Generating an Analytic Signal

Total Page:16

File Type:pdf, Size:1020Kb

Generating an Analytic Signal Generating Complex Baseband and Analytic Bandpass Signals by Richard Lyons, Nov. 2011 There are so many different time- and frequency-domain methods for generating complex baseband and analytic bandpass signals that I had trouble keeping those techniques straight in my mind. Thus, for my own benefit, I created a kind of reference table showing those methods. I present that table for your viewing pleasure in this blog. For clarity, I define a complex baseband signal as follows: derived from an input analog xbp(t)bandpass signal whose spectrum is shown in Figure 1(a), or discrete input xbp(n) bandpass signal whose spectrum is shown in Figure 1(b), a complex baseband signal is an xBB(n) sequence whose spectrum is that shown in Figure 1(c). The sample rate of an xbp(n) input sequence is defined as fs Hz. Spectrum of analog xbp(t) input signals. (a) –f 0 f Freq c c (Hz) Spectrum of discrete xbp(n) input signals. (b) . –f –f /2 –f 0 f f /2 f Freq s s c c s s (Hz) Spectrum of discrete xBB(n) baseband output signal. (c) . –f –f /2 0 f /2 f Freq s s s s (Hz) Spectrum of discrete xABP(n) analytic bandpass output signal. (d) . 0 –fs –fs/2 –fc fc fs/2 fs 3fs/2 Freq (Hz) Figure 1. Based on the same analog xbp(t) or discrete xbp(n) input bandpass signal, an analytic bandpass signal is an xABP(n) sequence whose spectrum is that shown in Figure 1(d). I realize that, by strict definition, an analytic signal has no negative- frequency spectral energy. And because our xABP(n) output bandpass signal is a discrete sequence it has spectral replications in its negative-frequency spectral region—so calling xABP(n) an analytic signal seems incorrect. We'll bypass that controversy by saying that a discrete sequence is analytic if it has no spectral energy in the frequency range of –fs/2 to zero Hz. 1 Table 1, below, presents my Hit Parade of complex baseband and analytic bandpass signal generation methods. In that table "LPF" means a lowpass, linear-phase, tapped-delay line, FIR filter. All discrete Fourier transforms (DFTs) are implemented with radix-2 fast Fourier transforms (FFTs). Table 1: Complex baseband and analytic bandpass signal generation methods Process Input/Output Comments Quadrature Sampling Input: analog Uses analog mixing x (t) i(t) bandpass signal and analog lowpass cos x (n) LPF A/D I centered at fc filters. Difficult to control the exact x (t) Hz, with sample bp cos(2πf t) phase delays and c f rate of fs Hz.. s gains of the i(t) x (t) q(t) and q(t) signals. sin x (n) LPF A/D Q Output: discrete complex xBB(n) –sin(2πf t) x (n) = x (n) + jx (n) c BB I Q baseband signal, centered at zero Hz, with sample rate of fs Hz. Quadrature Sampling Input: analog All-digital bandpass signal downconversion centered at f facilitates exact x (n) c cos Hz, with sample control of phases LPF xI(n) x (t) and gains of the bp xbp(n) rate of fs Hz. A/D xI(n) and xQ(n) cos(2πn/4) LPF x (n) signals. A/D's fs x (n) Q Output: discrete f = 4f =1/t sin sample rate normally s c s complex x (n) –sin(2πn/4) x (n) = x (n) + jx (n) BB equal to 4fc, but BB I Q baseband signal, setting f = 0.8f centered at zero s c allows bandpass Hz, with sample sampling to reduce rate of fs Hz. the fs sample rate. fs must greater than twice the bandwidth of xbp(t). See [1] or Section 8.9 of [2]. Discrete Complex Downconversion Input: discrete Uses a time-domain real bandpass Hilbert transformer uI(n) vI(p) wI(p) xI(n') Delay 2 hhp(k) 2 signal centered and a half-band xbp(n) at fs/4 Hz, with highpass FIR filter. sample rate of fs If hhilb(k) has an Hilbert u (n) v (p) w (p) x (n') odd number of taps, filter Q 2 Q h (k) Q 2 Q Hz. hp then half its [h (k)] hilb coefficients will be x (n') = x (n') + jx (n') BB I Q Output: discrete zero-valued. See [3] complex xBB(n) or Section 13.43 of baseband signal, [2]. centered at zero Hz, with sample rate of fs/4 Hz. 2 Table 1 Cont'd: Discrete Complex Downconversion Input: discrete Uses a time-domain x (n') real bandpass Hilbert transformer Delay 4 Delay I signal centered and simple three-tap xbp(n) at fs/4 Hz, with highpass FIR Hilbert x (n') compensation filter Compensation Q sample rate of fs filter 4 as shown in Part (a) [h (k)] filter [h(k)] Hz. (a) hilb of figure. Efficient implementation shown in Part (b) of xBB(n') = xI(n') + jxQ(n') Output: discrete complex xBB(n) figure. For reasonably xbp(n) x (n') baseband signal, –1 4 –1 I z z centered at zero acceptable operation, the –1 Hz, with sample z x (n') bandwidth of x (n) z–1 Q rate of fs/4 Hz. bp + – must not be larger than, say, fs/10. 4 z–1 See Section 13.43 of Hilbert filter [2]. (b) h(0) = –1/32 h(0) h(1) h(1) = 1/2 + 1/16 Compensation filter Discrete Complex Downconversion Input: discrete Standard complex real bandpass downconversion and xi(n) LPF 2 xI(n') signal centered lowpass FIR xbp(n) at fs/4 Hz, with filtering, with decimation by two as cos(πn/4) sample rate of fs Hz.. shown in Part (a) of LPF 2 x (n') figure. Due to the (a) x (n) Q q decimation, a very –sin(πn/4) Output: discrete efficient x (n') = x (n') + jx (n') BB I Q complex xBB(n) implementation is baseband signal, that shown in Part xi(n) In-phase (b) of figure. The x (n') centered at zero LPF I Hz, with sample coefficients of the x (n) In-phase and bp rate of f /2 Hz. 1, –1, 1, –1, ... s Quadrature phase LPFs are decimated (b) –1 Quadrature z xQ(n') versions of the x (n) LPF q coefficients in the identical Part (a) LPFs. If the Part (a) LPFs are half- band filters, the Part (b) In-phase and Quadrature phase LPFs will be even more computationally efficient. See [4], [5], or Section 13.1.3 of [2]. 3 Table 1 Cont'd: Discrete Complex Downconversion Input: discrete Negative-frequency real bandpass Set negative- components of xbp(n) x (n) X (m) signal centered are attenuated in bp N-point bp frequency at f Hz, with DFT samples c the frequency equal to zero sample rate of fs domain. Frequency 2 downconversion X (m) Hz. ABP performed by Assumes X (m=0) bp Circular rotate shifting (rotating) and Xbp(m=N/2) are zero-valued all samples in Output: discrete the frequency-domain negative-freq complex xBB(n) indices of the direction baseband signal, positive-frequency centered at zero spectral samples XBB(m) Hz, with sample prior to inverse N-point Real xI(n) DFT. inverse rate of fs Hz. Imag. DFT xQ(n) x (n) = x (n) + jx (n) BB I Q Discrete Complex Downconversion Input: discrete Negative-frequency real bandpass components of xbp(n) xbp(n) N-point signal centered are attenuated in DFT at kfs/D Hz, with the frequency 2 Xbp(m) sample rate of fs domain. Frequency Assumes X (m=0) bp downconversion Set negative- and X (m=N/2) Hz. bp performed by frequency are zero-valued samples decimation in the equal to zero Output: discrete time domain. See complex x (n) Section 13.29 of X (m) BB ABP baseband signal, [2]. Real D x (n') N-point I centered at zero inverse Hz, with sample DFT Imag. D xQ(n') rate of fs/D Hz. x (n') = x (n') + jx (n') BB I Q Analytic Bandpass Signal Generation Input: discrete Standard time- x (n) bandpass signal domain, tapped-delay bp (N-1) -sample delay x (n) centered at fc line, FIR Hilbert 2 I Hz, with sample transform method of discrete analytic N-tap FIR, h (k), rate of fs Hz. hilb bandpass signal xQ(n) Hilbert transformer generation. For Output: discrete proper time xABP(n) = xI(n) + jxQ(n) analytic xABP(n) synchronization of bandpass signal, xI(n) and xQ(n), N centered at fc must be an odd Hz, with sample number—in which case rate of fs Hz. half the hhilb(k) coefficients will be zero-valued. See Note 61 of [7] or Section 9.4.1 of [2]. 4 Table 1 Cont'd: Analytic Bandpass Signal Generation Input: discrete A prototype lowpass bandpass signal filter (LPF) is x (n) bp hcos(k), real FIR filter x (n) centered at fc designed to have a [Real part of h (k)] I BP Hz, with sample two-sided bandwidth slightly greater rate of fs Hz. h (k), real FIR filter than the bandwidth sin x (n) Q of xbp(n). The LPF's [Imag. part of hBP(k)] Output: discrete coefficients are x (n) = x (n) + jx (n) analytic x (n) multiplied by cosine ABP I Q ABP bandpass signal, and sine sequences centered at fc whose frequencies Windowing the two sets of filter Hz, with sample are fc Hz, creating coefficients will minimize passband rate of fs Hz.
