7786738
A Modified Multi-Secret Sharing Visual Cryptography Scheme
A modified threshold visual cryptography scheme (k, l, n)-VCS is proposed, which is used to analyze the relationship between the pixel expansion and the range of the participants¿ number. Based on (k, l, n)-VCS, a more precise definition of multi-secret sharing visual cryptography scheme is given, and a new method is presented. Experimental results demonstrate that both pixel expansion and relative difference of this method are better than those of the previous ones.
16677111
A Verifiable Visual Cryptography Scheme
To solve the problem of cheating in the reconstructing process of visual cryptography, a verifiable visual cryptography was proposed by adding a verifier to common visual cryptography scheme (VCS). This scheme can be used in any access structure and can check every share to prevent attackers from spoiling and grabbing the secret information.
42516817
Low delay perceptually lossless coding of audio signals
A novel predictive lossless coding scheme is proposed. The prediction is based on a new weighted cascaded least mean squared (WCLMS) method. To obtain both a high compression ratio and a very low encoding and decoding delay, the residuals from the prediction are encoded using either a variant of adaptive Huffman coding or a version of adaptive arithmetic coding. WCLMS is especially designed for music/speech signals. It can be used either in combination with psycho-acoustically pre-filtered signals to obtain perceptually lossless coding, or as a stand-alone lossless coder. Experiments on a database of moderate size and a variety of pre-filtered mono-signals show that the proposed lossless coder (which needs about 2 bit/sample for pre-filtered signals) outperforms competing lossless coders, such as ppmz, bzip2, Shorten, and LPAC, in terms of compression ratios. The combination of WCLMS with either of the adaptive coding schemes is also shown to achieve better compression ratios and lower delay than an earlier scheme combining WCLMS with Huffman coding over blocks of 4096 samples.
9449451
The Tree Representation of the Graph Used in Binary Image Processing
Abstract A very useful structure, line adjacency graph, used in binary image processing is mapped onto an extended binary tree in this paper. The latter is more convenient for storing, traversing and pruning, and it occupies less memory space than the former.
15008871
Large Sample Properties of Weighted Monte Carlo Estimators
A general approach to improving simulation accuracy uses information about auxiliary control variables with known expected values to improve the estimation of unknown quantities. We analyze weighted Monte Carlo estimators that implement this idea by applying weights to independent replications. The weights are chosen to constrain the weighted averages of the control variables. We distinguish two cases (unbiased and biased), depending on whether the weighted averages of the controls are constrained to equal their expected values or some other values. In both cases, the number of constraints is usually smaller than the number of replications, so there may be many feasible weights. We select maximally uniform weights by minimizing a separable convex function of the weights subject to the control variable constraints. Estimators of this form arise (sometimes implicitly) in several settings, including at least two in finance: calibrating a model to market data (as in the work of Avellaneda et al. 2001) and calculating conditional expectations to price American options. We analyze properties of these estimators as the number of replications increases. We show that in the unbiased case, weighted Monte Carlo reduces asymptotic variance, and that all convex objective functions within a large class produce estimators that are very close to each other in a strong sense. In contrast, in the biased case the choice of objective function does matter. We show explicitly how the choice of objective determines the limit to which the estimator converges.
19636079
Information In The Non-Stationary Case
Information estimates such as the ``direct method'' of Strong et al. (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by instead estimating the difference between the marginal and conditional entropies of the response. While this is an effective estimation strategy, it tempts the practitioner to ignore the role of the stimulus and the meaning of mutual information. We show here that, as the number of trials increases indefinitely, the direct (or ``plug-in'') estimate of marginal entropy converges (with probability 1) to the entropy of the time-averaged conditional distribution of the response, and the direct estimate of the conditional entropy converges to the time-averaged entropy of the conditional distribution of the response. Under joint stationarity and ergodicity of the response and stimulus, the difference of these quantities converges to the mutual information. When the stimulus is deterministic or non-stationary the direct estimate of information no longer estimates mutual information, which is no longer meaningful, but it remains a measure of variability of the response distribution across time.
15309050
Prediction of Transmembrane Helical Segments in Membrane Proteins Using Back Propagation Neural Network with Wavelet Analysis
Transmembrane proteins possess important physiological function. The increasing transmembrane protein sequences from genome projects raise the needs for theoretical methods to predict their structures, especially transmembrane helical segments (TMHs). wnnTM, a new method of BP neural network based on wavelet multiresolution analysis (MRA) is initially developed to predict the number and location of TMHs in membrane proteins. 80 proteins with known 3D-structure randomly selected from Mptopo database are used as test set to evaluate the prediction accuracy of the method. The final results indicate that the proposed method is more effective than BP neural network model only.