numbers that can be represented. As the range increases, though, the available precision goes down, since fewer bits lie to the right of the binary point. In 16 bits, the formats 16.0, 15.1, 14.2, 13.3, 12.4, 11.5, 10.6, 9.7, 8.8, 7.9, 6.10, 5.11, 4.12, 3.13, 2.14, and 1.15 are possible. The dynamic range, calculated as 20log (Full Scale Range/Smallest2= 96.3 dB.Resolvable Difference), remains the same for all 16-bit formats, 20log16Figure 6.3 Van Neumann architectureFigure 6.4 Harvard architectureFloating point DSPs represent real numbers using a mantissa and an exponent , similar to scientific notation : Many combine mantissa and exponent into a 32-bit number. The dynamic range for floating point devices is calculated from the largest and smallest multipliers E 2, where E is the exponent. Thus, for a representation that uses 24 bits for the mantissa and 8 bits for the signed exponent, the dynamic range is 20 log (1281272/2-) = 1535.3 dB. A large dynamic range means the system has great power to represent a wide range of input signals, from very small to very large.Assembly language is the command language for DSPs. DSPs often have specialized instructions that make programming for common DSP tasks more convenient and more efficient. For example, most DSPs offer multi-function instructions that exploit their parallel architecture . Other constructs that are frequently offered are efficient looping schemes , since so many DSP operations involve a great deal of repetition.Choosing a DSP for a particular application is not always easy. The first decision is on whether tochoose a fixed point or a floating point device []3. Generally, fixed point devices are cheaper and quicker,but floating point devices are more convenient to program and more suited to calculation-intensive algorithms . Second, the data width of the DSP determines how accurately it can represent numbers. Speed is another issue, not only how many cycles occur in each second, but also how many instructions execute in each cycle and how much work each of these instructions accomplishes. One way to assess the minimum requirements for the DSP is to estimate how many instructions must be executed for each received sample. When this number is multiplied by the sampling frequency , the minimum required number of instructions per second is obtained.The specific hardware and software features offered by a particular DSP can make one choice betterthan another, as can the amount of on-chip memory available []4. Sometimes DSPs are chosen becausewell-matched supporting hardware, particularly A/D and D/A converters, is obtainable. Frequently, the quality and convenience of the software tools, for both low level and high level programming languages, are also major factors, as is the availability of third party software. As always, cost is a factor. In fact, quite often, the DSP that is fastest and offers the most features, but also fits the budget, is the one selected.DSPs can be purchased in three forms, as a core, as a processor, and as a board level product. In DSP, the term "core" refers to the section of the processor where the key tasks are carried out, including the data registers, multiplier, ALU, address generator, and program sequencer. A complete processor requires combining the core with memory and interfaces to the outside world. While the core and these peripheral sections are designed separately, they will be fabricated on the same piece of silicon, making the processor a single integrated circuit.Suppose you build cellular telephones and want to include a DSP in the design. You will probably want to purchase the DSP as a processor, that is, an integrated circuit that contains the core, memory and other internal features. To incorporate this IC in your product, you have to design a printed circuit board where it will be soldered in next to your other electronics. This is the most common way that DSPs are used.Now, suppose the company you work for manufactures its own integrated circuits. In this case, you might not want the entire processor, just the design of the core. After completing the appropriate licensing agreement, you can start making chips that are highly customized to your particular application. This gives you the flexibility of selecting