3 research outputs found
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Symmetric 3dB filtering power divider with equal output power ratio for communication systems
This paper presents a two-way filtering power divider (FPD) with an equal output power ratio of 1:1. This implies that each of the FPD output port would receive 50% of the power at the input port. To achieve miniaturisation, a common square open-loop resonator is used to distribute energy between the two integrated Chebyshev bandpass filters. In addition to distributing energy, the common resonator also contributes one pole to each integrated bandpass filter (BPF), hence, reducing the number of individual resonating elements used in achieving the integrated FPD. To demonstrate the proposed design technique, a prototype FPD centred at 2.6 GHz with a 3 dB fractional bandwidth of 3% is designed, simulated and presented. The circuit model and microstrip layout results of the FPD show good agreement. The microstrip layout simulation responses show that a less than 1.1dB insertion loss and a greater than 16.5dB in-band return loss were achieved. The overall footprint of the integrated FPD is 37mm by 13mm (i.e. 0.32位g x 0.11位g, for 位g = guided-wavelength of the 50惟 microstrip line at 2.6 GHz). The integrated FPD reported in this paper shows some promising merits when compared to similar devices recently reported in literature
Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All
Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets