Y. Zhang, H. M. Liu, S. Li, Z. B. Wang, S. J. Fang [references] [full-text] [DOI: 10.13164/re.2025.0001] [Download Citations]
Compact Wideband High-Selectivity Filtering Power Divider Using Four-Coupled-Lines

In the paper, a compact wideband filtering power divider (FPD) with high frequency selectivity is presented, which is merely based on the four-coupled-lines (FCLs) and isolated resistors. Since the FCL with diagonal short-circuited of input port has filtering response, an FPD without adding extra resonators can be easily realized. Further, two types of FCLs are cascaded as multi-mode resonators for bandwidth enhancement, and two resistors are added for isolation improvement. For validation, a 3-dB prototype with a size of 0.4λg × 0.07λg is implemented. Measurements show that the proposed FPD has a fractional bandwidth of more than 80%. Besides, the stopband rejection is over 35 dB with a rectangle coefficient (|BW20dB/BW3dB|) of 1.28, which indicates high frequency selectivity.

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3. GUO, X., LIU, Y. H., WU, W. Wideband unequal filtering power divider with arbitrary constant power ratio and phase difference. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 2, p. 421-425. DOI: 10.1109/TCSII.2022.3212675
4. ZHANG, S. R., LIU, H. M., CHEN, S. Y., et al. Wideband filtering power divider with unequal power division ratio and all-frequency input absorptive feature. IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, vol. 71, no. 3, p. 1136-1140. DOI: 10.1109/TCSII.2023.3324915
5. ZHU, Y. H., CAI, J., CAO, Y., et al. Compact wideband absorptive filtering power divider with a reused composite T-shape network. IEEE Transactions on Circuits and Systems II: Express Briefs, 2023, vol. 70, no. 3, p. 899-903. DOI: 10.1109/TCSII.2022.3217462
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Keywords: Filtering power divider (FPD), high selectivity, four-coupled-line (FCL), wideband, miniaturization

J. Wang, Y. Luo, J. Zhang, L. Hou, J. Wang, B. Liu [references] [full-text] [DOI: 10.13164/re.2025.0009] [Download Citations]
A Wide-band High-performance Voltage-controlled Oscillator for 5G IoT Wireless Communication

A low phase noise and power-efficient class-B/C hybrid voltage-controlled oscillator (VCO) is presented for applying to 5G Internet of Things (IoT) wireless communication in this paper. The proposed three sets of switch capacitor array (SCA) are adopted first to widen the bandwidth by dividing the VCO output into eight overlapped frequency bands while maintaining the flexible frequency tuning. Then a multiple bias variable capacitor array (VCA) is designed to realize the fine-grain tuning of output frequency, which also improves the linearity within frequency-voltage tuning, the curvature variation in tunable gain, while minimizes the phase noise and stabilize tuning control on output frequency. After circuit implementation based on 180nm/1.2V CMOS standard process, the post-layout simulation results demonstrate that the proposed VCO achieves a wide frequency output from 4.63 GHz to 5.13 GHz, with consuming a total consumption of 0.19 mW at 1.2 V power supply voltage. The key phase noise is -115.1 dBc/Hz@1MHz on the 4.82 GHz center frequency, and the figure of merit (FoM) value can reach up to -195.6 dBc/Hz, which can surpass the performance to comparable similar class VCO design cases.

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9. XU, H., YAN, Y., WANG, Y., et al. A low voltage class-D VCO with implicit common-mode resonator implemented in 55 nm CMOS technology. Electronics, 2023, vol. 12, no. 10, p. 1-13. DOI: 10.3390/electronics12102262
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Keywords: LC voltage-controlled oscillator, Internet of Things communication, low phase noise, low power

Y. Wang, Z. H. Guo, K. Sun, H. B. Xiao, W. M. Wang [references] [full-text] [DOI: 10.13164/re.2025.0018] [Download Citations]
Youth Depression Diagnosis Algorithm Based on 3D-WGMobileNet and Transfer Learning

Depression is a common mental illness that not only profoundly infests the psychological state of patients, but also tends to cause damage to the functioning of patients' brain areas. To construct a comprehensive and detailed framework for a supporting diagnostic network that will help physicians make accurate and timely diagnoses when dealing with patients at different stages of depression, a network model based on three-dimensional (3D) weight group MobileNet (3D-WGMobileNet) and transfer learningis proposed. Firstly, fMRI data is preprocessed, and regional homogeneity analysis is used to reduce the dimension of the image. Then, the characteristics of Alzheimer's disease are learned by transfer learning and transferred to the proposed model. Next, the dynamic group convolution was used to construct the expert weight matrix of the convolution kernel, and the sliding window group convolution was used to compress the parameters of the model to improve the expression ability and computing power of the model. By using 5-fold cross-validation, we conducted experiments using data from HCP and REST-meta-MDD. The experiment results show that the proposed model gives a superior performance compared with other state-of-the-art methods, especially on the classification of the healthy group with major depression groups, where the two datasets achieve 88% and 91% accuracy, respectively, which verifies the feasibility and effectiveness of our model.

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Keywords: Depression, functional magnetic resonance imaging, transfer learning, MobileNet, dynamic group convolution