In the design and development of front-end communication systems, the arbitrary impedance environments are encountered frequently. In this article, a three-port dual-band equal power divider is proposed which also transforms all types of impedance environments at its ports inherently. The power divider is suitable for a wide range of arbitrary frequency ratios and impedance terminations at each of the input and output ports. A systematic design procedure with a design flow graph is provided. The closed-form design equations make the design process simple and quick for prototyping purposes. The proposed design approach enhances the design flexibility significantly with the presence of multiple independent variables. Design examples with distinct design specifications are provided to demonstrate the effectiveness and flexibility of the architecture. A prototype working at 1/2.6 GHz and with frequency-dependent complex load (FDCL) terminations at the input and output ports is fabricated and measured to validate the proposed architecture and design methodology. The proposed dual-band impedance transforming power divider exhibits controllable bandwidths, planar structure without any reactive element, compact size, and the ability to provide port matching for any real, complex, and frequency-dependent complex impedance terminations.INDEX TERMS Frequency varying impedances, dual-band power divider, frequency-dependent impedance transformer, high frequency ratio, impedance matching, impedance transforming power divider, microstrip power dividers, transmission line.
<p>Major depressive disorder (MDD) is a common mental disorder affecting the lives of about 280 million people and increasing rates of suicidal mortality. The current methods of diagnosis of depression are subjective, time-consuming, expensive, and inaccurate because of its heterogeneous symptoms that overlap with other disorders. In this paper, we exploit the potential of the fusion of artificial intelligence (AI) and electroencephalogram (EEG) to revolutionize the automatic diagnosis of depression and compare the classification performance of machine learning (ML) and deep learning (DL) based techniques. Results from the analysis of data recorded from 46 subjects (23 MDD and 23 Control) show that the ML methods, particularly the ensemble model with the Dempster-Shafer combination rule outperforms other models, achieving an accuracy of 99.62% and showing robustness to the variations in the data. Our work also includes a study on the effect of various hyper-parameters, in particular the number of EEG channels, feature selection methods, number of selected features, and segmentation length on the model performance. The AI-EEG integration can enhance the accuracy of diagnosis, enable personalized treatment plans, and improve patient outcomes. Continued research, development, and validation of AI algorithms, in conjunction with ethical considerations, will be crucial to harness the full potential of this technology in mental healthcare.</p>
<p>Major depressive disorder (MDD) is a common mental disorder affecting the lives of about 280 million people and increasing rates of suicidal mortality. The current methods of diagnosis of depression are subjective, time-consuming, expensive, and inaccurate because of its heterogeneous symptoms that overlap with other disorders. In this paper, we exploit the potential of the fusion of artificial intelligence (AI) and electroencephalogram (EEG) to revolutionize the automatic diagnosis of depression and compare the classification performance of machine learning (ML) and deep learning (DL) based techniques. Results from the analysis of data recorded from 46 subjects (23 MDD and 23 Control) show that the ML methods, particularly the ensemble model with the Dempster-Shafer combination rule outperforms other models, achieving an accuracy of 99.62% and showing robustness to the variations in the data. Our work also includes a study on the effect of various hyper-parameters, in particular the number of EEG channels, feature selection methods, number of selected features, and segmentation length on the model performance. The AI-EEG integration can enhance the accuracy of diagnosis, enable personalized treatment plans, and improve patient outcomes. Continued research, development, and validation of AI algorithms, in conjunction with ethical considerations, will be crucial to harness the full potential of this technology in mental healthcare.</p>
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