Metamaterials (MM) are artificially designed materials that possess unique properties due to their geometrical design. They also display some peculiar properties, such as negative refractive index, Snell’s law reversal, Doppler effect reverse, and left-handed behavior. MMs are used in a myriad of applications, including invisibility cloaking, perfect lensing, perfect absorption, and sensing. In this review article, the property of electromagnetic absorption by structures known as metamaterial absorbers (MMAs) is discussed. An MMA is a composite made up of many layers of metallic patterns separated by dielectric. This novel device helps in achieving near-unity absorption by various mechanisms, which are investigated in this article. The MMAs are classified based on their absorption characteristics, such as polarization tunability, broadband operation, and multiband absorption, in different frequency regimes.

Twitter sentiment detectors (TSDs) provide a better solution to evaluate the quality of service and product than other traditional technologies. The classification accuracy and detection performance of TSDs, which are extremely reliant on the performance of the classification techniques, are used, and the quality of input features is provided. However, the time required is a big problem for the existing machine learning methods, which leads to a challenge for all enterprises that aim to transform their businesses to be processed by automated workflows. Deep learning techniques have been utilized in several real-world applications in different fields such as sentiment analysis. Deep learning approaches use different algorithms to obtain information from raw data such as texts or tweets and represent them in certain types of models. These models are used to infer information about new datasets that have not been modeled yet. We present a new effective method of sentiment analysis using deep learning architectures by combining the “universal language model fine-tuning” (ULMFiT) with support vector machine (SVM) to increase the detection efficiency and accuracy. The method introduces a new deep learning approach for Twitter sentiment analysis to detect the attitudes of people toward certain products based on their comments. The extensive results on three datasets illustrate that our model achieves the state-of-the-art results over all datasets. For example, the accuracy performance is 99.78% when it is applied on the Twitter US Airlines dataset.

In this paper, a surrogate model based on a sparsely-connected back propagation neural networks (SC-BPNN) is proposed to reduce the large computational cost of conventional multi-objective antenna optimization problems. In this model, the connection parameters and network structure can be adaptively tuned by a hybrid real-binary particle swarm optimization (HPSO) algorithm for better network global optimization capability. Also, a time-varying transfer function is introduced to improve the problem of easily trapping into local optimum and to accelerate network convergence. Further, a fast multi-objective optimization framework based on the proposed SC-BPNN is established for multi-parameter antenna structures. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, indicating that the proposed model predicts antenna performance more accurately and saves considerable computational cost compared to those previously published approaches.

In this paper, a terahertz (THz) metamaterial absorber (MTMA), incorporating surface Pythagorean tree fractal resonators, was designed and experimentally fabricated on the flexible substrate of polyethylene terephthalate. The design presented two peaks with strong absorption of more than 97% at 0.49 and 0.69 THz. The dual-band absorption peaks were seen to be shifted with the change in the refractive index of the surrounding medium, with a corresponding sensitivity of 0.0968 and 0.1182 THz/RIU. The spectral shift of the reflection resonance dip was utilized as an assessment index to evaluate the sensing performance of the new structure, and it was found to be 2.08 and 2.98 for the two resonance peaks, respectively. It was observed that the proposed structure acted as an epsilon negative material at the first resonance and as a mu negative material at the second resonance. Further investigations on the electric field, magnetic field, and surface current distributions were carried out to elaborate on the absorption characteristics at various resonance frequencies. The proposed sensor is a highly sensitive MTMA which can be used to investigate the interaction of matter with THz waves.

An improved binary particle swarm optimization (BPSO) algorithm is proposed for the design of high-dimensional, multifunctional, and compact fragment-type antenna (FTA). First, orthogonal array (OA) based initialization instead of randomized initialization is employed to uniformly sample the design space for better population diversity. Then, a new transfer function with a time-variant transfer factor is proposed to improve the problem of easily falling into local optimum in basic BPSO. Experimental results of the two miniaturized FTA designs show that the proposed BPSO exhibits better convergence performance than other published discrete optimization algorithms and can provide excellent candidates for the internal miniaturized antenna designs in wireless and portable applications.

A triple band metamaterial absorber in the terahertz range, incorporating a three closed circle ring resonator, was designed and fabricated on flexible polyethylene terephthalate “PET” substrate. The proposed design was investigated theoretically and experimentally. Computer simulation technology “CST” was used to study the designed structure, while lithography technique was used to fabricate the absorber and terahertz time-domain spectroscopy was utilized to measure the reflectivity. Results showed the presence of three intensive peaks at the resonance frequencies of ‘0.43, 0.61, and 0.88 THz’, which correspond to the absorptivity of 98%, 91%, and 98%, respectively. The sensitivity of the three peaks was found to be ‘70.5, 133, and 149.5 GHz/RIU’, respectively. The parametric studies and field distributions were analysed. Furthermore, the proposed design exhibited polarization insensitivity for both transverse electric “TE” and transverse magnetic “TM” modes from 00 to 900. It was concluded that the proposed design can be specifically viable for some important applications such as ‘THz’ images, filtering, biological sensing, and optical communications.