Digital Signal communication, processing, data transitionand and advance electronics.pptx

Digital signal processing

1. Digital Signal Processing

2. DSP Digital signal processing (DSP) is the process of analyzing and modifying a signal to optimize or improve its efficiency or performance. It involves applying various mathematical and computational algorithms to analog and digital signals to produce a signal that's of higher quality than the original signal . OR DSP is primarily used to detect errors, and to filter and compress analog signals in transit. It is a type of signal processing performed through a digital signal processor or a similarly capable device that can execute DSP specific processing algorithms. Typically, DSP first converts an analog signal into a digital signal and then applies signal processing techniques and algorithms. For example, when performed on audio signals, DSP helps reduce noise and distortion. Some of the applications of DSP include audio signal processing, digital image processing, speech recognition, biomedicine and more.

3. DSP manipulates different types of signals with the intention of filtering, measuring, or compressing and producing analog signals. Analog signals differ by taking information and translating it into electric pulses of varying amplitude, whereas digital signal information is translated into binary format where each bit of data is represented by two distinguishable amplitudes. Another noticeable difference is that analog signals can be represented as sine waves and digital signals are represented as square waves. DSP can be found in almost any field, whether it's oil processing, sound reproduction, radar and sonar, medical image processing, or telecommunications-- essentially any application in which signals are being compressed and reproduced. 

4. A DSP contains 4 key components: Computing Engine: Mathematical manipulations, calculations, and processes by accessing the program, or task, from the Program Memory and the information stored in the Data Memory. Data Memory: This stores the information to be processed and works hand in hand with program memory.  Program Memory: This stores the programs, or tasks, that the DSP will use to process, compress, or manipulate data. I/O: This can be used for various things, depending on the field DSP is being used for, i.e. external ports, serial ports, timers, and connecting to the outside world.

5. DSP look like in a general system configuration. 

6. BASIC ELEMENTS OF DSP

7. ADC & DAC Electric equipment is heavily used in nearly every field. Analog to Digital Converters (ADC) and Digital to Analog Converters (DAC) are essential components for any variation of DSP in any field. These two converting interfaces are necessary to convert real world signals to allow for digital electronic equipment to pick up any analog signal and process it. Take a microphone for example: the ADC converts the analog signal collected by an input to audio equipment into a digital signal that can be outputted by speakers or monitors .

8. Analog Digital Signal Analog signal is a continuous signal which represents physical measurements. Digital signals are discrete time signals generated by digital modulation. Waves Denoted by sine waves Denoted by square waves Representation Uses continuous range of values to represent information Uses discrete or discontinuous values to represent information Example Human voice in air, analog electronic devices. Computers, CDs, DVDs, and other digital electronic devices. Technology Analog technology records waveforms as they are. Samples analog waveforms into a limited set of numbers and records them. Data transmissions Subjected to deterioration by noise during transmission and write/read cycle. Can be noise-immune without deterioration during transmission and write/read cycle. Response to Noise More likely to get affected reducing accuracy Less affected since noise response are analog in nature Flexibility Analog hardware is not flexible. Digital hardware is flexible in implementation. Uses Can be used in analog devices only. Best suited for audio and video transmission. Best suited for Computing and digital electronics. Applications Thermometer PCs, PDAs Bandwidth Analog signal processing can be done in real time and consumes less bandwidth. There is no guarantee that digital signal processing can be done in real time and consumes more bandwidth to carry out the same information. Memory Stored in the form of wave signal Stored in the form of binary bit Power Analog instrument draws large power Digital instrument draws only negligible power Cost Low cost and portable Cost is high and not easily portable Errors Analog instruments usually have a scale which is cramped at lower end and give considerable observational errors. Digital instruments are free from observational errors like parallax and approximation errors.

9. S ignal Anything that carries information can be called as signal. It can also be defined as a physical quantity that varies with time, temperature, pressure or with any independent variables such as speech signal or video signal. The process of operation in which the characteristics of a signal (Amplitude, shape, phase, frequency, etc.) undergoes a change is known as signal processing. Note  − Any unwanted signal interfering with the main signal is termed as noise. So, noise is also a signal but unwanted.

