...Samer Abboud Signal Processing Final Report 29/5/2014 There are there objectives to this report, as we will see in the three following problems. The first objective is to build a VI to find the magnitude response of each filter while changing the 5 designs of filters(Elliptical, Chebychev, Inverse Chebychev, Butterworth and Bessel) and changing the filter types (Lowpass, Highpass, Bandpass, Bandstop). The second objective which is seen in problem 2 is to generate a pulse with uniform white noise, passing it through a median type filter. Theoretical Part 1: In problem 1 we have a case structure for the 4 different types of filters. Those four types ( Lowpass, Highpass, Bandpass, Bandstop) require nearly the same input, changing slightly. the inputs are filter design, filter type, impulse response, high and low frequencies, the order of the filter, sampling rate and the attenuation and ripples in those filters that need those inputs. At the output of the case structure, we are transforming the signal into a Fourier transform and passing it through an array subset, multiplying with a logarithm of base 10, passing it through a bundle to finally be displayed on the waveform graph. All this is put inside a while loop with a 50 ms wait. Theoretical Part 2: In problem 2 we are inserting a certain number of samples into a pulse VI as well as a uniform white noise VI, while specifying some characteristics like width and delay and noise level. After we add the pulse to the...
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...Week 17 : Signal Processing Test - Attempt #1 Top of Form |Time Remaining: [pic] | | |Page: 1 2 | Page 1 [pic] |Question 1. 1. (TCO 3) What is the expression for the transform admittance for an unfluxed inductance of 4H? (Points : 6) | | | | [pic] 0.25/s | | [pic] 4/s | | [pic] 0.25s | | [pic] 4s | | | | ...
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...------------------------------------------------- Contents * SECTION :2 * SECTION :3 * SECTION :4 * SECTION :5 ------------------------------------------------- SECTION :2 Multiply the following polynomials in z by using the fft algorithm a=1+2*z^{-1}+ 4*z^{-2}+7*z^{-3}+12*z^{-4}+25*z^{-5} 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 ------------------------------------------------- ...
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...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 Domain 9 Figure 6: Graphical Representation of an Ideal Low Pass Filter 10 Figure 7: Image that was smoothed 11 Figure 8: Graphical...
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...Digital Signal Processing The acronym DSP can stand for one of two things: digital signal processing which refers to the technique used to process signals digitally or digital signal processor which would refer to a specialized kind of microprocessor. Since the introduction of general-purpose microprocessors in the 1980’s, a wider range of modern technology now includes digital signal processing technology. This includes cellular phones, CD players, computers, DVD players, video recorders, computer monitors, and many other similar devices. DSP is also taking over analog circuitry in TV sets and telephones. One of DSP’s major applications is signal compression and decompression. Signal compression enables things such as call waiting and image projection on computer screens. DSP involves a great deal of math, both complex and simple. The design of a DSP chip enables it to perform these functions very rapidly which produces hundreds of millions of samples very quickly. This provides “real time” performance meaning the signal is live. This can be seen in loud speakers and cell phones. Most major electronics companies utilize DSP in their technology including Texas Instruments, Intel, and Motorola. An example of “real time” performance could be viewed in a lab that we did. We had two separate groups: one group made a “receiver” and the other group made a “transmitter.” Across the room, one group had their receiver and set up the microphone on a stand a certain amount of space...
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...signal Speech Signal Processing Speech Production Speech Waveform Characteristics ● ● ● Loudness Voiced/Unvoiced. Pitch. – Fundamental frequency. Formants. ● Spectral envelope. – Speech Waveform Characteristics Voiced Unvoiced s s Short-Time Speech Analysis ● Segments (or frames, or vectors) are typically of length 20 ms. – – Speech characteristics are constant. Allows for relatively simple modeling. ● Often overlapping segments are extracted. The Spectrogram ● A classic analysis tool. – Consists of DFTs of overlapping, and windowed frames. ● Displays the distribution of energy in time and frequency. A spectrogram Short time ACF /m/ /ow/ /s/ ACF |DFT| Sound Propagation Sound propagates from the source to the receiver through a combination of four main propagation modes: ● ● ● ● direct propagation path reflection from walls diffraction around objects refraction due to temperature differences in the layers of air. For that reason sound is delayed and attenuated by different amounts. Reflection One happens when a sound wave encounters a medium with different impedance from which it is travelling in, for example when the sound propagating in the air hits the walls of a room (fig. 1). Sound reflects from walls, objects, etc. Acoustically, reflection results in: Sound reverberation - for small round-trip delays (less than 100 ms), Echo - for longer round-trip delays. fig. 1 ● ● ...
