scholarly journals Soft computing based compressive sensing techniques in signal processing: A comprehensive review

2020 ◽  
Vol 30 (1) ◽  
pp. 312-326 ◽  
Author(s):  
Ishani Mishra ◽  
Sanjay Jain
Keyword(s):  
Signal Processing ◽  
Compressive Sensing ◽  
High Energy ◽  
Sparse Recovery ◽  
Modern World ◽  
Mean Square ◽  
Reconstruction Accuracy ◽  
Comprehensive Review ◽  
Memory Space ◽  
Processing Signal

Abstract In this modern world, a massive amount of data is processed and broadcasted daily. This includes the use of high energy, massive use of memory space, and increased power use. In a few applications, for example, image processing, signal processing, and possession of data signals, etc., the signals included can be viewed as light in a few spaces. The compressive sensing theory could be an appropriate contender to manage these limitations. “Compressive Sensing theory” preserves extremely helpful while signals are sparse or compressible. It very well may be utilized to recoup light or compressive signals with less estimation than customary strategies. Two issues must be addressed by CS: plan of the estimation framework and advancement of a proficient sparse recovery calculation. The essential intention of this work expects to audit a few ideas and utilizations of compressive sensing and to give an overview of the most significant sparse recovery calculations from every class. The exhibition of acquisition and reconstruction strategies is examined regarding the Compression Ratio, Reconstruction Accuracy, Mean Square Error, and so on.

Optimization of Signal Processing Applications Using Parameterized Error Models for Approximate Adders

2021 ◽  
Vol 20 (2) ◽  
pp. 1-25
Author(s):  
Celia Dharmaraj ◽  
Vinita Vasudevan ◽  
Nitin Chandrachoodan
Keyword(s):  
Signal Processing ◽  
Circuit Design ◽  
Computational Efficiency ◽  
Noise Power ◽  
Mean Square ◽  
Error Statistics ◽  
Error Models ◽  
Optimization Framework ◽  
Accuracy Constraint ◽  
Multi Level

Approximate circuit design has gained significance in recent years targeting error-tolerant applications. In the literature, there have been several attempts at optimizing the number of approximate bits of each approximate adder in a system for a given accuracy constraint. For computational efficiency, the error models used in these routines are simple expressions obtained using regression or by assuming inputs or the error is uniformly distributed. In this article, we first demonstrate that for many approximate adders, these assumptions lead to an inaccurate prediction of error statistics for multi-level circuits. We show that mean error and mean square error can be computed accurately if static probabilities of adders at all stages are taken into account. Therefore, in a system with a certain type of approximate adder, any optimization framework needs to take into account not just the functionality of the adder but also its position in the circuit, functionality of its parents, and the number of approximate bits in the parent blocks. We propose a method to derive parameterized error models for various types of approximate adders. We incorporate these models within an optimization framework and demonstrate that the noise power is computed accurately.

Error Compensation Techniques for Fixed-Width Array Multiplier Design — A Technical Survey

2016 ◽  
Vol 26 (03) ◽  
pp. 1730003 ◽  
Author(s):  
S. Balamurugan ◽  
P. S. Mallick
Keyword(s):  
Error Compensation ◽  
Absolute Error ◽  
Mean Square ◽  
Maximum Absolute Error ◽  
Comprehensive Review ◽  
Related Research ◽  
Error Formula ◽  
Error Metrics ◽  
Array Multiplier ◽  
Future Work

This paper provides a comprehensive review of various error compensation techniques for fixed-width multiplier design along with its applications. In this paper, we have studied different error compensation circuits and their complexities in the fixed-width multipliers. Further, we present the experimental results of error metrics, including normalized maximum absolute error [Formula: see text], normalized mean error [Formula: see text] and normalized mean-square error [Formula: see text] to evaluate the accuracy of fixed-width multipliers. This survey is intended to serve as a suitable guideline and reference for future work in fixed-width multiplier design and its related research.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals Application of Stochastic Process in Signal Processing

2020 ◽  
Vol 7 (4) ◽  
pp. 71-75
Author(s):  
N John Britto
Keyword(s):  
Stochastic Process ◽  
Signal Processing ◽  
Bayesian Inference ◽  
Queuing Theory ◽  
Basic Result ◽  
Signal Strength ◽  
Internal Communication ◽  
Intermediate Node ◽  
Numerical Examples ◽  
Processing Signal

In this paper introduction about birth and death Poisson process basic result of the markovian application in queuing theory used in signal processing, signal transfer from some to passion based on the intermediate node, each intermediate node are transformed from signal strength S is directly proportional to 1/√p based on the formula using the internal communication a dependent can be characterised by the Gilbert model. Two state Markov model signals, distance when signal strength is greater the distance should be reduced. Bayesian inference is used, few numerical examples are studied.

Bernoulli's Chaotic Map-Based 2D ECG Image Steganography

2019 ◽  
pp. 208-241 ◽  
Author(s):  
Anukul Pandey ◽  
Barjinder Singh Saini ◽  
Butta Singh ◽  
Neetu Sood
Keyword(s):  
Signal Processing ◽  
Chaotic Map ◽  
Image Steganography ◽  
Cover Image ◽  
Ecg Signal ◽  
Mean Square ◽  
Secret Key ◽  
Mean Square Difference ◽  
Electrocardiogram Ecg ◽  
Ecg Steganography

Signal processing technology comprehends fundamental theory and implementations for processing data. The processed data is stored in different formats. The mechanism of electrocardiogram (ECG) steganography hides the secret information in the spatial or transformed domain. Patient information is embedded into the ECG signal without sacrificing the significant ECG signal quality. The chapter contributes to ECG steganography by investigating the Bernoulli's chaotic map for 2D ECG image steganography. The methodology adopted is 1) convert ECG signal into the 2D cover image, 2) the cover image is loaded to steganography encoder, and 3) secret key is shared with the steganography decoder. The proposed ECG steganography technique stores 1.5KB data inside ECG signal of 60 seconds at 360 samples/s, with percentage root mean square difference of less than 1%. This advanced 2D ECG steganography finds applications in real-world use which includes telemedicine or telecardiology.

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