Chapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING. 1.1 Introduction 1.2 The Sampling Process
|
|
- Letitia Ellis
- 5 years ago
- Views:
Transcription
1 Chapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING 1.1 Introduction 1.2 The Sampling Process Copyright c Andreas Antoniou Victoria, BC, Canada aantoniou@ieee.org January 31, 2008 Frame # 1 Slide # 1 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
2 Introduction Signal processing emerged soon after World War I in the form electrical filtering. Frame # 2 Slide # 2 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
3 Introduction Signal processing emerged soon after World War I in the form electrical filtering. With the invention of the digital computer and the rapid advances in VLSI technology during the 1960s, a new way of processing signals emerged: digital signal processing. Frame # 2 Slide # 3 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
4 Introduction Signal processing emerged soon after World War I in the form electrical filtering. With the invention of the digital computer and the rapid advances in VLSI technology during the 1960s, a new way of processing signals emerged: digital signal processing. This and the next two presentations provide a brief historical summary of the emergence of signal processing and its applications. Frame # 2 Slide # 4 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
5 Introduction Signal processing emerged soon after World War I in the form electrical filtering. With the invention of the digital computer and the rapid advances in VLSI technology during the 1960s, a new way of processing signals emerged: digital signal processing. This and the next two presentations provide a brief historical summary of the emergence of signal processing and its applications. To start with, a classification of the various types of signals encountered in today s technological world is provided. Frame # 2 Slide # 5 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
6 Introduction Signal processing emerged soon after World War I in the form electrical filtering. With the invention of the digital computer and the rapid advances in VLSI technology during the 1960s, a new way of processing signals emerged: digital signal processing. This and the next two presentations provide a brief historical summary of the emergence of signal processing and its applications. To start with, a classification of the various types of signals encountered in today s technological world is provided. Then the sampling process is described as a means of converting analog into digital signals. Frame # 2 Slide # 6 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
7 Signals Typically one assumes that a signal is an electrical signal, for example, a radio, radar, or TV signal. Frame # 3 Slide # 7 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
8 Signals Typically one assumes that a signal is an electrical signal, for example, a radio, radar, or TV signal. However, in DSP a signal is any quantity that depends on one or more independent variables. Frame # 3 Slide # 8 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
9 Signals Typically one assumes that a signal is an electrical signal, for example, a radio, radar, or TV signal. However, in DSP a signal is any quantity that depends on one or more independent variables. A radio signal represents the strength of an electromagnetic wave that depends on one independent variable, namely, time. Frame # 3 Slide # 9 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
10 Signals Cont d In our generalized definition of a signal, there may be more than one independent variables and the independent variables may be any quantity other than time. Frame # 4 Slide # 10 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
11 Signals Cont d In our generalized definition of a signal, there may be more than one independent variables and the independent variables may be any quantity other than time. For example, a digitized image may be thought of as light intensity that depends on two independent variables, the distances along the x and y axes; as such a digitized image is, in effect, a 2-dimensional signal. Frame # 4 Slide # 11 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
12 Signals Cont d In our generalized definition of a signal, there may be more than one independent variables and the independent variables may be any quantity other than time. For example, a digitized image may be thought of as light intensity that depends on two independent variables, the distances along the x and y axes; as such a digitized image is, in effect, a 2-dimensional signal. A video signal is made up of a series of images which change with time; thus a video signal is light intensity that depends on the distances along the x and y axes and also on the time; in effect, a video signal is a 3-dimensional signal. Frame # 4 Slide # 12 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
13 Signals Cont d In our generalized definition of a signal, there may be more than one independent variables and the independent variables may be any quantity other than time. For example, a digitized image may be thought of as light intensity that depends on two independent variables, the distances along the x and y axes; as such a digitized image is, in effect, a 2-dimensional signal. A video signal is made up of a series of images which change with time; thus a video signal is light intensity that depends on the distances along the x and y axes and also on the time; in effect, a video signal is a 3-dimensional signal. Some signals arise naturally, others are man-made. Frame # 4 Slide # 13 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
14 Signals Cont d Natural signals are found, for example, in: Acoustics, e.g., speech signals, sounds made by dolphins and whales Frame # 5 Slide # 14 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
15 Signals Cont d Natural signals are found, for example, in: Acoustics, e.g., speech signals, sounds made by dolphins and whales Astronomy, e.g., cosmic signals originating galaxies and pulsars, astronomical images Frame # 5 Slide # 15 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
16 Signals Cont d Natural signals are found, for example, in: Acoustics, e.g., speech signals, sounds made by dolphins and whales Astronomy, e.g., cosmic signals originating galaxies and pulsars, astronomical images Biology, e.g., signals produced by the brain and heart Frame # 5 Slide # 16 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
17 Signals Cont d Natural signals are found, for example, in: Acoustics, e.g., speech signals, sounds made by dolphins and whales Astronomy, e.g., cosmic signals originating galaxies and pulsars, astronomical images Biology, e.g., signals produced by the brain and heart Seismology, e.g., signals produced by earthquakes and volcanoes Frame # 5 Slide # 17 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
18 Signals Cont d Natural signals are found, for example, in: Acoustics, e.g., speech signals, sounds made by dolphins and whales Astronomy, e.g., cosmic signals originating galaxies and pulsars, astronomical images Biology, e.g., signals produced by the brain and heart Seismology, e.g., signals produced by earthquakes and volcanoes Physical sciences, e.g., signals produced by lightnings, the room temperature, the atmospheric pressure Frame # 5 Slide # 18 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
19 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Frame # 6 Slide # 19 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
