The Scientist and Engineer's Guide to Digital Signal Processing
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Moving Average Filters
CHAPTER 15 Moving Average Filters The moving average is the most common filter in DSP, mainly because it is the easiest digital filter to understand and use. In spite of its simplicity, the moving average filter is optimal for a common task: reducing random noise while retaining a sharp step response. This makes it the premier filter for time domain encoded signals. However, the moving average is the worst filter for frequency domain encoded signals, with little ability to separate one band of frequencies from another. Relatives of the moving average filter include the Gaussian, Blackman, and multiple- pass moving average. These have slightly better performance in the frequency domain, at the expense of increased computation time. Implementation by Convolution As the name implies, the moving average filter operates by averaging a number of points from the input signal to produce each point in the output signal. In equation form, this is written: EQUATION 15-1 Equation of the moving average filter. In M &1 this equation, x[ ] is the input signal, y[ ] is ' 1 % y[i] j x [i j ] the output signal, and M is the number of M j'0 points used in the moving average. This equation only uses points on one side of the output sample being calculated. Where x[ ] is the input signal, y[ ] is the output signal, and M is the number of points in the average. For example, in a 5 point moving average filter, point 80 in the output signal is given by: x [80] % x [81] % x [82] % x [83] % x [84] y [80] ' 5 277 278 The Scientist and Engineer's Guide to Digital Signal Processing As an alternative, the group of points from the input signal can be chosen symmetrically around the output point: x[78] % x[79] % x[80] % x[81] % x[82] y[80] ' 5 This corresponds to changing the summation in Eq. -
Linear Filtering of Random Processes
Linear Filtering of Random Processes Lecture 13 Spring 2002 Wide-Sense Stationary A stochastic process X(t) is wss if its mean is constant E[X(t)] = µ and its autocorrelation depends only on τ = t1 − t2 ∗ Rxx(t1,t2)=E[X(t1)X (t2)] ∗ E[X(t + τ)X (t)] = Rxx(τ) ∗ Note that Rxx(−τ)=Rxx(τ)and Rxx(0) = E[|X(t)|2] Lecture 13 1 Example We found that the random telegraph signal has the autocorrelation function −c|τ| Rxx(τ)=e We can use the autocorrelation function to find the second moment of linear combinations such as Y (t)=aX(t)+bX(t − t0). 2 2 Ryy(0) = E[Y (t)] = E[(aX(t)+bX(t − t0)) ] 2 2 2 2 = a E[X (t)] + 2abE[X(t)X(t − t0)] + b E[X (t − t0)] 2 2 = a Rxx(0) + 2abRxx(t0)+b Rxx(0) 2 2 =(a + b )Rxx(0) + 2abRxx(t0) −ct =(a2 + b2)Rxx(0) + 2abe 0 Lecture 13 2 Example (continued) We can also compute the autocorrelation Ryy(τ)forτ =0. ∗ Ryy(τ)=E[Y (t + τ)Y (t)] = E[(aX(t + τ)+bX(t + τ − t0))(aX(t)+bX(t − t0))] 2 = a E[X(t + τ)X(t)] + abE[X(t + τ)X(t − t0)] 2 + abE[X(t + τ − t0)X(t)] + b E[X(t + τ − t0)X(t − t0)] 2 2 = a Rxx(τ)+abRxx(τ + t0)+abRxx(τ − t0)+b Rxx(τ) 2 2 =(a + b )Rxx(τ)+abRxx(τ + t0)+abRxx(τ − t0) Lecture 13 3 Linear Filtering of Random Processes The above example combines weighted values of X(t)andX(t − t0) to form Y (t). -
Designing Filters Using the Digital Filter Design Toolkit Rahman Jamal, Mike Cerna, John Hanks
NATIONAL Application Note 097 INSTRUMENTS® The Software is the Instrument ® Designing Filters Using the Digital Filter Design Toolkit Rahman Jamal, Mike Cerna, John Hanks Introduction The importance of digital filters is well established. Digital filters, and more generally digital signal processing algorithms, are classified as discrete-time systems. They are commonly implemented on a general purpose computer or on a dedicated digital signal processing (DSP) chip. Due to their well-known advantages, digital filters are often replacing classical analog filters. In this application note, we introduce a new digital filter design and analysis tool implemented in LabVIEW with which developers can graphically design classical IIR and FIR filters, interactively review filter responses, and save filter coefficients. In addition, real-world filter testing can be performed within the digital filter design application using a plug-in data acquisition board. Digital Filter Design Process Digital filters are used in a wide variety of signal processing applications, such as spectrum analysis, digital image processing, and pattern recognition. Digital filters eliminate a number of problems associated with their classical analog counterparts and thus are preferably used in place of analog filters. Digital filters belong to the class of discrete-time LTI (linear time invariant) systems, which are characterized by the properties of causality, recursibility, and stability. They can be characterized in the time domain by their unit-impulse response, and in the transform domain by their transfer function. Obviously, the unit-impulse response sequence of a causal LTI system could be of either finite or infinite duration and this property determines their classification into either finite impulse response (FIR) or infinite impulse response (IIR) system. -
