Abstract-Images are very good information carriers but they depart from their original condition during transmission and are corrupted by different kind of noise. The purpose is to remove the noisy coefficients such that minimum amount of information is lost and maximum amount of noise is suppressed or reduced. We considered Generalized Gaussian distribution for modeling of noise. In the proposed technique, statistical thresholding methods are used for the estimation of threshold value while Biorthogonal wavelet has been envisaged for image decomposition and reconstruction. A qualitative and quantitative analysis of thresholding methods on different images shows significant results for statistical thresholding methods based on objective and subjective quality as compared to other de-noising methods.
RF Spectrum Decision in Cognitive Radio enables unlicensed users of wireless communication systems to occupy the vacant spectrum slots as a solution to scarce spectrum. Internet of Things (IoT) is a wide-reaching network of unified entities. IoT capable things will be interconnected through wireless communication technologies offering cost-effectiveness and accessibility to remote users making quality life style. IoT implementation suffers from challenges of vulnerabilities to dynamic environmental conditions, ease of access, bandwidth allocation and utilization, and cost to purchase RF spectrum. As RF spectrum is a precious commodity and there is a dearth of RF spectrum, hence IoT connections are drifting towards Cognitive Radio Networks (CRNs). Permeating things with cognitive abilities will be able to make RF spectrum decisions to achieve interference-free and wireless connectivity as per their QoS requirements. The wireless systems are rapidly advancing. The leap from packet switching along with circuit switching with 144 kbps data rate (2G and 2.5G) to Long Term Evolution Advanced (LTE-A), i.e., 4G occurred in one decade time frame. As the current wireless connectivity is aimed at higher capacity, higher data rate, low end-to-end latency, massive device connectivity, reduced cost and consistent Quality of Experience (QoE) provision, therefore, 4G is being replaced with 5G. Presently the Radio Frequency (RF) spectrum band is fully sold out and allocated to various wireless operators and applications. On the other hand, new wireless applications are emerging and there is a serious dearth of frequency spectrum to be allocated to emerging wireless services. The efficient utilization of assigned RF spectrum which is otherwise underutilized due to the typical usage by the licensed users known as Primary Users (PUs) is the one of the best possible way to implement IoT in 5G. Thus the Spectrum Decision by unlicensed users of CR holds a significance in CR-based IoT in 5G and beyond network. This chapter describes a scientific supported spectrum decision support framework for CR Network. The main goal of this chapter is to discuss how CR technology can be helpful for the IoT paradigm.
Abstract-Fixed size kernels are used to extract differential structure of images. Increasing the kernal size reduces the localization accuracy and noise along with increase in computational complexity. The computational cost of edge extraction is related to the image resolution or scale. In this paper wavelet scale correlation for edge detection along with scalability in edge detector has been envisaged. The image is decomposed according to its resolution, structural parameters and noise level by multilevel wavelet decomposition using Quadrature Mirror Filters (QMF). The property that image structural information is preserved at each decomposition level whereas noise is partially reduced within subbands, is being exploited. An innovative wavelet synthesis approach is conceived based on scale correlation of the concordant detail bands such that the reconstructed image fabricates an edge map of the image. Although this technique falls short to spot few edge pixels at contours but the results are better than the classical operators in noisy scenario and noise elimination is significant in the edge maps keeping default threshold constraint.
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