20 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Frame # 6 Slide # 20 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
21 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Telemetry, e.g., signals originating from weather stations and satellites Frame # 6 Slide # 21 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
22 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Telemetry, e.g., signals originating from weather stations and satellites Control systems, e.g., feedback control signals Frame # 6 Slide # 22 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
23 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Telemetry, e.g., signals originating from weather stations and satellites Control systems, e.g., feedback control signals Medicine, e.g., electrocardiographs, X-rays, magnetic resonance imaging Frame # 6 Slide # 23 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
24 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Telemetry, e.g., signals originating from weather stations and satellites Control systems, e.g., feedback control signals Medicine, e.g., electrocardiographs, X-rays, magnetic resonance imaging Space technology, e.g., the velocity of a space craft Frame # 6 Slide # 24 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
25 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Telemetry, e.g., signals originating from weather stations and satellites Control systems, e.g., feedback control signals Medicine, e.g., electrocardiographs, X-rays, magnetic resonance imaging Space technology, e.g., the velocity of a space craft Politics, e.g., the popularity ratings of a political party Frame # 6 Slide # 25 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
26 Signals Cont d Man-made signals are found in: Audio systems, e.g., music signals Communications, e.g., radio, telephone, TV signals Telemetry, e.g., signals originating from weather stations and satellites Control systems, e.g., feedback control signals Medicine, e.g., electrocardiographs, X-rays, magnetic resonance imaging Space technology, e.g., the velocity of a space craft Politics, e.g., the popularity ratings of a political party Economics, e.g., the price of a stock at the TSX, the TSX index, the gross national product Frame # 6 Slide # 26 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
27 Signals Cont d Two general classes of signals can be identified: Continuous-time signals Discrete-time signals Frame # 7 Slide # 27 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
28 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Frame # 8 Slide # 28 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
29 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Typical examples are: Frame # 8 Slide # 29 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
30 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Typical examples are: An electromagnetic wave originating from a distant galaxy Frame # 8 Slide # 30 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
31 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Typical examples are: An electromagnetic wave originating from a distant galaxy The sound wave produced by a dolphin Frame # 8 Slide # 31 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
32 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Typical examples are: An electromagnetic wave originating from a distant galaxy The sound wave produced by a dolphin The ambient temperature Frame # 8 Slide # 32 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
33 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Typical examples are: An electromagnetic wave originating from a distant galaxy The sound wave produced by a dolphin The ambient temperature The light intensity along the x and y axes in a photograph Frame # 8 Slide # 33 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
34 Continuous-Time Signals A continuous-time signal is a signal that is defined at each and every instant of time. Typical examples are: An electromagnetic wave originating from a distant galaxy The sound wave produced by a dolphin The ambient temperature The light intensity along the x and y axes in a photograph A continuous-time signal can be represented by a function x(t) where < t < Frame # 8 Slide # 34 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
35 Continuous-Time Signals Cont d x(t) t Frame # 9 Slide # 35 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
36 Discrete-Time Signals A discrete-time signal is a signal that is defined at discrete instants of time. Frame # 10 Slide # 36 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
37 Discrete-Time Signals A discrete-time signal is a signal that is defined at discrete instants of time. Typical examples are: Frame # 10 Slide # 37 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
38 Discrete-Time Signals A discrete-time signal is a signal that is defined at discrete instants of time. Typical examples are: The closing price of a particular commodity on the stock exchange Frame # 10 Slide # 38 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
39 Discrete-Time Signals A discrete-time signal is a signal that is defined at discrete instants of time. Typical examples are: The closing price of a particular commodity on the stock exchange The daily precipitation Frame # 10 Slide # 39 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
40 Discrete-Time Signals A discrete-time signal is a signal that is defined at discrete instants of time. Typical examples are: The closing price of a particular commodity on the stock exchange The daily precipitation The daily temperature of a patient as recorded by a nurse Frame # 10 Slide # 40 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
41 Discrete-Time Signals Cont d A discrete-time signal can be represented as a function x(nt ) where < n < and T is a constant. Frame # 11 Slide # 41 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
42 Discrete-Time Signals Cont d A discrete-time signal can be represented as a function x(nt ) where < n < and T is a constant. The quantity x(nt ) can represent a voltage or current level or any other quantity. Frame # 11 Slide # 42 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
43 Discrete-Time Signals Cont d A discrete-time signal can be represented as a function x(nt ) where < n < and T is a constant. The quantity x(nt ) can represent a voltage or current level or any other quantity. In DSP, x(nt ) always represents a series of numbers. Frame # 11 Slide # 43 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
44 Discrete-Time Signals Cont d A discrete-time signal can be represented as a function x(nt ) where < n < and T is a constant. The quantity x(nt ) can represent a voltage or current level or any other quantity. In DSP, x(nt ) always represents a series of numbers. Constant T usually represents time but it could be any other physical quantity depending on the application. Frame # 11 Slide # 44 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
45 Discrete-Time Signals Cont d x(nt) nt T Frame # 12 Slide # 45 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
46 Discrete-Time Signals Cont d Frame # 13 Slide # 46 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
47 Discrete-Time Signals Cont d Frame # 14 Slide # 47 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
48 Discrete-Time Signals Cont d Note: The signals in the previous two slides are discrete-time signals since a mutual fund or the TSX index has only one closing value per day. They are plotted as if they were continuous-time signals for the sake of convenience. Frame # 15 Slide # 48 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
