Competent realisation of cooperative spectrum sensing in cognitive radio systems

Pavithra, S.; Karthikeyan, S.; Sonti, V.J.K. Kishor; Jayashri, S.

doi:10.1504/ijesms.2015.068658

Cited by 7 publications

(1 citation statement)

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“…Model using Welch periodogram and FFT is designed in [6].In this method Neyman Pearson hypothesis is considered. In [7] Gaussian noise is described in [9] and [10]. This method provides less false detection.…”

Section: Introductionmentioning

confidence: 99%

Spectrum Sensing in Cognitive Radio using Frequency Domain

2019

IJEAT

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An efficient bandwidth allocation and dynamic bandwidth access away from its previous limits is referred as cognitive radio (CR).The limited spectrum with inefficient usage requires the advances of dynamic spectrum access approach, where the secondary users are authorized to utilize the unused temporary licensed spectrum. For this reason it is essential to analyze the absence/presence of primary users for spectrum usage. So spectrum sensing is the main requirement and developed to sense the absence/ presence of a licensed user. This paper shows the design model of energy detection based spectrum sensing in frequency domain utilizing Binary Symmetric Channel (BSC) ,Additive white real Gaussian channel (AWGN), Rayleigh fading channel users for 16-Quadrature Amplitude Modulation(QAM) which is utilized for the wide band sensing applications at low Signal to noise Ratio(SNR) level to reduce the false error identification. The spectrum sensing techniques has least computational complexity. Simulink model for the energy detection based spectrum sensing using frequency domain in MATLAB 2014a.

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Section: Introductionmentioning

confidence: 99%

Spectrum Sensing in Cognitive Radio using Frequency Domain

2019

IJEAT

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Laryngeal Tumor Recognition and Classification Using Neural Networks

Karthikeyan

Farhath

Vineesha

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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One of the highly challenging fields is Biomedical Image Processing. Image Processing involves many techniques used to detect and classify the tumor more efficiently than doctors. Manual classification is a time consuming and gives inaccurate results compared to automatic classification. Imaging methods like CT (Computer Tomography) scans, MRI (Magnetic Resonance Image), X-rays plays a major role in tumor detection and classification. Out of these MRI is widely used because it does not involve any radiation and also identifies even the smallest abnormalities present in throat. The whole process of detecting the laryngeal tumor from these scanned images can be categorised into four steps: Pre-processing, Segmentation, Feature Extraction and Classification. In proposed method, classification involves hybrid classifiers to improve accuracy.

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Secure and Trustable Routing in Wireless Sensor Networks Using Active Detection Routing Protocol

Karthikeyan

Reddy

et al. 2019

IOP Conf. Ser.: Mater. Sci. Eng.

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A Wireless Sensor Network (WSN) contains gadgets that are self-sufficiently conveyed in space used to direct the physical and natural conditions with the assistance of sensors. They are primarily utilized as a part of security basic applications, for example, checking nature, following the environment and controlling regions. As a result of the asset impediment and computational limitations the WSNs are straightforward to different security assaults. Dark opening assault is a genuine assault which seriously influences the bundle exchange from source to goal. Here the Active Trust conspire is utilized to stay away from the Black opening assault. The Active Trust conspire keeps away from the dark openings through Active discovery directing and information steering which enhances the course security. Both complete hypothetical investigation and exploratory outcomes demonstrate that the execution of the Active Trust conspire is superior to anything that of past examinations. Dynamic Trust can fundamentally enhance the information course achievement likelihood and capacity against dark gap assaults and can enhance arrange lifetime.

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Competent realisation of cooperative spectrum sensing in cognitive radio systems

Cited by 7 publications

References 0 publications

Spectrum Sensing in Cognitive Radio using Frequency Domain

Spectrum Sensing in Cognitive Radio using Frequency Domain

Laryngeal Tumor Recognition and Classification Using Neural Networks

Secure and Trustable Routing in Wireless Sensor Networks Using Active Detection Routing Protocol

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