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Nowadays, the spectrum becomes more crowded and to solve this problem we use the cognitive radio technology, cognitive radio is a promising technology used to improve of spectrum utilization. Among important functions of the cognitive radio is the spectrum sensing. Most of the research works on the spectrum sensing for the cognitive radio networks are considered in a fixed temporal state, they are ignored impact of the mobility of a secondary user. We interested in the concept of the spectrum sensing in realtime. In this paper, we propose an algorithm that examined the impact of the mobility of a secondary user to determine the parameters that affect the spectrum sensing in cognitive radio networks. The performance of the algorithm proposed is evaluated with simulations and results, and of course we will finish by a conclusion and a future perspective.

<p>Handover is a process that allows a mobile node to change its attachment point. A mobile node connected to a network can, in order to improve the quality of service, have the need to leave it to connect to a cell either of the same network or of a new network. The present paper introduce three techniques using adaptive Variable Step-Size Least Mean Square (VSSLMS) filter combined with spectrum sensing probability method to detect the triggering of handover in heterogeneous LTE networks. These techniques are Normalized LMS (NLMS), Kwong-NLMS and Li-NLMS. The simulation environment is composed of two femtocells belonging to a macrocell. Five User Equipements (UEs) are positioned in one femtocell and are assumed closest to its circumference. Simulation results show that sensing probability with Li-NLMS algorithm has a better performance compared with classical NLMS and Kwong-NLMS.</p>

In the shadow of the coronavirus (Covid-19) pandemic, the sterilization has become a major necessity for humans to avoid exponential Infection. In order to avoid touching contaminated surfaces, we develop an embedded system prototype, which allows the bottle or any other medium to pour the sterilization product without touching it. The human hands are detected using two sensors HC-SR04 and LM35. To overcome the stability problem due to the problem of sensor noise and enhance the system performance a Kalman filter algorithm is implemented to ensure stable hands detection. The efficiency of the prototype mounted on an Arduino board is checked. After the completion of the prototype, a comprehensive cost analysis is conducted.