how much memory is included, how the chip receives and transmits data, how it is packaged, and so on.Custom devices of this type are an increasingly important segment of the DSP marketplace.There are several dozen companies that will sell you DSPs already mounted on a printed circuit board. These have such features as extra memory, A/D and D/A converters, EPROM sockets, multiple processors on the same board, and so on. While some of these boards are intended to be used as stand alone computers, most are configured to be plugged into a host, such as a personal computer. Companies that make these types of boards are called Third Party Developers. The best way to find them is to ask the manufacturer of the DSP you want to use. Look at the DSP manufacturer's website; if you don't find a list there, send them an e-mail. They will be more than happy to tell you who are using their products and how to contact them.Keep in mind that the distinction between DSPs and other microprocessors is not always a clear line. For instance, look at how Intel describes the MMX technology addition to its Pentium processor: "Intel engineers have added 57 powerful new instructions specifically designed to manipulate and process video, audio and graphical data efficiently. These instructions are oriented to the highly parallel, repetitivesequences often found in multimedia operations . "In the future, we will undoubtedly see more DSP-like functions merged into traditional microprocessors and microcontrollers. The Internet and other multimedia applications are a strong driving force for these changes. These applications are expanding so rapidly, in twenty years it is very possible that the Digital Signal Processor may be the "traditional" microprocessor.Notes1. “冯·诺伊曼结构”取名字美国杰出的数学家—约翰·冯·诺伊曼（John Von Neumann,1903~1957）。
DSP的发展、现况及其应用中英文翻译
DSP的发展、现况及其应用数字信号处理（Digital Signal Processing，简称DSP）是一门涉及许多学科而又广泛应用于许多领域的新兴学科。
DSP有两种含义：digital Signal Processing（数字信号处理）、Digital Signal Processor（数字信号处理器）。
我们常说的DSP指的是数字信号处理器。
数字信号处理器是一种适合完成数字信号处理运算的处理器。
20世纪60年代以来，随着计算机和信息技术的飞速发展，数字信号处理技术应运而生并得到迅速的发展。
在过去的二十多年时间里，数字信号处理已经在通信等领域得到极为广泛的应用。
数字信号处理是利用计算机或专用处理设备，以数字形式对信号进行采集、变换、滤波、估值、增强、压缩、识别等处理，以得到符合人们需要的信号形式。
数字信号处理是以众多学科为理论基础的，它所涉及的范围极其广泛。
例如，在数学领域，微积分、概率统计、随机过程、数值分析等都是数字信号处理的基本工具，与网络理论、信号与系统、控制论、通信理论、故障诊断等也密切相关。
近来新兴的一些学科，如人工智能、模式识别、神经网络等，都与数字信号处理密不可分。
可以说，数字信号处理是把许多经典的理论体系作为自己的理论基础，同时又使自己成为一系列新兴学科的理论基础。
DSP主要应用在数字信号处理中，目的是为了能够满足实时信号处理的要求，因此需要将数字信号处理中的常用运算执行的尽可能快，这就决定了DSP的特点和关键技术。
适合数字信号处理的关键技术：DSP包含乘法器、累加器、特殊地址发生器、领开销循环等；提高处理速度的关键技术：流水线技术、并行处理技术、超常指令（VLIW）、超标量技术、DMA等。
从广义上讲，DSP、微处理器和微控制器（单片机）等都属于处理器，可以说DSP是一种CPU。
DSP和一般的CPU又不同，最大的区别在于：CPU是冯.诺伊曼结构的；DSP是数据和地址空间分开的哈佛结构。
数字信号处理词汇英文翻译
complex conjugate pairs复共轭对
151
quantization effects in digital filters数字滤波器中的量化效应
152
roundofferror舍入误差
153
sample-by-sample processing algorithm逐个样本处理算法
96
unit step单位阶跃信号
97
alternating step正负交替的阶跃信号
98
Z-transform Z变换
99
positive正的
100
negative负的
101
region of convergence收敛域
102
marginally stable临界稳定
103
polynomial多项式
208
Decimation-in-time radix-2 FFT algorithm按时间抽取的基二FFT算法
209
butterfly merging equations蝶形组合公式
210
shuffling重排
211
bit reversal码位倒置
212
fast convolution快速卷积
213
104
denominator分母
105
numerator分子
106
peak峰
107
dip谷
108
partial fraction expansion method部分分式展开法
109
unit circle单位圆
110
double sided complex sinusoid双边复正弦
数字信号处理英文文献及翻译
Digital Signal Processing数字信号处理院系:专业:学号:姓名:【英文原文】Digital Signal Processing1、IntroductionDigital signal processing is will signal to digitally says and deal with the theory and technology. Digital signal processing and analog signal processing is signal processing subset.Digital signal processing algorithm need to use special processing equipment such as computer or digital signal processor and application-specific integrated circuits, etc. Digital signal processing technology and equipment with flexible, precies anti-jamming of strong, equipment of small size, low cost, speed such outstanding advantages, these are simulation signal processing technology and equipment and incomparable.Since the goal of DSP is usually to measure or filter continuous real-world analog signals, the first step is usually to convert the signal from an analog to a digital form, by using an analog to digital converter. Often, the required output signal is