49 Nonquantized and Quantized Signals Signals can also be classified as: Nonquantized Quantized Frame # 16 Slide # 49 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
50 Nonquantized and Quantized Signals Signals can also be classified as: Nonquantized Quantized A nonquantized signal is a signal that can assume any value within a given range, e.g., the ambient temperature. Frame # 16 Slide # 50 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
51 Nonquantized and Quantized Signals Signals can also be classified as: Nonquantized Quantized A nonquantized signal is a signal that can assume any value within a given range, e.g., the ambient temperature. A quantized signal is a signal that can assume only a finite number of discrete values, e.g., the ambient temperature as measured by a digital thermometer. Frame # 16 Slide # 51 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
52 Nonquantized and Quantized Signals Cont d x(t) x(nt) (a) Continuous-time, nonquantized t nt (b) Discrete-time, nonquantized x(t) x(nt) (c) Continuous-time, quantized t nt (d) Discrete-time, quantized Frame # 17 Slide # 52 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
53 Alternative Notation A discrete-time signal x(nt ) is often represented in terms of the alternative notations x(n) and x n Frame # 18 Slide # 53 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
54 Alternative Notation A discrete-time signal x(nt ) is often represented in terms of the alternative notations x(n) and x n In the early presentations, x(nt ) will be used most of the time to emphasize the fact that a discrete-time signal is typically generated by sampling a continuous-time signal x(t) at instant t = nt. Frame # 18 Slide # 54 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
55 Alternative Notation A discrete-time signal x(nt ) is often represented in terms of the alternative notations x(n) and x n In the early presentations, x(nt ) will be used most of the time to emphasize the fact that a discrete-time signal is typically generated by sampling a continuous-time signal x(t) at instant t = nt. In later presentations, the more economical notation x(n) will be used where appropriate. Frame # 18 Slide # 55 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
56 Sampling Process To be able to process a nonquantized continuous-time signal by a digital system, we must first sample it to generate a discrete-time signal. Frame # 19 Slide # 56 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
57 Sampling Process To be able to process a nonquantized continuous-time signal by a digital system, we must first sample it to generate a discrete-time signal. We must then quantize it to get a quantized discrete-time signal. Frame # 19 Slide # 57 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
58 Sampling Process To be able to process a nonquantized continuous-time signal by a digital system, we must first sample it to generate a discrete-time signal. We must then quantize it to get a quantized discrete-time signal. That way, we can generate a numerical representation of the signal that entails a finite amount of information. Frame # 19 Slide # 58 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
59 Sampling Process Cont d A sampling system comprises three essential components: sampler quantizer encoder Frame # 20 Slide # 59 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
60 Sampling Process Cont d Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Sampling system Frame # 21 Slide # 60 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
61 Sampling Process Cont d A sampler in its bare essentials is a switch controlled by a clock signal which closes momentarily every T seconds thereby transmitting the level of the input signal x(t) at instant nt, i.e., x(nt ), to its output. Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Sampling system Frame # 22 Slide # 61 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
62 Sampling Process Cont d A sampler in its bare essentials is a switch controlled by a clock signal which closes momentarily every T seconds thereby transmitting the level of the input signal x(t) at instant nt, i.e., x(nt ), to its output. Parameter T is called the sampling period. Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Sampling system Frame # 22 Slide # 62 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
63 Sampling Process Cont d A quantizer is a device that will sense the level of its input and produce as output the nearest available level, say, x q (nt ), from a set of allowed levels, i.e., a quantizer will produce a quantized continuous-time signal. Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Sampling system Frame # 23 Slide # 63 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
64 Sampling Process Cont d An encoder is essentially a digital device that will sense the voltage or current level of its input and produce a corresponding binary number at its output, i.e., it will convert a quantized continuous-time signal into a corresponding discrete-time signal in binary form. Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Sampling system Frame # 24 Slide # 64 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
65 Sampling Process Cont d The sampling system described is essentially an analog-to-digital converter and its implementation can assume numerous forms. Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Frame # 25 Slide # 65 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
66 Sampling Process Cont d The sampling system described is essentially an analog-to-digital converter and its implementation can assume numerous forms. These devices go by the acronym of A/D converter or ADC and are available in VLSI chip form as off-the-shelf devices. Sampler x(t) x(nt) Quantizer x q (nt) Encoder x q '(nt) Clock nt Frame # 25 Slide # 66 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
67 Sampling Process Cont d A quantized discrete-time signal produced by an A/D converter is, of course, an approximation of the original nonquantized continuous-time signal. Frame # 26 Slide # 67 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
68 Sampling Process Cont d A quantized discrete-time signal produced by an A/D converter is, of course, an approximation of the original nonquantized continuous-time signal. The accuracy of the representation can be improved by increasing the sampling rate, and/or the number of allowable quantization levels in the quantizer Frame # 26 Slide # 68 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
69 Sampling Process Cont d A quantized discrete-time signal produced by an A/D converter is, of course, an approximation of the original nonquantized continuous-time signal. The accuracy of the representation can be improved by increasing the sampling rate, and/or the number of allowable quantization levels in the quantizer The sampling rate is simply 1/T = f s in Hz or 2π/T = ω s in radians per second (rad/s). Frame # 26 Slide # 69 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
70 Sampling Process Cont d Once a discrete-time signal is generated which is an accurate representation of the original continuous-time signal, any required processing can be perform by a digital system. Frame # 27 Slide # 70 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
71 Sampling Process Cont d Once a discrete-time signal is generated which is an accurate representation of the original continuous-time signal, any required processing can be perform by a digital system. If the processed discrete-time signal is intended for a person, e.g., a music signal, then it must be converted back into a continuous-time signal. Frame # 27 Slide # 71 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