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...Primary Cognitive PU4 Frequency Sense the spectral environment over a wide bandwidth Transmit in “white space” & Adapt bandwidth and power Detect if primary user appears Move to new white space Cognitive Radio System Design Network Management Sensing MAC Sensing Signal Processing Sensing radio Spectrum Allocation Network Link Layer Wideband signaling Wideband radio Physical Layer Spectrum sensing is the key enabling functionality How do we implement spectrum sensing in a system? Spectrum Sensing Problem Primary User Cognitive Radio users must guarantee non-interference requirement Tx Rx CR CR Decoding SNR Sensing SNR distance Distance and channel not known Cognitive radio can only observe (sense) primary system Tx signals Need to sense signals in highly negative SNR Sensing SNR < Decoding SNR – worst case channel Sensing SNR < [5dB to 20 dB] – [20 dB to 40 dB] = [-35 to 0 dB] Designing Spectrum Sensors – Sensing Requirements set by Primary User system ● Signal level (dBm) ● Maximum detection time (s) ● Interference protection (%) – Can we use standard detection techniques? ● Energy detection ● Pilot detection ● Feature detection – Can a radio sense primary signals robustly and guarantee noninterference to primary users in negative SNR regimes?...
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...Imagining a robot to behave just like a human is one of those new-age fantasies of almost everyone who can bottle even a tiny glimpse of a vision of the future. For people like us who sometimes manage to think of things other than just the daily survival have a lot of room for all kinds of strange dreams. Sometimes this also leads us to build all kinds of blue-sky thinking. Stepping off the quicksand let me classify ourselves as a group of individuals who have much time to think so as to recycle all kinds of emotions into this moment all over again. We very well know how it does feel when we are ecstatic about something, or when we are embarrassed, or sometimes get a shock of our lives, sometimes feel like crying our hearts out or sometimes plain bored to death. We can relate to all kind of feelings that ranges between these exaggerated memes, can’t we? But how do you relate if you were a robot? I would say that this is where you would feel the hint of a brainteaser, even if I assume your IQ to surpass Einstein’s. The fact that there is even a problem here seem to elude most people, it's hard to realize what it is and even harder to explain it. There is this default position that consciousness is, in principle, knowable and explainable in the framework of modern neurology and that there are no reasons to think otherwise. But did I mention earlier that I, for some daunting series of events, have started conceiving myself as a robot?! You might and should guffaw over this, but...
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...VOL. 3, NO. 4, DECEMBER 2010 Microcontroller based Power Efficient Signal Conditioning Unit for Detection of a Single Gas using MEMS based Sensor P. Bhattacharyya*, D. Verma and D.Banerjee Department of Electronics and Telecommunication Engineering, Bengal Engineering and Science University, Shibpur- 711103, Howrah, West Bengal, India *Corresponding author: Tel.: +913326684561; fax: +913326682916 E-mail: pb_etc_besu@yahoo.com Abstract-A low power MEMS based sensor along with the embedded power efficient signal conditioning unit (Microcontroller based), which can be used with any suitable sensor-network to detect and quantify variations in a particular gas concentration, has been reported in this paper. The power consumption of the MEMS gas sensor is ~ 70mW to 100mW depending upon its operating temperature (150-250°C) and that of entire signal conditioning unit (consisting of low noise amplifier, switch, microcontroller and power management chip) is ~ 36mW in the ON state and only ~7.2µW in OFF state (sleep mode). The test gas in this particular case was methane for which sensor resistance varied from 100KΩ to 10KΩ. This hybrid sensor system is very much suitable for detecting a single gas with display of corresponding gas concentrations and subsequent alarming if the threshold limit is crossed. Index terms: MEMS, Gas sensor, Low power, Microcontroller, Signal Conditioning I. INTRODUCTION A Signal-conditioning unit for gas Detection has in the recent years been a very...
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...Bandwidth (signal processing) From Wikipedia, the free encyclopedia Jump to: navigation, search Baseband bandwidth. Here the bandwidth equals the upper frequency. Bandwidth is the difference between the upper and lower frequencies in a contiguous set of frequencies. It is typically measured in hertz, and may sometimes refer to passband bandwidth, sometimes to baseband bandwidth, depending on context. Passband bandwidth is the difference between the upper and lower cutoff frequencies of, for example, an electronic filter, a communication channel, or a signal spectrum. In case of a low-pass filter or baseband signal, the bandwidth is equal to its upper cutoff frequency. The term baseband bandwidth always refers to the upper cutoff frequency, regardless of whether the filter is bandpass or low-pass. Bandwidth in hertz is a central concept in many fields, including electronics, information theory, radio communications, signal processing, and spectroscopy. A key characteristic of bandwidth is that a band of a given width can carry the same amount of information, regardless of where that band is located in the frequency spectrum (assuming equivalent noise level). For example, a 5 kHz band can carry a telephone conversation whether that band is at baseband (as in your POTS telephone line) or modulated to some higher (passband) frequency. In computer networking and other digital fields, the term bandwidth often refers to a data rate measured in bits per second, for example network...