Recommended publications
  • Optical Single Sideband for Broadband and Subcarrier Systems
    University of Alberta Optical Single Sideband for Broadband And Subcarrier Systems Robert James Davies 0 A thesis submitted to the faculty of Graduate Studies and Research in partial fulfillrnent of the requirernents for the degree of Doctor of Philosophy Department of Electrical And Computer Engineering Edmonton, AIberta Spring 1999 National Library Bibliothèque nationale du Canada Acquisitions and Acquisitions et Bibliographie Services services bibliographiques 395 Wellington Street 395, rue Wellington Ottawa ON KlA ON4 Ottawa ON KIA ON4 Canada Canada Yom iUe Votre relérence Our iSie Norre reference The author has granted a non- L'auteur a accordé une licence non exclusive licence allowing the exclusive permettant à la National Library of Canada to Bibliothèque nationale du Canada de reproduce, loan, distribute or sell reproduire, prêter, distribuer ou copies of this thesis in microform, vendre des copies de cette thèse sous paper or electronic formats. la forme de microfiche/nlm, de reproduction sur papier ou sur format électronique. The author retains ownership of the L'auteur conserve la propriété du copyright in this thesis. Neither the droit d'auteur qui protège cette thèse. thesis nor substantial extracts fkom it Ni la thèse ni des extraits substantiels may be printed or otheMrise de celle-ci ne doivent être Unprimés reproduced without the author's ou autrement reproduits sans son permission. autorisation. Abstract Radio systems are being deployed for broadband residential telecommunication services such as broadcast, wideband lntemet and video on demand. Justification for radio delivery centers on mitigation of problems inherent in subscriber loop upgrades such as Fiber to the Home (WH)and Hybrid Fiber Coax (HFC).
    [Show full text]
  • 3 Characterization of Communication Signals and Systems
    63 3 Characterization of Communication Signals and Systems 3.1 Representation of Bandpass Signals and Systems Narrowband communication signals are often transmitted using some type of carrier modulation. The resulting transmit signal s(t) has passband character, i.e., the bandwidth B of its spectrum S(f) = s(t) is much smaller F{ } than the carrier frequency fc. S(f) B f f f − c c We are interested in a representation for s(t) that is independent of the carrier frequency fc. This will lead us to the so–called equiv- alent (complex) baseband representation of signals and systems. Schober: Signal Detection and Estimation 64 3.1.1 Equivalent Complex Baseband Representation of Band- pass Signals Given: Real–valued bandpass signal s(t) with spectrum S(f) = s(t) F{ } Analytic Signal s+(t) In our quest to find the equivalent baseband representation of s(t), we first suppress all negative frequencies in S(f), since S(f) = S( f) is valid. − The spectrum S+(f) of the resulting so–called analytic signal s+(t) is defined as S (f) = s (t) =2 u(f)S(f), + F{ + } where u(f) is the unit step function 0, f < 0 u(f) = 1/2, f =0 . 1, f > 0 u(f) 1 1/2 f Schober: Signal Detection and Estimation 65 The analytic signal can be expressed as 1 s+(t) = − S+(f) F 1{ } = − 2 u(f)S(f) F 1{ } 1 = − 2 u(f) − S(f) F { } ∗ F { } 1 The inverse Fourier transform of − 2 u(f) is given by F { } 1 j − 2 u(f) = δ(t) + .
