Digital Signal Processing: Theory and Practice, Hardware and Software
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Discrete Cosine Transform Based Image Fusion Techniques VPS Naidu MSDF Lab, FMCD, National Aerospace Laboratories, Bangalore, INDIA E.Mail: [email protected]
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by NAL-IR Journal of Communication, Navigation and Signal Processing (January 2012) Vol. 1, No. 1, pp. 35-45 Discrete Cosine Transform based Image Fusion Techniques VPS Naidu MSDF Lab, FMCD, National Aerospace Laboratories, Bangalore, INDIA E.mail: [email protected] Abstract: Six different types of image fusion algorithms based on 1 discrete cosine transform (DCT) were developed and their , k 1 0 performance was evaluated. Fusion performance is not good while N Where (k ) 1 and using the algorithms with block size less than 8x8 and also the block 1 2 size equivalent to the image size itself. DCTe and DCTmx based , 1 k 1 N 1 1 image fusion algorithms performed well. These algorithms are very N 1 simple and might be suitable for real time applications. 1 , k 0 Keywords: DCT, Contrast measure, Image fusion 2 N 2 (k 1 ) I. INTRODUCTION 2 , 1 k 2 N 2 1 Off late, different image fusion algorithms have been developed N 2 to merge the multiple images into a single image that contain all useful information. Pixel averaging of the source images k 1 & k 2 discrete frequency variables (n1 , n 2 ) pixel index (the images to be fused) is the simplest image fusion technique and it often produces undesirable side effects in the fused image Similarly, the 2D inverse discrete cosine transform is defined including reduced contrast. To overcome this side effects many as: researchers have developed multi resolution [1-3], multi scale [4,5] and statistical signal processing [6,7] based image fusion x(n1 , n 2 ) (k 1 ) (k 2 ) N 1 N 1 techniques. -
Detection and Estimation Theory Introduction to ECE 531 Mojtaba Soltanalian- UIC the Course
Detection and Estimation Theory Introduction to ECE 531 Mojtaba Soltanalian- UIC The course Lectures are given Tuesdays and Thursdays, 2:00-3:15pm Office hours: Thursdays 3:45-5:00pm, SEO 1031 Instructor: Prof. Mojtaba Soltanalian office: SEO 1031 email: [email protected] web: http://msol.people.uic.edu/ The course Course webpage: http://msol.people.uic.edu/ECE531 Textbook(s): * Fundamentals of Statistical Signal Processing, Volume 1: Estimation Theory, by Steven M. Kay, Prentice Hall, 1993, and (possibly) * Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory, by Steven M. Kay, Prentice Hall 1998, available in hard copy form at the UIC Bookstore. The course Style: /Graduate Course with Active Participation/ Introduction Let’s start with a radar example! Introduction> Radar Example QUIZ Introduction> Radar Example You can actually explain it in ten seconds! Introduction> Radar Example Applications in Transportation, Defense, Medical Imaging, Life Sciences, Weather Prediction, Tracking & Localization Introduction> Radar Example The strongest signals leaking off our planet are radar transmissions, not television or radio. The most powerful radars, such as the one mounted on the Arecibo telescope (used to study the ionosphere and map asteroids) could be detected with a similarly sized antenna at a distance of nearly 1,000 light-years. - Seth Shostak, SETI Introduction> Estimation Traditionally discussed in STATISTICS. Estimation in Signal Processing: Digital Computers ADC/DAC (Sampling) Signal/Information Processing Introduction> Estimation The primary focus is on obtaining optimal estimation algorithms that may be implemented on a digital computer. We will work on digital signals/datasets which are typically samples of a continuous-time waveform. -
Digital Signals
