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Ultra-wideband (UWB) antennas cover a frequency range of 3.1 to 10.6 GHz and have sparked a lot of research interest as an essential part of wireless communication systems as they provide high data transmission speeds, are less expensive, and consume less power. UWB antennas are widely used in radar imaging, radio frequency identification, public security, and other high-accuracy positioning devices such as altimetry. Some smart applications of UWB antennas are vehicular radar systems, surveillance systems, software-defined radios, spectrum analysis, proximity fuses, etc. Multiple-input-multiple-output (MIMO) is a multiplexing technology that adopts multiple antennas both at the transmitter and receiver, which can enhance the channel capacity. MIMO technology is extensively used in several applications, such as in portable devices, wireless body area networks (WBANs), vehicular communication, and satellite–terrestrial networks. Generally, the MIMO antennas are used to obtain high reliability, high capacity, high throughput, and high security. The UWB MIMO antennas (UMAs) are considered the best choice for wireless communication systems as they offer reliability and wide transmission capacity, in contrast to unit antenna elements (AEs), without increasing system bandwidth or transmission power. The present-day and future communications systems need higher throughput to meet the demands of users. The signal transfer rate can be improved by improving channel bandwidth or increasing the number of receiving antennas. However, the main issue in designing UMAs is to provide high isolation between AEs because mutual coupling interactions between them affect the generated radiation patterns, leading to worse performance and failing to meet the operative constraints and requirements. When introducing decoupling techniques (DTs), researchers experience numerous challenges, including an increase in antenna size, design complexity, and cross-polarization. This article offers an organized review and simulated study of the various DTs in UMAs. The simulated study has been carried out through the implementation of various types of DTs on the same two-port UMA, which consists of two microstrip-fed circular-shaped AEs with open-end slotted partial ground. In contrast with previously reported review articles, this article provides a detailed study of various types of DTs reported so far and a better understanding for selecting appropriate DTs, which help in designing UMAs with better performance. [ABSTRACT FROM AUTHOR]

The fifth generation (5G) of mobile networks is a significant technological advancement in telecommunications that provides faster data speeds, lower latency, and greater network capacity. One of the key technologies that enables 5G is multiple-input/multiple-output (MIMO) antenna systems, which allow for the transmission and reception of multiple data streams simultaneously, improving network performance and efficiency. MIMO is essential to meeting the demand for higher data rates and improved network performance in 5G networks. This work presents a four-element MIMO antenna system dedicated to the upper 5G millimeter-wave (mmWave) spectrum. The suggested antenna system is designed using an ultra-thin RO5880 substrate having total dimensions of 20 × 20 × 0.254 mm 3 with symmetrical geometry. The proposed antenna covers a fractional bandwidth of 46.875% (25–38 GHz), covering potential 5G bands of 26, 28, and 32 GHz, and offers isolation of >18 dB. The proposed MIMO system is fabricated and tested in-house. The antenna showed efficiency >88% at the potential band of interest and a peak gain of 3.5 dBi. The orthogonal arrangement of the resonating elements provides polarization diversity. Also, the MIMO parameters obtained, such as mean effective gain (MEG), envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and total active reflection coefficient (TARC), are found to have good performance. The measured results obtained are found to be in good agreement with simulations, hence making the proposed MIMO antenna suitable for handheld mmWave 5G devices. [ABSTRACT FROM AUTHOR]

In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks. [ABSTRACT FROM AUTHOR]

It is essential to use highly antigenic epitope areas, since the development of peptide vaccines heavily relies on the precise design of epitope regions that can elicit a strong immune response. Choosing epitope regions experimentally for the production of the SARS-CoV-2 vaccine can be time-consuming, costly, and labor-intensive. Scientists have created in silico prediction techniques based on machine learning to find these regions, to cut down the number of candidate epitopes that might be tested in experiments, and, as a result, to lessen the time-consuming process of their mapping. However, the tools and approaches involved continue to have low accuracy. In this work, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and long short-term memory (LSTM) for the classification of peptides into epitopes or non-epitopes. Numerous transfer learning strategies were utilized, and the fine-tuned method gave the best result, with an AUC of 0.979, an f1 score of 0.902, and 95.1% accuracy, which was far better than the performance of the model trained from scratch. The experimental results obtained show that this model has superior performance when compared to other methods trained on IEDB datasets. Using bioinformatics tools such as ToxinPred, VaxiJen, and AllerTop2.0, the toxicities, antigenicities, and allergenicities, respectively, of the predicted epitopes were determined. In silico cloning and codon optimization were used to successfully express the vaccine in E. coli. This work will help scientists choose the best epitope for the development of the COVID-19 vaccine, reducing cost and labor and thereby accelerating vaccine production. [ABSTRACT FROM AUTHOR]

A radio is adaptive if it can autonomously analyze the communications environment and instantly modify its settings to achieve the best possible efficiency. In orthogonal frequency division multiplexing (OFDM) transmissions, identifying the space frequency block coding (SFBC) category utilized is one of the most important tasks of an adaptive receiver. Previous approaches to this problem did not take into consideration the fact that real systems typically suffer from transmission defects. This study offers a novel maximum likelihood recognizer capable of distinguishing between SFBC OFDM waveforms in the context of inphase and quadrature phase differences (IQDs). The theoretical findings show that IQDs arising from the transmitter and recipient can be combined with channel paths to generate so-called effective channel paths. The conceptual examination demonstrates that the outlined maximum likelihood strategy of the SFBC recognition and effective channel estimation processes is implemented by an expectation maximization tool utilizing the error control decoders' soft outputs. The simulations results reveal that the suggested strategy delivers a much greater recognition accuracy than the typical approaches outlined in the comparable literature. At a signal-to-noise ratio (SNR) of 14 dB, for example, the proposed approach achieves a bit error rate (BER) of 0.00002, which is very close to the case of perfect estimation and compensation for IQDs, outperforming the previous reported works which achieved BERs of 0.01 and 0.02. [ABSTRACT FROM AUTHOR]

Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions. [ABSTRACT FROM AUTHOR]