    [Show full text]
  • Design and Application of a Hilbert Transformer in a Digital Receiver
    Proceedings of the SDR 11 Technical Conference and Product Exposition, Copyright © 2011 Wireless Innovation Forum All Rights Reserved DESIGN AND APPLICATION OF A HILBERT TRANSFORMER IN A DIGITAL RECEIVER Matt Carrick (Northrop Grumman, Chantilly, VA, USA; [email protected]); Doug Jaeger (Northrop Grumman, Chantilly, VA, USA; [email protected]); fred harris (San Diego State University, San Diego, CA, USA; [email protected]) ABSTRACT A common method of down converting a signal from an intermediate frequency (IF) to baseband is using a quadrature down-converter. One problem with the quadrature down-converter is it requires two low pass filters; one for the real branch and one for the imaginary branch. A more efficient way is to transform the real signal to a complex signal and then complex heterodyne the resultant signal to baseband. The transformation of a real signal to a complex signal can be done using a Hilbert transform. Building a Hilbert transform directly from its sampled data sequence produces suboptimal results due to time series truncation; another method is building a Hilbert transformer by synthesizing the filter coefficients from half Figure 1: A quadrature down-converter band filter coefficients. Designing the Hilbert transform filter using a half band filter allows for a much more Another way of viewing the problem is that the structured design process as well as greatly improved quadrature down-converter not only extracts the desired results. segment of the spectrum it rejects the undesired spectral image, the spectral replica present in the Hermetian 1. INTRODUCTION symmetric spectra of a real signal. Removing this image would result in a single sided spectrum which being non- The digital portion of a receiver is typically designed to Hermetian symmetric is the transform of a complex signal.
    [Show full text]
  • An FPGA-BASED Implementation of a Hilbert Filter for Real-Time Estimation of Instantaneous Frequency, Phase and Amplitude of Power System Signals
    International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 23 (2018) pp. 16333-16341 © Research India Publications. http://www.ripublication.com An FPGA-BASED implementation of a Hilbert filter for Real-time Estimation of Instantaneous Frequency, Phase and Amplitude of Power System Signals Q. Bart and R. Tzoneva Department of Electrical, Electronic and Computer Engineering, Cape Peninsula University of Technology, Cape town, 7535, South Africa. Abstract apriori knowledge for a particular fixed sampling rate. The DFT is periodic in nature and assumes that the window of The instantaneous parameters, amplitude, phase and sampled data contains an integer number of waveform cycles. frequency of power system signals provide valuable During frequency perturbations the sampled data may not information about the status of the power system, particularly comprise an integer number of cycles resulting in when these parameters can be obtained simultaneously from discontinuities at the endpoints. This is one of the locations that are remotely located from each other. Phasor disadvantages of the DFT for real-time estimation of Measurement Units (PMU’s) placed at these remote locations synchrophasors and this phenomenon is commonly known as can perform these simultaneous time synchronized spectral leakage. In order to accommodate the dynamic measurements. In this work digital signal processing nature of power system signals an extension of the DFT techniques and Field Programmable Gate Array (FPGA) known as the Taylor-Fourier Transform [4],[5] has also been technology was used for the real-time estimation of power utilized as a synchrophasor estimator. Alternative techniques system signal parameters. These parameters were estimated such as Kalman Filtering [6] and Enhanced Phase Locked based upon an approach utilizing the Hilbert transform and Loop techniques [7] have also been proposed.
    [Show full text]
  • Arxiv:1611.05269V3 [Cs.IT] 29 Jan 2018 Graph Analytic Signal, and Associated Amplitude and Frequency Modulations Reveal Com
    On Hilbert Transform, Analytic Signal, and Modulation Analysis for Signals over Graphs Arun Venkitaraman, Saikat Chatterjee, Peter Handel¨ Department of Information Science and Engineering, School of Electrical Engineering and ACCESS Linnaeus Center KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden . Abstract We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and mod- ulation analysis in conventional signal processing, and the observation that comple- mentary insight is often obtained by viewing conventional signals in the graph setting. Our definitions of Hilbert transform and analytic signal use a conjugate-symmetry-like property exhibited by the graph Fourier transform (GFT), resulting in a ’one-sided’ spectrum for the graph analytic signal. The resulting graph Hilbert transform is shown to possess many interesting mathematical properties and also exhibit the ability to high- light anomalies/discontinuities in the graph signal and the nodes across which signal discontinuities occur. Using the graph analytic signal, we further define amplitude, phase, and frequency modulations for a graph signal. We illustrate the proposed con- cepts by showing applications to synthesized and real-world signals. For example, we show that the graph Hilbert transform can indicate presence of anomalies and that arXiv:1611.05269v3 [cs.IT] 29 Jan 2018 graph analytic signal, and associated amplitude and frequency modulations reveal com- plementary information in speech signals. Keywords: Graph signal, analytic signal, Hilbert transform, demodulation, anomaly detection. Email addresses: [email protected] (Arun Venkitaraman), [email protected] (Saikat Chatterjee), [email protected] (Peter Handel)¨ Preprint submitted to Signal Processing 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 (a) (b) Figure 1: Anomaly highlighting behavior of the graph Hilbert transform for 2D image signal graph.
    [Show full text]
  • Complex Exponentials and Spectrum Representation
    Music 270a: Complex Exponentials and Spectrum Representation Tamara Smyth, [email protected] Department of Music, University of California, San Diego (UCSD) October 21, 2019 1 Exponentials The exponential function is typically used to describe • the natural growth or decay of a system’s state. An exponential function is defined as • t/τ x(t)= e− , where e =2.7182..., and τ is the characteristic time constant, the time it takes to decay by 1/e. τ Exponential, e−t/ 1 0.9 0.8 0.7 0.6 0.5 0.4 1/e 0.3 0.2 0.1 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Time (s) Figure 1: Exponentials with characteristic time constants, .1, .2, .3, .4, and .5 Both exponential and sinusoidal functions are aspects • of a slightly more complicated function. Music 270a: Complex Exponentials and Spectrum Representation 2 Complex numbers Complex numbers provides a system for • 1. manipulating rotating vectors, and 2. representing geometric effects of common digital signal processing operations (e.g. filtering), in algebraic form. In rectangular (or Cartesian) form, the complex • number z is defined by the notation z = x + jy. The part without the j is called the real part, and • the part with the j is called the imaginary part. Music 270a: Complex Exponentials and Spectrum Representation 3 Complex Numbers as Vectors A complex number can be drawn as a vector, the tip • of which is at the point (x, y), where x , the horizontal coordinate—the real part, y , the vertical coordinate—the imaginary part.
    [Show full text]
  • Applications of Analytic Function Theory to Analysis of Single-Sideband Angle-Modulated Systems
    TECHNICAL NASA NOTE NASA TNA D-5446 =-­ e. z c 4 c/I 4 z LOAN COPY: RETURN TC.) AFWL (WWL-2) KIRTLAND AFB, N MEX APPLICATIONS OF ANALYTIC FUNCTION THEORY TO ANALYSIS OF SINGLE-SIDEBAND ANGLE-MODULATED SYSTEMS by John H. Painter r Langley Research Center i ! Langley Station, Hdmpton, Vd. &iii I NATIONAL AERONAUTICS AND SPACE ADMINISTRATION WASHINGTON, D. C. SEPTEMBER 1969 71 d TECH LIBRARY KAFB, "I \lllllllllll. .. I1111 Illlllllllllllllllllllll 01320b2 1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. NASA TN D-5446 I 4. Title and Subtitle 5. Report Date September 1969 APPLICATIONS OF ANALYTIC FUNCTION THEORY TO ANALYSIS 6. Performing Organization Co OF SINGLE-SIDEBAND ANGLE-MODULATED SYSTEMS 7. Author(s) 8. Performing Orgonization Re John H. Painter L-6562 9. Performing Orgonization Name and Address 0. Work Unit No. 125-21-05-01-23 NASA Langley Research Center Hampton, Va. 23365 1. Contract or Grant No. 3. Type of Report and Period 2. Sponsoring Agency Name ond Address Technical Note National Aeronautics and Space Administration Washington, D.C. 20546 4. Sponsoring Agency Code 15. Supplementary Notes 16. Abstract This paper applies the theory and notation of complex analytic ti" unctions and stochastic process6 to the investigation of the single-sideband angle-modulation process. Low-deviation modulation and linear product detection in the presence of noise are carefully examined for the case of sinusoidal modulation. Modulation by an arbitrary number of sinusoids or by modulated subcarriers is considered. Also treated is a method for increasing modulation efficiency. The paper concludes with an examination of the asymp­ totic (low-noise) performance of nonlinear detection of a single-sideband carrier which is heavily frequenc modulated by a Gaussian process.