another analog output signal, which requires a digital to analog converter. Even if this process is more complex than analog processing and has a discrete value range, the stability of digital signal processing thanks to error detection and correction and being less vulnerable to noise makes it advantageous over analog signal processing for many, though not all, applications.DSP algorithms have long been run on standard computers, on specialized processors called digital signal processors (DSP)s, or on purpose-built hardware such as application-specific integrated circuit (ASICs). Today there are additional technologies used for digital signal processing including more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial applications such as motor control), and stream processors, among others.In DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information that is the frequency spectrum. Autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space.2、Signal SamplingWith the increasing use of computers the usage of and need for digital signal processing has increased. In order to use an analog signal on a computer it must be digitized with an analog to digital converter (ADC). Sampling is usually carried out in two stages, discretization and quantization. In the discretization stage, the space ofsignals is partitioned into equivalence classes and quantization is carried out by replace the signal with representative signal values are approximated by values from a finite set.The Nyquist-Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the samples if the sampling frequency is greater than twice the highest frequency of the signal. In practice, the sampling frequency is often significantly more than twice the required bandwidth.A digital to analog converter (DAC) is used to convert the digital signal back to analog signal. The use of a digital computer is a key ingredient in digital control systems.3 、Time and Space DomainsThe most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Filtering generally consists of some transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters, for example: A “linear” filter is a linear transformation of input samples; other filters are “non-linear.” Linear filters satisfy the superposition cond ition, i.e. if an input is a weighted linear combination of different signals, the output is an equally weighted linear combination of the corresponding output signals.A “causal” filter uses only previous samples of the input or output signals; while a “n on-causal” filter uses future input samples. A non-causal filter can usually be changed into a causal filter by adding a delay to it.A “time-invariant” filter has constant properties over time; other filters such as adaptive filters change in time.Some f ilters are “stable”, others are “unstable”. A stable filter produces an output that converges to a constant value with time, or remains bounded within a finite interval. An converges to a constant value with time, or remains bounded within a finite interval. An unstable filter can produce an output that grows without bounds, with bounded or even zero input.A “Finite Impulse Response” (FIR) filter uses only the input signal, while an “Infinite Impulse Response” filter (IIR) uses both the input signal and pr evious samples of the output signal. FIR filters are always stable, while IIR filters may be unstable.Most filters can be described in Z-domain (a superset of the frequency domain) by their transfer functions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response. The output of an FIR filter to any given input may be calculated by convolving the input signal with the impulse response. Filters can also be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions.4、Frequency DomainSignals are converted from time or space domain to the frequency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fourier transformis converted to the power spectrum, which is the magnitude of each frequency component squared.The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are presentin the input signal and which are missing.Filtering, particularly in non real-time work can also be achieved by converting to the frequency domain, applying the filter and then converting back to the time