This paper presents an efficient method for recovery of SIMS signals from strongly noised blurred discrete data. This technique is based on Tikhonov-Miller regularization where a priori model of solution is included. The latter is a denoisy signal obtained using the Kalman filter. This is an interesting estimation method, but it can only be used when the system is described precisely. By comparing the results of the proposed technique with those of the literature, our algorithm gives the best results without artifacts and oscillations related to noise and significant improvement of the depth resolution. While, the gain in FWHM is less improved than those obtained by the wavelet technique. Therefore, this new algorithm can push the limits of SIMS measurements towards its ultimate resolution.

The present paper deals with a real-time assessment of a fuzzy –backstepping based control applied to a battery-supercapacitor (SC) hybrid energy storage system (HESS). To properly emulate the behavior of an electric vehicle, the proposed topology is extended to a PMSM drive, that represents the traction part. The proposed control scheme is divided into two parts: The first part plans a fuzzy logic power management approach, to operate the system in a smart way: First, It ensures an optimal load power-sharing, focusing the operation of the involved sources in a safe mode. Second, a quite regulation of both the dc bus and the SC voltage without additional controllers. The second part proposes a back-stepping direct torque control (BS-DTC), associated to a space vector modulation (SVM) strategy, to ensure decoupled torque and flux control of the PMSM machine. The experimental results, conducted on a small …

Identification schemes based on multivariate polynomials have been receiving attraction in different areas due to the quantum secure property. Identification is one of the most important elements for the IoT to achieve communication between objects, gather and share information with each other. Thus, identification schemes which are post-quantum secure are significant for Internet-of-Things (IoT) devices. Various polar forms of multivariate quadratic and cubic polynomial systems have been proposed for these identification schemes. There is a need to define polar form for multivariate dth degree polynomials, where d >= 4 . In this paper, we propose a solution to this need by defining constructions for multivariate polynomials of degree d >= 4 . We give a generic framework to construct the identification scheme for IoT and RFID applications. In addition, we compare identification schemes and curve-based cryptoGPS which is currently used in RFID applications.

In this paper, we present a new ultra fast detector called Low Gain Avalanche Detector (LGAD) with low internal gain. The LGAD is fabricated with conventional APD technology with a modified doping profile, in the multiplication region, which affects the device performance such as: breakdown, multiplication gain and noise factor. For this reason, a numerical method based on Newton-Raphson calculation is proposed to estimate the electrostatic potential and electric field models of low gain avalanche detectors (LGADs) in order to investigate their performances. These models have been validated by their agreement with TCAD numerical simulation results. The effect of Boron doping profile, with different doses in the multiplication region, on the LGAD electrical performance is studied for various device structures in order to extend the device capability to its limit. In addition, LGAD devices are simulated for different temperature considering the effect of the temperature on the multiplication gain.