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.

Correction: Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction.

[This corrects the article DOI: 10.1371/journal.pone.0229367.].

Multi-stage feature selection (MSFS) algorithm for UWB-based early breast cancer size prediction.

Breast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multi- stage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi- stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.