Digital audio watermarking embeds inaudible information into digital audio data for the purposes of copyright protection, ownership verification, covert communication, and/or auxiliary data carrying. In this paper, we first describe the desirable characteristics of digital audio watermarks. Previous work on audio watermarking, which has primarily focused on the inaudibility of the embedded watermark and its robustness against attacks such as compression and noise, is then reviewed. In this research, special attention is paid to the synchronization attack caused by casual audio editing or malicious random cropping, which is a low-cost yet effective attack to watermarking algorithms developed before. A digital audio watermarking scheme of low complexity is proposed in this research as an effective way to deter users from misusing or illegally distributing audio data. The proposed scheme is based on audio content analysis using the wavelet filterbank while the watermark is embedd in the Fourier transform domain. A blind watermark detection technique is developed to identify the embedded watermark under various types of attacks.
Background/purpose
We assessed the mobility of single-root teeth by using Miller's mobility index (MMI) and to analyze the validity of MMI for the diagnosis of periodontitis.
Materials and methods
A total of 30 patients were included and the Spearman correlation coefficient was used to assess the correlation between MMI, clinical attachment level (CAL), and probing depth (PD). The validity of MMI for the diagnosis of the severity of periodontitis was evaluated using the receiver operating characteristic (ROC) curve, area under curve (AUC) value, positive predictive value (PPV).
Results
Strong correlations were observed between MMI and CAL (
r
= 0.92) and between MMI and PD (
r
= 0.76). When the CAL = 3–4 mm and CAL ≥5 mm groups were pooled together, the AUC value was 0.81. The AUC was 0.86 for diagnosis with MMI in the CAL ≥5 mm group. A PPV of 100% was achieved for all grades when MMI >1. When the teeth with PD ≥ 5 to <7 mm and PD ≥ 7 mm groups were pooled together, the AUC value for MMI was 0.80. The PPV was 98.8%, 99%, and 100% for MMI Grade 1, Grade 2, and Grade 3, respectively. When PD ≥ 7 mm was defined as severe periodontitis, the AUC value for MMI was 0.72.
Conclusion
MMI may provide valuable information for the diagnosis of moderate and severe periodontitis when CAL is not obtainable during routine practice.
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