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Summary In this paper, an energy management strategy of direct current (DC) microgrids (MGs) is presented. The proposed MG consists of fuel cells, batteries, and supercapacitors, along with associated DC/DC and DC/AC converters. Coordination between different sources of MG is implemented using a multi‐agent system. Multi‐agent algorithm is implemented using an open source agent building toolkit, Java Agent Development framework (JADE). The energy source components are modeled and implemented using MatLab/Simulink (power system library). The proposed energy management strategy is designed using proportional‐integral (PI) controllers, which is implemented using the JADE. Interface between MatLab/Simulink and multi‐agent system (JADE) is done with the help of MACSimJX. The proposed multi‐agent framework is presented and the interface between the JADE and MatLab/Simulink is described in details. Then, the design and implementation steps of the PI controller using JADE are presented. Simulation work is carried out, and the results show that the proposed multi‐agent‐based controller effectively coordinated with variable loads in MG.

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Convolutional Neural Networks (CNNs) are efficient tools for pattern recognition applications. They have found applications in wireless communication systems such as modulation classification from constellation diagrams. Unfortunately, noisy channels may render the constellation points deformed and scattered, which makes the classification a difficult task. This paper presents an efficient modulation classification algorithm based on CNNs. Constellation diagrams are generated for each modulation type and used for training and testing of the CNNs. The proposed work depends on the application of Radon Transform (RT) to generate more representative patterns for the constellation diagrams to be used for training and testing. The RT has a good ability to represent discrete points in the spatial domain as curved lines. Several pre-trained networks including AlexNet, VGG-16, and VGG-19 are used as classifiers for modulation type from the spatial-domain constellation diagrams or their RTs. Several simulation experiments are presented in this paper to compare different scenarios for modulation classification at different Signal-to-Noise Ratios (SNRs) and fading channel conditions.

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last decades. It achieves safe access to systems using biometrics instead of the traditional passwords. The utilization of a biometric in its original format makes it usable only once. Therefore, a cancelable biometric template should be used, so that it can be replaced when it is attacked. Cancelable biometrics aims to enhance the security and privacy of biometric authentication. Digital encryption is an efficient technique to be used in order to generate cancelable biometric templates. In this paper, a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification systems. The considered biometric in this paper is the speech signal. The speech signal is transformed into its spectrogram. Then, the spectrogram is encrypted using two cascaded optical encryption algorithms. The first algorithm is the Optical Scanning Holography (OSH) for its efficiency as an encryption tool. The OSH encrypted spectrogram is encrypted using Double Random Phase Encoding (DRPE) by implementing two Random Phase Masks (RPMs). After the two cascaded optical encryption algorithms, the cancelable template is obtained. The verification is implemented through correlation estimation between enrolled and test templates in their encrypted format. If the correlation value is larger than a threshold value, the user is authorized. The threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two curves. The implementation of optical encryption is adopted using its software rather than the optical setup. The efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation results. It can improve the biometric data security without deteriorating the recognition accuracy. Simulation results give close-to-zero