Understanding Digital Signal Processing
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Dsp Notes Prepared
DSP NOTES PREPARED BY Ch.Ganapathy Reddy Professor & HOD, ECE Shaikpet, Hyderabad-08 Ch Ganapathy Reddy, Prof and HOD, ECE, GNITS id:[email protected],9052344333 1 DIGITAL SIGNAL PROCESSING A signal is defined as any physical quantity that varies with time, space or another independent variable. A system is defined as a physical device that performs an operation on a signal. System is characterized by the type of operation that performs on the signal. Such operations are referred to as signal processing. Advantages of DSP 1. A digital programmable system allows flexibility in reconfiguring the digital signal processing operations by changing the program. In analog redesign of hardware is required. 2. In digital accuracy depends on word length, floating Vs fixed point arithmetic etc. In analog depends on components. 3. Can be stored on disk. 4. It is very difficult to perform precise mathematical operations on signals in analog form but these operations can be routinely implemented on a digital computer using software. 5. Cheaper to implement. 6. Small size. 7. Several filters need several boards in analog, whereas in digital same DSP processor is used for many filters. Disadvantages of DSP 1. When analog signal is changing very fast, it is difficult to convert digital form .(beyond 100KHz range) 2. w=1/2 Sampling rate. 3. Finite word length problems. 4. When the signal is weak, within a few tenths of millivolts, we cannot amplify the signal after it is digitized. 5. DSP hardware is more expensive than general purpose microprocessors & micro controllers. Ch Ganapathy Reddy, Prof and HOD, ECE, GNITS id:[email protected],9052344333 2 6. -
Signal and Image Processing in Biomedical Photoacoustic Imaging: a Review
Review Signal and Image Processing in Biomedical Photoacoustic Imaging: A Review Rayyan Manwar 1,*,†, Mohsin Zafar 2,† and Qiuyun Xu 2 1 Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA 2 Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA; [email protected] (M.Z.); [email protected] (Q.X.) * Correspondence: [email protected] † These authors have equal contributions. Abstract: Photoacoustic imaging (PAI) is a powerful imaging modality that relies on the PA effect. PAI works on the principle of electromagnetic energy absorption by the exogenous contrast agents and/or endogenous molecules present in the biological tissue, consequently generating ultrasound waves. PAI combines a high optical contrast with a high acoustic spatiotemporal resolution, al- lowing the non-invasive visualization of absorbers in deep structures. However, due to the optical diffusion and ultrasound attenuation in heterogeneous turbid biological tissue, the quality of the PA images deteriorates. Therefore, signal and image-processing techniques are imperative in PAI to provide high-quality images with detailed structural and functional information in deep tissues. Here, we review various signal and image processing techniques that have been developed/implemented in PAI. Our goal is to highlight the importance of image computing in photoacoustic imaging. Keywords: photoacoustic; signal enhancement; image processing; SNR; deep learning 1. Introduction Citation: Manwar, R.; Zafar, M.; Xu, Photoacoustic imaging (PAI) is a non-ionizing and non-invasive hybrid imaging Q. Signal and Image Processing in modality that has made significant progress in recent years, up to a point where clinical Biomedical Photoacoustic Imaging: studies are becoming a real possibility [1–6]. -
Noise Will Be Noise: Or Phase Optimized Recursive Filters for Interference Suppression, Signal Differentiation and State Estimation (Extended Version) Hugh L