Finite Impulse Response (FIR) Digital Filters (II) Ideal Impulse Response Design Examples Yogananda Isukapalli
Finite Impulse Response (FIR) Digital Filters (II) Ideal Impulse Response Design Examples Yogananda Isukapalli 1 • FIR Filter Design Problem Given H(z) or H(ejw), find filter coefficients {b0, b1, b2, ….. bN-1} which are equal to {h0, h1, h2, ….hN-1} in the case of FIR filters. 1 z-1 z-1 z-1 z-1 x[n] h0 h1 h2 h3 hN-2 hN-1 1 1 1 1 1 y[n] Consider a general (infinite impulse response) definition: ¥ H (z) = å h[n] z-n n=-¥ 2 From complex variable theory, the inverse transform is: 1 n -1 h[n] = ò H (z)z dz 2pj C Where C is a counterclockwise closed contour in the region of convergence of H(z) and encircling the origin of the z-plane • Evaluating H(z) on the unit circle ( z = ejw ) : ¥ H (e jw ) = åh[n]e- jnw n=-¥ 1 p h[n] = ò H (e jw )e jnwdw where dz = jejw dw 2p -p 3 • Design of an ideal low pass FIR digital filter H(ejw) K -2p -p -wc 0 wc p 2p w Find ideal low pass impulse response {h[n]} 1 p h [n] = H (e jw )e jnwdw LP ò 2p -p 1 wc = Ke jnwdw 2p ò -wc Hence K h [n] = sin(nw ) n = 0, ±1, ±2, …. ±¥ LP np c 4 Let K = 1, wc = p/4, n = 0, ±1, …, ±10 The impulse response coefficients are n = 0, h[n] = 0.25 n = ±4, h[n] = 0 = ±1, = 0.225 = ±5, = -0.043 = ±2, = 0.159 = ±6, = -0.053 = ±3, = 0.075 = ±7, = -0.032 n = ±8, h[n] = 0 = ±9, = 0.025 = ±10, = 0.032 5 Non Causal FIR Impulse Response We can make it causal if we shift hLP[n] by 10 units to the right: K h [n] = sin((n -10)w ) LP (n -10)p c n = 0, 1, 2, …. -
Frequency Response
EE105 – Fall 2015 Microelectronic Devices and Circuits Frequency Response Prof. Ming C. Wu [email protected] 511 Sutardja Dai Hall (SDH) Amplifier Frequency Response: Lower and Upper Cutoff Frequency • Midband gain Amid and upper and lower cutoff frequencies ωH and ω L that define bandwidth of an amplifier are often of more interest than the complete transferfunction • Coupling and bypass capacitors(~ F) determineω L • Transistor (and stray) capacitances(~ pF) determineω H Lower Cutoff Frequency (ωL) Approximation: Short-Circuit Time Constant (SCTC) Method 1. Identify all coupling and bypass capacitors 2. Pick one capacitor ( ) at a time, replace all others with short circuits 3. Replace independent voltage source withshort , and independent current source withopen 4. Calculate the resistance ( ) in parallel with 5. Calculate the time constant, 6. Repeat this for each of n the capacitor 7. The low cut-off frequency can be approximated by n 1 ωL ≅ ∑ i=1 RiSCi Note: this is an approximation. The real low cut-off is slightly lower Lower Cutoff Frequency (ωL) Using SCTC Method for CS Amplifier SCTC Method: 1 n 1 fL ≅ ∑ 2π i=1 RiSCi For the Common-Source Amplifier: 1 # 1 1 1 & fL ≅ % + + ( 2π $ R1SC1 R2SC2 R3SC3 ' Lower Cutoff Frequency (ωL) Using SCTC Method for CS Amplifier Using the SCTC method: For C2 : = + = + 1 " 1 1 1 % R3S R3 (RD RiD ) R3 (RD ro ) fL ≅ $ + + ' 2π # R1SC1 R2SC2 R3SC3 & For C1: R1S = RI +(RG RiG ) = RI + RG For C3 : 1 R2S = RS RiS = RS gm Design: How Do We Choose the Coupling and Bypass Capacitor Values? • Since the impedance of a capacitor increases with decreasing frequency, coupling/bypass capacitors reduce amplifier gain at low frequencies. -
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. -
Classic Filters There Are 4 Classic Analogue Filter Types: Butterworth, Chebyshev, Elliptic and Bessel. There Is No Ideal Filter
Classic Filters There are 4 classic analogue filter types: Butterworth, Chebyshev, Elliptic and Bessel. There is no ideal filter; each filter is good in some areas but poor in others. • Butterworth: Flattest pass-band but a poor roll-off rate. • Chebyshev: Some pass-band ripple but a better (steeper) roll-off rate. • Elliptic: Some pass- and stop-band ripple but with the steepest roll-off rate. • Bessel: Worst roll-off rate of all four filters but the best phase response. Filters with a poor phase response will react poorly to a change in signal level. Butterworth The first, and probably best-known filter approximation is the Butterworth or maximally-flat response. It exhibits a nearly flat passband with no ripple. The rolloff is smooth and monotonic, with a low-pass or high- pass