domain. This is a fast, O (n log n) operation, and can give essentially any filter shape including excellent approximations to brickwall filters.There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the frequency components with smaller magnitude while retaining the order of magnitudes of frequency components. Frequency domain analysis is also called spectrum or spectral analysis.5、Signal ProcessingSignals commonly need to be processed in a variety of ways. For example, the output signal from a transducer may well be contaminated with unwanted electrical “noise”. The electrodes attached to a patient’s chest when an ECG is taken measure tiny electrical voltage changes due to the activity of the heart and other muscles. The signal is often strongly affected by “mains pickup” due to electrical interference from the mains supply. Processing the signal using a filter circuit can remove or at least reduce the unwanted part of the signal. Increasingly nowadays, the filtering of signals to improve signal quality or to extract important information is done by DSP techniques rather than by analog electronics.6、Development of DSPThe development of digital signal processing dates from the 1960’s with the use of mainframe digital computers number-crunching applications such an the Fast Fourier Transform (FFT), which allows the frequency spectrum of a signal to be computed rapidly. These techniques are not widely used at that time, because suitable computing equipment was generally available only in universities and other scientific research institutions.7、Digital Signal Processors (DSPs)The introduction of the microprocessor in the late 1970’s and early 1980’s made it possible for DSP techniques to be used in a much wider range of applications. However, general-purpose microprocessors such as the Inter x86 family are not ideally suited to the numerically-intensive requirements of DSP, and during the 1980’s the increasing importance of DSP led several major electronics manuf acturers (such as Texas Instruments, Analog Devices and Motorola) to develop Digital Signal Processor chips-specialized microprocessors with architectures designed specifically for the types of operations required in digital signal processing.(Note that the acronym DSP can variously mean Digital Signal Processing, the term used for a wide range of techniques for processing signals digitally, or Digital Signal Processor,a specialized type of microprocessor chip). Like a general-purpose microprocessor, a DSP is a programmable device, with its own native instruction code. DSP chip are capable of carrying out millions of floating point operations per second, and like their better-known general-purpose cousins, faster and more powerful versions are continually being introduced. DSPs can also be embedded within complex “system-on-chip” devices, often containing both analog and digital circuitry.8、Applications of DSPGeneral speaking, digital signal processing is the study method of using a digital signal, analysis, transformation, filtering, detetion, modulation and a fast aloorithm door technology subject.DSP technology is nowadays commonplace in such devices as mobile phones, multimedia computers, video recorders, CD players, hard disc drive controllers and modems, and will soon replace analog circuitry in TV sets and telephones. An important application of DSP is in signal compression and decompression. Signal compression is used in digital cellular phones to allow a greater number of calls to be handled simul taneously within each local “cell”. DSP signal compression technology allows people not only to talk to one another but also to see one anther on their computer screens, using small video cameras mounted on the computer monitors, with only a conventional telephone line linking them together. In audio CD systems, DSP technology is used to perform complex error detection and correction on the raw data as it is read from the CD.Although some of the mathematical theory underlying DSP techniques, such as Fourier and Hilbert transforms, digital filter design and signal compression, can be fairly complex, the numerical operations required actually to implement these techniques are very simple, consisting mainly of operations that could be done on a cheap