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2024-07-25
PIER M
Vol. 128, 31-39, 2024
download: 29
Filter Design Based on Multilayer Wide Side Coupling Structure
Wu-Sheng Ji , Hanglin Du , Ying-Yun Tong , Xiaochun Ji and Liying Feng
In this paper, three kinds of filters are designed, all of which are based on the basic multi-layer structure of microstrip-slot wire-microstrip wide edge coupling. The ultra-wideband filter is realized by three-class connection. The intermediate coupling layer of coplanar waveguide and multimode resonator is designed to realize the double broadband filter. The ultra-wideband filter is realized by using a curved T SIR structure and changing the middle coupling slot structure. The purpose of this paper is to construct a stable and easy to generalize multilayer filter design method, which can achieve broadband and high selectivity, and can realize dual passbands.
Filter Design Based on Multilayer Wide Side Coupling Structure
2024-07-23
PIER M
Vol. 128, 21-30, 2024
download: 69
Convex Optimization-Based Linear and Planar Array Pattern Nulling
Tong Van Luyen , Nguyen Van Cuong and Phan Dang Hung
In the landscape of wireless communication, smart antennas, or adaptive array antennas, have emerged as vital components, offering heightened gains and spectral efficiency in advanced communication systems such as 5G and beyond. However, augmenting network coverage, capacity, and quality of service remains a pressing concern amid advancing communication technologies and escalating user demands. Array antennas with reduced sidelobe levels, high directivity, and increased beam steering capabilities are sought after to address these challenges. This paper explores convex optimization as a potent tool for array synthesis problems, offering robust performance and solution efficiency. By formulating optimization problems as convex programming, sidelobe reduction challenges can be efficiently addressed. The paper presents a comprehensive investigation into convex optimization-based approaches for array pattern nulling, assessing their performance and computational efficiency in various scenarios. Numerical examples demonstrate the efficacy of the proposed methods in maintaining the main lobe, controlling sidelobe levels, and placing nulls at interfering directions, thereby advancing the state-of-the-art in smart antenna technology.
Convex Optimization-based Linear and Planar Array Pattern Nulling
2024-07-20
PIER M
Vol. 128, 11-20, 2024
download: 67
Compact Dual-Band Antenna Based on Dual-Cap Metasurface
Xue Chen and Haipeng Dou
A novel compact dual-band antenna based on dual-cap metasurface (MS) is proposed. By etching circumferential circular ring slots on one side of the substrate and large cruciform slot on the other side, the dual-cap MS operates in two frequency bands. In addition, by placing the dual-cap MS at the back of a circular ring planar antenna which serves as a reflector, the impedance characteristic of the antenna in lower band and gain both in two bands are improved. The results show that this dual-cap MS antenna operates in the Wireless Local Area Network (WLAN) bands of 2.43-2.6 GHz and 5.48-6.05 GHz. Moreover, the maximum gains in lower and upper bands can reach 6.9 and 5.8 dBi, respectively.
Compact Dual-band Antenna Based on Dual-cap Metasurface
2024-07-17
PIER M
Vol. 128, 1-9, 2024
download: 86
A 3-Band Iteration Method to Transfer Knowledge Learned in RGB Pretrained Models to Hyperspectral Domain
Lei Wang and Sailing He
We propose a 3-band iteration method to transfer knowledge learned from RGB (red, green and blue) data pretrained models to the hyperspectral domain. We demonstrate classification of a Multi-spectral Choledoch database for cholangiocarcinoma diagnosis. The results show quicker and more stable training progress: 92%+ top-1 accuracy in the initial 3 epochs. Some advanced training techniques in the RGB computer vision field can be easily utilized and transferred to the hyperspectral domain without adding more parameters to the original architecture. The computational cost and hardware requirements remain the same. After voting, the highest top-1 accuracy on the validation set reached 95.4%, and the highest top-1 accuracy on the test set reached 94.3%. We can directly use our models trained on high-dimensional spectral images to test and infer on RGB color images. We visualized some results by Grad-CAM (Gradient-weighted Class Activation Mapping) on RGB test data, and it shows the transferability of knowledge. We trained the models solely on classification task on spectral data, and these models showed their ability to predict on RGB images with different fields of views. The results indicate good segmentation even when the model has never been trained on any segmentation task.
A 3-band Iteration Method to Transfer Knowledge Learned in RGB Pretrained Models to Hyperspectral Domain