Recently, the chaotic compressive sensing paradigm has been widely used in many areas, due to its ability to reduce data acquisition time with high security. For cognitive radio networks (CRNs), this mechanism aims at detecting the spectrum holes based on few measurements taken from the original sparse signal. To ensure a high performance of the acquisition and recovery process, the choice of a suitable sensing matrix and the appropriate recovery algorithm should be done carefully. In this paper, a new chaotic compressive spectrum sensing (CSS) solution is proposed for cooperative CRNs based on the Chebyshev sensing matrix and the Bayesian recovery via Laplace prior. The chaotic sensing matrix is used first to acquire and compress the high-dimensional signal, which can be an interesting topic to be published in symmetry journal, especially in the data-compression subsection. Moreover, this type of matrix provides reliable and secure spectrum detection as opposed to random sensing matrix, since any small change in the initial parameters generates a different sensing matrix. For the recovery process, unlike the convex and greedy algorithms, Bayesian models are fast, require less measurement, and deal with uncertainty. Numerical simulations prove that the proposed combination is highly efficient, since the Bayesian algorithm with the Chebyshev sensing matrix provides superior performances, with compressive measurements. Technically, this number can be reduced to 20% of the length and still provides a substantial performance.

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Spectrum sensing aims at searching and finding the unused frequency bands in specific radio spectrum. It monitors the frequency bands to detect the activity of primary/licensed users and decide if secondary users can use these bands or not. In order to improve the efficiency of spectrum sensing in wideband cognitive radio networks, compressive sensing framework has been recommended and studied in many papers since it helps the system to get better and faster results using the sparse structure of the radio spectrum. Therefore, this paper represents an in-depth survey of the best requirements of compressive sensing and spectrum sensing techniques for robust combination and effective solution for wideband cognitive radio networks. It also provides examples of innovative applications of compressive spectrum sensing including IoT, smart city and 5th generation of mobile networks. To sum up some challenges and research directions related to compressive spectrum sensing technique are given at the end.

Coronavirus (COVID-19) is continuing its spread across the world, with more than seven million confirmed cases. The findings could be important as lockdown restrictions begin to be eased, and they highlight the need for the introduction of increasingly effective techniques to deal with this spread and help effectively identify new infections more quickly, at a reasonable cost and with a minimum error rate. The use of machine learning models constitutes a new approach, used more and more in this field. In this proposed work, we built a new classification model named CovStacknet and it based on StackNet metamodeling methodology combined with deep convolutional neural network as the basis for features extraction from X-Ray images. The proposed model has reached an accuracy score of 98%, which is better than that achieved by the basic models.