Technical Information Digital Signals 1 1 bit t Part 1 Fundamentals Technical Information Part 1: Fundamentals Part 2: Self-operated Regulators Part 3: Control Valves Part 4: Communication Part 5: Building Automation Part 6: Process Automation Should you have any further questions or suggestions, please do not hesitate to contact us: SAMSON AG Phone (+49 69) 4 00 94 67 V74 / Schulung Telefax (+49 69) 4 00 97 16 Weismüllerstraße 3 E-Mail: [email protected] D-60314 Frankfurt Internet: http://www.samson.de Part 1 ⋅ L150EN Digital Signals Range of values and discretization . 5 Bits and bytes in hexadecimal notation. 7 Digital encoding of information. 8 Advantages of digital signal processing . 10 High interference immunity. 10 Short-time and permanent storage . 11 Flexible processing . 11 Various transmission options . 11 Transmission of digital signals . 12 Bit-parallel transmission. 12 Bit-serial transmission . 12 Appendix A1: Additional Literature. 14 99/12 ⋅ SAMSON AG CONTENTS 3 Fundamentals ⋅ Digital Signals V74/ DKE ⋅ SAMSON AG 4 Part 1 ⋅ L150EN Digital Signals In electronic signal and information processing and transmission, digital technology is increasingly being used because, in various applications, digi- tal signal transmission has many advantages over analog signal transmis- sion. Numerous and very successful applications of digital technology include the continuously growing number of PCs, the communication net- work ISDN as well as the increasing use of digital control stations (Direct Di- gital Control: DDC). Unlike analog technology which uses continuous signals, digital technology continuous or encodes the information into discrete signal states (Fig. 1). When only two discrete signals states are assigned per digital signal, these signals are termed binary si- gnals. -
Z-Transformation - I
Digital signal processing: Lecture 5 z-transformation - I Produced by Qiangfu Zhao (Since 1995), All rights reserved © DSP-Lec05/1 Review of last lecture • Fourier transform & inverse Fourier transform: – Time domain & Frequency domain representations • Understand the “true face” (latent factor) of some physical phenomenon. – Key points: • Definition of Fourier transformation. • Definition of inverse Fourier transformation. • Condition for a signal to have FT. Produced by Qiangfu Zhao (Since 1995), All rights reserved © DSP-Lec05/2 Review of last lecture • The sampling theorem – If the highest frequency component contained in an analog signal is f0, the sampling frequency (the Nyquist rate) should be 2xf0 at least. • Frequency response of an LTI system: – The Fourier transformation of the impulse response – Physical meaning: frequency components to keep (~1) and those to remove (~0). – Theoretic foundation: Convolution theorem. Produced by Qiangfu Zhao (Since 1995), All rights reserved © DSP-Lec05/3 Topics of this lecture Chapter 6 of the textbook • The z-transformation. • z-変換 • Convergence region of z- • z-変換の収束領域 transform. -変換とフーリエ変換 • Relation between z- • z transform and Fourier • z-変換と差分方程式 transform. • 伝達関数 or システム • Relation between z- 関数 transform and difference equation. • Transfer function (system function). Produced by Qiangfu Zhao (Since 1995), All rights reserved © DSP-Lec05/4 z-transformation(z-変換) • Laplace transformation is important for analyzing analog signal/systems. • z-transformation is important for analyzing discrete signal/systems. • For a given signal x(n), its z-transformation is defined by ∞ Z[x(n)] = X (z) = ∑ x(n)z−n (6.3) n=0 where z is a complex variable. Produced by Qiangfu Zhao (Since 1995), All rights reserved © DSP-Lec05/5 One-sided z-transform and two-sided z-transform • The z-transform defined above is called one- sided z-transform (片側z-変換). -
Basics on Digital Signal Processing
Basics on Digital Signal Processing z - transform - Digital Filters Vassilis Anastassopoulos Electronics Laboratory, Physics Department, University of Patras Outline of the Lecture 1. The z-transform 2. Properties 3. Examples 4. Digital filters 5. Design of IIR and FIR filters 6. Linear phase 2/31 z-Transform 2 N 1 N 1 j nk n X (k) x(n) e N X (z) x(n) z n0 n0 Time to frequency Time to z-domain (complex) Transformation tool is the Transformation tool is complex wave z e j e j j j2k / N e e With amplitude ρ changing(?) with time With amplitude |ejω|=1 The z-transform is more general than the DFT 3/31 z-Transform For z=ejω i.e. ρ=1 we work on the N 1 unit circle X (z) x(n) z n And the z-transform degenerates n0 into the Fourier transform. I -z z=R+jI |z|=1 R The DFT is an expression of the z-transform on the unit circle. The quantity X(z) must exist with finite value on the unit circle i.e. must posses spectrum with which we can describe a signal or a system. 4/31 z-Transform convergence We are interested in those values of z for which X(z) converges. This region should contain the unit circle. I Why is it so? |a| N 1 N 1 X (z) x(n) zn x(n) (1/zn ) n0 n0 R At z=0, X(z) diverges ROC The values of z for which X(z) diverges are called poles of X(z). -
