This paper presents a method for using neighboring cascaded quadruplet (CQ) units sharing resonators to decrease the order of filter so as to reduce the size of a high temperature superconducting (HTS) linear phase filter. The main advantage is that it will not reduce the number of the filter's transmission zeros, and will not increase the difficulty of circuit design and tuning. Based on this method, this paper presents a 10-order HTS linear phase filter with two pairs of transmission zeros on double-sided YBCO/LaAlO 3 /YBCO films with a size of 20.3 mm×20.92 mm, a thickness of 0.5 mm and a dielectric constant of 24.04. At 77 K, the filter's measured center frequency is 830.03 MHz with a bandwidth of 10 MHz, an edge out-ofband rejection greater than 30 dB MHz −1 , and a group delay variation of less than ±10 ns over 60% of the filter bandwidth.
A novel broadband traveling-wave power divider/combiner structure with waveguide ports and microstrip probes is presented at Ku-band for the first time. Design procedure was simplified by mathematical modeling and fullwave simulations. A design of Ku-band 2-way power divider/combiner was carried out to verify the approach. Some fabrication details of conventional machining were taken into account by setting up simulations. The measured results showed a minimum overall insertion loss of 0.42dB corresponding to a combining efficiency greater than 90%, and 1dB bandwidth of 36.2%.
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<p>Swarm intelligence algorithms are relatively simple and highly applicable algorithms, especially for solving optimization problems with high reentrancy, high stochasticity, large scale, multi-objective and multi-constraint characteristics. The sparrow search algorithm (SSA) is a kind of swarm intelligence algorithm with strong search capability, but SSA has the drawback of easily falling into local optimum in the iterative process. Therefore, a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is proposed for addressing this deficiency. Firstly, the Tent chaos mapping is invoked in the initialization population stage to improve the population diversity; secondly, the positive cosine algorithm incorporating random inertia weights is introduced in the discoverer position update, so as to improve the probability of the algorithm jumping out of the local optimum and speed up the convergence; finally, the firefly perturbation is used to firefly perturb the sparrows, and all sparrows are updated with the optimal sparrows using the firefly perturbation method to improve their search-ability. Thirteen benchmark test functions were chosen to evaluate SFSSA, and the results were compared to those computed by existing swarm intelligence algorithms, as well as the proposed method was submitted to the Wilcoxon rank sum test. Furthermore, the aforesaid methods were evaluated in the CEC 2017 test functions to further validate the optimization efficiency of the algorithm when the optimal solution is not zero. The findings show that SFSSA is more favorable in terms of algorithm performance, and the method's searchability is boosted. Finally, the suggested algorithm is used to the locating problem of emergency material distribution centers to further validate the feasibility and efficacy of SFSSA.</p>
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