rolloff rate of 20 dB/decade (6 dB/octave) for every pole. Thus, a 5th-order Butterworth low-pass filter would have an attenuation rate of 100 dB for every factor of ten increase in frequency beyond the cutoff frequency. It has a reasonably good phase response. Figure 1 Butterworth Filter Chebyshev The Chebyshev response is a mathematical strategy for achieving a faster roll-off by allowing ripple in the frequency response. As the ripple increases (bad), the roll-off becomes sharper (good). The Chebyshev response is an optimal trade-off between these two parameters. Chebyshev filters where the ripple is only allowed in the passband are called type 1 filters. Chebyshev filters that have ripple only in the stopband are called type 2 filters , but are are seldom used. -
Chapter 3 FILTERS
Chapter 3 FILTERS Most images are a®ected to some extent by noise, that is unexplained variation in data: disturbances in image intensity which are either uninterpretable or not of interest. Image analysis is often simpli¯ed if this noise can be ¯ltered out. In an analogous way ¯lters are used in chemistry to free liquids from suspended impurities by passing them through a layer of sand or charcoal. Engineers working in signal processing have extended the meaning of the term ¯lter to include operations which accentuate features of interest in data. Employing this broader de¯nition, image ¯lters may be used to emphasise edges | that is, boundaries between objects or parts of objects in images. Filters provide an aid to visual interpretation of images, and can also be used as a precursor to further digital processing, such as segmentation (chapter 4). Most of the methods considered in chapter 2 operated on each pixel separately. Filters change a pixel's value taking into account the values of neighbouring pixels too. They may either be applied directly to recorded images, such as those in chapter 1, or after transformation of pixel values as discussed in chapter 2. To take a simple example, Figs 3.1(b){(d) show the results of applying three ¯lters to the cashmere ¯bres image, which has been redisplayed in Fig 3.1(a). ² Fig 3.1(b) is a display of the output from a 5 £ 5 moving average ¯lter. Each pixel has been replaced by the average of pixel values in a 5 £ 5 square, or window centred on that pixel. -
Feedback Amplifiers
UNIT II FEEDBACK AMPLIFIERS & OSCILLATORS FEEDBACK AMPLIFIERS: Feedback concept, types of feedback, Amplifier models: Voltage amplifier, current amplifier, trans-conductance amplifier and trans-resistance amplifier, feedback amplifier topologies, characteristics of negative feedback amplifiers, Analysis of feedback amplifiers, Performance comparison of feedback amplifiers. OSCILLATORS: Principle of operation, Barkhausen Criterion, types of oscillators, Analysis of RC-phase shift and Wien bridge oscillators using BJT, Generalized analysis of LC Oscillators, Hartley and Colpitts’s oscillators with BJT, Crystal oscillators, Frequency and amplitude stability of oscillators. 1.1 Introduction: Feedback Concept: Feedback: A portion of the output signal is taken from the output of the amplifier and is combined with the input signal is called feedback. Need for Feedback: • Distortion should be avoided as far as possible. • Gain must be independent of external factors. Concept of Feedback: Block diagram of feedback amplifier consist of a basic amplifier, a mixer (or) comparator, a sampler, and a feedback network. Figure 1.1 Block diagram of an amplifier with feedback A – Gain of amplifier without feedback. A = X0 / Xi Af – Gain of amplifier with feedback.Af = X0 / Xs β – Feedback ratio. β = Xf / X0 X is either voltage or current. 1.2 Types of Feedback: 1. Positive feedback 2. Negative feedback 1.2.1 Positive Feedback: If the feedback signal is in phase with the input signal, then the net effect of feedback will increase the input signal given to the amplifier. This type of feedback is said to be positive or regenerative feedback. Xi=Xs+Xf Af = = = Af= Here Loop Gain: The product of open loop gain and the feedback factor is called loop gain. -
Unit I Microwave Transmission Lines