3F3 – Digital Signal Processing (DSP)
3F3 – Digital Signal Processing (DSP) Simon Godsill www-sigproc.eng.cam.ac.uk/~sjg/teaching/3F3 Course Overview • 11 Lectures • Topics: – Digital Signal Processing – DFT, FFT – Digital Filters – Filter Design – Filter Implementation – Random signals – Optimal Filtering – Signal Modelling • Books: – J.G. Proakis and D.G. Manolakis, Digital Signal Processing 3rd edition, Prentice-Hall. – Statistical digital signal processing and modeling -Monson H. Hayes –Wiley • Some material adapted from courses by Dr. Malcolm Macleod, Prof. Peter Rayner and Dr. Arnaud Doucet Digital Signal Processing - Introduction • Digital signal processing (DSP) is the generic term for techniques such as filtering or spectrum analysis applied to digitally sampled signals. • Recall from 1B Signal and Data Analysis that the procedure is as shown below: • is the sampling period • is the sampling frequency • Recall also that low-pass anti-aliasing filters must be applied before A/D and D/A conversion in order to remove distortion from frequency components higher than Hz (see later for revision of this). • Digital signals are signals which are sampled in time (“discrete time”) and quantised. • Mathematical analysis of inherently digital signals (e.g. sunspot data, tide data) was developed by Gauss (1800), Schuster (1896) and many others since. • Electronic digital signal processing (DSP) was first extensively applied in geophysics (for oil-exploration) then military applications, and is now fundamental to communications, mobile devices, broadcasting, and most applications of signal and image processing. There are many advantages in carrying out digital rather than analogue processing; among these are flexibility and repeatability. The flexibility stems from the fact that system parameters are simply numbers stored in the processor. -
How I Came up with the Discrete Cosine Transform Nasir Ahmed Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131
mxT*L. BImL4L. PRocEsSlNG 1,4-5 (1991) How I Came Up with the Discrete Cosine Transform Nasir Ahmed Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, New Mexico 87131 During the late sixties and early seventies, there to study a “cosine transform” using Chebyshev poly- was a great deal of research activity related to digital nomials of the form orthogonal transforms and their use for image data compression. As such, there were a large number of T,(m) = (l/N)‘/“, m = 1, 2, . , N transforms being introduced with claims of better per- formance relative to others transforms. Such compari- em- lh) h = 1 2 N T,(m) = (2/N)‘%os 2N t ,...) . sons were typically made on a qualitative basis, by viewing a set of “standard” images that had been sub- jected to data compression using transform coding The motivation for looking into such “cosine func- techniques. At the same time, a number of researchers tions” was that they closely resembled KLT basis were doing some excellent work on making compari- functions for a range of values of the correlation coef- sons on a quantitative basis. In particular, researchers ficient p (in the covariance matrix). Further, this at the University of Southern California’s Image Pro- range of values for p was relevant to image data per- cessing Institute (Bill Pratt, Harry Andrews, Ali Ha- taining to a variety of applications. bibi, and others) and the University of California at Much to my disappointment, NSF did not fund the Los Angeles (Judea Pearl) played a key role. -
CONVOLUTION: Digital Signal Processing .R .Hamming, W
CONVOLUTION: Digital Signal Processing AN-237 National Semiconductor CONVOLUTION: Digital Application Note 237 Signal Processing January 1980 Introduction As digital signal processing continues to emerge as a major Decreasing the pulse width while increasing the pulse discipline in the field of electrical engineering, an even height to allow the area under the pulse to remain constant, greater demand has evolved to understand the basic theo- Figure 1c, shows from eq(1) and eq(2) the bandwidth or retical concepts involved in the development of varied and spectral-frequency content of the pulse to have increased, diverse signal processing systems. The most fundamental Figure 1d. concepts employed are (not necessarily listed in