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Digital Signal Processing by N. G. Palan: A Practical and Theoretical Guide for DSP Students and Engineers

Digital Signal Processing by N. G. Palan: A Comprehensive Guide

Digital signal processing (DSP) is a branch of engineering that deals with the analysis, manipulation and synthesis of signals using digital techniques. Signals can be anything that carries information, such as sound, image, video, speech, radar, etc. DSP is widely used in various applications such as communication, multimedia, biomedical, control, robotics, etc.

digital signal processing by n g palan

One of the popular books that covers the fundamentals of DSP is Digital Signal Processing by N. G. Palan. This book is written for undergraduate students of electronics and telecommunication engineering. It covers the topics such as signals and systems, Z-transform, digital filter structure, DTFT, DFT and FFT in a clear and concise manner. The book also provides numerous solved examples, illustrations, diagrams, tables and questions from university papers to help the students understand the concepts better.

The book by N. G. Palan has many features and benefits for the readers. Some of them are:

  • It follows the syllabus prescribed by various universities in India.

  • It explains the theoretical concepts with mathematical derivations and proofs.

  • It provides practical examples and applications of DSP in real life scenarios.

  • It includes MATLAB programs for implementing some of the DSP algorithms.

  • It offers online access to additional resources such as e-books, videos, quizzes, etc.

Signals and Systems

The first chapter of the book introduces the basic concepts and classifications of signals and systems. Signals are functions of one or more independent variables that represent some physical phenomenon. Systems are devices or processes that perform some operations on the input signals and produce output signals. Some of the topics covered in this chapter are:

  • Continuous and discrete time signals: These are signals that are defined for continuous or discrete values of time. For example, a sine wave is a continuous time signal, while a sequence of numbers is a discrete time signal.

  • Continuous valued or discrete valued signals: These are signals that have continuous or discrete values of amplitude. For example, a voltage signal is a continuous valued signal, while a binary signal is a discrete valued signal.

  • Periodic and non-periodic signals: These are signals that repeat or do not repeat themselves after a fixed interval of time. For example, a cosine wave is a periodic signal, while a random noise is a non-periodic signal.

  • Even and odd signals: These are signals that have symmetric or anti-symmetric properties with respect to the origin. For example, an even signal satisfies x(t) = x(-t), while an odd signal satisfies x(t) = -x(-t).

  • Energy and power signals: These are signals that have finite or infinite values of energy or power. For example, an energy signal has finite energy and zero power, while a power signal has infinite energy and finite power.

  • Deterministic and random signals: These are signals that have predictable or unpredictable behavior. For example, a deterministic signal can be described by a mathematical equation, while a random signal cannot be predicted in advance.

  • Multichannel and multidimensional signals: These are signals that have more than one component or dimension. For example, a multichannel signal can be a stereo audio signal with two channels, while a multidimensional signal can be an image with two spatial dimensions.

The chapter also explains the representation of discrete time signals as arrays of values, such as sequences, vectors and matrices. It also introduces the standard test signals and basic operations on discrete time signals, such as time shifting, time scaling, amplitude scaling, addition, subtraction, multiplication and convolution.

One of the important concepts in this chapter is the linear shift invariant (LSI) system. An LSI system is a system that satisfies two properties: linearity and shift invariance. Linearity means that the output of the system is proportional to the input and follows the superposition principle. Shift invariance means that the output of the system does not change if the input is shifted by any amount of time. An LSI system can be characterized by its impulse response, which is the output of the system when the input is a unit impulse function.

The chapter also explains how to perform linear convolution on discrete time signals using the convolution sum formula. Linear convolution is an operation that combines two signals to produce a third signal that reflects the effect of one signal on the other. Linear convolution is widely used in digital filtering, modulation, coding, etc.

Another important concept in this chapter is the difference equation. A difference equation is an equation that relates the output and input samples of a discrete time system using differences. A difference equation can be used to model and analyze various types of systems, such as recursive filters, digital oscillators, etc.

The chapter also introduces the concept of correlation. Correlation is a measure of similarity between two signals. Correlation can be used to detect patterns, identify features, estimate parameters, etc. The chapter explains how to find the cross-correlation and auto-correlation functions of discrete time signals using formulas and examples.

The last topic in this chapter is the analog to digital (A/D) conversion process. A/D conversion is the process of converting an analog signal into a digital signal. A/D conversion involves three steps: sampling, quantization and encoding. Sampling is the process of taking discrete samples of an analog signal at regular intervals of time. Quantization is the process of approximating each sample value to a finite number of levels. Encoding is the process of assigning binary codes to each quantized level.


The second chapter of the book introduces the Z-transform and its applications in DSP. The Z-transform is a mathematical tool that converts a discrete time signal into a complex function of a complex variable z. The Z-transform can be used to analyze and design various types of systems, such as filters, controllers, modulators, etc.

Some of the topics covered in this chapter are:

  • What are the advantages and disadvantages of digital signal processing over analog signal processing? Digital signal processing has some advantages over analog signal processing, such as:

  • It can perform complex operations that are difficult or impossible to implement using analog circuits.

  • It can achieve higher accuracy and precision by avoiding noise and distortion that affect analog signals.

  • It can store and transmit digital signals more efficiently and securely than analog signals.

  • It can be easily modified and updated by changing the software or firmware without changing the hardware.

  • Digital signal processing also has some disadvantages over analog signal processing, such as:

  • It requires analog to digital (A/D) and digital to analog (D/A) converters that introduce quantization error and aliasing.

  • It has limited dynamic range and resolution due to finite word length and fixed point arithmetic.

  • It has higher latency and power consumption due to computational complexity and memory requirements.

  • It may suffer from numerical errors and instability due to roundoff, overflow, underflow, etc.

This is the end of the article. I hope you enjoyed reading it and learned something new about digital signal processing by N. G. Palan. Thank you for your attention and feedback. 71b2f0854b


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