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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.

In cognitive radio networks (CRNs), the topic of spectrum sensing (SS) or primary signal strength (PSS) has attracted a great deal of interest from academics and researchers to protect the legacy users spectrum. However, sensing enhances the robustness and reliability of spectrum management in CRNs environments. In order to improve the SS performance, the method used by secondary user (SU) or cognitive radio user (CRU) should be robust to analyze the transmitted signal strength by primary user (PU) or base station of primary networks (BSPNs). The spectrum sensing policy in the design of CRN systems is an important function, it determines the quality and performance of the system. In this context, we use the digital energy detector (DED) method which is employed by SU to sense the presence/or absence of the local PSS via two different channels, namely: The AWGN-link channel and the MRF-link (Multipath Rayleigh Fading) channel on two principal scenarios for the CRNs, a non-cooperative spectrum sensing (NCPSS) and a cooperative primary spectrum sensing (CPSS). Simulation results are investigated in terms of the detection probability (DP), false alarm probability (FAP) and the received signal-to-noise ratio (SNR) at SU.

Recently, the issue of spectrum sensing (SS) in cognitive radio networks (CRNs) has been widely reported in the literature. This paper presents a new strategy to improve the reliability of SS. One of the challenges in CRNs is to sense all information about a secondary user (SU) that is moving, especially in a cluttered environment, because the mobility of that user has a considerable impact on the sensing performance of the primary user. Thereby, an algorithm that ensures a high sensing level, in the case of a SU moving with a low velocity in a cluttered environment, is proposed. Besides, the shadowing element becomes important in cluttered environments. Therefore, it is important to provide a weighted averaging mechanism to obtain a stable measure of the signal strength at the SU. To do this, a simple recursive estimator is used. In the end, the algorithm proposed is evaluated through simulations and results.

Mobile communication systems present an actuality subject in academic and industrial research activities due to several phenomena such as interferences, multipath, fading, and shadowing. All this lead to a severe perturbation on handover mechanism which depends on specific reports, essentially, reference signal received power (RSRP) and signal-to-interference and noise ratio(SINR). In this paper, we design a new technique in handover domain; it consists of combining energy detection method used in cognitive radio with least mean square (LMS) process in order to prognosticate the handover impact in a realistic scenario of heterogeneous LTE network. More exactly, technique sense of the word "triggering" will be changed to a probability of detection Pd. The proposed algorithm cycle follows two main steps; Firstly, predict at what time the absence of spectrum (primary user) will occur, using a predicted sensing probability Pˆd (t+p) . Secondly, search others spectrums in this time by calculating Pd(t) for each sensed signal and hand-off secondary user in the best spectrum. The results achieved of the simulation are evaluating, it shows that the proposed method predict the original Pd correctly with minimal errors and select the best spectrum successfully.