72 Sampling Process Cont d Once a discrete-time signal is generated which is an accurate representation of the original continuous-time signal, any required processing can be perform by a digital system. If the processed discrete-time signal is intended for a person, e.g., a music signal, then it must be converted back into a continuous-time signal. Just like the sampling process, the conversion from a discrete- to a continuous-signal requires a suitable digital-to-analog interface. Frame # 27 Slide # 72 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
73 Sampling Process Cont d Typically, the digital-to-analog interface requires a series of two cascaded modules, a digital-to-analog (or D/A) converter and a smoothing device: y(nt) D/A converter y (nt) Smoothing device y(t) Frame # 28 Slide # 73 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
74 Sampling Process Cont d A D/A converter will receive an encode digital signal in binary form like that in Fig. (a) as input and produce a corresponding quantized continuous-time signal such as that in Fig. (b). The stair-like nature of the quantized signal is, of course, undesirable and a D/A converter is normally followed by some type of smoothing device, typically a lowpass filter, that will eliminate the uneveness in the signal. y(nt) y'(t) (a) nt (b) t Frame # 29 Slide # 74 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
75 Sampling Process Cont d Complete DSP system Sampler Quantizer Encoder Digital system D/A converter Smoothing device x(t) x(nt) x q(nt) x' q(nt) y(nt) y (nt) y(t) Clock nt Frame # 30 Slide # 75 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
76 Sampling Process Cont d The quality of the conversion from a continuous- to a discrete-time signal and back to a continuous-time signal can be improved by understanding the processes involved and/or by designing the components of the sampling system carefully. Frame # 31 Slide # 76 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
77 Sampling Process Cont d The quality of the conversion from a continuous- to a discrete-time signal and back to a continuous-time signal can be improved by understanding the processes involved and/or by designing the components of the sampling system carefully. This subject will be treated at a higher level of sophistication in Chap. 6. Frame # 31 Slide # 77 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
78 Signal Processing Signal processing is the science of analyzing, synthesizing, sampling, encoding, transforming, decoding, enhancing, transporting, archiving, and generally manipulating signals in some way or another. Frame # 32 Slide # 78 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
79 Signal Processing Signal processing is the science of analyzing, synthesizing, sampling, encoding, transforming, decoding, enhancing, transporting, archiving, and generally manipulating signals in some way or another. These presentations are concerned primarily with the branch of signal processing that entails the manipulation of the spectral characteristics of signals. Frame # 32 Slide # 79 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
80 Signal Processing Signal processing is the science of analyzing, synthesizing, sampling, encoding, transforming, decoding, enhancing, transporting, archiving, and generally manipulating signals in some way or another. These presentations are concerned primarily with the branch of signal processing that entails the manipulation of the spectral characteristics of signals. If the processing of a signal involves modifying, reshaping, or transforming the spectrum of the signal in some way, then the processing involved is usually referred to as filtering. Frame # 32 Slide # 80 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
81 Signal Processing Signal processing is the science of analyzing, synthesizing, sampling, encoding, transforming, decoding, enhancing, transporting, archiving, and generally manipulating signals in some way or another. These presentations are concerned primarily with the branch of signal processing that entails the manipulation of the spectral characteristics of signals. If the processing of a signal involves modifying, reshaping, or transforming the spectrum of the signal in some way, then the processing involved is usually referred to as filtering. If the filtering is carried out by digital means, then it is referred to as digital filtering. Frame # 32 Slide # 81 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
82 This slide concludes the presentation. Thank you for your attention. Frame # 33 Slide # 82 A. Antoniou Digital Signal Processing Secs. 1.1, 1.2
Chapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING 1.6 Analog Filters 1.7 Applications of Analog Filters
Chapter 1 INTRODUCTION TO DIGITAL SIGNAL PROCESSING 1.6 Analog Filters 1.7 Applications of Analog Filters Copyright c 2005 Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org July 14, 2018
More informationChapter 5 THE APPLICATION OF THE Z TRANSFORM Aliasing
Chapter 5 THE APPLICATION OF THE Z TRANSFORM 5.5.4 Aliasing Copyright c 2005- Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org February 14, 2008 Frame # 1 Slide # 1 A. Antoniou Digital Signal
More informationChapter 6 CONTINUOUS-TIME, IMPULSE-MODULATED, AND DISCRETE-TIME SIGNALS. 6.6 Sampling Theorem 6.7 Aliasing 6.8 Interrelations
Chapter 6 CONTINUOUS-TIME, IMPULSE-MODULATED, AND DISCRETE-TIME SIGNALS 6.6 Sampling Theorem 6.7 Aliasing 6.8 Interrelations Copyright c 2005- Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org
More informationTE 302 DISCRETE SIGNALS AND SYSTEMS. Chapter 1: INTRODUCTION
TE 302 DISCRETE SIGNALS AND SYSTEMS Study on the behavior and processing of information bearing functions as they are currently used in human communication and the systems involved. Chapter 1: INTRODUCTION
More informationChapter 5 THE APPLICATION OF THE Z TRANSFORM. 5.6 Transfer Functions for Digital Filters 5.7 Amplitude and Delay Distortion
Chapter 5 THE APPLICATION OF THE Z TRANSFORM 5.6 Transfer Functions for Digital Filters 5.7 Amplitude and Delay Distortion Copyright c 2005- Andreas Antoniou Victoria, BC, Canada Email: aantoniou@ieee.org
More informationPulse Code Modulation
Pulse Code Modulation Modulation is the process of varying one or more parameters of a carrier signal in accordance with the instantaneous values of the message signal. The message signal is the signal
More informationDigital Signal Processing Lecture 1
Remote Sensing Laboratory Dept. of Information Engineering and Computer Science University of Trento Via Sommarive, 14, I-38123 Povo, Trento, Italy Digital Signal Processing Lecture 1 Prof. Begüm Demir
More informationСтатистическая обработка сигналов. Введение
Статистическая обработка сигналов. Введение А.Г. Трофимов к.т.н., доцент, НИЯУ МИФИ lab@neuroinfo.ru http://datalearning.ru Курс Статистическая обработка временных рядов Сентябрь 2018 А.Г. Трофимов Введение
More informationece 429/529 digital signal processing robin n. strickland ece dept, university of arizona ECE 429/529 RNS
ece 429/529 digital signal processing robin n. strickland ece dept, university of arizona 2007 SPRING 2007 SCHEDULE All dates are tentative. Lesson Day Date Learning outcomes to be Topics Textbook HW/PROJECT
More informationDIGITAL SIGNAL PROCESSING. Chapter 1 Introduction to Discrete-Time Signals & Sampling
DIGITAL SIGNAL PROCESSING Chapter 1 Introduction to Discrete-Time Signals & Sampling by Dr. Norizam Sulaiman Faculty of Electrical & Electronics Engineering norizam@ump.edu.my OER Digital Signal Processing
More informationOverview of Signal Processing
