Earticle

This paper proposes a zero-watermark scheme for identification photos based on QR code and visual cryptography. The method makes no changes to original images while embedding the QR code watermark. In order to ensure the security, we use Arnold transformation to scrambling the watermark. In the scheme, we use discrete wavelet transform and matrix norm computing to generate the invariant feature values against print-scan attacks. Then the VC scheme is applied to generate the secret image from the feature values and the disturbed watermark by using a codebook. In the extraction scheme, we get the secret image which is registered to certification authority and the feature values extracted from the examined image with VC scheme, and then apply inverse Arnold transformation to restore the watermark, finally repair position patterns of the QR code. The experimental results show that the proposed algorithm is effective and robust against attacks such as JPEG compression, add noise, multiple filters, scale, little angle rotation and crop, especial print-scan attacks.

Presented in this paper is a generalized norm bound for the continuous and discrete algebraic Riccati equations. The generalized norm bound provides a lower bound of the Riccati solutions specified by any kind of submultiplicative matrix norms including the spectral, Frobenius and $\ell_1$ norms.

본 논문에서는 능동 소나 시스템을 이용한 표적 탐지를 위해 비음수 행렬 분해 기법을 활용하여 잔향을 제거하는 기법에 대한 연구를 진행하였다. 본 논문에서는 기존의 기법이 반복 추정을 통해 표적 및 잔향 기저를 추정하여 연산 시간이 오래 걸린다는 단점에 주목하였다. 이를 개선하기 위하여 L1-노름을 활용하여 반복 초기에 원하는 해로 빠르게 수렴할 수 있도록 하였다. 또한, 기존의 기법이 활용하고 있는 휴리스틱 곱셈 갱신 기법은 추가적인 비용 함수를 활용할 경우 수학적으로 수렴을 보장하지 못하기 때문에, 수렴을 보장할 수 있는 주요화최소화 기법을 통해 갱신식을 새롭게 도출하였다. 본 알고리즘을 검증하기 위하여 시뮬레이션을 수행하였으며, 제안하는 알고리즘이 기존 대비 약 3 dB 향상된 신호대잔향비 성능을 보였다.

In this paper, we conduct research on an algorithm to suppress reverberation using a non-negative matrix factorization technique for target detection using an active sonar system. In this paper, we focus on the disadvantage that conventional algorithm estimates the target echo and reverberation bases through iterative estimation, which takes a long time to calculate. To improve this, we use the L1-norm to quickly converge to the desired solution at the beginning of the iteration. In addition, considering the fact that the heuristic multiplicative update method used in the conventional algorithm does not mathematically guarantee the convergence of additional penalty functions, the update equation is developed through a majorization-minimization technique that can guarantee convergence. Simulations were performed to verify the algorithm, and the results showed that the signal-to-noise ratio performance of the proposed algorithm was improved by 3 dB compared to the conventional algorithm.