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- ItemMiniaturization of Power Dividers by Using Asymmetric CMRC Structures and QWLTs with Low-Cost Materials(Radioengineering Society, 2024-09) Kittiwittayapong, S.; Torrungrueng, D.; Phaebua, K.; Sukpreecha, K.; Lertwiriyaprapa, T.; Janpugdee, P.This paper presents the miniaturization of power dividers using asymmetric compact-microstrip-resonant-cell (CMRC) structures employing low-cost materials based on a quarter-wave-like transformer (QWLT). The proposed CMRC-based QWLT power divider is intended for operation at a frequency of 2.4 GHz, utilizing the FR-4 print circuit board (PCB) with a dielectric constant of 4.3 and a substrate thickness of 1.6 mm. The CMRC dimensions include a width of 5.32 mm and a length of 8.52 mm. It is found that a significant 50% size reduction of length is achieved compared to a conventional power divider, while maintaining an insertion loss (IL) of 3.3 dB, as well as achieving the return loss and isolation loss of 20 dB.
- ItemMicrostrip Circularly-Polarized Leaky-Wave Antenna with Wide Axial Ratio Bandwidth for X-Band Application(Radioengineering Society, 2024-09) Wang, Z.; Zhang, D.; Gao, M.; Liu, H.; Fang, S.A microstrip circularly polarized (CP) leaky-wave antenna (LWA) operating in the X-band, and having the characteristics of a broad axis-ratio bandwidth is proposed. The proposed LWA is made up of 13 unit cells in series through microstrip feeding lines. Elliptical and rectangular slots are etched in each unit cell to achieve the radiation of CP waves. The open stopband at the broadside frequency can be suppressed by shifting the feeding line position and etching two circular notches on both sides of each radiation patch. To validate the proposed method, a prototype antenna operating in the X-band is manufactured and measured. The measured result demonstrates that the �10-dB impedance bandwidth of the microstrip CP LWA is 42.2% (7.96-12.22 GHz); the 3-dB axial ratio bandwidth is 26.4% (9.2-12.2 GHz); the gain of the antenna is 16.0 dBic. Besides, the main beam maintains good CP radiation properties while it continues to scan from �22° to +18°.
- ItemAESA Antennas using Machine Learning with Reduced Dataset(Radioengineering Society, 2024-09) Zaib, A.; Masood, A. R.; Abdullah, M. A.; Khattak, S.; Saleem, A. B.; Ullah, I.This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns.
- ItemEFU Net: Edge Information Fused 3D Unet for Brain Tumor Segmentation(Radioengineering Society, 2024-09) Wang, Y.; Tian, H.; Liu, M.Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part.
- ItemSoil Moisture Content Inversion by Coupling AEA and ARMA(Radioengineering Society, 2024-09) Feng, Y.; Nie, J.; Xie, G.; Lv, H.This study aimed to explore the inversion method of soil moisture content by using numerical simulation and field detection. The researchers used the early signal amplitude envelope (AEA) method to directly invert soil moisture in the shallow part of the soil, which avoided the transmission error of the Topp formula. The Auto-Regressive Moving Average Model (ARMA) was used to calculate the power spectrum of radar signals, and the BP neural network was used to train the power spectrum of different Gaussian windows, so as to improve the inversion accuracy. According to the study, the average error of soil moisture content inverted by AEA method was 0.45% in the range of 0-0.41m, while the error of ARMA method in depth range of 0.1-1.0m was less than 1%. The results showed that the combination of the two methods can effectively invert the soil moisture content within the radar detection range.
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