In this study, a new spectrum sensing method for cognitive radio (CR) is proposed for primary user (PU) signals which are modelled as random with unknown power. It is also assumed that, being dynamic, the PU might exhibit multiple status changes in the channel within the sensing period. As a novel approach for detecting dynamic and randomly modelled PUs, we propose a cumulative sum based weighted energy detector (CuS-WED) that only utilizes the samples between the last status change point (LSCP) of the PU and the end of the sensing period contrary to using all the observed data samples in the sensing period. It is shown that the proposed sensing strategy provides noticeable performance improvement in detecting dynamic and randomly modelled PUs. Probability of detection performance of the proposed sensing scheme is investigated for strictly one, at most two, and at most three status changes of the PU. Based on simulation results, it is concluded that the proposed method performs better than the other existing techniques, especially for low probability of false alarm values.

Unimodular sequences with low‐autocorrelation sidelobes are employed in various applications of radar and communication systems. Suppressing some predetermined autocorrelation sidelobes of a designed sequence can be accomplished by performing minimisation of the metric of weighted integrated sidelobe level (WISL). In this study, the authors propose a new algorithm to design unimodular sequences utilising the majorisation minimisation (MM) method for directly minimising WISL in the frequency domain. The proposed algorithm allows the design of long sequences in a computationally efficient manner. As the first step of the algorithm, a function majorising the frequency domain representation of the WISL metric is introduced. Then, a closed‐form solution for the minimisation of WISL is derived followed by its realisation as an iterative algorithm. They also provide an acceleration scheme to allow efficient implementation of the designed algorithm. Numerical experiments show that the proposed algorithm not only outperforms existing cyclic algorithms with respect to computation time and suppression of desired autocorrelation sidelobes but also converges in less number of iterations than its time domain MM‐based counterparts.

Multi-dimensional transfer functions (MDTF) are occasionally designed as two-step approaches. At the first step, the constructed domain is modelled coarsely using global volume statistics and an initial transfer function (TF) is designed. Then, a finer classification is performed using local information to refine the TF design. In this study, both a new TF domain and a novel two-step MDTF strategy are proposed for visualization of abdominal organs. The proposed domain is generated by aligning the histograms of the slices, which are reconstructed based on user aligned majority axis/regions through an interactive Multi-Planar Reconstruction graphical user interface. It is shown that these user aligned histogram stacks (UAHS) exploit more a priori information by providing tissue specific inter-slice spatial domain knowledge. For initial TF design, UAHS are approximated using a multi-scale hierarchical Gaussian mixture model, which is designed to work in quasi real time. Then, a finer classification step is carried out for refinement of the initial result. Applications to several MRI data sets acquired with various sequences demonstrate improved visualization of abdomen.

In cognitive radio, users of the spectrum are grouped as primary and secondary users. Primary users are license holders and a certain interval of band is assigned to them. On the other hand, secondary users determine the idle frequency intervals which are not used by primary users. To determine whether a spectrum band is idle or in use, various spectrum sensing techniques have been proposed in the literature. In the studies on goodness of fit (GOF) tests, it is assumed that primary user does not change its status during spectrum analysis. However, when the duration of analysis is long, the status of primary user may change. In this study, effect of the primary user traffic on the sensing performance of GOF test based spectrum sensing methods is investigated. According to simulation results, it has been observed that changes in the primary user status adversely affect the sensing performance.