Overview of Signal Processing Chapter Intended Learning Outcomes: (i) Understand basic terminology in signal processing (ii) Differentiate digital signal processing and analog signal processing (iii) Describe
More informationSignal Processing of Discrete-time Signals
Signal Processing of Discrete-time Signals Andrew C. Singer and David C. Munson Jr. January 26, 2009 2 Chapter 1 Overview of Discrete-time Signal Processing 1 DSP overview 2 Continuous-time signals 3 Discrete-time
More informationOverview of Digital Signal Processing
Overview of Digital Signal Processing Chapter Intended Learning Outcomes: (i) Understand basic terminology in digital signal processing (ii) Differentiate digital signal processing and analog signal processing
More informationANALOGUE AND DIGITAL COMMUNICATION
ANALOGUE AND DIGITAL COMMUNICATION Syed M. Zafi S. Shah Umair M. Qureshi Lecture xxx: Analogue to Digital Conversion Topics Pulse Modulation Systems Advantages & Disadvantages Pulse Code Modulation Pulse
More informationCascaded Noise-Shaping Modulators for Oversampled Data Conversion
Cascaded Noise-Shaping Modulators for Oversampled Data Conversion Bruce A. Wooley Stanford University B. Wooley, Stanford, 2004 1 Outline Oversampling modulators for A/D conversion Cascaded noise-shaping
More informationMicrocomputer Systems 1. Introduction to DSP S
Microcomputer Systems 1 Introduction to DSP S Introduction to DSP s Definition: DSP Digital Signal Processing/Processor It refers to: Theoretical signal processing by digital means (subject of ECE3222,
More informationIslamic University of Gaza. Faculty of Engineering Electrical Engineering Department Spring-2011
Islamic University of Gaza Faculty of Engineering Electrical Engineering Department Spring-2011 DSP Laboratory (EELE 4110) Lab#4 Sampling and Quantization OBJECTIVES: When you have completed this assignment,
More informationLecture Schedule: Week Date Lecture Title
http://elec3004.org Sampling & More 2014 School of Information Technology and Electrical Engineering at The University of Queensland Lecture Schedule: Week Date Lecture Title 1 2-Mar Introduction 3-Mar
More informationContinuous time and Discrete time Signals and Systems
Continuous time and Discrete time Signals and Systems 1. Systems in Engineering A system is usually understood to be an engineering device in the field, and a mathematical representation of this system
More informationPRINCIPLES OF COMMUNICATION SYSTEMS. Lecture 1- Introduction Elements, Modulation, Demodulation, Frequency Spectrum
PRINCIPLES OF COMMUNICATION SYSTEMS Lecture 1- Introduction Elements, Modulation, Demodulation, Frequency Spectrum Topic covered Introduction to subject Elements of Communication system Modulation General
More informationMusic 270a: Fundamentals of Digital Audio and Discrete-Time Signals
Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals Tamara Smyth, trsmyth@ucsd.edu Department of Music, University of California, San Diego October 3, 2016 1 Continuous vs. Discrete signals
More informationPULSE CODE MODULATION (PCM)
PULSE CODE MODULATION (PCM) 1. PCM quantization Techniques 2. PCM Transmission Bandwidth 3. PCM Coding Techniques 4. PCM Integrated Circuits 5. Advantages of PCM 6. Delta Modulation 7. Adaptive Delta Modulation
More informationEE 351M Digital Signal Processing
EE 351M Digital Signal Processing Course Details Objective Establish a background in Digital Signal Processing Theory Required Text Discrete-Time Signal Processing, Prentice Hall, 2 nd Edition Alan Oppenheim,
More informationContinuous vs. Discrete signals. Sampling. Analog to Digital Conversion. CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals
Continuous vs. Discrete signals CMPT 368: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 22,
More informationII Year (04 Semester) EE6403 Discrete Time Systems and Signal Processing
Class Subject Code Subject II Year (04 Semester) EE6403 Discrete Time Systems and Signal Processing 1.CONTENT LIST: Introduction to Unit I - Signals and Systems 2. SKILLS ADDRESSED: Listening 3. OBJECTIVE
More informationSignals and Systems. Lecture 13 Wednesday 6 th December 2017 DR TANIA STATHAKI
Signals and Systems Lecture 13 Wednesday 6 th December 2017 DR TANIA STATHAKI READER (ASSOCIATE PROFFESOR) IN SIGNAL PROCESSING IMPERIAL COLLEGE LONDON Continuous time versus discrete time Continuous time
More informationThe quality of the transmission signal The characteristics of the transmission medium. Some type of transmission medium is required for transmission:
Data Transmission The successful transmission of data depends upon two factors: The quality of the transmission signal The characteristics of the transmission medium Some type of transmission medium is
More informationCommunications IB Paper 6 Handout 3: Digitisation and Digital Signals
Communications IB Paper 6 Handout 3: Digitisation and Digital Signals Jossy Sayir Signal Processing and Communications Lab Department of Engineering University of Cambridge jossy.sayir@eng.cam.ac.uk Lent
More informationEECS 452 Midterm Exam Winter 2012
EECS 452 Midterm Exam Winter 2012 Name: unique name: Sign the honor code: I have neither given nor received aid on this exam nor observed anyone else doing so. Scores: # Points Section I /40 Section II
More informationAPPLICATIONS OF DSP OBJECTIVES
APPLICATIONS OF DSP OBJECTIVES This lecture will discuss the following: Introduce analog and digital waveform coding Introduce Pulse Coded Modulation Consider speech-coding principles Introduce the channel
More informationSignal Characteristics
Data Transmission The successful transmission of data depends upon two factors:» The quality of the transmission signal» The characteristics of the transmission medium Some type of transmission medium
More informationEC 6501 DIGITAL COMMUNICATION UNIT - II PART A
EC 6501 DIGITAL COMMUNICATION 1.What is the need of prediction filtering? UNIT - II PART A [N/D-16] Prediction filtering is used mostly in audio signal processing and speech processing for representing
More informationLecture 7 Frequency Modulation
Lecture 7 Frequency Modulation Fundamentals of Digital Signal Processing Spring, 2012 Wei-Ta Chu 2012/3/15 1 Time-Frequency Spectrum We have seen that a wide range of interesting waveforms can be synthesized
More informationCMPT 318: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals
CMPT 318: Lecture 4 Fundamentals of Digital Audio, Discrete-Time Signals Tamara Smyth, tamaras@cs.sfu.ca School of Computing Science, Simon Fraser University January 16, 2006 1 Continuous vs. Discrete
More informationQUESTION BANK. SUBJECT CODE / Name: EC2301 DIGITAL COMMUNICATION UNIT 2
QUESTION BANK DEPARTMENT: ECE SEMESTER: V SUBJECT CODE / Name: EC2301 DIGITAL COMMUNICATION UNIT 2 BASEBAND FORMATTING TECHNIQUES 1. Why prefilterring done before sampling [AUC NOV/DEC 2010] The signal
More informationModule 1: Introduction to Experimental Techniques Lecture 2: Sources of error. The Lecture Contains: Sources of Error in Measurement
The Lecture Contains: Sources of Error in Measurement Signal-To-Noise Ratio Analog-to-Digital Conversion of Measurement Data A/D Conversion Digitalization Errors due to A/D Conversion file:///g /optical_measurement/lecture2/2_1.htm[5/7/2012
More informationDIGITAL SIGNAL PROCESSING. Introduction
DIGITAL SIGNAL PROCESSING Introduction What is Signal? A SIGNAL is a measurement of a physical quantity of certain medium. Examples of signals: Audio patterns (voice, speech, music) Visual patterns (written
More informationA/D Converter An electronic circuit that transforms an analog signal into a digital form that can be used by a computer or other digital circuits.
