...The Convolution of Love Virginia Fisher Student ID# 20441228 RS 180 Love & Friendship / Professor Carolyn Whitney-Brown 19/09/2013 The Convolution of Love “Is love a utopian dream or a possibility within our reach?” (Nouwen, 24). This essay will first, explore Henri Nouwen’s theory of reversibility or irreversibility of evil. Next, Nouwen’s perspective will be applied to the relationship between Louis and Janine and finally, this paper will examine the possibility of their capacity for love to change throughout the course of the book. Henri Nouwen composes a succinct vision of his reversibility or irreversibility of evil theory. There are two distinctive domains within, that of the “taking form of existence” and conversely, that of the “forgiving form of love”. The “taking form” is markedly comprised of the insidious need of destructive power over others and is enmeshed in a foundation of fear. One of the most prevalent forms of this destructive power is the use of an individual’s past which can become “the most lethal weapon in human relationships” spawning “shame, guilt, moral and even physical death” (Nouwen, 26). This hypothesis of irreversible evil is “definitive and unchangeable” and determines that all “mistakes are final and unforgiveable” which in turn, predestines us to the “impossibility of love” (Nouwen, 27). Conversely, the “forgiving form” embodies truthfulness, tenderness and a complete “disarmament” of self through “the confession of our total self to each...
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...b=1-3*z^{-1}+ 7*z^{-2}+15*z^{-3}-12*z^{-4}+13*z^{-5} Transform polynomials equation to vector ------------------------------------------------- a = [1 2 4 7 12 25]; ------------------------------------------------- b = [1 -3 7 15 -12 13]; ------------------------------------------------- % FFT function in Matlab using Circular Convolution, instead avoid the ------------------------------------------------- % affect of circular convolution, we add zero paddles at end of the vector ------------------------------------------------- ------------------------------------------------- a0 = [a zeros(1, 5)]; ------------------------------------------------- b0 = [b zeros(1 , 5)]; ------------------------------------------------- ------------------------------------------------- % Convolution in time domain equivalent the mutiply in frequency domain. ------------------------------------------------- % First equation doing convolution operation of two polynomials equation in ------------------------------------------------- % time domain. ------------------------------------------------- % Secon equation doing mutiply operation of two polynomials equation in ------------------------------------------------- % frequency domain, then convert back to time domain...
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...Fourier Transform and its applications Jatin Kumar Murray State University Abstract It has been widely recognized that waveforms are an integral part of the various universe phenomenon. Waveforms can be used to represent almost everything in the world. Therefore it is understandable that concepts related to waveforms or signals are extremely important as their applications exist in a broad variety of fields. The processes and ideas related to waveforms play a vital role in different areas of science and technology such as communications, optics, quantum mechanics, aeronautics, image processing to name a few. Even though the physical nature of signals might be completely different in various disciplines, all waveforms follow one fundamental principle; they can be represented by functions of one or more independent variables. This paper would focus on the concept of Fourier Transform, the technique through which signals can be deconstructed and represented as sum of various elementary signals. It briefly describes Linear Time Invariant systems and their response to superimposed signals. Fourier transform has many applications in physics and Engineering. This paper would also cover some of Fourier Transform applications in telecommunication and its impact on society. Introduction Some of the basic signals that exist in the world and are useful in various technology fields are continuous and discrete time...
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...Lab 2: Linear Time-Invariant Systems In this experiment, you will study the output response of linear time-invariant (LTI) systems using MATLAB, and learn how to use MATLAB to implement the convolution sum. You will also investigate the properties of LTI systems. The objective of this experiment is: (1) to study how to compute the output of LTI systems, and (2) to study the properties of discrete-time LTI systems. 1. Introduction to Linear Time-Invariant (LTI) Systems In discrete time, linearity provides the ability to completely characterize a system in terms of its response [pic] to a signal of the form [pic] for all [pic]. If a linear system is also time-invariant, then the responses [pic] will become [pic]. The combination of linearity and time-invariance therefore allows a system to be completely described by its impulse response [pic]. The output of the system [pic] is related to the input [pic] through the convolution sum as follows: [pic] Similarly, the output [pic] of a continuous-time LTI system is related to the input [pic] and the impulse response [pic] through the following convolution integral: [pic] The convolution of discrete-time sequences [pic] and [pic] represented mathematically by the expression given in [pic] can be viewed pictorially as the operation of flipping the time axis of the sequence [pic] and shifting it by [pic] samples, then multiplying [pic] by[pic] and summing the resulting product sequence...
