A compact multiple-input-multiple-output (MIMO) dielectric resonator antenna (DRA) that is suitable for internet of things (IoT) sensor networks is proposed with reduced coupling between elements. Two rectangular-shaped DRAs have been placed on the opposite sides of a Rogers substrate and each is fed using a coplanar waveguide (CPW) feed with slots etched in a dedicated metal ground plane that is located under the DRA. Moreover, locating the elements at the opposite sides of the substrate has improved the isolation by 27 dB without the need to incorporate additional complex structures, which has reduced the overall antenna size. Furthermore, a dual band operation is achieved since each antenna resonates at two frequencies: 28 GHz and 38 GHz with respective impedance matching bandwidths of 18% and 13%. As a result, the corresponding data rates are also increased independently. In addition to the advantages of improved isolation, compact size and dual band operation, the proposed configuration offers a diversity gain (DG), envelope correlation coefficient (ECC) and channel capacity loss (CCL) of 9.98 dB, 0.007, 0.06 bits/s/Hz over the desired bands, respectively. A prototype has been built with good agreement between simulated and measured results.

Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research’s primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field.

This paper is a comprehensive review of the recent literature studies on the developments and applications of millimeter-wave (mm-wave) dielectric resonator antennas (DRAs). Different designs and techniques for linear and circular polarized DRAs are discussed thoroughly. In addition, array and multiple-input multiple-output (MIMO) DRAs operating in the K, Ka, and V bands are illustrated. These applications are highly advantageous on many levels, resulting in the improved performance of the DRA in terms of obtaining a higher gain, lower losses, a higher efficiency, and a lower profile. This work reviews the fundamental research trends in antennas to meet the demands of fifth-generation (5G) communications and beyond. The reviewed studies are scholarly sources which contain measurement-based results. This paper concludes by highlighting the limitations of the studies and the implications for future research.

A wideband hemispherical dielectric resonator antenna (DRA) with enhanced gain is proposed for a frequency band of 20 to 28 GHz. The precise alignment and assembly of the DRA represent key challenges at such frequencies that were addressed using three approaches: the first was based on outlining the DRA position on the ground plane, the second involved creating a groove in the compound ground plane in which the DRA is placed, and the third was based on the 3D-printing of the DRA on a perforated substrate. In all cases, the same DRA was utilized and excited in a higher-order mode using an annular ring slot. The high gain was achieved by exciting a higher-order mode, and the wideband was obtained by merging the bandwidths of the two excited modes. The alignment methods used expedite the DRA prototyping by saving substantial time that is usually spent in adjusting the DRA position with respect to the feeding slot. The proposed configurations were measured, with an impedance bandwidth of 33.33% and a maximum gain of 10 dBi observed. Close agreement was achieved between the measured and simulated results.

A significant number of cameras regularly generate massive amounts of data, demanding hardware, time, and labor resources to acquire, process, and monitor. Asymmetric frames within videos pose a challenge to automatic summarization of videos, making it challenging to capture key content. Developments in computer vision have accelerated the seamless capture and analysis of high-resolution video content. Video summarization (VS) has garnered considerable interest due to its ability to provide concise summaries of lengthy videos. The current literature mainly relies on a reduced set of representative features implemented using shallow sequential networks. Therefore, this work utilizes an optimal feature-assisted visual intelligence framework for representative feature selection and summarization. Initially, the empirical analysis of several features is performed, and ultimately, we adopt a fine-tuning InceptionV3 backbone for feature extraction, deviating from conventional approaches. Secondly, our strategic encoder–decoder module captures complex relationships with five convolutional blocks and two convolution transpose blocks. Thirdly, we introduced a channel attention mechanism, illuminating interrelations between channels and prioritizing essential patterns to grasp complex refinement features for final summary generation. Additionally, comprehensive experiments and ablation studies validate our framework’s exceptional performance, consistently surpassing state-of-the-art networks on two benchmarks (TVSum and SumMe) datasets.

A novel approach is proposed to design a circularly polarized (CP) hemispherical dielectric resonator antenna (DRA) with a wide axial ratio (AR) bandwidth by incorporating an additional dielectric substrate between the antenna and the ground plane. This is in addition to the lower feeding substrate that is located between the ground plane on one side and the feeding microstrip line on the other side. Adding another substrate on top of the ground plane provided an additional degree of freedom in the design that facilitated the achievement of ab 18% AR bandwidth. In addition, an integrated hemispherical DRA and perforated substrate configuration was utilized to achieve optimum effective substrate permittivity and overcome the DRA alignment and assembly challenges while maintaining the achieved wide CP bandwidth. A close agreement was achieved between measurements and simulations.