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...[Fourier analysis of Control System] [Fourier analysis of Control System] Submitted to: Dr. S. K. Raghuwanshi Submitted By: Rishi Kant Sharan Semester: V Branch: Electronics & Communication Engineering Submitted to: Dr. S. K. Raghuwanshi Submitted By: Rishi Kant Sharan Adm. No: 2010JE1117 Semester: V Branch: Electronics & Communication Engineering Abstract The assignment focuses on the Fourier analysis of Control System. Which leads to frequency domain analysis of control system. The scope of estimation and controlling the behavior a system by means of Fourier transformation of its transfer function and analyzing its frequency response. Abstract The assignment focuses on the Fourier analysis of Control System. Which leads to frequency domain analysis of control system. The scope of estimation and controlling the behavior a system by means of Fourier transformation of its transfer function and analyzing its frequency response. ACKNOWLEDGEMENT There is an old adage that says that you never really learn a subject until you teach it. I now know that you learn a subject even better when you write about it. Preparing this term paper has provided me with a wonderful opportunity to unite my love of concept in CONTROL SYSTEM. This term paper is made possible through the help and support from everyone, including: professor, friends, parents, family, and in essence, all sentient beings. Especially, please allow me to dedicate...
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...estimation problem: 1) the filtering problem is to find the filtered output y , , ( t ) , where n . Y,!(t)S Cgl'(t)xi(t), i=l 1ItIT; (1.2) 2) the identification problem is to find the filter weights g ; ( t ) , i = 1;. ., n, for any t I. T This generalization of the least-squares estimation problem is important whenever practical space-time or multichannel filtering arises, such as in adaptive antenna arrays, I. INTRODUCTION decision feedback and fractionally spaced channel equalizINIMUM mean-square estimation is an old and ma- ers, etc. In the previous formalization of the least-squares ture subject, pervading throughout much of the com- problem, we do not need formal stochastic characterizations munication and signal processing literature [l]. Specifically, of the sequences but deal only with observed deterministic various versions...
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...A Novel Channel Estimation Algorithm for 3GPP LTE Downlink System Using Joint Time-Frequency Two-Dimensional Iterative Wiener Filter Jinfeng Hou, Jian Liu School of Communication and Information Engineering University of Electronic Science and Technology of China (UESTC) Chengdu 611731, China Email: houjinfeng@gmail.com, liuj@uestc.edu.cn Abstract—The channel estimation algorithms are employed in 3GPP Long Term Evolution (LTE) downlink system to assist the coherent demodulation of Orthogonal Frequency Division Multiplexing (OFDM) symbols. Based on the comparison of several exiting different channel estimation algorithms, we propose a joint time-frequency two-dimensional iterative Wiener filter (IWF) channel estimation algorithm for 3GPP LTE downlink system. In this scheme, we first apply the linear minimum mean square error (LMMSE) algorithm based on singular value decomposition (SVD) for IWF in frequency domain, and then the values after the first filtering in frequency domain are used to achieve the second IWF in time domain. Comparing to the conventional algorithms, the channel estimation algorithm proposed by this paper brings up lower bit error rate (BER) and adds little computational complexity. I. I NTRODUCTION In December 2004, the Third Generation Partnership Program (3GPP) members started a feasibility study on the enhancement of the Universal Terrestrial Radio Access (UTRA) in the aim of continuing the long time frame competitiveness of the 3G Universal Mobile Telecommunications...
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...technical papers is also appended for a quick reference. Streszczenie. Techniki PLL i synchronizacji są ważnymi elementami przetworników w systemach sieciowych, takich jak: rozproszonych systemach mocy, FACTs czy HVDC. Artykuł przedstawia przegląd tego typu metod a następnie porównanie tych metod. Na końcu ponad 40 podstawowych artykułów z tej tematyki jest przedstawionych.(Metody synchronizacji i PLL w przetwornikach sieciowych – przegląd). Keywords: Grid-interfaced converters, phase locked loop, synchronization Słowa kluczowe: uchłady PLL, synchronizacja. Introduction The basic phase locked loop (PLL) concept was originally published by Appleton in 1923 and Bellescize in 1932, which was mainly used for synchronous reception of radio signals [1-2]. After that, PLL techniques were widely used in various industrial fields such as communication systems [36], motor control systems [7-8], induction heating power supplies [9] and contactless power supplies [10]. Recently, PLL techniques have been used for synchronization between grid-interfaced converters and the utility network. An ideal PLL can provide the fast and accurate synchronization information with a high degree of immunity and insensitivity to...
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...LTE Initial Access [pic] Like all mobile communication systems, in LTE a terminal must perform certain steps before it can receive or transmit data. These steps can be categorized in cell search and cell selection, derivation of system information, and random access. The complete procedure is known as LTE Initial Access and is shown in the Figure below. After the initial access procedure, the terminal is able to receive and transmit its user data. [pic] Initial synchronization [pic] Successful execution of the cell search and selection procedure as well as acquiring initial system information is essential for the UE before taking further steps to communicate with the network. For this reason, it is important to take a closer look at this fundamental physical layer procedure. This section focuses on the cell-search scheme defined for LTE and the next chapter describes reception of the essential system information. As in 3G (WCDMA), LTE uses a hierarchical cell-search procedure in which an LTE radio cell is identified by a cell identity, which is comparable to the scrambling code that is used to separate base stations and cells in WCDMA. To avoid the need for expensive and complicated network and cell planning, 504 physical layer cell identities of is sufficiently large. With a hierarchical cell search scheme, these identities are divided into 168 unique cell layer identity groups in the physical layer, in which each group consists of three...
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