In this paper, a joint spatio–radio frequency resource allocation and hybrid beamforming scheme for the massive multiple-input multiple-output (MIMO) systems is proposed. We consider limited feedback two-stage hybrid beamformimg for decomposing the precoding matrix at the base-station. To reduce the channel state information (CSI) feedback of massive MIMO, we utilize the channel covariance-based RF precoding and beam selection. This beam selection process minimizes the inter-group interference. The regularized block diagonalization can mitigate the inter-group interference, but requires substantial overhead feedback. We use channel covariance-based eigenmodes and discrete Fourier transforms (DFT) to reduce the feedback overhead and design a simplified analog precoder. The columns of the analog beamforming matrix are selected based on the users’ grouping performed by the K-mean unsupervised machine learning algorithm. The digital precoder is designed with joint optimization of intra-group user utility function. It has been shown that more than 50 % feedback overhead is reduced by the eigenmodes-based analog precoder design. The joint beams, users scheduling and limited feedbacK-based hybrid precoding increases the sum-rate by 27 . 6 % compared to the sum-rate of one-group case, and reduce the feedback overhead by 62 . 5 % compared to the full CSI feedback.

Steganography is considered the first line of defense in information security as it hides a secret message (payload) inside an innocent looking file (container) to transfer the payload under the adversary's nose without noticing it. Steganographic systems only use the container to hide the payload. In this paper, we present a steganographic system that uses the container not only to hide the payload, but also to give misleading information to the adversary. To achieve this goal, we use quick response (QR) code as a container. QR codes generated by our proposed system can carry its ordinary message in addition to the payload. Anyone can read the message, but the payload can only be obtained using a secret key. The message and the payload are unrelated; i.e. any message can be generated regardless of the payload and vise versa. We can take advantage of that by generating a message that gives misleading information to the adversary. We test the proposed system and show that the generated QR code is (valid) i.e indistinguishable from an ordinary QR code which makes it look innocent and less susceptible to an adversary's attack. Moreover, it is space-efficient, has an acceptable level of noise immunity and is prone to steganalysis attacks.

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Solar energy, one of many types of renewable energy, is considered to be an excellent alternative to non-renewable energy sources. Its popularity is increasing rapidly, especially because fuel energy consumes and depletes finite natural resources, polluting the environment, whereas solar energy is low- cost and clean. To produce a reliable supply of energy, however, solar energy must also be consistent. The energy we derive from a photovoltaic (PV) array is dependent on changeable factors such as sunlight, positioning of the array, covered area, and status of the solar cell. Every change adds potential for the creation of error in the array. Therefore, thorough research and a protocol for fast, efficient location and correction of all kinds of errors must be an urgent priority for researchers.For this project we used machine learning (ML) with voltage and current sensors to detect, localize and classify common faults including open circuit, short circuit, and hot-spot. Using the proposed algorithm, we have improved the accuracy of fault detection, classification and localization to 100%. Further, the proposed method can execute all three tasks (detection, classification, and localization) simultaneously.

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