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...Digital Image Processing: PIKS Inside, Third Edition. William K. Pratt Copyright © 2001 John Wiley & Sons, Inc. ISBNs: 0-471-37407-5 (Hardback); 0-471-22132-5 (Electronic) DIGITAL IMAGE PROCESSING DIGITAL IMAGE PROCESSING PIKS Inside Third Edition WILLIAM K. PRATT PixelSoft, Inc. Los Altos, California A Wiley-Interscience Publication JOHN WILEY & SONS, INC. New York • Chichester • Weinheim • Brisbane • Singapore • Toronto Designations used by companies to distinguish their products are often claimed as trademarks. In all instances where John Wiley & Sons, Inc., is aware of a claim, the product names appear in initial capital or all capital letters. Readers, however, should contact the appropriate companies for more complete information regarding trademarks and registration. Copyright 2001 by John Wiley and Sons, Inc., New York. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic or mechanical, including uploading, downloading, printing, decompiling, recording or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 605 Third Avenue, New York, NY 10158-0012, (212) 850-6011, fax (212) 850-6008, E-Mail: PERMREQ @ WILEY.COM. This publication is designed...
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...Signals and Systems Midterm 10:20a.m. ~ 12:20p.m., May 1, Fri., 2009 Closed book, but open 1 sheet (both sides, 2 pages) of personal notes of A4 size Total score: 120 Total 4 pages in one B4 sheet 1. [12] Suppose x and y denote input and output, respectively, of each of the three systems: System A: y (t ) = x(t + 2) sin(ω t + 2), ω ≠ 0 System B: y[n] = ( − 1 ) ( x[n] + 1) 2 System C: y[n] = ∑ x 2 [k + 1] − x[k ] k =1 n n Answer the following questions for each system and justify your answer. (a) Is the system linear? (b) Is the system time invariant? (c) Is the system causal? (d) Is the system stable? 2. We want to develop an edge detector that is robust against additive noise. Consider a discrete-time (DT) linear time-invariant (LTI) system H 2 with h2 [ n] = h[ n] ∗ h[ n + 1] as its impulse response shown below, where h[ n] = δ [ n] − δ [ n − 1] . (a) [4] Assume there is no noise, i.e., d [n] = 0 and x[n] = p[n] . Sketch the output y[n] of the system assuming the input p[n] to the system is the following signal: (b) [4] Assume the noise is d [ n] = −δ [ n + 1] and the input p[n] remains the same. Sketch the output y[n] of the system. (c) [4] In order to use system H 2 as a part of an edge detector, we would like to add an LTI system H s whose unit impulse response hs [n] is shown below. System H s smoothes out effect of noise on x[n] . The overall system can be represented as below: Sketch the output ys [n] of the system with d [n] and p[n] specified...
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...Task 1: Task one was intended to build off of ideas implemented in the previous lab. More specifically formulating a V-I curve for the resistor circuit displayed in the prelab. Graph 1 shows the resulting V-I curve. Graph 1: V-I curve for the potentiometer. Like the previous lab, the signal was generated by a function generator and then sent through a potentiometer followed by a precision resistor in series. Figure 1 shows the LabView Block diagram that was used to record voltages of the two resistors. Figure 1: LabView Block diagram used to record voltages across the precision resistor and potentiometer. Because the resistance of the precision resistor was known to be 10Ω the recorded voltage was used to calculate the current using Ohm’s Law shown in equation 1. V=I*R Equation 1: Ohm’s law. Used to calculate the current. Task 2: Task two was designed to demonstrate how different sampling rates and different frequencies affect data and how it appears. In this section, the function generator was set to 5 Vpp with a frequency of 1000Hz and plugged into the compact DAQ module. Next the experiment was performed using five sampling rates (500, 1000, 2000, 3752, & 20000 ) with samples of 10, 40, 50, 200, and 2000. The LabView block diagram shown in figure 2 was responsible for collecting the mean and standard deviation of the amplitude and frequency. ...
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...Abstract— Face recognition is a biometric system used to verify or identify a person from a digital image. This involve extracting features of face and then recognize it, regardless of expression, lighting, illumination, transformations (translate, rotate and scale image), ageing and pose. The existing approaches are deblurring based, joint deblurring and recognition, deriving blur invariant features and direct approach, which uses convolution model for performing face recognition in the presence of blur. So these methods cannot handle non uniform blurring situations that frequently arise from rotations and tilts in hand held cameras. In this paper, face recognition is done, in the presence of space varying motion blur. We have taken the concept that the set of all images obtained by non-uniformly blurring a given image form a convex set. We develop an algorithm based on TSF (Transformation Spread Function) model. On each focused...
