A novel robust watermarking scheme based on image connectivity is proposed. Having obtained the connected objects according to the selected connectivity pattern, the gravity centers are calculated in several larger objects as the reference points for watermark embedding. Based on these reference points and the center of the whole image, several sectors are formed, and the same version watermarks are embedded into these sectors. Thanks to the very stable gravity center of the connected objects, watermark detection is synchronized successfully. Simulation results show that our scheme can survive under both local and global geometrical distortions.

DTW-based text-dependent speaker verification technology is an effective scheme for protecting personal information in personal electronic products from others. To enhance the performance of a DTW-based system, an impostor database covering all possible passwords is generally required for the matching scores normalization. However, it becomes impossible in our practical application scenario since users are not restricted in their choice of password. We propose a method to generate pseudo-impostor data by employing an acoustic codebook. Based on the pseudo-impostor data, two normalization algorithms are developed. Besides, a template compression approach based on the codebook is introduced. Some modifications to the conventional DTW global constraints are also made for the compressed template. Combining the normalization and template compression methods, we obtain more than 66% and 35% relative reduction in storage and EER, respectively. We expect that other DTW-based tasks may also benefit from our methods.

The adaptive cross-spectral (ACS) technique recently introduced by Okuno et al. provides an attractive solution to acoustic echo cancellation (AEC) as it does not require double-talk (DT) detection. In this paper, we first introduce a generalized ACS (GACS) technique where a step-size parameter is used to control the magnitude of the incremental correction applied to the coefficient vector of the adaptive filter. Based on the study of the effects of the step-size on the GACS convergence behaviour, a new variable step-size ACS (VSS-ACS) algorithm is proposed, where the value of the step-size is commanded dynamically by a special finite state machine. Furthermore, the proposed algorithm has a new adaptation scheme to improve the initial convergence rate when the network connection is created. Experimental results show that the new VSS-ACS algorithm outperforms the original ACS in terms of a higher acoustic echo attenuation during DT periods and faster convergence rate.

In this paper, a digital processing method is described for modifying tone contrast that is defined as the greatest difference in frequencies between peaks and valleys of pitch curves in monosyllable utterances. Under quiet and noisy backgrounds, modified Mandarin tone words were presented to hearing-im- paired Chinese listeners with moderate to severe sensorineural hearing loss. The listeners were asked to identify four alternative monosyllable words which were distinguishable by tones 1, 2, 3 and 4 respectively. Employing this method, it was found that modified speech with enhanced tone contrast yielded moderate gains in the percentage of correct identification of the tones when compared to unmodified speech tones with only compression amplification. It was likewise found that reducing tone contrast generally reduced the degree of correct tone identification. These findings therefore offer support to the assertion that a hearing aid with tone modifications is indeed effective for hearing-impaired Chinese.

2D (two-dimensional) convolution is a basic operation in image processing and requires intensive computation. Although the SIMD model is considered suitable for 2D convolution, previous 2D convolution algorithms on the SIMD model assume unbounded number of PEs (Processing Elements) available, which we call unbounded case. Unbounded case could not be satisfied on real computers. In this paper, time-optimal data-parallel 2D convolution is studied on mesh-connected SIMD computers with bounded number of PEs. Because the optimal computation complexity is not difficult to achieve, the main concern of this paper is how to achieve optimal communication complexity. Firstly the lower bound computation complexity is analyzed. Then the lower bound communication complexities are analyzed under two typical data-distribution strategies: block-mapping and cyclic-mapping. Based on the analysis result, an optimal algorithm is presented under the block-mapping. The algorithm achieves the lower bound complexity both in computation and in communication.