V. Divyapraba ¹, K. Kishore Kumar ¹, R. Pratheepa ¹, V. Elamaran ¹

Affiliations
1 Department of Electronics and Communication Engineering, School of EEE, SASTRA University, Thanjavur - 613401, Tamil Nadu, IN

Abstract

Spectrum sensing is the major tasks of cognitive radio for utilizing spectrum resources efficiently thereby identifying and making use of spectrum holes is the objective of sensing. Energy detection technique is adopted to compare the performance of different modulated signals in poor and good SNR. Probability of detection and false alarm are the metrics used to identify the number of primary users over a bandwidth of 5MHz. False alarm is kept constant to increase the detection probability. Simulink model for detections based on FFT and Welch periodogram are implemented. Modulated signals having high data rates can occupy more number of users thereby increasing the usage of spectrum. Also increase in noise should not affect the detection of primary users and reduce the probability of misdetection. Receiver operating characteristic curve is used to analyze the detections with respect to false alarm probability for different ranges of SNR. Noise uncertainty and SNR wall which can limit the performances are also taken into consideration. A unique approach to energy detection based on FFT shows increase in the sensing in the form of spectrum estimation with four primary users and the noise is assumed to be additive white Gaussian. Neyman Pearson hypothesis is considered for determining the presence and absence of users. Noise is the factor which decreases the performance of energy detector in case of blind sensing which can be limited with the help of highly modulated signals. The growing demand for spectrum makes cognitive radio an important technology to be focused in which the sensing process can reduce the interference caused to the primary users.

Keywords

Energy Detection, FFT, Modulation Techniques, Spectrum Sensing, Welch Periodogram

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V. Nandhini ¹, R. Pratheepa ¹, N. Anjana ¹, V. Elamaran ¹

Affiliations
1 Department of Electronics and Communication Engineering, School of EEE, SASTRA University, Thanjavaur – 613401, Tamil Nadu, IN

Abstract

In the field of image processing, contrast enhancement is a vital area to enhance contrast of images which are having poor contrast. Histogram Equalization (HE) is the method used to increase contrast in images. To improve contrast in images several HE methods persist. By determining the Probability Density Function (PDF) and Cumulative Density Function (CDF), HE expands the distribution of pixels. The overall brightness would be altered while the histogram equalization is being applied is a one kind of disadvantage. The drawback which is mentioned here can be avoided by, classifying the image based on intensity exposure and is divided into sub images based on the median value. To minimize the over enhancement, the sub images are clipped using the threshold value. Equalization can be done separately for each sub images, and the equalized sub images are combined to form a single image. Thus, to keep the brightness and to bring limitation in enhancement rate the classification and subdivision based HE method was proposed which equalizes the image. For color images, this method of equalization performs better than gray scale images. The simulation results for several test images are obtained using Matlab software tool. The results show that the entropy of the proposed method is compared with the standard HE method and it determines the amount of information available in the image. The proposed method provides better enhancement than other methods of equalization by controlling the enhancement rate. Improvements can be done by selecting the threshold values for clipping and intensity exposure. Contrast enhancement is applied in the areas of photography, medical imaging and video surveillance systems to enhance quality in images and the image looks natural.

Keywords

Classifcation, Clipping, Contrast Enhancement, Entropy, Exposure, Histogram Equalization

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