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...check method flowchart IV. EXPERIMENT AND RESULT 4.1 Thresholding Approach- Figure 2 (a) shows input picture of lung. Threshold segmentation is connected on the picture which is appeared in the figure 7 (b).This is the range with the intensity values higher than the characterized limit. High intensity zones generally includes disease cell. So through threshold segmentation we can indicate the area of tumor cell. Figure 7- (a) Original Image (b)Image by Threshold Segmentation 4.2 Gabor Filter A Gabor filter is a direct channel whose drive reaction is characterized by a consonant capacity duplicated by a Gaussian capacity. As a result of the increase convolution property (Convolution hypothesis), the Fourier change of a Gabor channel's drive reaction is the convolution of the Fourier change of the symphonious capacity and the Fourier change of the Gaussian capacity [6]. Figure 2 depicts (a) the original picture and (b) the enhanced picture utilizing Gabor Filter. Fig. 8 (a) Original image (b) Enhanced by Gabor Fig 9- Plot of Accuracy of Threshold vs. Gabor Filter V. CONCLUSION Lung cancer is the most risky and across the board on the planet as per stage the revelation of the tumor cells in the lungs, this gives us the sign that the procedure of identification this malady plays a vital and crucial part to maintain a strategic distance from genuine stages and...
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...The responses from the impulses are estimated by analyzing the training sequence. Virtual multipath is created by applying the original signal to some sort of change in phase and amplitude and later add them up to get the aggregated fake signal. The hacker introduces a second transmitter at the receiver to confuse the target. At the receiver end the received symbols is represented by y=h*x+n. Here convolution operation takes place, convolution is the method a compare to impulses, here we add ‘n’ as it is noise. The most challenging part for the attacker is to obtain weights, initially the length of Xa is L+M-1, which gets changed as Va which will be L+(L+M-1). To defend against these attacks the receiver uses two different training sequence to estimate the impulse and sends two training sequence, for which the attacker has to solve both the weights w1 and w2. These weights do not necessarily help the same channel estimation. Thus there will be a change of estimated responses and at the helper can indicate the existence of threats caused from the virtual multipath attack. The parameters dhelp is measured against the threshold the helper activity is used to find...
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...EXPANSION JOINTS T. N. GOPINATH 1. INTRODUCTION When piping lacks inherent flexibility due to routing and/or develops large reactions or detrimental overstrain on the strain sensitive equipments, the Piping Engineer considers provision of expansion joints to overcome the same. Expansion joints are also provided to isolate the vibrating equipment from piping and also to facilitate free movement of the equipment mounted on load cells. 2. TYPES OF EXPANSION JOINTS The expansion joints can be slip type or the bellows type. 2.1 Slip Type of Expansion Joint In slip type of expansion joint one pipe slides into another and the assembly is sealed by means of packing between the sliding pipes. This device has the limitation that it permits only axial movement in the direction of pipe axis. Small amount of lateral and/ or angular movement will cause binding and eventually leakage. It is extremely difficult to seal it off completely. The limitations on packing makes it suitable only for very low temperature and low pressure services. Fig 2.1 indicates the general arrangement of a slip type expansion joint. [pic] Fig 2.1 2.2 Bellow Type Expansion Joint The bellow type expansion joint is extensively used as the most efficient and functionally reliable elongation compensator and/ or vibration isolator. These are capable of compensating for large amounts of axial and/...
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...that this report is my own original work. Where other people’s work has been used (either from a printed source, Internet or any other source), this has been properly acknowledged and referenced in accordance with departmental requirements. 3. I have not used work previously produced by another student or any other person to hand in as my own. 4. I have not allowed, and will not allow, anyone to copy my work with the intention of passing it off as his or her own work. Name | Student number | Signature | | | | Date | | Contents Table of Figures 3 Acronyms Used 3 PART 1 Filtering in the Frequency Domain 3 Introduction 3 Review of Prior Knowledge 4 Complex Numbers 4 Fourier series 4 Fourier Transform 4 Convolution Theorem 5 Overview 5 2-D FFT 6 DFT 6 IDFT 7 2-D FFT 7 Comparison with 1-D FFT 8 2-D FFT and Image Processing 8 Image Smoothing and Sharpening 9 Smoothing 9 Sharpening 11 Conclusion 13 PART 2 Application of Filtering in the Frequency Domain 13 Introduction 13 Gaussian Filter Theoretical Analysis 13 Gaussian Low Pass Filter 14 Gaussian High Pass Filter 14 Gaussian Filter Design 15 Practical Results 16 Conclusion 17 References 18 Table of Figures Figure 1: Input Signal Transformed to Frequency Domain 5 Figure 2: Flow Diagram of Filtering in the Frequency Domain 5 Figure 3: Spatial VS Frequency Domain 5 Figure 4: 2-D Sinusoidal Wave 7 Figure 5: Flow Diagram of Filtering in the Frequency...
