Digital: In digital commimication, we use discretc signals to represent data using binary
numbers.
Signal: A signal is anything that carries some information. It's a physical quantity that conveys data and varies with time, space, or any other independent variable. It can be in the time/frequency domain. It can be one-dimensional or two-dimensional. Here are all the major types of signals.
Processing: The performing of operations on any dnta in accordance with some protocol or instniction is known as processing.
System: A system is a physical entity that is responsible for thc processing. It has the necessary hardWare to perform the required arithmetic or logical oparations on a signal. Here are all the major types of systems.
Putting all these together, we can get a definition for DSP.
RESOURCE PERSON:
Mr. P. Veernath Associate Professor, Dept. of ECE, Joginpally B R Engineering College
Session has conducted through online mode started with introduction of resource person Mr. P. Veemath Assosiate Professor, Dept. of ECE, Joginpally B R Engineering College. by Mrs.P.Shirisha. Resource person has explained about organization of digital signal processing course. He also explained about importance of digital signal processing and their real time applications. He explained about importance's of systems and different types of filters and processers in detailed. He delivered the effective lecture regarding systems and design of filter method techniques in the session with technical examples
Signals:
Classify signals : The methods we use in processing a signal or in analyzing the response of a system to a signal depend heavily on the characteristic attributes of the specific signal. There are techniques that apply only to specific families of signals. Consequently, any investigation in signal processing should start with a classification of the signals involved in the specific application.
Multi channel signal: The signal which is generated by multiple sources or multiple sensors and are represented in vector form is called as multichannel signal. Example : Earth qualce generated wave , E1ectrocardiogram(ECG ) 3-channe1,12- channel .
Multi-dimensional signal: The signal which is a function oY more than one independent variables are called as mu1tidimen5iona1 signal. Example : f(x,y) = x2 + 2y+3 Continuous time signal :The signal which can be defined for every point of time in an interval is called as continuous time signal Example: X (t) = coszt
Discrete time signal: The signal which is only defined on specific point of time is called discrete time signal. Example: X(n) = 2n , nd 0 = 1 , nd 0 v t.
Continuous valued signal: The signal which takes on all possible values on a finite or an infinite range is called as continuous valued signal.
Discrete valued signal: The signal which talces on values from a finite set of possible values is called as discrete valued signal.
DSP Technology and Applications:
Digital signal processing is the technology that represents and processes signals in the form of digits, which belongs to signal processing lilce analog signal processing.
T1@ purpose Of DSP teclmology is to measure or filter continuous analog signals in the real world. Therefore, before performing digital signal processing, the signal needs to be converted from the analog domain to the digital domain, which is usually achieved by an analog-to-digital converter. And its output is often transformed into the analog domain by a digital-to-analog converter.
The algorithms of digital signal processing require the use of a computer or special processing equipment such as digital signal processors and application specific integrated circuits (ASICs). DSP techno1op•y and equipment havc outstanding advantages such as flexibility, high precision, strong anti-interference performanccs, small size, low cost, and fast speed, which are unmatched by analog signal processing technology. Digital signal procGssing is based on many disciplines, which covers a very wide range. For example, in the field of mathematics, calculus, probability statistics, stochastic processes, and numerical analysis are all basic tools for digital signal processing. It is also closely related to networli theory, signals and systems, cybernetics, communication theory, and fault diagnosis. Besides, some recent emerging disciplines, such as artificial ñitelligence, pattern recognition, and neural networks, are also inseparable from digital signal processing. It can be said that DSP technology talces many classic theoretical systems as its theoretical foundation, and at the same time malces itself a theoretical foundation fur a series of emerging disciplines
DTFT:
The Discrete-Time Fourier Transform (DTFT) is the cornerstone of all DSP, because it tells us that from a discrete set of samples of a continuous function, we can create a periodic summation of that function's Fourier transform. At the very least, we can recreate an approximation of the actual transform and its inverse, the original continuous function. And under certain idealized conditions, we can recreate the original function with no distortion at all. That famous theorem is called the Nyquist-Shannon sampling theorem.
DTFS:
In this module, we will derive an expansion for discrete-time, periodic functions, and in doing
so, derive the Discrete Time Fourier Series (DTFS), or the Discrete Fourier Transform (DFT).
Since complex exponentials (Section 1.8) are eigenfunctions of linear time-invariant (LTI) systems calculating the output of an LTI system H given ejmn as an input amounts to simple