Digital Audio Terms A/D Converter An electronic circuit that transforms an analog signal into a digital form that can be used by a computer or other digital circuits. Aliasing An undesirable effect that
More informationAnalog-Digital Interface
Analog-Digital Interface Tuesday 24 November 15 Summary Previous Class Dependability Today: Redundancy Error Correcting Codes Analog-Digital Interface Converters, Sensors / Actuators Sampling DSP Frequency
More informationIntroduction to Digital Signal Processing (Discrete-time Signal Processing)
Introduction to Digital Signal Processing (Discrete-time Signal Processing) Prof. Chu-Song Chen Research Center for Info. Tech. Innovation, Academia Sinica, Taiwan Dept. CSIE & GINM National Taiwan University
More informationCHAPTER 4. PULSE MODULATION Part 2
CHAPTER 4 PULSE MODULATION Part 2 Pulse Modulation Analog pulse modulation: Sampling, i.e., information is transmitted only at discrete time instants. e.g. PAM, PPM and PDM Digital pulse modulation: Sampling
More informationIntroduction to Discrete-Time Control Systems
Chapter 1 Introduction to Discrete-Time Control Systems 1-1 INTRODUCTION The use of digital or discrete technology to maintain conditions in operating systems as close as possible to desired values despite
More informationMultirate DSP, part 3: ADC oversampling
Multirate DSP, part 3: ADC oversampling Li Tan - May 04, 2008 Order this book today at www.elsevierdirect.com or by calling 1-800-545-2522 and receive an additional 20% discount. Use promotion code 92562
More informationEE19D Digital Electronics. Lecture 1: General Introduction
EE19D Digital Electronics Lecture 1: General Introduction 1 What are we going to discuss? Some Definitions Digital and Analog Quantities Binary Digits, Logic Levels and Digital Waveforms Introduction to
More informationEITF25 Internet Techniques and Applications L2: Physical layer. Stefan Höst
EITF25 Internet Techniques and Applications L2: Physical layer Stefan Höst Data vs signal Data: Static representation of information For storage Signal: Dynamic representation of information For transmission
More informationIntroduction. Stefano Ferrari. Università degli Studi di Milano Methods for Image Processing. academic year
Introduction Stefano Ferrari Università degli Studi di Milano stefano.ferrari@unimi.it Methods for Image Processing academic year 2015 2016 Image processing Computer science concerns the representation,
More informationFigure 1: Block diagram of Digital signal processing
Experiment 3. Digital Process of Continuous Time Signal. Introduction Discrete time signal processing algorithms are being used to process naturally occurring analog signals (like speech, music and images).
More informationIntroduction to signals and systems
CHAPTER Introduction to signals and systems Welcome to Introduction to Signals and Systems. This text will focus on the properties of signals and systems, and the relationship between the inputs and outputs
More informationCommunications I (ELCN 306)
Communications I (ELCN 306) c Samy S. Soliman Electronics and Electrical Communications Engineering Department Cairo University, Egypt Email: samy.soliman@cu.edu.eg Website: http://scholar.cu.edu.eg/samysoliman
More informationUNIT-1. Basic signal processing operations in digital communication
UNIT-1 Lecture-1 Basic signal processing operations in digital communication The three basic elements of every communication systems are Transmitter, Receiver and Channel. The Overall purpose of this system
More informationIn this lecture. System Model Power Penalty Analog transmission Digital transmission
System Model Power Penalty Analog transmission Digital transmission In this lecture Analog Data Transmission vs. Digital Data Transmission Analog to Digital (A/D) Conversion Digital to Analog (D/A) Conversion
More informationLesson 7. Digital Signal Processors
Lesson 7 Digital Signal Processors Instructional Objectives After going through this lesson the student would learn o Architecture of a Real time Signal Processing Platform o Different Errors introduced
More informationMultirate DSP, part 1: Upsampling and downsampling
Multirate DSP, part 1: Upsampling and downsampling Li Tan - April 21, 2008 Order this book today at www.elsevierdirect.com or by calling 1-800-545-2522 and receive an additional 20% discount. Use promotion
More informationImplementation of FPGA based Design for Digital Signal Processing
e-issn 2455 1392 Volume 2 Issue 8, August 2016 pp. 150 156 Scientific Journal Impact Factor : 3.468 http://www.ijcter.com Implementation of FPGA based Design for Digital Signal Processing Neeraj Soni 1,
More informationDigital Signal Processing (Subject Code: 7EC2)
CIITM, JAIPUR (DEPARTMENT OF ELECTRONICS & COMMUNICATION) Notes Digital Signal Processing (Subject Code: 7EC2) Prepared Class: B. Tech. IV Year, VII Semester Syllabus UNIT 1: SAMPLING - Discrete time processing
More informationEE 210 Lab Exercise #4 D/A & A/D Converters
EE 210 Lab Exercise #4 D/A & A/D Converters Introduction This lab deals with simple resistive circuits to perform Digital-to-Analog (D/A) conversion. We also introduce the use of a basic Analog-to-Digital
More informationOF HIGH QUALITY AUDIO SIGNALS
COMPRESSION OF HIGH QUALITY AUDIO SIGNALS 1. Description of the problem Fairlight Instruments, who brought the problem to the MISG, have developed a high quality "Computer Musical Instrument" (CMI) which
More informationCHAPTER 5. Digitized Audio Telemetry Standard. Table of Contents
CHAPTER 5 Digitized Audio Telemetry Standard Table of Contents Chapter 5. Digitized Audio Telemetry Standard... 5-1 5.1 General... 5-1 5.2 Definitions... 5-1 5.3 Signal Source... 5-1 5.4 Encoding/Decoding
More informationWeek 1 Introduction of Digital Signal Processing with the review of SMJE 2053 Circuits & Signals for Filter Design
SMJE3163 DSP2016_Week1-04 Week 1 Introduction of Digital Signal Processing with the review of SMJE 2053 Circuits & Signals for Filter Design 1) Signals, Systems, and DSP 2) DSP system configuration 3)
More informationDigital Image Processing and Machine Vision Fundamentals
Digital Image Processing and Machine Vision Fundamentals By Dr. Rajeev Srivastava Associate Professor Dept. of Computer Sc. & Engineering, IIT(BHU), Varanasi Overview In early days of computing, data was
More informationCHAPTER -15. Communication Systems
CHAPTER -15 Communication Systems COMMUNICATION Communication is the act of transmission and reception of information. COMMUNICATION SYSTEM: A system comprises of transmitter, communication channel and
More informationSignals and Systems EE235. Leo Lam
Signals and Systems EE235 Leo Lam Today s menu Lab detailed arrangements Homework vacation week From yesterday (Intro: Signals) Intro: Systems More: Describing Common Signals Taking a signal apart Offset
More informationCh 5 Hardware Components for Automation
Ch 5 Hardware Components for Automation Sections: 1. Sensors 2. Actuators 3. Analog-to-Digital Conversion 4. Digital-to-Analog Conversion 5. Input/Output Devices for Discrete Data Computer-Process Interface
More informationFundamentals of Digital Communication
Fundamentals of Digital Communication Network Infrastructures A.A. 2017/18 Digital communication system Analog Digital Input Signal Analog/ Digital Low Pass Filter Sampler Quantizer Source Encoder Channel
More informationLaboratory Assignment 2 Signal Sampling, Manipulation, and Playback
Laboratory Assignment 2 Signal Sampling, Manipulation, and Playback PURPOSE This lab will introduce you to the laboratory equipment and the software that allows you to link your computer to the hardware.
