The optimal design of Multiple-Input Multiple-Output (MIMO) antennas is significant for next-generation smartphone solutions with the appearance of further communication. This paper describes the design, simulation, and measurements of a ten-element MIMO antenna for n79 at 4.4–5.0 GHz, n102 at 5.925–6.425 GHz, and n104 at 6.425–7.125 GHz ranges that will be implemented in today’s smartphones. The proposed antenna system has a maximum dimension of 158 mm × 60 mm and will use the low-cost FR-4 as the dielectric material. The design procedure is carried out with a lot of care to ensure that it does not compromise the spatial characteristics and at the same time minimizes the mutual coupling between the nearby antenna dimensions. High isolation and low ECC values are simulated using capturing techniques on the antenna geometry and its element position for proper MIMO performance improvement. The constructed antenna system portrays good performances in the intended bands as evidenced by the reflection coefficient values, radiation patterns, and individual gains. The performance of the above proposed ten-element MIMO antenna system in terms of bandwidth efficiency, diversity gain, and channel capacity loss is discussed. It also satisfies the demands of current standards of wireless communications and provides future possibilities for higher data transmission rates and message reliability in next-generation smartphone applications. Here, the application of the FR-4 substrate is remarkable as it provides comparatively better performance with significantly easier manufacturing and less cost, and thus, the design has good possibilities to be commercially viable. This work can be understood to form part of ongoing research in enhancing the advancement of the smartphone’s antenna technology, which offers a scalable approach to the prospective spectrum altitude and the emergent surge in the data rates of mobile communication.

For automobile and aerospace engineers, implementing Hall currents and thermal radiation in cooling systems helps increase the performance and durability of an engine. In the case of solar energy systems, the effectiveness of heat exchangers and solar collectors can be enhanced by the best use of hybrid nanofluids and the implementation of a Hall current, thermophoresis, Brownian motion, a heat source/sink, and thermal radiation in a time-dependent hybrid nanofluid flow over a disk for a Bayesian regularization ANN backpropagation algorithm. In the current physical model of Cobalt ferrite $CoF{e}_2{O}_4$ and aluminum oxide $A{l}_2{O}_3$ mixed with water, a new category of the nanofluid is called the hybrid nanofluid. The study uses MATLAB bvp4c to unravel such intricate relations, transforming PDEs into ODEs. This analysis enables the numerical solution of several BVPs that govern the system of the given problem. Hall currents resulting from the interaction between magnetic fields and the electrically conducting nanofluid, and thermal radiation as an energy transfer mechanism operating through absorption and emission, are central factors for controlling these fluids for use in various fields. The graphical interpretation assists in demonstrating the character of new parameters. The heat source/sink parameter is advantageous to thermal layering, but using a high Schmidt number limits the mass transfer. Additionally, a backpropagation technique with Bayesian regularization is intended for solving ordinary differential equations. Training state, performance, error histograms, and regression demonstration are used to analyze the output of the neural network. In addition to this, there is a decrease in the fluid velocity as magnetic parameter values decrease and a rise in the fluid temperature while the disk is spinning. Thermal radiation adds another level to the thermal behavior by altering how the hybrid nanofluid receives, emits, and allows heat to pass through it.

A two element Multiple Input Multiple Output UWB (Ultra-wide band) antenna is presented in this paper. The antenna is backed by the FR-4 substrate with a thickness of 1.6 mm and dielectric constant of 4.3 covering an area of 52.8 × 29 mm2. The proposed MIMO antenna consists of two single UWB antennas and a stub placed between the two antennas to reduce the mutual coupling. The proposed antenna achieves the voltage standing wave ratio (VSWR) of less than 2 and more than 86% radiation efficiency over the whole band of interest. Performance of the antenna is examined using both simulation and experiment. The design exhibits good performance characteristics in terms of bandwidth, gain, efficiency, return loss and VSWR. The proposed antenna is analyzed using the CST Microwave Studio software. The prototype model of the antenna is fabricated, tested, and measured to validate the simulation return loss results. There is acceptable agreement between the measured and simulated results.

A novel ground slot-based massive multiple-input multiple-output (MIMO) system with 14 elements for dual-band application of next-generation smartphones is developed. Every single antenna in the array is placed on the borders of a smartphone’s PCB, operating within the 3.55 GHz to 3.7 GHz (n48) and 5.855 GHz to 5.925 GHz (n47) frequency bands. This study also further assesses the performance of this system with analytical results, in which the self-s-parameters are less than −6 dB for each antenna, whereas isolation between components is less than −10 dB. At the same time, the overall efficiency reaches 65.55% and higher in the whole band. The evaluation of MIMO performance parameters such as envelope correlation coefficient (ECC), and DG of the MIMO system is well performed. A proposed MIMO antenna system is deployed and analyzed, which is a massive MIMO antenna, and the simulation results are compared to the measurement. The results validate the fact that the design of this particular MIMO system suits it to be incorporated in the n47 and n48 frequency bands, and therefore the presented MIMO system is suitable to be an option for integration into the upcoming smartphone architecture.

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost.