16174340
A new encryption algorithm for image cryptosystems
Abstract There are two major differences of the characteristics of the text data and image data. One difference is that the size of image data is usually much larger than that of text data. The other is that plain data rarely permit loss when a compression technique is used, but image data do. In this paper, we design an efficient cryptosystem for images. Our method is based on vector quantization, which is one of the popular image compression techniques. Our method can achieve the following two goals. One goal is to design a high security image cryptosystem. The other goal is to reduce computational complexity of the encryption and decryption algorithms.
23513255
A public verifiable copy protection technique for still images
It is well known that a digital signature scheme and digital timestamp are two well-defined security methods for copyright protection. However, these techniques are not suitable for directly dealing with digital images. For this reason, digital watermarking has received considerable attention for copyright protection. Unfortunately, there are still some challenges in the proposed watermarking schemes. Here, we will introduce cryptographic tools into watermarking, in such a way that both the advantages of cryptographic tools and digital watermarking are available. The publicly verifiable property and tolerant distortion property are therefore possible. Based on this idea, a lossless and robust copyright-protection scheme, distinct from conventional watermarking techniques, is proposed. Our scheme is strong enough to resist common image processing, geometric distortions and some intentional attacks. Some of the attacks are still challenges for the proposed watermarking methods. A series of experiments are conducted to prove the robustness property. The StirMark and unZign attacks are adopted as benchmark verification.
5643714
On Some Characterisation Problems of Subdomain Testing
Subdomain testing is a very general approach to the selection of test cases. It captures the characteristics of testing strategies that require the test suite to cover some predefined testing requirements. This paper attempts to characterise precisely the failure distributions for the best and worst case of any given subdomain testing strategy. Our analysis has revealed some crucial factors and principles that affect the effectiveness of subdomain testing strategies.
9048292
Updating mixture of principal components for error concealment
We present a new statistical modeling technique called "updating mixture of principal components" (UMPC). UMPC specifically captures the non-stationary as well as the multi-modal characteristics of the data. Real-world data such as video data typically have these two characteristics. The video content changes over time and has a multi-modal probability distribution. We apply UMPC to perform error concealment for video data transmitted over networks with losses, and show that UMPC outperforms conventional error concealment methods.
2051959
Performance Evaluation of Forwarding Strategies for Location Management in Mobile Networks
This paper presents a methodology for evaluating the performance of forwarding strategies for location management in a personal communication services (PCS) mobile network. A forwarding strategy in the PCS network can be implemented by two mechanisms: a forwarding operation which follows a chain of databases to locate a mobile user and a resetting operation which updates the databases in the chain so that the current location of a mobile user can be known directly without having to follow a chain of databases. In this paper, we consider the PCS network as a server whose function is to provide services to the mobile user for ‘updating the location of the user as the user moves across a database boundary’ and ‘locating the mobile user’. We use a Markov chain to describe the behavior of the mobile user and analyze the best time when forwarding and resetting should be performed in order to optimize the service rate of the PCS network. We demonstrate the applicability of our approach with hexagonal and mesh coverage models for the PCS network and provide a physical interpretation of the result.
6145819
HapBlock: haplotype block partitioning and tag SNP selection software using a set of dynamic programming algorithms
UNLABELLED
Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed with regions of low LD. Such LD patterns make it possible to select a set of single nucleotide polymorphism (SNPs; tag SNPs) for genome-wide association studies. We have developed a suite of computer programs to analyze the block-like LD patterns and to select the corresponding tag SNPs. Compared to other programs for haplotype block partitioning and tag SNP selection, our program has several notable features. First, the dynamic programming algorithms implemented are guaranteed to find the block partition with minimum number of tag SNPs for the given criteria of blocks and tag SNPs. Second, both haplotype data and genotype data from unrelated individuals and/or from general pedigrees can be analyzed. Third, several existing measures/criteria for haplotype block partitioning and tag SNP selection have been implemented in the program. Finally, the programs provide flexibility to include specific SNPs (e.g. non-synonymous SNPs) as tag SNPs.


AVAILABILITY
The HapBlock program and its supplemental documents can be downloaded from the website http://www.cmb.usc.edu/~msms/HapBlock.
4650146
A New Coarse-to-Fine Framework for 3D Brain MR Image Registration
Registration, that is, the alignment of multiple images, has been one of the most challenging problems in the field of computer vision. It also serves as an important role in biomedical image analysis and its applications. Although various methods have been proposed for solving different kinds of registration problems in computer vision, the results are still far from ideal when it comes to real world biomedical image applications. For instance, in order to register 3D brain MR images, current state of the art registration methods use a multi-resolution coarse-to-fine algorithm, which typically involves starting with low resolution images and working progressively through to higher resolutions, with the aim to avoid the local maximum "traps". However, these methods do not always successfully avoid the local maximum. Consequently, various rather sophisticated optimization methods are developed to attack this problem. In this paper, we propose a novel viewpoint on the coarse-to-fine registration, in which coarse and fine images are distinguished by different scales of the objects instead of different resolutions of the images. Based on this new perspective, we develop a new image registration framework by combining the multi-resolution method with novel multi-scale algorithm, which could achieve higher accuracy and robustness on 3D brain MR images. We believe this work has great contribution to biomedical image analysis and related applications.