More informationSignals & Signal Processing
Chapter 1 Signals & Signal Processing 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 #33120 Original PowerPoint slides prepared by S. K. Mitra 1-1-1 Signal & Signal Processing Signal: quantity that carries information
More informationSignals & Signal Processing
Chapter 1 Signals & Signal Processing 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 #33120 Original PowerPoint slides prepared by S. K. Mitra 1-1-1 Signal & Signal Processing Signal: quantity that carries information
More informationFinal Exam. EE313 Signals and Systems. Fall 1999, Prof. Brian L. Evans, Unique No
Final Exam EE313 Signals and Systems Fall 1999, Prof. Brian L. Evans, Unique No. 14510 December 11, 1999 The exam is scheduled to last 50 minutes. Open books and open notes. You may refer to your homework
More informationEE482: Digital Signal Processing Applications
Professor Brendan Morris, SEB 3216, brendan.morris@unlv.edu EE482: Digital Signal Processing Applications Spring 2014 TTh 14:30-15:45 CBC C222 Lecture 01 Introduction 14/01/21 http://www.ee.unlv.edu/~b1morris/ee482/
More informationChapter-1: Introduction
Chapter-1: Introduction The purpose of a Communication System is to transport an information bearing signal from a source to a user destination via a communication channel. MODEL OF A COMMUNICATION SYSTEM
More informationQuarterly Progress and Status Report. The 51-channel spectrum analyzer - a status report
Dept. for Speech, Music and Hearing Quarterly Progress and Status Report The 51-channel spectrum analyzer - a status report Garpendahl, G. and Liljencrants, J. and Rengman, U. journal: STL-QPSR volume:
More informationChapter 3 Data Transmission COSC 3213 Summer 2003
Chapter 3 Data Transmission COSC 3213 Summer 2003 Courtesy of Prof. Amir Asif Definitions 1. Recall that the lowest layer in OSI is the physical layer. The physical layer deals with the transfer of raw
More informationLecture 390 Oversampling ADCs Part I (3/29/10) Page 390-1
Lecture 390 Oversampling ADCs Part I (3/29/0) Page 390 LECTURE 390 OVERSAMPLING ADCS PART I LECTURE ORGANIZATION Outline Introduction Deltasigma modulators Summary CMOS Analog Circuit Design, 2 nd Edition
More informationSampling and Reconstruction of Analog Signals
Sampling and Reconstruction of Analog Signals Chapter Intended Learning Outcomes: (i) Ability to convert an analog signal to a discrete-time sequence via sampling (ii) Ability to construct an analog signal
More informationCOMP211 Physical Layer
COMP211 Physical Layer Data and Computer Communications 7th edition William Stallings Prentice Hall 2004 Computer Networks 5th edition Andrew S.Tanenbaum, David J.Wetherall Pearson 2011 Material adapted
More informationChapter-15. Communication systems -1 mark Questions
Chapter-15 Communication systems -1 mark Questions 1) What are the three main units of a Communication System? 2) What is meant by Bandwidth of transmission? 3) What is a transducer? Give an example. 4)
More informationDigital Signal Processing
Digital Signal Processing Lecture 9 Discrete-Time Processing of Continuous-Time Signals Alp Ertürk alp.erturk@kocaeli.edu.tr Analog to Digital Conversion Most real life signals are analog signals These
More informationStream Information. A real-time voice signal must be digitized & transmitted as it is produced Analog signal level varies continuously in time
, German University in Cairo Stream Information A real-time voice signal must be digitized & transmitted as it is produced Analog signal level varies continuously in time Th e s p ee ch s i g n al l e
More informationEXPERIMENT 4 PULSE CODE MODULATION
EXPERIMENT 4 PULSE CODE MODULATION 1.0 OBJECTIVES 1.1 To generate sampled signal using SCILAB software. 1.2 To perform Pulse Code Modulation system using SCILAB. 2.0 EQUIPMENT/APPARATUS SCILAB Software
More informationAdvanced Digital Signal Processing Part 2: Digital Processing of Continuous-Time Signals
Advanced Digital Signal Processing Part 2: Digital Processing of Continuous-Time Signals Gerhard Schmidt Christian-Albrechts-Universität zu Kiel Faculty of Engineering Institute of Electrical Engineering
More informationAdvanced AD/DA converters. ΔΣ DACs. Overview. Motivations. System overview. Why ΔΣ DACs
Advanced AD/DA converters Overview Why ΔΣ DACs ΔΣ DACs Architectures for ΔΣ DACs filters Smoothing filters Pietro Andreani Dept. of Electrical and Information Technology Lund University, Sweden Advanced
More informationLecture 2: SIGNALS. 1 st semester By: Elham Sunbu
Lecture 2: SIGNALS 1 st semester 1439-2017 1 By: Elham Sunbu OUTLINE Signals and the classification of signals Sine wave Time and frequency domains Composite signals Signal bandwidth Digital signal Signal
More informationEE 438 Final Exam Spring 2000
2 May 2000 Name: EE 438 Final Exam Spring 2000 You have 120 minutes to work the following six problems. Each problem is worth 25 points. Be sure to show all your work to obtain full credit. The exam is
More informationContents. Telecom Service Chae Y. Lee. Data Signal Transmission Transmission Impairments Channel Capacity
Data Transmission Contents Data Signal Transmission Transmission Impairments Channel Capacity 2 Data/Signal/Transmission Data: entities that convey meaning or information Signal: electric or electromagnetic
More informationECE 556 BASICS OF DIGITAL SPEECH PROCESSING. Assıst.Prof.Dr. Selma ÖZAYDIN Spring Term-2017 Lecture 2
ECE 556 BASICS OF DIGITAL SPEECH PROCESSING Assıst.Prof.Dr. Selma ÖZAYDIN Spring Term-2017 Lecture 2 Analog Sound to Digital Sound Characteristics of Sound Amplitude Wavelength (w) Frequency ( ) Timbre
More informationSAMPLING AND RECONSTRUCTING SIGNALS
CHAPTER 3 SAMPLING AND RECONSTRUCTING SIGNALS Many DSP applications begin with analog signals. In order to process these analog signals, the signals must first be sampled and converted to digital signals.
