This paper emphasizes the function of the split ring resonator (SRR) within the design of ultra-wideband (UWB) antennas. We presented an antenna that belongs to the category of UWB antenna with two rejected bands based on square SRR. At first, we presented a UWB antenna. This antenna is designed to operate in the band 2.3–11.5 GHz. Then, with the integration of two SRR cells, we were capable of filtering the bands 3.7–3.9 GHz and 8.7–8.9 GHz without losing the UWB characteristics outside these rejected bands. Next, to ensure the proper performance of the MIMO system, we studied the use of metamaterials in the design of MIMO (Multiple-Input Multiple-Output) antennas for miniaturization and antenna performance.

In this paper, we propose a compact Multiple Input Multiple Output (MIMO) antenna system with high isolation for wireless applications in 5G connected devices. This MIMO antenna system with the size of 92 × 88 mm² consists of two elliptical antennas symmetrically arranged next to each other. Two decoupling methods which are neutralization and Defected Ground Structure (DGS) are applied to ensure diversity of the proposed MIMO antenna. The single and MIMO antennas are simulated and analyzed then fabricated and measured. A good agreement between measurements and simulations is obtained. These configurations, dedicated to covering the 3.4 GHz band ‐3.8 GHz, have shown very satisfactory performances more than ‐30 dB in terms of reduction of mutual coupling between the antennas constituting our system. MIMO diversity parameters, such as Envelope Correlation (ECC), Diversity Gain (DG), and total efficiency, are also studied for each proposed MIMO system. Thus, results demonstrate that our two proposed antenna configurations are very suitable for 5G MIMO applications.

Skin lesion segmentation is a critical step in computer-aided diagnosis systems for dermatology, aiding in the early detection of skin diseases. In this research, we present a robust approach for skin lesion segmentation utilizing the PH2 dataset, which comprises 200 dermoscopic images along with their medical segmentations. The proposed method integrates the U-Net architecture using VGG16 as a backbone, a well-established convolutional neural network model, and employs data augmentation techniques to enhance the segmentation performance. The results of the study demonstrate exceptional segmentation accuracy and effectiveness. The quantitative evaluation metrics showcase the model's proficiency, with an accuracy of 0.9459, an Intersection over Union (IoU) score of 0.9483, a Dice coefficient of 0.8826, precision of 0.9353, and a recall of 0.9151. These remarkable metrics highlight the model's precision in outlining skin lesions within dermoscopic images. This research contributes to the field of dermatology and medical imaging by providing a reliable and efficient approach for skin lesion segmentation. The combination of the U-Net architecture with data augmentation techniques significantly improves the accuracy and robustness of skin lesion segmentation, which is vital for early disease detection and accurate diagnosis. This work has the potential to advance the development of computer-aided diagnostic systems in dermatology, ultimately improving patient outcomes and facilitating more efficient clinical workflows.

This paper introduces a database-based design methodology aimed at optimizing a 26 GHz MIMO antenna system through machine learning (ML) techniques. The procedure is divided into two primary phases. Initially, a rectangular microstrip patch antenna is designed and enhanced using analytical models alongside ML algorithms that are trained on a detailed dataset of geometric parameters. This yields effective impedance matching (S11 < −45 dB) and a high gain (~6.64 dBi), which serve as the foundation for the MIMO structure. In the second phase, split ring resonator (SRR) configurations are integrated between the antenna elements to reduce mutual coupling. A specialized dataset, featuring varied dimensions of SRR, quantities of unit cells, and spatial placements, is utilized to train Random Forest models that forecast arrangements achieving optimal isolation (S21 < −40 dB) while maintaining low reflection losses. Additionally, a secondary dataset is constructed to investigate the best strategies for SRR placement, ensuring an optimal balance between isolation and return loss. The ultimate MIMO design is validated via comprehensive full-wave electromagnetic simulations and experimental measurements. The proposed system exhibits noteworthy performance enhancements, including an envelope correlation coefficient (ECC) < 0.005, diversity gain (DG) ≈ 9.99 dB, channel capacity loss (CCL) < 0.3 bits/s/Hz, total active reflection coefficient (TARC) < −30 dB, radiation efficiency exceeding 80%, and a maximum gain increase up to 10.22 dB. The close correlation between predicted and measured outcomes validates the effectiveness of the ML-driven approach in expediting antenna optimization for 5G and future applications.

Summary: In this paper, a new hybrid method formulation to resolve the mixed potential integral equation in an efficient and a fast approach, which is based on a simultaneous formulation in both spatial and spectral domains, is proposed. The entries of the method of moments matrix are then given by the sum of two integrals. The first one is expressed in the spatial domain. This part is analytically evaluated after a development in Taylor series of the exponential terms in the function to integrate. The integrals expressed in the spectral domain have a finite range, and they are calculated using numerical integration. Then the convergence problem is avoided in this approach. A good agreement between the simulated and measured results has been achieved. Copyright © 2015 John Wiley & Sons, Ltd.

Lung and colon cancers are among the top causes of death and morbidity in humans. Their simultaneous growth within organs can have a detrimental impact on human life. If cancer is not detected early, it is likely to spread to both of those organs. In this research, transfer learning method based on a numerous model is presented and compared to automate lung and colon cancers diagnosis in histopathological data. Many studies in this field employ the LC25000 dataset. In fact, it includes 25000 histopathological images belonging to 5 distinct classes which are benign colonic tissue, colon adenocarcinoma, lung squamous cell carcinoma, lung adenocarcinoma, and benign lung tissue. For the transfer Learning method, the used models which were compared are EfficientNetsV2 from B0 to B3, and Small, MobileNetV2, ResNetV2, and Xception. The best results were reached when Employing EfficientNetV2B1. The scored accuracy, F1-score, AUC are equal to 1. Moreover, the loss is $6.06. 10^{-5}$, respectively. Furthermore, the accuracy, F1score, AUC, Sensitivity, Specificity, attained 1 for the totality of the 5 classes. The proposed method with a transfer learning strategy is extremely efficient for lung and colon cancer automated diagnosis, outperforming several current methods. This method might help doctors provide more accurate diagnoses and improve patient outcomes.