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The main problem in designing a multiband bandpass filter (BPF) is making each passband response highly independent, where each bandwidth of multiband BPF can be controlled and adjusted separately. To overcome this problem, this paper proposes a highly independent multiband BPF based on a multicoupled line stub-SIR with a folding structure. The proposed multiband BPF is constructed as a multicoupled line to generate a highly independent inter-passband. Moreover, the multiband performance is produced separately and independently by using three sets of resonators: resonator A1/A2 (R A1 and R A2), resonator B1/B2 (R B1 and R B2), and resonator C1/C2 (R C1 and R C2). The three passband frequencies can be independently arranged and designed. To miniaturize the multiband BPF, a folding structure is also proposed. As a result, the multiband BPF has a compact size that is reduced by over 61.29 % compared to previous structures. The even-odd excitation model and the equivalent circuit model are used to analyze the multiband BPF structure. This BPF is designed for GPS applications at 1.57 GHz, WCDMA (3G) at 1.8 GHz, WLAN (WiFi) at 2.4 GHz, LTE (4G) at 2.6 GHz, and 5G communication at 3.5 GHz. To evaluate and validate the proposed structure of the multiband BPF, the circuits are fabricated and tested. The simulated and measured results of the multiband BPF show good agreement. In conclusion, the proposed multiband BPF structure has a highly independent inter passband response and a compact size. INDEX TERMS Compact, folding structure, highly independent, multiband BPF.

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This paper implements static hand gesture recognition in recognizing the alphabetical sign from "A" to "Z", number from "0" to "9", and additional punctuation mark such as "Period", "Question Mark", and "Space" in Sistem Isyarat Bahasa Indonesia (SIBI). Hand gestures are obtained by evaluating the contour representation from image segmentation of the glove wore by user. Then, it is classified using Artificial Neural Network (ANN) based on the training model previously built from 100 images for each gesture. The accuracy rate of hand gesture translation is calculated to be 90%. Moreover, speech translation recognizes NATO phonetic letter as the speech input for translation.

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