UNIT I MICROWAVE TRANSMISSION LINES INTRODUCTION Microwaves are electromagnetic waves with wavelengths ranging from 1 mm to 1 m, or frequencies between 300 MHz and 300 GHz. Apparatus and techniques may be described qualitatively as "microwave" when the wavelengths of signals are roughly the same as the dimensions of the equipment, so that lumped-element circuit theory is inaccurate. As a consequence, practical microwave technique tends to move away from the discrete resistors, capacitors, and inductors used with lower frequency radio waves. Instead, distributed circuit elements and transmission-line theory are more useful methods for design, analysis. Open-wire and coaxial transmission lines give way to waveguides, and lumped-element tuned circuits are replaced by cavity resonators or resonant lines. Effects of reflection, polarization, scattering, diffraction, and atmospheric absorption usually associated with visible light are of practical significance in the study of microwave propagation. The same equations of electromagnetic theory apply at all frequencies. While the name may suggest a micrometer wavelength, it is better understood as indicating wavelengths very much smaller than those used in radio broadcasting. The boundaries between far infrared light, terahertz radiation, microwaves, and ultra-high-frequency radio waves are fairly arbitrary and are used variously between different fields of study. The term microwave generally refers to "alternating current signals with frequencies between 300 MHz (3×108 Hz) and 300 GHz (3×1011 Hz)."[1] Both IEC standard 60050 and IEEE standard 100 define "microwave" frequencies starting at 1 GHz (30 cm wavelength). Electromagnetic waves longer (lower frequency) than microwaves are called "radio waves". Electromagnetic radiation with shorter wavelengths may be called "millimeter waves", terahertz radiation or even T-rays. -
Wave Guides & Resonators
UNIT I WAVEGUIDES & RESONATORS INTRODUCTION Microwaves are electromagnetic waves with wavelengths ranging from 1 mm to 1 m, or frequencies between 300 MHz and 300 GHz. Apparatus and techniques may be described qualitatively as "microwave" when the wavelengths of signals are roughly the same as the dimensions of the equipment, so that lumped-element circuit theory is inaccurate. As a consequence, practical microwave technique tends to move away from the discrete resistors, capacitors, and inductors used with lower frequency radio waves. Instead, distributed circuit elements and transmission-line theory are more useful methods for design, analysis. Open-wire and coaxial transmission lines give way to waveguides, and lumped-element tuned circuits are replaced by cavity resonators or resonant lines. Effects of reflection, polarization, scattering, diffraction, and atmospheric absorption usually associated with visible light are of practical significance in the study of microwave propagation. The same equations of electromagnetic theory apply at all frequencies. While the name may suggest a micrometer wavelength, it is better understood as indicating wavelengths very much smaller than those used in radio broadcasting. The boundaries between far infrared light, terahertz radiation, microwaves, and ultra-high-frequency radio waves are fairly arbitrary and are used variously between different fields of study. The term microwave generally refers to "alternating current signals with frequencies between 300 MHz (3×108 Hz) and 300 GHz (3×1011 Hz)."[1] Both IEC standard 60050 and IEEE standard 100 define "microwave" frequencies starting at 1 GHz (30 cm wavelength). Electromagnetic waves longer (lower frequency) than microwaves are called "radio waves". Electromagnetic radiation with shorter wavelengths may be called "millimeter waves", terahertz Page 1 radiation or even T-rays. -
Electronic Filters Design Tutorial - 3
Electronic filters design tutorial - 3 High pass, low pass and notch passive filters In the first and second part of this tutorial we visited the band pass filters, with lumped and distributed elements. In this third part we will discuss about low-pass, high-pass and notch filters. The approach will be without mathematics, the goal will be to introduce readers to a physical knowledge of filters. People interested in a mathematical analysis will find in the appendix some books on the topic. Fig.2 ∗ The constant K low-pass filter: it was invented in 1922 by George Campbell and the Running the simulation we can see the response meaning of constant K is the expression: of the filter in fig.3 2 ZL* ZC = K = R ZL and ZC are the impedances of inductors and capacitors in the filter, while R is the terminating impedance. A look at fig.1 will be clarifying. Fig.3 It is clear that the sharpness of the response increase as the order of the filter increase. The ripple near the edge of the cutoff moves from Fig 1 monotonic in 3 rd order to ringing of about 1.7 dB for the 9 th order. This due to the mismatch of the The two filter configurations, at T and π are various sections that are connected to a 50 Ω displayed, all the reactance are 50 Ω and the impedance at the edges of the filter and filter cells are all equal. In practice the two series connected to reactive impedances between cells.