Available online at https://arxiv.org/ 1 Noise will be noise: Or phase optimized recursive filters for interference suppression, signal differentiation and state estimation (extended version) Hugh L. Kennedy Abstract— The increased temporal and spectral resolution of oversampled systems allows many sensor-signal analysis tasks to be performed (e.g. detection, classification and tracking) using a filterbank of low-pass digital differentiators. Such filters are readily designed via flatness constraints on the derivatives of the complex frequency response at dc, pi and at the centre frequencies of narrowband interferers, i.e. using maximally-flat (MaxFlat) designs. Infinite-impulse-response (IIR) filters are ideal in embedded online systems with high data-rates because computational complexity is independent of their (fading) ‘memory’. A novel procedure for the design of MaxFlat IIR filterbanks with improved passband phase linearity is presented in this paper, as a possible alternative to Kalman and Wiener filters in a class of derivative-state estimation problems with uncertain signal models. Butterworth poles are used for configurable bandwidth and guaranteed stability. Flatness constraints of arbitrary order are derived for temporal derivatives of arbitrary order and a prescribed group delay. As longer lags (in samples) are readily accommodated in oversampled systems, an expression for the optimal group delay that minimizes the white-noise gain (i.e. the error variance of the derivative estimate at steady state) is derived. Filter zeros are optimally placed for the required passband phase response and the cancellation of narrowband interferers in the stopband, by solving a linear system of equations. Low complexity filterbank realizations are discussed then their behaviour is analysed in a Teager-Kaiser operator to detect pulsed signals and in a state observer to track manoeuvring targets in simulated scenarios. -
Overview Textbook Prerequisite Course Outline
ESE 337 Digital Signal Processing: Theory Fall 2018 Instructor: Yue Zhao Time and Location: Tuesday, Thursday 7:00pm - 8:20pm, Javits Lecture Center 101 Contact: Email: [email protected], Office: 261 Light Engineering Office Hours: Tuesday, Thursday 1:30pm - 3:00pm, or by appointment Teaching Assistants, and Office Hours: • Jiaming Li ([email protected]): THU 12:30pm - 2:00pm, or by appointment, Location: 208 Light Engineering (Changes of hours, if any, will be updated on Blackboard.) Overview Digital Signal Processing (DSP) lies at the heart of modern information technology in many fields including digital communications, audio/image/video compression, speech recognition, medical imaging, sensing for health, touch screens, space exploration, etc. This class covers the basic principles of digital signal processing and digital filtering. Skills for analyzing and synthesizing algorithms and systems that process discrete time signals will be developed. 3 credits. Textbook • A.V. Oppenheim and R.W. Schafer, Discrete Time Signal Processing, Prentice Hall, Third Edition, 2009 Prerequisite • ESE 305, Deterministic Signals and Systems Course Outline Week 1 DT signals, DT systems and properties, LTI systems Week 2 Convolution, properties of LTI systems Week 3 Examples of LTI systems, eigen functions, frequency response of DT systems, DTFT 1 ESE 337 Syllabus Week 4 Convergence and properties of DTFT Week 5 Theorems of DTFT, useful DTFT pairs, examples, Z-transform Week 6 Examples of Z-transform, properties of ROC Week 7 Inverse Z-transform, -
Digital Camera Identification Using Sensor Pattern Noise for Forensics Applications
Citation: Lawgaly, Ashref (2017) Digital camera identification using sensor pattern noise for forensics applications. Doctoral thesis, Northumbria University. This version was downloaded from Northumbria Research Link: http://nrl.northumbria.ac.uk/32314/ Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University’s research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html DIGITAL CAMERA IDENTIFICATION USING SENSOR PATTERN NOISE FOR FORENSICS APPLICATIONS Ashref Lawgaly A thesis submitted in partial fulfilment of the requirements of the University of Northumbria at Newcastle for the degree of Doctor of Philosophy Research undertaken in the Faculty of Engineering and Environment January 2017 DECLARATION I declare that no outputs submitted for this degree have been submitted for a research degree of any other institution. I also confirm that this work fully acknowledges opinions, ideas and contributions from the work of others. Any ethical clearance for the research presented in this commentary has been approved. -