four-function calculator. The architecture of a DSP chip is designed to carry out such operations incredibly fast, processing hundreds of millions of samples every second, to provided real-time performance: that is , the ability to process a signal “live” as it is sampled and then output the processed signal, for example to a loudspeaker or video display. All of the practical examples of DSP applications mentioned earlier, such as hard disc drives and mobile phones, demand real-time operation.The major electronics manufacturers have invested heavily in DSP technology. Because they now find application in mass-market products, DSP chips account for a substantial proportion of the world market for electronic devices. Sales amount to billions of dollars annually, and seem likely to continue to increase rapidly.The main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, RADAR, SONAR, seismology, and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room matching equalization of sound in hi-fi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes.数字信号处理一、数字信号处理的概述数字信号处理是将信号以数字方式表示并处理的理论和技术。
- 1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
- 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
- 3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。
毕业设计(论文)外文资料翻译专业：自动化姓名：学号：外文题目：The Breadth and Depth of DSP外文出处：The Scientist and Engineer's Guideto DSP1 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。
1.1 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。
1.2 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。
1.2.1 复用在世界上大约有10亿电话。
在按几个按钮，开关网络允许其中任何一项，只有几秒钟的任何其他连接。
这项任务的艰巨，是超乎想象！直到20世纪60年代，两个电话之间的连接需要通过机械开关和放大器的模拟语音信号。
一个连接需要一对导线。
相比之下，DSP音频信号转换成串行数字数据流。
由于位可以轻松地交织在一起，后来分开，很多电话交谈可以传输渠道单一。
例如，一个电话标准，被称为T载波系统可以同时传送24个语音信号。
每个语音信号进行采样，每秒8000次，使用一个8位集成的（对数压缩）模拟到数字的转换。
这个结果在64,000比特/秒，所有24个被包含在1.544兆比特/秒的渠道代表每个语音信号。
这个信号可以传输，使用普通电话线，22号铜线，一个典型的互连距离约6000英尺。
数字传输的资金优势是巨大的。
线和模拟开关是昂贵的数字逻辑门价格便宜。
1.2.2 压缩当语音信号数字化，在8000样本/秒，大多数的数字信息是多余的。
也就是说，任何一个样本进行信息主要由邻近的样品重复。
DSP算法已发展到几十个数字化语音信号转换成数据流，需要较少的比特/秒。
这些被称为数据压缩算法。
匹配解压缩算法，用于恢复其原来的形式的信号。
这些算法不同的金额达到压缩和音质。
在一般情况下，减少64千比特/秒的数据传输速率为32千比特/秒的结果，在不损失音质。
当压缩到8千比特/秒的数据传输速率，声音明显受到影响，但仍然可用的长途电话网络。
达到的最高压缩约2千比特/秒，高度扭曲的声音，但可用于某些应用，如军事和海底通信。
1.2.3 回声控制回声是一个严重的问题，在长途电话连接。
当你走进一个电话，你的声音信号传播连接的接收器，它的一部分返回的回声。
如果连接是几百公里内，接收回声所用的时间只有几毫秒。
人类的耳朵习惯于听到这些小的时间延迟的回声，连接听起来很正常。
随着距离变大，回声变得越来越明显和刺激性。
延迟是几百毫秒洲际通信，特别是反感。
数字信号处理攻击这类型的问题，通过测量返回信号，并产生适当的反信号取消违规回声。
同样的技术，允许免提电话用户听取和不战而音频反馈（啸）在同一时间发言。
它也可用于减少环境噪声，取消它与数字产生抗噪。
1.3 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。
1.3.1 音乐从音乐家的麦克风，高保真的扬声器的路径是相当长。
数字数据表示，重要的是要防止通常与模拟存储和操作相关的退化。
这是非常熟悉的人与光盘，录音带的音乐素质。
在一个典型的场景，音乐作品在多个频道或曲目的录音室录制。
在某些情况下，这甚至涉及个别乐器和歌手分别记录。
这样做是为了给录音师更大的灵活性，创造的最终产品。
被称为复杂的过程，结合到最终产品的个别曲目的缩混。
DSP可以在组合提供几个重要的功能，包括：过滤，加法和减法信号，信号的编辑，等等。
最有趣的音乐准备的DSP应用之一是人工混响。
如果各个渠道的简单相加，导致一块听起来体弱及摊薄，音乐家多，如果在户外玩耍。
这是因为听众都深受影响的音乐，通常是在录音室最小的回声或混响内容。
DSP允许人造回声和混响加在混合模拟各种理想的听音环境。
几百毫秒延迟的回声，给像位置的大教堂的印象。
10-20毫秒的延迟添加回声提供更多的适度规模聆听室的看法。
1.3.2 语音生成语音生成和识别被用于人类和机器之间的沟通。
而不是用你的双手和眼睛，你用你的嘴和耳朵。
当你的手和眼睛应做别的东西，如：驾驶汽车，进行手术，或不幸敌人发射你的武器，这是非常方便。
两种方法用于计算机生成的讲话：数码录音和声道模拟。
在数码录音，一个人的扬声器的声音数字化处理和储存，通常在压缩形式。
在播放过程中，存储的数据压缩和转换成模拟信号。
整个小时的录音讲话要求只有约3兆字节的存储空间，即使是小规模的计算机系统内的功能。
这是今天使用的数字语音代最常用的方法。
声道模拟器比较复杂，试图模仿人类创造讲话的物理机制。
人类声道是声腔与商会的大小和形状确定的共振频率。
声音源于声道声和摩擦音，在两种基本方式之一。
浊音，声带振动产生周期脉冲附近的空气进入声乐腔。
相比之下，摩擦音源于在嘈杂的空气湍流，如牙齿和嘴唇，窄缢。
声道模拟器操作产生类似于激发这两种类型的数字信号。
共鸣腔的特点是通过类似共振的激励信号，通过数字滤波器的模拟。
这种方法是在一个非常早期的DSP成功故事，讲拼写，广泛销售的儿童电子学习援助。
1.3.3 语音识别人类语音的自动识别是非常多讲话一代困难。
语音识别是一个经典的东西，人类的大脑好例子，但数码电脑做的很差。
数码电脑可以存储和调用大量数据，在炽烈速度执行数学计算，并没有变得无聊或低效重复的任务。
不幸的是，现今电脑执行得非常糟糕时，面临着与原始的感官数据。
教学计算机发送给您每月的电费是很容易的。
在同一台计算机教学，以了解你的声音，是一大创举。
数字信号处理一般接近语音识别的问题，在两个步骤：特征提取，特征匹配。
传入的音频信号中的每个单词是孤立的，然后分析激发和共鸣频率识别的类型。
这些参数与前面的例子中，找出最接近的说话。
通常情况下，这些系统只有几百字的限制，只能接受具有鲜明的字与字之间的停顿的讲话，以及必须为各扬声器培训。
虽然这是许多商业应用提供足够的，这些限制是震撼人心相比，人类的听觉能力。
有巨大的财政奖励那些生产成功的商业产品，要在这方面做的大量工作。
1.4 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。
1.4.1 雷达雷达是无线电探测和测距的缩写。
在最简单的雷达系统，无线电发射机产生的无线电频率能量的脉冲长几微秒。
此脉冲被送入一个高度定向天线，以光的速度在产生的无线电波传播距离。
飞机在这一波的路径将反映能源回向接收天线的一小部分，位于附近的传输站点。
脉冲传输和接收到的回波之间的运行时间从计算到物体的距离。
发现对象的方向更简单地说，你知道你指出的定向天线时收到回音。
经营范围的雷达系统是由两个参数决定：多少能源是在初始脉冲，无线电接收机的噪声水平。
不幸的是，增加脉冲能量通常需要较长的脉搏。
反过来，在较长的脉冲减少经过时间的测量准确度和精密度。
这两个重要参数之间的冲突结果：能够在远距离探测的对象，并能准确地判断一个对象的距离。
DSP具有革命性的雷达三个方面，所有这些都涉及到这个基本问题。
首先，DSP可以压缩后收到的脉冲，提供更好的距离决心，没有减少的经营范围。