    [Show full text]
  • Bandpass Filters and Hilbert Transform
    Bandpass filters and Hilbert Transform Summary of Chapter 14 In Analyzing Neural Time Series Data: Theory and Practice Lauritz W. Dieckman A quick message from Mike X I've created a googlegroups for the book. If you have other questions about the book, analyses, or code, feel free to post them there. https://groups.google.com/forum/#!forum/a nalyzingneuraltimeseriesdata Filters ‐Everywhere • Music ‐analog recordings ~44,100 Hz sampling ‐typically bandpass filtered to range of human hearing ‐Or, purposefully Used in artistic creation ‐One persons distortion is another persons art Why? Filter‐Hilbert Method vs Complex Wavelet Convolution Similarities • Used to create a complex (real and imaginary) time series (analytic signal) from real signal data • Analytic signal used to determine phase and power ‐Methods described in Chapter 13 • The signal must be bandpass filtered before Why? Filter‐Hilbert Method vs Complex Wavelet Convolution Differences • Signal must first be bandpass filtered • Analytic signal acquired by creating the “phase quadrature component” (one quarter‐cycle) by rotating sections of a Fourier spectrum. • PRO: Filter‐Hilbert provides superior control over frequency filtering ‐not limited in shape whereas bandpass filters can take many shapes • CON: However, Filter‐Hilbert requires signal processing toolbox for kernel creation (unlike Morlet wavelets). • CON: Bandpass filtering is slower than wavelets Bandpass Filtering • First step is Bandpass filtering ‐Don’t worry Hilbert fans, we’ll get back to Hilbert transform soon • Highly recommended to separate frequencies before the transform ‐e.g. lower frequencies may dominate combined signal Filters: To Infinity and… much shorter • Finite Impulse Response (FIR) SOON I will reign –The responsesupreme to an and impulse ends at some point.infinite over your ‐Stability analytic signal ‐Computation time ‐ Risk of phase distortions You will never triumph Infinite ImpulseButterworth.
    [Show full text]
  • Instantaneous Frequency and Amplitude of Complex Signals Based on Quaternion Fourier Transform
    Instantaneous frequency and amplitude of complex signals based on quaternion Fourier transform Nicolas Le Bihan∗ Stephen J. Sangwiney Todd A. Ellz August 8, 2012 Abstract plex modulation can be represented mathematically by a polar representation of quaternions previously The ideas of instantaneous amplitude and phase are derived by the authors. As in the classical case, there well understood for signals with real-valued samples, is a restriction of non-overlapping frequency content based on the analytic signal which is a complex sig- between the modulating complex signal and the or- nal with one-sided Fourier transform. We extend thonormal complex exponential. We show that, un- these ideas to signals with complex-valued samples, der these conditions, modulation in the time domain using a quaternion-valued equivalent of the analytic is equivalent to a frequency shift in the quaternion signal obtained from a one-sided quaternion Fourier Fourier domain. Examples are presented to demon- transform which we refer to as the hypercomplex rep- strate these concepts. resentation of the complex signal. We present the necessary properties of the quaternion Fourier trans- form, particularly its symmetries in the frequency do- 1 Introduction main and formulae for convolution and the quater- nion Fourier transform of the Hilbert transform. The The instantaneous amplitude and phase of a real- hypercomplex representation may be interpreted as valued signal have been understood since 1948 from an ordered pair of complex signals or as a quater- the work of Ville [1] and Gabor [2] based on the nion signal. We discuss its derivation and proper- analytic signal. A critical discussion of instanta- ties and show that its quaternion Fourier transform neous amplitude and phase was given by Picinbono is one-sided.
