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Rizki Rivai Ginanjar,Dong-Seong Kim,Chang-Bae Moon 대한전자공학회 2019 IEIE Transactions on Smart Processing & Computing Vol.8 No.1
In this paper, a novel, high-capacity and transparent blind audio watermarking system based on rounding reduced-arc M-ary phase shift keying (MPSK) is presented. Signal samples of the audio file are divided into several frames and then transformed into the frequency domain using fast Fourier transform. The watermark is embedded in the phase element of the selected samples based on several criteria. Two modified modulation schemes (256-PSK and binary phase shift keying) are used for the embedding process to compare the effects of the M-ary number used in the system. To improve the system, a genetic algorithm is utilized to obtain the best embedding parameters, which produces the optimal output based on the perceptual quality of the watermarked audio and the robustness of the extracted watermark. Experimental results show that the proposed audio watermarking system produces high-quality watermarked audio while loading a huge amount of data.
Toward Deep Learning-based Low Latency Communication in Industrial IoT
Ade Pitra Hermawan,Rizki Rivai Ginanjar,Dong-Seong Kim,Jae-Min Lee 한국통신학회 2019 한국통신학회 학술대회논문집 Vol.2019 No.6
This paper proposes a new direction in order to achieve high throughput and low latency communication in Industrial Internet of Things (IIoT) by utilizing Deep Learning(DL) technique. In order to achieve the goals, congestion in the network shall be avoided. We compare the performance of some DL algorithms in solving network congestion issue. In addition, future research trends regarding to maximize the system performance and to achieve high throughput and low latency communication in IIoT are suggested.