Exceptional advancement has been made in the development of graphene optical nanoantennas. They are incorporated with optoelectronic devices for plasmonics application and have been an active research area across the globe. The interest in graphene plasmonic devices is driven by the different applications they have empowered, such as ultrafast nanodevices, photodetection, energy harvesting, biosensing, biomedical imaging and high-speed terahertz communications. In this article, the aim is to provide a detailed review of the essential explanation behind graphene nanoantennas experimental proofs for the developments of graphene-based plasmonics antennas, achieving enhanced light–matter interaction by exploiting graphene material conductivity and optical properties. First, the fundamental graphene nanoantennas and their tunable resonant behavior over THz frequencies are summarized. Furthermore, incorporating graphene–metal hybrid antennas with optoelectronic devices can prompt the acknowledgment of multi-platforms for photonics. More interestingly, various technical methods are critically studied for frequency tuning and active modulation of optical characteristics, through in situ modulations by applying an external electric field. Second, the various methods for radiation beam scanning and beam reconfigurability are discussed through reflectarray and leaky-wave graphene antennas. In particular, numerous graphene antenna photodetectors and graphene rectennas for energy harvesting are studied by giving a critical evaluation of antenna performances, enhanced photodetection, energy conversion efficiency and the significant problems that remain to be addressed. Finally, the potential developments in the synthesis of graphene material and technological methods involved in the fabrication of graphene–metal nanoantennas are discussed.

This paper presents a micro strip patch antenna for future 5G-communication technology at centered frequency 38GHz and 54GHz having bandwidth 1.94GHz and 2GHz respectively with low cost substrate and small size patch best suited for miniaturized devices. It consists of Rogers RT5880 (lossy) substrate with dielectric constant 2.2 with loss tangent of 0.0013 and standard thickness 0.508mm, PEC patch and PEC ground. Substrate of dimensions 6mm×6.25mm and patch with dimensions 2mm×2mm is used. Microstrip-line feeding technique is used. Array having 4 elements with 4mm spacing has been proposed to achieve 12 dB gain for mobile data Applications on millimeter wave frequencies at 38.6GHz, 47.7GHz and 54.3GHz having bandwidth 3.5GHz, 2.5GHz and 1.3GHz respectively with Tapered line feeding. Overall size of antenna is 6mm×6.25mm×0.578mm. The proposed Antenna design is simulated on CST Microwave Studio.

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient’s chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

Today, worldwide more than five billion of wireless devices are directly communicating for voice and data transmission. The amount of data utilization has increased remarkably and here comes 5G technology with more prominent features, offering high data rate, low latency rate, efficient EM spectrum utilization, an immense machine-2-machine communication, etc. The efficient implementation of 5G technologies requires efficient and compact antennas. This work presents a novel multiband rectangular dielectric resonator antenna for future 5G wireless communication system, having stacked radiator with semi-circular slots etched on the left and right sides of an upper radiator. Additionally, a semi-elliptical slots rectangular microstrip patch antenna of the same dimensions for the purpose of comparison is designed. 28 and 38 GHz, which are the proposed 5G bands by most researchers, are the core target of this work. Alumina with a high relative permittivity of 9.8 is used as a radiator in the design of DRA, while common in the design of both proposed antennas, Rogers RT/DUROID 5880 with a relative permittivity of 2.2 having standard thickness is used as substrate material. Both the proposed antennas have an overall same size of 13 × 11.25 mm 2. The proposed dielectric antenna resonates at 25.4, 34.6 and 38 GHz with 7.34, 4.04, and 3.30 GHz of wide impedance bandwidth covering the targeted 5G, 28, and 38 GHz bands, having a good return loss of-34.7,-31.8, and-33.5 dB, respectively. Further, the proposed dielectric antenna has a maximum radiation efficiency of 97.63%, with overall radiation efficiency greater than 90%, and maximum gain of 7.6 dBi is also noted. On the other hand, the proposed microstrip antenna resonates at 28 and 38 GHz with a 1.49 and 1.01 GHz of moderate impedance bandwidth, having-23.6 and-27.1 dB of satisfactory return loss. Further, the proposed patch antenna has a maximum radiation efficiency of 90.33% at 28 GHz, with overall radiation efficiency of greater than 84%, and moderate gain of 5.45 dBi is also noted. Both the proposed antennas have a nearly omnidirectional radiation pattern at resonance frequencies, with VSWR less than 2. Comparative study of the two proposed antennas regarding radiation efficiency, return loss, gain, data rate, and impedance bandwidth evidently shows that performance of DRA over MPA at millimeter wave is very good. The proposed antennas are simulated in CST Microwave studio v18.