the order Further altering the pulse to that of Figure 1e provides for an [ ] of importance) the sampling theorem 1 , Fourier transforms even broader bandwidth, Figure 1f. If the pulse is finally al- [ ][] 2 3, convolution, covariance, etc. tered to the limit, i.e., the pulsewidth being infinitely narrow The intent of this article will be to address the concept of and its amplitude adjusted to still maintain an area of unity convolution and to present it in an introductory manner under the pulse, it is found in 1g and 1h the unit impulse hopefully easily understood by those entering the field of produces a constant, or ``flat'' spectrum equal to 1 at all digital signal processing. frequencies. Note that if ATe1 (unit area), we get, by defini- It may be appropriate to note that this article is Part II (Part I tion, the unit impulse function in time. -
Reception Performance Improvement of AM/FM Tuner by Digital Signal Processing Technology
Reception performance improvement of AM/FM tuner by digital signal processing technology Akira Hatakeyama Osamu Keishima Kiyotaka Nakagawa Yoshiaki Inoue Takehiro Sakai Hirokazu Matsunaga Abstract With developments in digital technology, CDs, MDs, DVDs, HDDs and digital media have become the mainstream of car AV products. In terms of broadcasting media, various types of digital broadcasting have begun in countries all over the world. Thus, there is a demand for smaller and thinner products, in order to enhance radio performance and to achieve consolidation with the above-mentioned digital media in limited space. Due to these circumstances, we are attaining such performance enhancement through digital signal processing for AM/FM IF and beyond, and both tuner miniaturization and lighter products have been realized. The digital signal processing tuner which we will introduce was developed with Freescale Semiconductor, Inc. for the 2005 line model. In this paper, we explain regarding the function outline, characteristics, and main tech- nology involved. 22 Reception performance improvement of AM/FM tuner by digital signal processing technology Introduction1. Introduction from IF signals, interference and noise prevention perfor- 1 mance have surpassed those of analog systems. In recent years, CDs, MDs, DVDs, and digital media have become the mainstream in the car AV market. 2.2 Goals of digitalization In terms of broadcast media, with terrestrial digital The following items were the goals in the develop- TV and audio broadcasting, and satellite broadcasting ment of this digital processing platform for radio: having begun in Japan, while overseas DAB (digital audio ①Improvements in performance (differentiation with broadcasting) is used mainly in Europe and SDARS (satel- other companies through software algorithms) lite digital audio radio service) and IBOC (in band on ・Reduction in noise (improvements in AM/FM noise channel) are used in the United States, digital broadcast- reduction performance, and FM multi-pass perfor- ing is expected to increase in the future. -
Next-Generation Photonic Transport Network Using Digital Signal Processing
Next-Generation Photonic Transport Network Using Digital Signal Processing Yasuhiko Aoki Hisao Nakashima Shoichiro Oda Paparao Palacharla Coherent optical-fiber transmission technology using digital signal processing is being actively researched and developed for use in transmitting high-speed signals of the 100-Gb/s class over long distances. It is expected that network capacity can be further expanded by operating a flexible photonic network having high spectral efficiency achieved by applying an optimal modulation format and signal processing algorithm depending on the transmission distance and required bit rate between transmit and receive nodes. A system that uses such adaptive modulation technology should be able to assign in real time a transmission path between a transmitter and receiver. Furthermore, in the research and development stage, optical transmission characteristics for each modulation format and signal processing algorithm to be used by the system should be evaluated under emulated quasi-field conditions. We first discuss how the use of transmitters and receivers supporting multiple modulation formats can affect network capacity. We then introduce an evaluation platform consisting of a coherent receiver based on a field programmable gate array (FPGA) and polarization mode dispersion/polarization dependent loss (PMD/PDL) emulators with a recirculating-loop experimental system. Finally, we report the results of using this platform to evaluate the transmission characteristics of 112-Gb/s dual-polarization, quadrature phase shift keying (DP-QPSK) signals by emulating the factors that degrade signal transmission in a real environment. 1. Introduction amplifiers—which is becoming a limiting factor In the field of optical communications in system capacity—can be efficiently used. -
