In this paper, we describe the development and design procedure of the new kind of coaxial TE_m,1 modes generator based on ring resonator with coupling apertures. The generator enables excitation of subsequent TE_m,1 modes in a cylindrical waveguide. The proposed design method allows to obtain high purity TE_m,1 modes. The angular mode number can be chosen by replacing the plate with coupling apertures. Structure and parameters of the generator was optimized using CST-Microwave Studio. The mode generator was fabricated and checked on the test bench in an anechoic chamber. The measured field distributions confirm excitation of the desired TE_m,1 modes. A good agreement between simulations and measurements is obtained. The presented mode generator, operating in non-rotating TE_m,1 modes, is easy to fabricate, and suitable for cold-test experiments of high power components and devices.

Convolutional neural networks have achieved tremendous success in the areas of image processing and computer vision. However, they experience problems with low-frequency information such as semantic and category content and background color, and high-frequency information such as edge and structure. We propose an efficient and accurate deep learning framework called the multi-frequency feature extraction and fusion network (MFFNet) to perform image processing tasks such as deblurring. MFFNet is aided by edge and attention modules to restore high-frequency information and overcomes the multiscale parameter problem and the low-efficiency issue of recurrent architectures. It handles information from multiple paths and extracts features such as edges, colors, positions, and differences. Then, edge detectors and attention modules are aggregated into units to refine and learn knowledge, and efficient multi-learning features are fused into a final perception result. Experimental results indicate that the proposed framework achieves state-of-the-art deblurring performance on benchmark datasets.