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Spectrum Sensing is accomplished at the physical layer of Cognitive Radio Networks. This letter presents a Fast Sequency-Ordered Complex Hadamard Transform (FSCHT) based Parzen window Entropy detection technique (PWED) for spectrum sensing. The energy compaction property of FSCHT leads to a discriminating sensing performance compared to Fast Fourier Transform (FFT) transform. In PWED, the kernelbased probability density estimation is employed to evaluate the entropy. The impact of orthogonal transforms on the computation of entropy is analyzed. The computational complexity of PWED technique is compared with Shannon entropy technique. A substantial improvement in the SNR wall is observed in the presence of noise uncertainty. The proposed technique detects the DVB-T signal up to -54 dB SNR with probability of detection (P d ) 0.9 and probability of false alarm (P f a ) 0.1.

It is very difficult for doctors to detect a brain tumor at an early stage. MRI images are more susceptible to noise and other environmental disturbances. Therefore, it becomes difficult for doctors to determine the tumor and its causes. So, we came up with a system in which the system will detect a brain tumor from images. Here we are converting an image to a grayscale image. We apply filters to the image to remove noise and other environmental clutter from the image. The system will process the selected image using preprocessing steps. At the same time, different algorithms are used to detect the tumor from the image. But the edges of the image will not be sharp in the early stages of a brain tumor. So here we are applying image segmentation to the image to detect the edges of the images. We have proposed an image segmentation process and a variety of image filtering techniques to obtain image characteristics. Through this entire process, accuracy can be improved. This system is implemented in the Matlab.

Cognitive Radio networks provide advancement to the wireless generations. Due to the fixed spectrum allocation policy, a wider portion of the spectrum is underutilized in many areas. Spectrum sensing adds intelligence to Cognitive Radios that dynamically identifies the spectrum holes. Many spectrum sensing algorithms are proposed in literature to sense the spectrum holes in a noisy environment. But, there are several challenges that are to be considered in simulating the algorithms in a real-time channel. In this paper, a review of various issues of spectrum sensing is discussed.