This work is devoted to the study of the emitting properties of azimuthally inhomogeneous segmental dielectric resonators excited by whispering gallery modes. Due to the diffraction of the azimuthal waves on local nonhomogeneities located equidistantly along the azimuthal coordinate, intense electromagnetic radiation is achieved in the azimuthal sector of the angles 0°-360°. A prototype of an all-around emitting antenna based on basis of a segmental dielectric resonator is proposed, and its characteristics in the far zone are studied. It is shown that such an antenna forms a multilobe radiation pattern (72 lobes) in the circle sector of angles. The antenna gain in the lobes at the resonant frequency reaches 12 dB. Antenna optimization is achieved in the proposed method because of the large gain produced by the antenna. It is also analyzed that these types of radiation patterns observed by antennas are well used for the Internet of Things- (IoT-) based applications.

In response to the persistent challenges posed by fall armyworm (FAW) outbreaks in Rwanda’s maize production since 2017, this research introduces an innovative strategy integrating fuzzy logic and neural network methodologies, originally developed for hydrometeor identification. The focus is on distinguishing flying adult FAW moths using four polarimetric radar parameters: horizontal reflectivity (DBZHC), correlation coefficient (RHOHV), differential reflectivity (ZDR), and specific differential phase (KDP). Demonstrating a remarkable accuracy with a fraction of echoes correctly identified (FEI) of 98.42% for FAW and 87.02% for other weather phenomena, validated by a Heidke skill score (HSS) of 0.9801, the system proves adept at discerning between weather and nonweather events. A significant strength of the developed method lies in its ability to detect FAW adult moths approximately four weeks earlier than ground-based observations identifying infestation outbreaks, This was evident in the context of FAW infestation in maize fields within the surveyed districts of Nyanza, Huye, and Gisagara in the Southern Province of Rwanda. This positions the weather radar method as a promising early warning system for FAW outbreaks, especially beneficial in less-monitored regions like East Africa. The study underscores the potential application of polarimetric C-band Doppler weather radar, providing valuable insights into the intricate dynamics of agricultural insect pest outbreaks. The method offers practical solutions for timely interventions and enhanced crop management strategies, contributing to more effective pest control and promoting sustainable agriculture practices.

A compact, dual-port, triband MIMO antenna is designed and tested for three sub-6 GHz WLAN bands for IoT applications. The size and performance of the antenna make it versatile for the emerging IoT applications communicating using the 2.4 GHz, 5.2 GHz, and 5.8 GHz WLAN frequencies. The single radiating element of antenna geometry includes a pair of modified and optimized rectangular patches. A complimentary split ring resonator (CSRR) structure is incorporated in the ground plane as a defect (defected ground structure) to attain the third operational band. The orientation of the elements, orthogonally arranged, along with DGS, enhances the isolation between the radiating elements keeping it below 18 dB throughout the three operating frequency bands. The size of the final antenna is as small as mm² build on commercially available and cheap FR4 substrate of thickness 1.5748 mm. A peak gain of 7.17 dBi is attained for the proposed MIMO system. This MIMO antenna also satisfies the diversity parameter requirements including , , dB, and bits/s/Hz. This makes the proposed antenna a good candidate for IoT applications employing WLAN frequencies.

The article is aimed at proposing the design and investigating the performance of a three-petalled flower-shaped wideband microstrip patch antenna for IoT and next-generation wireless applications. The proposed printed monopole antenna is provided with a microstrip feed line for excitation with a defected ground plane. The antenna is designed and analyzed using a finite-element-based simulator HFSS (version 15.0). The Optimetrics feature in the simulator is used for the performance optimization of the designed antenna that results in wide impedance bandwidth between 2.5 and 5.5 GHz, with add-on benefits such as less human efforts along with fast optimum results. The designed antenna holds an advantage of being low profile and reduced in size as overall diminutive dimensions of the proposed patch antenna are , making it suitable for use in Wi-Max- and WLAN-enabled IoT applications. The paper is aimed at proposing an innovative optimal design aiming at the concerns about the risks in the growth of IoT and mobile computing, particularly in wireless and mobile networks. The anticipated antenna, owing to its simple and compact design, can be easily integrated into portable mobile devices, and thus, it is considered suitable for 4G and 5G and other next-generation communication applications of IoT devices.

Leveraging weather radar technology for environmental monitoring, particularly the detection of biometeors like birds, bats, and insects, presents a significant challenge due to the dynamic nature of their behavior. Unlike hydrometeor targets, biometeor targets exhibit arbitrary changes in direction and position, which significantly alter radar wave polarization upon scattering. This study addresses this challenge by introducing a novel methodology utilizing Rwanda’s C-Band Polarization Radar. Our approach exploits the capabilities of dual-polarization radar by analyzing parameters such as differential reflectivity (ZDR) and correlation coefficient (RHOHV) to derive the Depolarization Ratio (DR). While existing radar metrics offer valuable insights, they have limitations in fully capturing depolarization effects. To address this, we propose an advanced fuzzy logic algorithm (FL_DR) integrating the DR parameter. The FL_DR’s performance was rigorously evaluated against a standard FL algorithm. Leveraging a substantial dataset comprising nocturnal clear air radar echoes collected during a Fall Armyworm (FAW) outbreak in maize fields from September 2020 to January 2021, the FL_DR demonstrated a notable improvement in accuracy compared to the existing FL algorithm. This improvement is evident in the Fraction of Echoes Correctly Identified (FEI), which increased from 98.42% to 98.93% for biological radar echoes and from 87.02% to 95.81% for meteorological radar echoes. This enhanced detection capability positions FL_DR as a valuable system for monitoring, identification, and warning of environmental phenomena in regions similar to tropical areas facing FAW outbreaks. Additionally, it could be tested and further refined for other migrating biological targets such as birds, insects, or bats.