The Scientist and Engineer's Guide to Digital Signal Processing Properties of Convolution
CHAPTER 7 Properties of Convolution A linear system's characteristics are completely specified by the system's impulse response, as governed by the mathematics of convolution. This is the basis of many signal processing techniques. For example: Digital filters are created by designing an appropriate impulse response. Enemy aircraft are detected with radar by analyzing a measured impulse response. Echo suppression in long distance telephone calls is accomplished by creating an impulse response that counteracts the impulse response of the reverberation. The list goes on and on. This chapter expands on the properties and usage of convolution in several areas. First, several common impulse responses are discussed. Second, methods are presented for dealing with cascade and parallel combinations of linear systems. Third, the technique of correlation is introduced. Fourth, a nasty problem with convolution is examined, the computation time can be unacceptably long using conventional algorithms and computers. Common Impulse Responses Delta Function The simplest impulse response is nothing more that a delta function, as shown in Fig. 7-1a. That is, an impulse on the input produces an identical impulse on the output. This means that all signals are passed through the system without change. Convolving any signal with a delta function results in exactly the same signal. Mathematically, this is written: EQUATION 7-1 The delta function is the identity for ( ' convolution. Any signal convolved with x[n] *[n] x[n] a delta function is left unchanged. This property makes the delta function the identity for convolution. This is analogous to zero being the identity for addition (a%0 ' a), and one being the identity for multiplication (a×1 ' a). -
Exploring Anti-Aliasing Filters in Signal Conditioners for Mixed-Signal, Multimodal Sensor Conditioning by Arun T
Texas Instruments Incorporated Amplifiers: Op Amps Exploring anti-aliasing filters in signal conditioners for mixed-signal, multimodal sensor conditioning By Arun T. Vemuri Systems Architect, Enhanced Industrial Introduction Figure 1. Multimodal, mixed-signal sensor-signal conditioner Some sensor-signal conditioners are used to process the output Analog Digital of multiple sense elements. This Domain Domain processing is often provided by multimodal, mixed-signal condi- tioners that can handle the out- puts from several sense elements Sense Digital Amplifier 1 ADC 1 at the same time. This article Element 1 Filter 1 analyzes the operation of anti- Processed aliasing filters in such sensor- Intelligent Output Compensation signal conditioners. Sense Digital Basics of sensor-signal Amplifier 2 ADC 2 Element 2 Filter 2 conditioners Sense elements, or transducers, convert a physical quantity of interest into electrical signals. Examples include piezo resistive bridges used to measure pres- sure, piezoelectric transducers used to detect ultrasonic than one sense element is processed by the same signal waves, and electrochemical cells used to measure gas conditioner is called multimodal signal conditioning. concentrations. The electrical signals produced by sense Mixed-signal signal conditioning elements are small and exhibit nonidealities, such as tem- Another aspect of sensor-signal conditioning is the electri- perature drifts and nonlinear transfer functions. cal domain in which the signal conditioning occurs. TI’s Sensor analog front ends such as the Texas Instruments PGA309 is an example of a device where the signal condi- (TI) LMP91000 and sensor-signal conditioners such as TI’s tioning of resistive-bridge sense elements occurs in the PGA400/450 are used to amplify the small signals produced analog domain.