10. Types of signal

11. Continuous Time Signals Continuous-time signals are defined along a continuum of time and are thus, represented by a continuous independent variable. Continuous-time signals are often referred to as analog signals. This type of signal shows continuity both in amplitude and time. These will have values at each instant of time. Sine and cosine functions are the best example of Continuous time signal.

12. Discrete Time signals The signals, which are defined at discrete times are known as discrete signals. Therefore, every independent variable has distinct value. Thus, they are represented as sequence of numbers. Although speech and video signals have the privilege to be represented in both continuous and discrete time format; under certain circumstances, they are identical. Amplitudes also show discrete characteristics. Perfect example of this is a digital signal; whose amplitude and time both are discrete.

13. Unit Impulse Signal It is denoted as δ(n) in discrete time domain and can be defined as; δ(n )={0,for n=0 Otherwise δ(n )={ 1,for n=1 Unit Step Signal Discrete time unit step signal is defined as; U ( n )= { 1,0, for n ≥ for n <0 Unit Ramp Signal A discrete unit ramp function can be defined as − r ( n )= { n ,0 , for n ≥ for n <0 Parabolic Signal Discrete unit parabolic function is denoted as p(n) and can be defined as; p ( n )= { n² ,0, for n ≥ for n <0

14. Sinusoidal Signal All continuous-time signals are periodic. The discrete-time sinusoidal sequences may or may not be periodic. They depend on the value of ω. For a discrete time signal to be periodic, the angular frequency ω must be a rational multiple of 2π. Discrete form of a sinusoidal signal can be represented in the format − x(n )= Asin ( ωn + θ )x (n )

15. Even Signal A signal is said to be even if it satisfies the following condition; x (−t)= x(t) x ( −t )= x(t ) Time reversal of the signal does not imply any change on amplitude here. Odd Signal A signal is said to be odd, if it satisfies the following condition x (−t)=−x(t)x(−t)=−x(t) Here, both the time reversal and amplitude change takes place simultaneously.

16. Some important results related to even and odd signals are given below. Even × Even = Even Odd × Odd = Even Even × Odd = Odd Even ± Even = Even Odd ± Odd = Odd Even ± Odd = Neither even nor odd

17. Periodic Signals Periodic signal repeats itself after certain interval of time. We can show this in equation form as − x(t) = x(t ) ± nTx (t) = x(t) ± nT Where , n = an integer (1,2,3……) T = Fundamental time period (FTP) ≠ 0 and ≠∞ Fundamental time period (FTP) is the smallest positive and fixed value of time for which signal is periodic. Here , the signal is repeating after every 1 sec. Therefore, we can say that the signal is periodic and its FTP is 1 sec.

18. Non-Periodic Signal Simply, we can say, the signals, which are not periodic are non-periodic in nature. As obvious, these signals will not repeat themselves after any interval time. Non-periodic signals do not follow a certain format; therefore, no particular mathematical equation can describe them.

19. DSP - Operations on Signals Shifting Time Shifting Time shifting means, shifting of signals in the time domain. Mathematically, it can be written as x(t ) → y( t+k )x(t ) → y( t+k ) This K value may be positive or it may be negative. According to the sign of k value, we have two types of shifting named as Right shifting and Left shifting. Shifting means movement of the signal, either in time domain (around Y-axis) or in amplitude domain (around X-axis). Accordingly, we can classify the shifting into two categories named as Time shifting and Amplitude shifting, these are subsequently discussed below. Amplitude Shifting Amplitude shifting means shifting of signal in the amplitude domain (around X-axis). Mathematically, it can be represented as − x(t) → x(t )+ K x (t ) → x(t)+K This K value may be positive or negative. Accordingly, we have two types of amplitude shifting which are subsequently discussed below.

20. Case 1 (K > 0) When K is greater than zero, the shifting of the signal takes place towards "left" in the time domain. Therefore, this type of shifting is known as Left Shifting of the signal . Case 2 (K < 0) When K is less than zero the shifting of signal takes place towards right in the time domain. Therefore, this type of shifting is known as Right shifting. Time shifting

21. Amplitude shifting Case 1 (K > 0) When K is greater than zero, the shifting of signal takes place towards up in the x-axis. Therefore, this type of shifting is known as upward shifting. Case 2 (K < 0) When K is less than zero shifting of signal takes place towards downward in the X- axis. Therefore, it is called downward shifting of the signal.