    [Show full text]
  • Signal-Processing Framework for Ultrasound Compressed Sensing Data: Envelope Detection and Spectral Analysis
    applied sciences Article Signal-Processing Framework for Ultrasound Compressed Sensing Data: Envelope Detection and Spectral Analysis Yisak Kim, Juyoung Park and Hyungsuk Kim * Department of Electrical Engineering, Kwangwoon University, Seoul 139-701, Korea; [email protected] (Y.K.); [email protected] (J.P.) * Correspondence: [email protected] Received: 20 August 2020; Accepted: 30 September 2020; Published: 4 October 2020 Abstract: Acquisition times and storage requirements have become increasingly important in signal-processing applications, as the sizes of datasets have increased. Hence, compressed sensing (CS) has emerged as an alternative processing technique, as original signals can be reconstructed using fewer data samples collected at frequencies below the Nyquist sampling rate. However, further analysis of CS data in both time and frequency domains requires the reconstruction of the original form of the time-domain data, as traditional signal-processing techniques are designed for uncompressed data. In this paper, we propose a signal-processing framework that extracts spectral properties for frequency-domain analysis directly from under-sampled ultrasound CS data, using an appropriate basis matrix, and efficiently converts this into the envelope of a time-domain signal, avoiding full reconstruction. The technique generates more accurate results than the traditional framework in both time- and frequency-domain analyses, and is simpler and faster in execution than full reconstruction, without any loss of information. Hence, the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems handling large datasets. Keywords: compressed sensing (CS); CS reconstruction; Fourier transform; envelope detection; Hilbert transform; analytic signal; ultrasound B-mode image; ultrasound attenuation map 1.
    [Show full text]
  • Easy Fourier Analysis
    Easy Fourier Analysis http://www.complextoreal.com/base.htm SpectrumDCInformationSignal term at 2w on csignal the positivenegative x-axis Half is up This half is shifted. down shifted. Charan Langton, The Carrier Editor fm = -1 fm = -9, fc-9,-9 -8, = -8, -8-7 -7 -9, -8, -7 SIGNAL PROCESSING & SIMULATION NEWSLETTER Baseband, Passband Signals and Amplitude Modulation The most salient feature of information signals is that they are generally low frequency. Sometimes this is due to the nature of data itself such as human voice which has frequency components from 300 Hz to app. 20 KHz. Other times, such as data from a digital circuit inside a computer, the low rates are due to hardware limitations. Due to their low frequency content, the information signals have a spectrum such as that in the figure below. There are a lot of low frequency components and the one-sided spectrum is located near the zero frequency. Figure 1 - The spectrum of an information signal is usually limited to low frequencies The hypothetical signal above has four sinusoids, all of which are fairly close to zero. The frequency range of this signal extends from zero to a maximum frequency of fm. We say that this signal has a bandwidth of fm. In the time domain this 4 component signal may looks as shown in Figure 2. Figure 2 - Time domain low frequency information signal 1 of 18 1/25/2008 3:42 AM Easy Fourier Analysis http://www.complextoreal.com/base.htm Now let’s modulate this signal, which means we are going to transfer it to a higher (usually much higher) frequency.
    [Show full text]
  • A New Discrete-Time Analytic Signal for Reducing Aliasing in Discrete Time–Frequency Distributions
    15th European Signal Processing Conference (EUSIPCO 2007), Poznan, Poland, September 3-7, 2007, copyright by EURASIP A NEW DISCRETE-TIME ANALYTIC SIGNAL FOR REDUCING ALIASING IN DISCRETE TIME–FREQUENCY DISTRIBUTIONS John M. O' Toole, Mostefa Mesbah and Boualem Boashash Perinatal Research Centre, University of Queensland, Royal Brisbane & Women's Hospital, Herston, QLD 4029, Australia. e-mail: [email protected] web: www.som.uq.edu.au/research/sprcg/ ABSTRACT tained from the finite discrete-time signal s(nT) of length N, The commonly used discrete-time analytic signal for discrete where T represents the sampling period. time–frequency distributions (DTFDs) contains spectral en- The requirements for an alias-free discrete-time, discrete- ergy at negative frequencies which results in aliasing in the frequency TFDs are as follows: a discrete-time TFD requires DTFD. A new discrete-time analytic signal is proposed that that a signal with half the Nyquist bandwidth is used, which approximately halves this spectral energy at the appropri- is the case for the analytic signal [2]. A discrete-frequency ate discrete negative frequencies. An empirical comparison TFD requires that a signal with half the time duration is shows that aliasing is reduced in the DTFD using the pro- used, that is s(nT) is zero for N < n ≤ 2N − 1 [3]. Thus posed analytic signal rather than the conventional analytic these two conditions must be combined to produce a discrete- signal. The time domain characteristics of the two analytic time, discrete-frequency TFD (simply referred to as a dis- signals are compared using an impulse signal as an exam- crete TFD, (DTFD)).
    [Show full text]