Averaging Correlation for Weak Global Positioning System Signal Processing
AVERAGING CORRELATION FOR WEAK GLOBAL POSITIONING SYSTEM SIGNAL PROCESSING A Thesis Presented to The Faculty ofthe Fritz J. and Dolores H. Russ College ofEngineering and Technology Ohio University In Partial Fulfillment ofthe Requirements for the Degree Master ofScience by ZhenZhu June, 2002 OHIO UNIVERSITY LIBRARY 111 ACKNOWLEDGEMENTS I want to thank all the people who had helped me on my research all through the past years. Particularly I would like to acknowledge some of them who did directly support me on this thesis. First I would express my sincere gratitude to my advisor, Dr. Starzyk. It is him who first led me into the area of GPS. He offered insight directions to all my research topics. His professional advices always inspired me. I would like to thank Dr. van Graas for his guidance, patience and encouragements. His effort is very important to my research. Also Dr. Uijt de Haag must be specially thanked for being my committee member. Sincere thanks are for Dr. Matolak. From his courses about mobile communication, I did learn a lot that I had never been taught in other classes. The assistance provided by all the faculty members in the EECS School will never be forgotten. This thesis would never be finished without the help ofmy friends, especially Jing Pang, Abdul Alaqeeli and Sanjeev Gunawardena. Their cooperation made it possible for me to complete my research quickly. Finally, the support from my family gives me the opportunity to study here. All my energy to work on my research is from them. IV TABLE OF CONTENTS LIST OF TABLES vi LIST OF ILLUSTRATIONS vii 1 INTRODUCTION 1 2 GLOBAL POSITIONING SySTEM 4 3 GPS SIGNAL PROCESSING 7 3.1 GPS Signal Structure 7 3.2 GPS Receiver Architecture 12 3.3 GPS Signal Processing- Acquisition 14 3.3.1 Acquisition 14 3.3.2 Signal Power and Misdetection Probability 16 3.3.3 Demodulating Frequency 24 3.4 Tracking 25 3.4.1 Sequential Time Domain Tracking Loop 25 3.4.2 Correlation Based Tracking 28 3.5 Software Radio Introductions 30 3.6 Hardware Implementations 32 4 AVERAGING 33 4.1 Averaging Up Sampled Signa1. -
ESE 531: Digital Signal Processing Today IIR Filter Design Impulse
Today ESE 531: Digital Signal Processing ! IIR Filter Design " Impulse Invariance " Bilinear Transformation Lec 18: March 30, 2017 ! Transformation of DT Filters IIR Filters and Adaptive Filters ! Adaptive Filters ! LMS Algorithm Penn ESE 531 Spring 2017 – Khanna Penn ESE 531 Spring 2017 - Khanna 2 IIR Filter Design Impulse Invariance ! Transform continuous-time filter into a discrete- ! Want to implement continuous-time system in time filter meeting specs discrete-time " Pick suitable transformation from s (Laplace variable) to z (or t to n) " Pick suitable analog Hc(s) allowing specs to be met, transform to H(z) ! We’ve seen this before… impulse invariance Penn ESE 531 Spring 2017 - Khanna 3 Penn ESE 531 Spring 2017 - Khanna 4 Impulse Invariance Impulse Invariance ! With Hc(jΩ) bandlimited, choose ! With Hc(jΩ) bandlimited, choose j ω j ω H(e ω ) = H ( j ), ω < π H(e ω ) = H ( j ), ω < π c T c T ! With the further requirement that T be chosen such ! With the further requirement that T be chosen such that that Hc ( jΩ) = 0, Ω ≥ π / T Hc ( jΩ) = 0, Ω ≥ π / T h[n] = Thc (nT ) Penn ESE 531 Spring 2017 - Khanna 5 Penn ESE 531 Spring 2017 - Khanna 6 1 IIR by Impulse Invariance Example jω ! If Hc(jω)≈0 for |ωd| > π/T, no aliasing and H(e ) = H(jω/T), ω<π jω ! To get a particular H(e ), find corresponding Hc and Td for which above is true (within specs) ! Note: Td is not for aliasing control, used for frequency scaling. Penn ESE 531 Spring 2017 - Khanna 7 Penn ESE 531 Spring 2017 - Khanna 8 Example Example 1 eat ←⎯L→ s − a Penn ESE 531 Spring -
Digitisation and Data Processing in Fourier Transform Nmr
