Volume-1 Issue-3, August 2021 – Indian Journal of Signal Processing (IJSP)

C1009081321

IJSP Journal — Tue, 10 Aug 2021 11:55:34 +0000

The Indian Journal of Signal Processing (IJSP) has ISSN 2582-8320 (online), open-access, peer-reviewed, periodical quarterly international journal, which is published by Lattice Science Publication (LSP) in February, May, August and November. The aim of the journal is to publish high quality peer–reviewed original articles in the area of Signal Processing that covers Analog Filter Design, Applications of Fourier Transforms, Coding Theory, Continuous Time Signals, Digital Communication Techniques, Digital Design and Synthesis, Digital Filter Structure, Digital Systems – Characterization, Description and Testing, Discrete Fourier Transform, DSP Algorithms and Architecture, IR Filters, IIR Filters, Linear Algebra for Signal Processing, Linear and Nonlinear Optimization, Multirate Signal Processing, Optical, Signal Processing, Random Processes, Signal Compression-Theory and Methods, Simple Digital Filters, Speech and Audio Processing, Wavelet Theory, Filter Design and Structures, Fast Algorithms, Adaptive filters, Nonlinear Signals and Systems, Multirate Filtering and Filter Banks, Signal Reconstruction, Time-Frequency Analysis, Spectral Estimation, Higher Order Spectrum Analysis, Parameter Estimation, Detection, Array Signal Processing, Statistical Signal Analysis, Signal and System Modelling, Cyclostationary Signal Analysis, Speech Production and Perception, Speech Analysis, Speech Synthesis, Speech Coding, Speech Recognition, Speech Enhancement and Noise Reduction, Active Noise Control, Active Noise Reduction, Echo Cancellation, Psychoacoustics, Broadband Audio Coding, Signal Processing for Music, Binaural Systems, Room Acoustics, Digital Transforms, HDTV, Multidimensional Systems, Machine Vision, Image Coding, Image Motion / Sequence / Video, Computed Imaging, Geophysical and Seismic Processing, Image Analysis and Segmentation, Image Filtering, Restoration and Enhancement, Image Representation and Modelling, Pattern Recognition, Neural Networks, Fuzzy Systems, Evolutionary Computation, Expert Systems, Multisensor Data Fusion, Architectures and VLSI Hardware, Programmable Signal Processors, Algorithms and Applications Mappings, Design Methology and CAD Tools, Languages and Real Time Software, Real Time System Estimation, Optimization Problems in Signal Processing, Radar, Sonar, Biomedical Processing, Geophysical Signal Processing, Underwater Signal Processing, Remote Sensing, Robotics, Astronomy, Classification, Crime on the Web, Security/Publicity/Privacy/Reputation, Frauds in Business Reputation, Satellite Signals Processing, Measure and Instrumentation. #Analog Filter Design #Applications of Fourier Transforms #Coding Theory #Continuous Time Signals #Digital Communication Techniques #Digital Design and Synthesis #Digital Filter Structure #Digital Systems – Characterization #Description and Testing #Discrete Fourier Transform #DSP Algorithms and Architecture #IR Filters #IIR Filters #Linear Algebra for Signal Processing #Linear and Nonlinear Optimization #Multirate Signal Processing #Neural Networks #Optical #Signal Processing #Random Processes #Signal Compression-Theory and Methods #Simple Digital Filters #Speech and Audio Processing #Wavelet Theory #Filter Design and Structures #Fast Algorithms #Adaptive filters #Nonlinear Signals and Systems #Multirate Filtering and Filter Banks #Signal Reconstruction #Time-Frequency Analysis #Spectral Estimation #Higher Order Spectrum Analysis #Parameter Estimation #Detection #Array Signal Processing #Statistical Signal Analysis #Signal and System Modelling #Cyclostationary Signal Analysis #Speech Production and Perception #Speech Analysis #Speech Synthesis #Speech Coding #Speech Recognition #Speech Enhancement and Noise Reduction #Active Noise Control #Active Noise Reduction #Echo Cancellation #Psychoacoustics #Broadband Audio Coding #Signal Processing for Music #Binaural Systems #Room Acoustics #Digital Transforms #HDTV #Multidimensional Systems #Machine Vision #Image Coding #Image Motion / Sequence / Video #Computed Imaging #Geophysical and Seismic Processing #Image Analysis and Segmentation #Image Filtering #Restoration and Enhancement #Image Representation and Modelling #Pattern Recognition #Neural Networks #Fuzzy Systems #Evolutionary Computation #Expert Systems #Multisensor Data Fusion #Architectures and VLSI Hardware #Programmable Signal Processors #Algorithms and Applications Mappings #Design Methology and CAD Tools #Languages and Real Time Software #Real Time System Estimation #Optimization Problems in Signal Processing #Radar #Sonar #Biomedical Processing #Geophysical Signal Processing #Underwater Signal Processing #Remote Sensing #Robotics #Astronomy #Classification #Crime on the Web #Security/Publicity/Privacy/Reputation #Frauds in Business Reputation #Satellite Signals Processing #Measure and Instrumentation #PhD #Academic #Scopus #SCI #LatticeScience #Springer, #ScienceDirect #IEEE #Mendeley #Research #Scholarship #UGC #SSRN #LatticeScience #ESCI #Science #Journal #Conference #SSRN #PubLons