More informationREADING ASSIGNMENTS. Signal Processing First. SYSTEMS Process Signals LECTURE OBJECTIVES. This Lecture: Lecture 8 Sampling & Aliasing.
Signal Proceing Firt Lecture 8 Sampling & Aliaing READING ASSIGNMENTS Thi Lecture: Chap 4, Section 4- and 4-2 Replace Ch 4 in DSP Firt, pp. 83-94 Other Reading: Recitation: Strobe Demo (Sect 4-3 Next Lecture:
More informationFall Music 320A Homework #2 Sinusoids, Complex Sinusoids 145 points Theory and Lab Problems Due Thursday 10/11/2018 before class
Fall 2018 2019 Music 320A Homework #2 Sinusoids, Complex Sinusoids 145 points Theory and Lab Problems Due Thursday 10/11/2018 before class Theory Problems 1. 15 pts) [Sinusoids] Define xt) as xt) = 2sin
More informationApplications of Music Processing
Lecture Music Processing Applications of Music Processing Christian Dittmar International Audio Laboratories Erlangen christian.dittmar@audiolabs-erlangen.de Singing Voice Detection Important pre-requisite
More informationRecall. Sampling. Why discrete time? Why discrete time? Many signals are continuous-time signals Light Object wave CCD
Recall Many signals are continuous-time signals Light Object wave CCD Sampling mic Lens change of voltage change of voltage 2 Why discrete time? With the advance of computer technology, we want to process
More informationAdvantages of Analog Representation. Varies continuously, like the property being measured. Represents continuous values. See Figure 12.
Analog Signals Signals that vary continuously throughout a defined range. Representative of many physical quantities, such as temperature and velocity. Usually a voltage or current level. Digital Signals
More informationDigital Communication Prof. Bikash Kumar Dey Department of Electrical Engineering Indian Institute of Technology, Bombay
Digital Communication Prof. Bikash Kumar Dey Department of Electrical Engineering Indian Institute of Technology, Bombay Lecture - 03 Quantization, PCM and Delta Modulation Hello everyone, today we will
More information6.976 High Speed Communication Circuits and Systems Lecture 17 Advanced Frequency Synthesizers
6.976 High Speed Communication Circuits and Systems Lecture 17 Advanced Frequency Synthesizers Michael Perrott Massachusetts Institute of Technology Copyright 2003 by Michael H. Perrott Bandwidth Constraints
More informationGSM BASED PATIENT MONITORING SYSTEM
GSM BASED PATIENT MONITORING SYSTEM ABSTRACT This project deals with the monitoring of the patient parameters such as humidity, temperature and heartbeat. Here we have designed a microcontroller based
More informationPrinciples of Baseband Digital Data Transmission
Principles of Baseband Digital Data Transmission Prof. Wangrok Oh Dept. of Information Communications Eng. Chungnam National University Prof. Wangrok Oh(CNU) / 3 Overview Baseband Digital Data Transmission
More informationVoice Transmission --Basic Concepts--
Voice Transmission --Basic Concepts-- Voice---is analog in character and moves in the form of waves. 3-important wave-characteristics: Amplitude Frequency Phase Telephone Handset (has 2-parts) 2 1. Transmitter
More informationIntroduction to Real-Time Digital Signal Processing
Real-Time Digital Signal Processing. Sen M Kuo, Bob H Lee Copyright # 2001 John Wiley & Sons Ltd ISBNs: 0-470-84137-0 Hardback); 0-470-84534-1 Electronic) 1 Introduction to Real-Time Digital Signal Processing
More informationEECS 452 Practice Midterm Exam Solutions Fall 2014
EECS 452 Practice Midterm Exam Solutions Fall 2014 Name: unique name: Sign the honor code: I have neither given nor received aid on this exam nor observed anyone else doing so. Scores: # Points Section
More informationDigital Signal Processing The Breadth and Depth of DSP
Digital Signal Processing The Breadth and Depth of DSP Moslem Amiri, Václav Přenosil Masaryk University Resource: The Scientist and Engineer's Guide to Digital Signal Processing (www.dspguide.com) By Steven
More informationEEE 311: Digital Signal Processing I
EEE 311: Digital Signal Processing I Course Teacher: Dr Newaz Md Syur Rahim Associated Proessor, Dept o EEE, BUET, Dhaka 1000 Syllabus: As mentioned in your course calendar Reerence Books: 1 Digital Signal
More information