Mitigating mutual coupling effects on circular polarization for improved bandwidth in MIMO systems: A novel approach

An improved mutual coupling compensation in circularly polarized (CP) multi-input multi-output (MIMO) dielectric resonator antenna (DRA) is presented in this paper. Using trimming approach, the mutual coupling (MC) between closely spaced DRA units at 0.3λ has been significantly reduced while axial ratio performance has been maintained. Mutual coupling reduction is obtained by trimming the DRA to ensure low mutual coupling below -20dB. The exclusive features of the proposed MIMO DRA include wide impedance matching bandwidth (BW), triple band circular polarization, and suppressed MC between the radiating elements. The impedance bandwidth matches perfectly with a triple band's 3 dB axial ratio (AR). It is designed with characteristic mode analysis with good agreement of the measurement that has been obtained. Using the probe feed method, the DRA and patch strip are coupled together to allow bandwidth widening of the pro-posed DRA. An impedance bandwidth of 34% at a lower frequency to around 2% at a higher frequency was achieved in all resonance frequencies. Thus, we refer to our newly designed DRA as a proposed method for effectively reducing the mutual coupling between DRAs. Additionally, the 3 dB AR bandwidth matched at 3.3 GHz, 4.6 GHz, and 6.3 GHz with a percentage of 11.66%, 3.04%, and 2.22% obtained at the three different frequencies. Note that the proposed DRA exhibits low mutual coupling (below -20 dB) at the targeted frequencies, which is suitable for better signal reception for MIMO applications. By computing, the metrics envelop correlation coefficient, diversity gain, channel capacity loss, and total active reflection coefficient, the MIMO performance of the proposed antenna is verified. The experiments show a close result between simulated and computed validation of the proposed DRA.

The Design and Optimization of an Acoustic and Ambient Sensing AIoT Platform for Agricultural Applications

The use of technology in agriculture has been gaining significant attention recently. By employing advanced tools and automation and leveraging the latest advancements in the Internet of Things (IoT) and artificial intelligence (AI), the agricultural sector is witnessing improvements in its crop yields and overall efficiency. This paper presents the design and performance analysis of a machine learning (ML) model for agricultural applications involving acoustic sensing. This model is integrated into an efficient Artificial Intelligence of Things (AIoT) platform tailored for agriculture. The model is then used in the design of a communication network architecture and for determining the distribution of the computing load between edge devices and the cloud. The study focuses on the design, analysis, and optimization of AI deployment for reliable classification models in agricultural applications. Both the architectural level and hardware implementation are taken into consideration when designing the radio module and computing unit. Additionally, the study encompasses the design and performance analysis of the hardware used to implement the sensor node specifically developed for sound classification in agricultural applications. The novelty of this work lies in the optimization of the integrated sensor node, which combines the proposed ML model and wireless network, resulting in an agricultural-specific AIoT platform. This co-design enables significant improvements in the performance and efficiency for acoustic and ambient sensing applications.