Performance Analysis of Cognitive Radios in a Cooperative Scheme over Nakagami Fading Channels
Sk Shariful Alam¹, Shishir Mallick², Al-Zadid Sultan Bin Habib³
¹Sk Shariful Alam, Astrocent, Nicolaus Copernicus Astronomical Center(CAMK-PAN), Warsaw, Poland.

²Shishir Mallick, Department of ECE, KUET, Khulna, Bangladesh.

³Al-Zadid Sultan Bin Habib, Department of ECE, KUET, Khulna, Bangladesh.

Manuscript received on 31 July 2021 | Revised Manuscript received on 07 August 2021 | Manuscript Accepted on 15 August 2021 | Manuscript published on 30 August 2021 | PP: 8-14 | Volume-1 Issue-3, August 2021 | Retrieval Number: 100.1/ijsp.C1009081321 | DOI: 10.54105/ijsp.C1009.081321

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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Radio spectrum is a primary requisite for wireless technologies and sensor networks. Due to the high demand and expense of the radio spectrum, it is guaranteed to extend its efficient utilization it. To expand the effective operation and serviceability of the radio spectrum in wireless communications, the notion of Cognitive Radio (CR) is presented in where the licensed spectrum of Primary User (PU) is used opportunistically by unlicensed CR users without interfering with the prioritized PU data transmission. Usually, a CR system is applied to detect empty radio bands by exploiting well-known spectrum sensing schemes and then unused spectrum is opportunistically used by the CR system. Various channels fading of the radio environment are to be considered while introducing different spectrum sensing approaches. In this regard, sensing time to find a vacant radio spectrum should be maintained minimum to reliably get the desired throughput. In this paper, an agreement issue is addressed between the time required for effective spectrum sensing and the achievable throughput of the CR network. Our proposed model illustrates the achievable throughput of CR node in cooperation provides better performance than stand-alone CR node. This is achieved by addressing the variation of the number of nodes under the Nakagami fading distribution. In conclusion, the maximum throughputs of the cooperative CR nodes are guaranteed as per simulation and data analysis.

Keywords: Cognitive radio, Spectrum sensing, Nakagami Fading distribution, Fusion rule, Achievable throughput.
Scope of the Article: Signal Processing

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C1008081321

IJSP Journal — Tue, 10 Aug 2021 11:34:46 +0000

Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink
Sheikh Md. Rabiul Islam¹, Md. Shakibul Islam²
¹Sheikh Md. Rabiul Islam, Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.

²Md. Shakibul Islam, Department of Electronics and Communication Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh.

Manuscript received on 24 June 2021 | Revised Manuscript received on 30 June 2021 | Manuscript Accepted on 15 August 2021 | Manuscript published on 30 August 2021 | PP: 1-7 | Volume-1 Issue-3, August 2021 | Retrieval Number: 100.1/ijsp.C1008081321 | DOI: 10.54105/ijsp.C1008.081321

Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

Keywords: EEG signal, EEG abnormalities, Neural Mass Model, Simulink.
Scope of the Article: Biomedical Processing

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