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.

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.

This article primarily focuses on the performance evaluation of a new methodology, imputation by feature importance (IBFI), to serve its imputed dataset in further regression scenarios when dealing with soil radon gas concentration (SRGC) time-series data. The time-series data have been collected spanning over fourteen(14) months period, which included four seismic events, and have been used for experimentation. The imputation by feature importance (IBFI) has been experimented and obtained results are found more efficient in the imputation of missing patterns in investigated time series when compared to traditionally used imputation methods viz. mean, median, mode, predictive mean matching (PMM), and hot-deck imputation.The IBFI methodology has been used in a variety of settings, such as data missing not at random (MNAR), missing completely at random (MCAR), and missing at random (MAR), with missingness percentages ranging from 10% to 30%. In this study, the imputed datasets, 9 for each imputation method, have been used further to predict the attribute of interest (radon concentration (RN)) keeping others as independent attributes such as thoron, temperature, relative humidity, and pressure time series. Support vector machine (SVM) with linear kernel has been used as a learning algorithm and its performance was evaluated based on the fact that how efficient and unbiased values were imputed. Statistical performance evaluation measures viz. root mean squared log error (RMSLE), root mean square error (RMSE), mean squared error (MSE),and mean absolute percentage error (MAPE) have been calculated for the assessment of performance. The findings of our study show that the IBFI imputed dataset has provided a betterfitted model. The model generation and predictions upon IBFI imputed time series result in more accurate predictions when compared to mean, median, mode, PMM, and hot-deck imputed time series. Furthermore, PMM and median imputed time series also perform closer to the IBFI imputed time series.

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Powerful cryptographic systems based on mathematically hard problems are utilized to ensure tighter security for data communication purposes. However, these traditional cryptographic systems are bound to fail in the ensuing era of quantum computing. Thus, Artificial Intelligence (AI) inspired security methods are needed to secure communications in the era of quantum computing. This paper presents a challenge-response password-based authentication system based on the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) AI hard problem. In this system, a server sends a challenge text to a client, then the client generates a random image and blends the challenge text inside this random image using his password. Then the client sends the generated image to the server. The server extracts the challenge text from the sent image using his copy of the client's password. If the extracted challenge text is the same as the sent challenge text, then both the client's and the server's copies of the password match and the client is authenticated. The efficiency of the proposed system is analyzed and the outcomes prove that the proposed system is efficient in terms of time and space. Also, a security investigation of the proposed system is employed, and the results prove that the system is probabilistic and very sensitive to changes in its parameters. It does not leak any statistical information about the client's password and the generated images cannot be distinguished from random images. In addition, the security of the proposed system is analyzed against two possible attacks; the brute force attack and the replay attack and the results prove that the proposed system is immune to these attacks. Finally, the proposed system is ensured to be indistinguishably secure against an adaptive chosen-challenge text attack (IND-ACCTA), based on the CAPTCHA AI hard problem when the hash function H is modeled as a random oracle.

In the sparse representation-based classification (SRC), the object recognition procedure depends on local sparsity identification from sparse coding coefficients, where many existing SRC methods have focused on the local sparsity and the samples correlation to improve the classifier performance. However, the coefficients often do not accurately represent the local sparsity due to several factors that affect the data acquisition process such as noise, blurring, and downsampling. Therefore, this paper presents an effective method that exploits nonlocal sparsity by estimating the sparse code changes, which can be done by adding a nonlocal constraint term to the local constraint one. In addition, for generality, the sparse coding and regularization parameters are adaptively estimated. A comparative study demonstrated that the proposed method has better accuracy rates compared to the existing state-of-the-art methods.