0079 6565/X0/0301 0027SO5.00/0 DIGITISATION AND DATA PROCESSING IN FOURIER TRANSFORM NMR J. C. LINDON and A. G. FERRIG~ Department of Physical Chemistry, Wellcome Research Laboratories, Langley Court, Beckenham, Kent BR3 3BS, 1J.K. (Received 17 December 1979) CONTENTS 1. Introduction 28 1,I. Aims of the Review 28 1.2. FTNMR Computer Systems 28 2. Digitisation 29 2.1 Signal-to-Noise 29 2.1.1. Signal averaging 29 2.1.2. Signal-to-noise in time and frequency domains 30 2.2. Hardware Requirements 30 2.2.1. Sample-and-hold 30 2.2.2. Analog-to-digital conversion 31 2.2.3. ADC resolution and error sources 32 2.3. Sampling Rates and Timing 33 2.4. Quantisation Errors 33 2.5. Signal Overflow 34 2.5.1. Introduction 34 2.5.2. Possible number of scans 34 2.5.3. Scaling of memory and ADC 35 2.5.4. Normalised averaging 36 2.5.5. Double length averaging 31 2.6. Signal Averaging in the Presence of Large Signals 37 2.6.1. Number of scans and ADC resolution 37 2.6.2. Spectroscopic methods of improving dynamic range 40 2.6.3. Block averaging 40 2.6.4. Double length averaging 41 2.7 Summary of Averaging Methods 42 3. Manipulations Prior to Fourier Transformation 43 3.1. Zero Filling 43 3.2 Sensitivity Enhancement 45 3.2.1. Introduction 45 3.2.2. Apodisation methods 45 3.3. Resolution Enhancement 47 3.3.1. Introduction and spectroscopic methods 47 3.3.2. -
Chap 4 Sampling of Continuous-Time Signals Introduction
Chap 4 Sampling of Continuous-Time Signals Introduction 4.1 Periodic Sampling 4.2 Frequency-Domain Representation of Sampling 4.3 Reconstruction of a Bandlimited Signal from its Samples 4.4 Discrete-Time Processing of Continuous-Time Signals 4.5 Continuous-Time Processing of Discrete-Time Signals 4.6 Changing the Sampling Rate Using Discrete-Time Processing 4.7 Multirate Signal Processing 4.8 Digital Processing of Analog Signals 4.9 Oversampling and Noise Shaping in A/D and D/A Conversion 2018/9/18 DSP 2 Periodic Sampling Sequence of samples x[n] is obtained from a continuous-time signal xc(t) : x[n] = xc(nT), - infinity < n < infinity T : sampling period fs = 1/T : sampling frequency, samples/second C/D Continuous-to-discrete-time Xc(t) converter X[n] = Xc(nT) T In a practical setting, the operation of sampling is often implemented by an analog-to-digital (A/D) converter which can be approximated to the ideal C/D converter. The sampling operation is generally not invertible. The inherent ambiguity in sampling is of primary concern in signal processing. 2018/9/18 DSP 3 Sampling with a Periodic Impulse Train s(t) C/D Converter Conversion from impulse train x[n] = x (nT) x (t) X C C xS(t)to discrete-time sequence xC(t) xC(t) xS(t) 2 Sampling Rates xS(t) -4T 0 2T -4T 0 2T x[n] 2 Output Sequences x[n] -4 0 2 -4 0 2 2018/9/18 DSP 4 Frequency-domain representation of sampling The conversion of xc(t) to xs(t) through modulating signal s(t) which is a periodic impulse train s(t) (t nT ) n xs (t) xc (t)s(t) xc (t) (t nT ) n by shifting property of the impulse xs (t) xc (nT)(t nT) n The Fourier transform of a periodic impulse train is a periodic impulse train. -
Introduction Simulation of Signal Averaging
Signal Processing Naureen Ghani December 9, 2017 Introduction Signal processing is used to enhance signal components in noisy measurements. It is especially important in analyzing time-series data in neuroscience. Applications of signal processing include data compression and predictive algorithms. Data analysis techniques are often subdivided into operations in the spatial domain and frequency domain. For one-dimensional time series data, we begin by signal averaging in the spatial domain. Signal averaging is a technique that allows us to uncover small amplitude signals in the noisy data. It makes the following assumptions: 1. Signal and noise are uncorrelated. 2. The timing of the signal is known. 3. A consistent signal component exists when performing repeated measurements. 4. The noise is truly random with zero mean. In reality, all these assumptions may be violated to some degree. This technique is still useful and robust in extracting signals. Simulation of Signal Averaging To simulate signal averaging, we will generate a measurement x that consists of a signal s and a noise component n. This is repeated over N trials. For each digitized trial, the kth sample point in the jth trial can be written as xj(k) = sj(k) + nj(k) Here is the code to simulate signal averaging: % Signal Averaging Simulation % Generate 256 noisy trials trials = 256; noise_trials = randn(256); % Generate sine signal sz = 1:trials; sz = sz/(trials/2); S = sin(2*pi*sz); % Add noise to 256 sine signals for i = 1:trials noise_trials(i,:) = noise_trials(i,:) + S; -