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...6.867 Final Project: Comparing Machine Learning Methods for Detecting Facial Expressions Vickie Ye and Alexandr Wang Abstract is used in this project uses the approach described in [3]. Kazemi et. al. uses localized histograms of gradient orienIn this project, we compared di↵erent methods for facial ex- tation in a cascade of regressors that minimize squared error, pression recognition using images from a Kaggle dataset re- in which each regressor is learned through gradient boostleased as a part of an ICML-2013 workshop on representation ing. This approach is robust to geometric and photometric learning. We found that classification using features extracted transformations, and showed less than 5% error on the LFPW manually from facial images using principal component anal- dataset. ysis yielded on average 40% classification accuracy. Using fea- The facial landmarks (eyes, eyebrows, nose, mouth) are intutures extracted by facial landmark detection, we received on itively the most expressive features in a face, and could also average 52% classification accuracy. However, when we used serve as good features for emotion classification. a convolutional neural network, we received 65% classification accuracy. 1.3 1 Support vector machines are widely used in classification problems, and is an optimization problem that can be solved in its dual form, Introduction Detecting facial expressions is an area of research within computer vision that has been studied...
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...History of Reverb and Echo in Audio Production Recording Natural occurring reverb goes further back than man on earth. Since before man, creatures and nature made sounds that created natural reverb and echo. But now in today’s age with men and the modern technology that they have created there are all sorts of ways to create reverb and echo. From big metal sheets to plug-ins in a DAW on a computer, engineers have found ways to incorporate and benefit from reverb and echo on a recorded audio track. But before skipping into today’s ways of recording reverb and echo let’s shed some light on what reverb and echo truly is and where it came from. Reverberation or as most people refer to it as reverb, refers to the way sound waves reflects of the surfaces around the sound source before it reaches the listeners ear. Reverb is basically a series of multiple fast echoes merged together so fast that the human ear cannot separate the sound into a delayed distinct duplicate sound. When hearing exact duplicates of the sound is when it becomes echo. In the early days of radio, they would send the radio signal through a line miles and miles away and bring it back. An easy way to hear reverb is to enter an empty room and clap. Immediately after the clap the person would hear the decaying sound of the clap; that is reverb. Recording reverb in the early days was as simple as just backing up a microphone from your sound source to reach the right amount of reverb you were looking for. There...
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...Computer Science Sonic Arts Research Centre m.vanwalstijn@qub.ac.uk course website: http://www.somasa.qub.ac.uk/~mvanwalstijn/ELE8059/ (all course material becomes available there) recommended text: Mulgrew, Grant & Thompson; “Digital Signal Processing: Concepts & Applications” DSP 1 Session 1: Introduction to DSP COURSE OUTLINE date session 1 26 Sep 2011 lecture tutorial questions Introduction to DSP session 2 3 Oct 2011 Signals and Spectral Representation X session 3 10 Oct 2011 Linear Systems X session 4 17 Oct 2011 Time-Domain Description and Convolution X session 5 24 Oct 2011 Sampled Data and Discrete-Time Systems X session 6 7 Oct 2011 The Discrete Fourier Transform X session 7 14 Nov 2011 The Fast Fourier Transform X session 8 21 Nov 2011 Fast Convolution X session 9 28 Nov 2011 Multi-Rate Processing X coursework assignment* 5 Dec 2012 feedback session 23 Jan 2012 session 10 13 Feb 2012 Continuous Filter Theory X session 11 20 Feb 2012 Infinite Impulse Response (IIR) Filters X session 12 27 Feb 2012 Finite Impulse Response (FIR) Filters X session 13 5 Mar 2012 Random Signal Analysis X session 14 12 Mar 2012 Optimum Filters X session 15 19 Mar 2012 Adaptive Filters X *The coursework assignment counts for 20% of the mark. DSP Session 1: Introduction to DSP ...
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