This paper presents a compact, uni-planer wide band Multiple-Input-Multiple-Output (MIMO) antenna. The designed antenna array has a compact size of (26mm x 31mm) covering a wide frequency band from 3.1GHz - 11GHz. A partial ground plane shared by two radiating patches is used. A ground stub and a single column Electromagnetic Bandgap (EBG) structure in between the two radiating patches results in very low mutual coupling in the designed antenna. The proposed MIMO-antenna was evaluated through different performance metric indicators like channel capacity loss, far-field radiation pattern, S-parameters, envelope correlation coefficient, peak gain, diversity-gain and radiation efficiency. Our designed MIMO antenna has a very high isolation amongst the MIMO antennas ( $\text{S}_{21} < -25$ dB), a high Diversity-Gain (DG $>9.995$ dB), a very low Envelope Co-relation Coefficient of (ECC < 0.001) and low Channel-Capacity-Loss (CCL < 0.1bits/s/Hz). The said antenna has an average radiation efficiency of 85.5% and a peak gain of (5.67dB) within the ultra-wideband (UWB) spectrum. A 0.8mm thick FR-4 substrate is used to fabricate the MIMO-antenna and tested in an anechoic chamber. The measured results closely match with the simulated results.

The art of paper folding also known as Origami plays an important role in various research areas of different scientific fields. Origami structures possess many useful characteristics such as reconfigurability, flexibility, deployability, compactness, and multi-functionality. These attributes are inevitable for modern communication systems. Additionally, they provide antenna engineers an extra degree of freedom while blueprinting next-generation designs. One of the desirable features of origami is its folding capability. Using this property, it can be transformed into a two-Dimensional (2D) sheet from a three-Dimensional (3D) structure and vice versa through external stimulus. In recent years, researchers have proposed numerous smart materials to optimize folding behavior. These smart materials extend the functionality of origami technology for designing self-folding structures required in space systems, small scale devices, and self-assembly systems. Origami structures feature many useful characteristics pertinent to modern day design challenges of communication systems such as reconfiguration, flexibility, compactness, and multifunctionality. In this work, a state of the art review is presented on conceptualization, design challenges, and fabrication of light-weight and low-cost deployable origami antennas. Furthermore, to provide critical insights deployment challenges and possible solutions are also provided. It is believed that this work will not only help peers to understand the basic working principles of different origami structures but will also lay the foundation for future research in the evolving field of origami-based antennas. Moreover, the design methodology proposed in this paper will also provide pragmatic solutions for origami antennas suitable for the harsh and rigid environments.

This article presents an extensive examination of antennas rooted in nature and biology, showcasing their remarkable performance across a wide spectrum of frequencies—from microwave to terahertz. The limitations of traditional antenna design have become increasingly evident in the face of burgeoning demands for novel communication technologies. Conventional analytical-equation-based approaches struggle to deliver the combined performance characteristics – encompassing bandwidth, gain, radiation pattern, and miniaturization – that emerging technologies necessitate. This has fueled an interest in bio-inspired antenna designs, a paradigm shift drawing inspiration from the ingenious structural solutions found in the living and non-living world, from plant leaves to bird feathers. These bio-inspired designs offer distinct advantages such as broader bandwidth and reduced sizes, making them highly appealing alternatives to the limitations of conventional antenna designs. This review explores a diverse range of bio-inspired designs. Among them are fractal geometries, inspired by self-repeating patterns in nature, which achieve optimal performance. Numerous designs in this category draw inspiration from nature, incorporating patterns observed in snowflakes, tree branches, clouds, and butterflies. Furthermore, nano-antennas have attracted significant attention for their vast potential applications in microwave and optical frequencies, playing a pivotal role in high-resolution spectroscopy, biomedical diagnosis and sensing, quantum photonics, and solar cell applications. By examining design methodologies and potential benefits, this article highlights the transformative potential of nature-inspired antennas. The compelling advantages of bio-inspired approaches necessitate a thorough exploration of their potential, paving the way for the development of next-generation communication systems with unprecedented capabilities.

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

Low-resolution Geological Survey of Pakistan (GSP) maps surrounding the region of interest show oolitic and fossiliferous limestone occurrences correspondingly in Samanasuk, Lockhart, and Margalla hill formations in the Hazara division, Pakistan. Machine-learning algorithms (MLAs) have been rarely applied to multispectral remote sensing data for differentiating between limestone formations formed due to different depositional environments, such as oolitic or fossiliferous. Unlike the previous studies that mostly report lithological classification of rock types having different chemical compositions by the MLAs, this paper aimed to investigate MLAs’ potential for mapping subclasses within the same lithology, i.e., limestone. Additionally, selecting appropriate data labels, training algorithms, hyperparameters, and remote sensing data sources were also investigated while applying these MLAs. In this paper, first, oolitic (Samanasuk), fossiliferous (Lockhart and Margalla) limestone-bearing formations along with the adjoining Hazara formation were mapped using random forest (RF), support vector machine (SVM), classification and regression tree (CART), and naïve Bayes (NB) MLAs. The RF algorithm reported the best accuracy of 83.28% and a Kappa coefficient of 0.78. To further improve the targeted allochemical limestone formation map, annotation labels were generated by the fusion of maps obtained from principal component analysis (PCA), decorrelation stretching (DS), X-means clustering applied to ASTER-L1T, Landsat-8, and Sentinel-2 datasets. These labels were used to train and validate SVM, CART, NB, and RF MLAs to obtain a binary classification map of limestone occurrences in the Hazara division, Pakistan using the Google Earth Engine (GEE) platform. The classification of Landsat-8 data by CART reported 99.63% accuracy, with a Kappa coefficient of 0.99, and was in good agreement with the field validation. This binary limestone map was further classified into oolitic (Samanasuk) and fossiliferous (Lockhart and Margalla) formations by all the four MLAs; in this case, RF surpassed all the other algorithms with an improved accuracy of 96.36%. This improvement can be attributed to better annotation, resulting in a binary limestone classification map, which formed a mask for improved classification of oolitic and fossiliferous limestone in the area.

Microwave medical imaging (MMI) is experiencing a surge in research interest, with antenna performance emerging as a key area for improvement. This work addresses this need by enhancing the directivity of a compact UWB antenna using a Yagi-Uda-inspired reflector antenna. The proposed reflector-loaded antenna (RLA) exhibited significant gain and directivity improvements compared to a non-directional reference antenna. When analyzed for MMI applications, the RLA showed a maximum increase of 4 dBi in the realized gain and of 14.26 dB in the transmitted field strength within a human breast model. Moreover, it preserved the shape of time-domain input signals with a high correlation factor of 94.86%. To further validate our approach, another non-directional antenna with proven head imaging capabilities was modified with a reflector, achieving similar directivity enhancements. The combined results demonstrate the feasibility of RLAs for improved performance in MMI systems.