Digital Signal Processing Lecture 8
Lecture 8 Recap Introduction CT->DT Digital Signal Processing Impulse Invariance Lecture 8 - Filter Design - IIR Bilinear Trans. Example Electrical Engineering and Computer Science University of Tennessee, Knoxville Overview Lecture 8 1 Recap Recap Introduction CT->DT 2 Introduction Impulse Invariance 3 Bilinear Trans. CT->DT Example 4 Impulse Invariance 5 Bilinear Trans. 6 Example Roadmap Lecture 8 Introduction Discrete-time signals and systems - LTI systems Unit sample response h[n]: uniquely characterizes an Recap LTI system Introduction Linear constant-coefficient difference equation CT->DT Frequency response: H(ej!) Impulse Invariance Complex exponential being eigenfunction of an LTI j! j! Bilinear Trans. system: y[n] = H(e )x[n] and H(e ) as eigenvalue. Example z transform P1 −n The z-transform, X(z) = n=−∞ x[n]z Region of convergence - the z-plane System function, H(z) Properties of the z-transform The significance of zeros 1 H n−1 The inverse z-transform, x[n] = 2πj C X(z)z dz: inspection, power series, partial fraction expansion Sampling and Reconstruction Transform domain analysis - nwz Review - Design structures Lecture 8 Different representations of causal LTI systems LCDE with initial rest condition H(z) with jzj > R+ and starts at n = 0 Recap Block diagram vs. Signal flow graph and how to determine Introduction system function (or unit sample response) from the graphs CT->DT Design structures Impulse Invariance Direct form I (zeros first) Bilinear Trans. Direct form II (poles first) - Canonic structure Example Transposed form (zeros first) IIR: cascade form, parallel form, feedback in IIR (computable vs. noncomputable) FIR: direct form, cascade form, parallel form, linear phase Metric: computational resource and precision Sources of errors: coefficient quantization error, input quantization error, product quantization error, limit cycles Pole sensitivity of 2nd-order structures: coupled form Coefficient quantization examples: direct form vs. -
IIR Filters (II)
Lecture 8 - IIR Filters (II) James Barnes ([email protected]) Spring 2009 Colorado State University Dept of Electrical and Computer Engineering ECE423 – 1 / 27 Lecture 8 Outline ● Introduction ● Digital Filter Design by Analog → Digital Conversion ● (Probably next lecture) ”All Digital” Design Algorithms ● (Next lecture) Conversion of Filter Types by Frequency Transformation Colorado State University Dept of Electrical and Computer Engineering ECE423 – 2 / 27 ❖ Lecture 8 Outline Introduction ❖ IIR Filter Design Overview Method: Impulse Invariance for IIR FIlters Approximation of Derivatives Bilinear Transform Matched Z-Transform Introduction Colorado State University Dept of Electrical and Computer Engineering ECE423 – 3 / 27 IIR Filter Design Overview ● Methods which start from analog design ✦ Impulse Invariance ✦ Approximation of Derivatives ✦ Bilinear Transform ✦ Matched Z-transform All are different methods of mapping the s-plane onto the z-plane ● Methods which are ”all digital” ✦ Least-squares ✦ McClellan-Parks Colorado State University Dept of Electrical and Computer Engineering ECE423 – 4 / 27 ❖ Lecture 8 Outline Introduction Method: Impulse Invariance for IIR FIlters ❖ Impulse Invariance ❖ Impulse Invariance (2) ❖ Impulse Invariance (3) ❖ Impulse Invariance (5) ❖ Impulse Invariance Procedure ❖ Impulse Invariance Example Method: Impulse Invariance for IIR FIlters ❖ Impulse Invariance Example (2) Approximation of Derivatives Bilinear Transform Matched Z-Transform Colorado State University Dept of Electrical and Computer Engineering ECE423 – 5 / 27 Impulse Invariance We start by sampling the impulse response of the analog filter: ha(t) h[n]= ha(nt0) t0 Sampling Theorem gives relation between Fourier Transform of sampled and continuous ”signals”: ∞ 1 ω 2πk H(z)|z=ejω = Ha(j − j ), (1) t0 t0 t0 k=X−∞ where ω =Ωt0 = 2πf/fs and f is the analog frequency in Hz.